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      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      In this interesting manuscript the authors present experiments examining the relationship between the obligate intracellular bacterium Chlamydia pneumoniae (Cpn) and the microtubule (MT) cytoskeleton of the host eukaryotic cell using both mammalian cells and yeast as a model. They demonstrate that host microtubule stability contributes to the rate of entry of the bacteria into the cells. Interphase MT architecture is therefore important and correspondingly they show that mitotic cells are less permissive to bacterial entry. Other experiments show that Cpn entry is accompanied by changes to MT stability (measured indirectly via post-translational modifications). Finally they investigate the effects of overexpressing a Cpn virulence factor Cpn0572 in mammalian cells and yeast that they have previously shown to interact with both the actin and MT cytoskeletal networks. Cpn0572 expression induces MT acetylation (stability) which correlates with their previous observations and when expressed ectopically in yeast Cpn0572 suppresses force-dependent MT catastrophe. They propose that chlamydial effectors like Cpn0572 influence MT architecture and stability during Cpn entry, revealing a previously unappreciated role for MT in the bacterial entry process.

      • *

      Our Response to Summary ____Reviewer #1

      We thank the reviewer for the summary of our study. We would like to clarify one point. The mammalian-cell experiments demonstrating that CPn0572 interacts with and alters the MT cytoskeleton, including increased MT acetylation, were reported in our previous work (Höhler et al., 2024; doi: 10.1242/jcs.263450) and are not experiments performed in the present study. In the current manuscript, we use controlled ectopic expression of CPn0572 in S. pombe to analyse its effects on MT dynamics by live-cell imaging and show that CPn0572 reduces catastrophe and depolymerization and suppresses the normal catastrophe response at the cell cortex.

      Experimental points

      * __Comment 1*__

      Fig 1A - to assist interpretation whole cell images similar to those in the supplementary file should be included. It is difficult to relate the images in the small immunofluorescence panels to the phenotypes depicted.* *

      Answer to comment 1

      We have provided whole cell images of the enlarged images shown in Fig 1A. These are shown in Supp. Fig. S1A. We have added the following statement in the revised results section (line 174): Whole cell images of the zoom images in Fig 1A are shown in Fig S1A.




      Comment 2

      Cold recovery assay: one wonders what happens to the actin and intermediate filament networks under these conditions and when treated with MT-targetted agents. Some data should be included to rule out that there are additional effects on these systems as off target effects on actin might also influence the data. This is an essential control to support the conclusions drawn.

      Answer to comment 2

      Cold treatment efficiently perturbs the MT cytoskeleton without causing a comparable gross disruption of the actin cytoskeleton. These images are now included in the revised version of the manuscript (new Supp. Fig. S2). We have added the following sentence to our revised manuscript:(lines 129-130) Cold-treatment did not lead to gross alteration of F-actin organization (Fig. S2A).

      Comment 3a

      *Is MT repolymerisation synchronous following cold recovery? Its difficult to assess from the included images and sample size. *

      Answer to comment 3a

      Cold-induced MT depolymerization followed by rewarming results in rapid and highly synchronized MT regrowth in U2OS cells. This has been quantitatively demonstrated by Didier et al. (2008), who showed that MT asters were detectable within 30 s after rewarming and that a centrosome-radiating MT network had reformed in 95% of control U2OS cells within 60 s (Didier et al., 2008; doi: 10.1091/mbc.E06-12-1140). The images shown in our study are representative examples and were not intended to independently quantify the synchrony of MT regrowth.

      We have clarified this point in the revised results section as follows: (lines 120–122) “Cold-induced MT depolymerization followed by rewarming results in rapid and highly synchronized re-polymerization of the MT cytoskeleton in U2OS cells [32].”

      Comment 3b

      Why was this method selected in preference to nocodazole treatment and washout, where synchrony is easier to establish.

      Answer to comment 3b

      We selected cold-induced MT depolymerization because this approach provides both rapid and synchronized MT regrowth upon rewarming. We do not consider synchrony of MT repolymerization to be inherently easier to establish following nocodazole treatment and washout. In U2OS cells, cold-induced MT depolymerization followed by rewarming is a well-established MT-regrowth assay, and rapid, highly coordinated repolymerization has been demonstrated previously (Didier et al., 2008; doi: 10.1091/mbc.E06-12-1140).

      Importantly, the cold-recovery approach is particularly suitable for our experimental question because transfer of the cells from ice to 37°C provides a precisely defined starting point for MT repolymerization and, at the same time, initiates the early infection period. This allows us to analyse C. pneumoniae entry during the first minutes of MT recovery. Nocodazole washout can likewise be used to induce synchronized MT regrowth, but requires drug removal and repeated washing before recovery can be initiated. We therefore chose cold-induced depolymerization as the more appropriate approach for coupling synchronized MT regrowth directly to the very early stages of infection.


      Comment 4

      Although EB are scored in Fig 1A, they are not shown alongside the microtubules and modified microtubules as described.

      Answer to comment 4

      We apologize for the misleading representation in Fig 1A. The upper part of the figure is a schematic illustration of the experimental setup, whereas the lower part shows representative microscopic images of the tubulin and acetylated MT phenotype at the indicated time points. The schematic depicts the experimental workflow subsequently used for the infection experiments and was not intended to indicate that EBs were visualized in the images shown in Fig 1A.

      We have modified the schematic in the revised manuscript to make this distinction clear and to avoid further confusion.

      Comment 5a (please note- we have divided this into several sub comments)

      What happens to actin/intermediate filaments following the treatments with Taxol?

      Answer to comment 5a

      Because actin has a central role in chlamydial entry, we examined whether the Taxol treatment used in our experiments causes a major reorganization of the actin cytoskeleton. U2OS cells treated with DMSO or 10 µM Taxol for 2 h showed prominent F-actin fibres under both conditions, with no obvious gross disruption of the actin cytoskeleton following Taxol treatment (included now as new Fig. S2B). We have added the following sentence to our revised manuscript:(lines 202-204): Under the conditions used, Taxol treated cells showed prominent F-actin fibres with no obvious gross disruption of the actin cytoskeleton (Fig S2B).

      We did not analyse intermediate filaments. To our knowledge, there are no data implicating intermediate filaments in chlamydial entry. In C. trachomatis, their reorganization has been described during later inclusion development (Kumar and Valdivia, 2008, doi:10.1016/j.chom.2008.05.018), while in C. pneumoniae-infected cells alterations of vimentin and keratins 8/18 were detected at 48-72 h post-infection (Savijoki et al., 2008, doi:10.1111/j.1574-695X.2008.00488.x).

      Comment 5b

      Is this specifically targeting the MT under these conditions?


      Answer to comment 5b

      Taxol directly binds β-tubulin within polymerized MTs and stabilizes the MT lattice (Xiao et al., 2006, doi:10.1073/pnas.0603704103). Nevertheless, because actin and MTs are functionally interconnected, we experimentally assessed the actin cytoskeleton under the exact Taxol conditions used in our infection assay and did not observe a major alteration of F-actin organization.

      The purpose of the Taxol/Tubacin comparison was specifically to distinguish MT stabilization from increased tubulin acetylation per se. Taxol stabilizes MTs and consequently increases their acetylation, whereas Tubacin inhibits HDAC6-mediated tubulin deacetylation and increases MT acetylation without stabilizing MTs (Haggarty et al., 2003, doi:10.1073/pnas.0430973100). Under our experimental conditions, both treatments produced a comparable increase in acetylated MTs, whereas only Taxol generated MTs resistant to cold-induced depolymerization. These data are already shown in Fig 2 and Fig S3 and described in the Results (lines 195-220).

      Thus, both Taxol and Tubacin increase MT acetylation, but only Taxol stabilizes the MT network and only Taxol increases EB internalization. This is the basis for our conclusion that the long-lived MT state, rather than acetylation alone, is associated with enhanced C. pneumoniae entry.

      Comment 5c

      How toxic are the treatments and how were they titrated - this does not seem to be included.


      Answer to comment 5c

      The concentrations and treatment times are already given in both the Fig 2 legend and the Materials and Methods. U2OS cells were treated with 10 µM Taxol or 10 µM Tubacin for 2 h at 37°C before infection (Fig 2, lines 222-232; Materials and Methods, lines 675-686).

      These conditions were not established by a de novo dose-response titration in the present study but were selected on the basis of established short-term treatments for manipulating MT stability and acetylation in U2OS cells. Importantly, Jansen et al. used the same conditions - 10 µM Taxol for 2 h and 10 µM Tubacin for 2 h in U2OS cells - to experimentally distinguish stable from acetylated MT populations (Jansen et al., 2023, doi:10.1083/jcb.202106105). Tubacin as an inhibitor of HDAC6-dependent tubulin deacetylation was originally characterized by Haggarty et al. (2003, doi:10.1073/pnas.0430973100).

      We additionally verified the intended differential effects of these treatments in our own U2OS cells: both Taxol and Tubacin increased MT acetylation, whereas only Taxol protected MTs against cold-induced depolymerization (Fig 2 and Fig S3; lines 199-209).

      We did not perform a separate quantitative cytotoxicity assay. However, treatment was limited to 2 h, we observed no obvious signs of acute cellular deterioration or major changes in cell morphology, and the new F-actin analysis shows no gross disruption of the actin cytoskeleton under the Taxol conditions used.

      Comment 6

      line 156 - it is unclear what 'microtubule subsets' are referred to here and how the authors arrive at the fact that ~9% of MT are acetylated.

      Answer to comment 6

      We apologize that the term “microtubule subsets” was not sufficiently defined. By this term, we referred to microtubules distinguished by post-translational modification, in this case acetylated versus non-acetylated MTs. U2OS cells vary considerably in the abundance of acetylated MTs, ranging from cells containing few to cells containing many acetylated MTs.

      We have changed the wording in the results section accordingly.

      Comment 7

      Can the effects of taxol be modulated by changing the bacterial load (MOI)? The dose dependency of taxol is considered but not the reciprocal i.e. whether the effect can be suppressed by increasing the number of bacteria.

      Answer to comment 7

      We understand the proposed experiment to mean varying the bacterial load (MOI) at a constant taxol concentration to determine whether the taxol-dependent increase in entry becomes less apparent at higher MOIs.

      While such an experiment could test how the magnitude of the taxol effect depends on bacterial input, increasing the MOI is not mechanistically reciprocal to the taxol treatment. Taxol alters a host-cell property before addition of C. pneumoniae EBs by stabilizing MTs and increasing the population of long-lived/acetylated MTs. Our experiment therefore addresses whether this pre-existing MT state influences bacterial entry. Increasing the number of bacteria does not reverse or otherwise alter this host-cell state.

      Moreover, at high MOIs, a reduced relative difference between control and taxol-treated cells could result simply from saturation of available entry sites or cellular uptake capacity rather than from suppression of the taxol effect. We therefore consider an MOI titration in taxol-treated cells difficult to interpret with respect to the specific question of whether a pre-existing stabilized MT state promotes C. pneumoniae entry.

      Comment 8

      Figure 1 shows limited co-localisation between EB and MT. Are the authors certain that this is not stochastic? How many EB align with F-actin stress fibres on intermediate filaments under similar conditions. This might be interesting and correct, but controls are lacking to demonstrate specificity, which would make the data more convincing.

      Answer to comment 8

      We addressed the possibility that the observed EB–MT co-localization reflects stochastic overlap by quantifying the fraction of the cellular area occupied by MTs at the 10-min time point. At this stage of MT recovery, MTs occupied approximately 14% of the cellular area, whereas 35% of internalized EBs co-localized with MTs. Thus, EB–MT association occurred substantially more frequently than expected from MT area coverage alone. Using the MT-covered cellular area as the probability of random overlap, the observed frequency was significantly higher than expected for a random spatial distribution (exact binomial test, p The association was also strongly biased toward a specific MT population. Of the MT-associated EBs, 71% were associated with acetylated MTs, although acetylated MTs represented only approximately 9% of the total MT population under these conditions. This strong enrichment further argues against stochastic overlap.

      We deliberately performed this analysis at 10 min after shifting the cells back to 37°C, when MT re-polymerization is still incomplete and individual MT filaments are clearly distinguishable. This minimizes apparent co-localization resulting simply from the dense MT network present in untreated interphase cells.

      We do not consider F-actin to provide an equivalent negative control for this question. F-actin remains extensively distributed under these conditions and, importantly, actin is directly involved in chlamydial entry; EB association with actin would therefore be biologically expected rather than a measure of nonspecific cytoskeletal overlap. We did not analyse intermediate filaments. We consider the comparison between the observed EB–MT association and the quantitatively determined probability of random overlap to provide the more direct test of stochastic association.

      We have revised the Results section accordingly (lines 154-166): “Next, we analysed the subcellular localization of internalized EBs. At the 10-min time point (Fig. 1A), MT re-polymerization was still incomplete and individual MT filaments were clearly distinguished. Approximately 14% of the cellular area was occupied by MTs, whereas 35% of internalized EBs co-localized with MTs (Fig. 1E, F). Thus, EB association with MTs occurred at a substantially higher frequency than expected from MT area coverage alone. Consistently, comparison with a random spatial distribution using the fraction of MT-covered cellular area as the probability of random EB–MT overlap showed that the observed association was significantly higher than expected by chance (exact binomial test, p __ __

      Comment 9

      Figure 3. The cell cycle block relies upon RO-3306 which shifts the mitotic cell population from 5% to 36%. Would a thymidine block and release to synchronise the population yield a higher proportion of cells in mitosis? Did the authors consider this approach and exclude it for a defined reason?


      Answer to comment 9

      Achieving the highest possible proportion of mitotic cells was not the primary requirement for our experiment. Rather, we required a sufficient number of cells entering mitosis within a defined time window after release.

      Thymidine arrests cells at the G1/S transition, and cells must subsequently progress through S and G2 before entering mitosis. In contrast, the CDK1 inhibitor RO-3306 arrests cells directly at the G2/M transition and therefore allows rapid and temporally defined entry into mitosis following washout. We therefore considered RO-3306 more suitable for our experimental design. Importantly, our analysis does not rely on the entire synchronized population being mitotic. Following RO-3306 release, mitotic and non-mitotic cells were identified and analysed separately at the single-cell level. Under our conditions, approximately 36% of the population was mitotic, providing sufficient numbers of mitotic cells for quantification of C. pneumoniae infection. Thus, increasing the overall percentage of mitotic cells would not alter the basis of our comparison between mitotic and non-mitotic cells.

      Comment 10

      Many properties change in mitotic cells in addition to MT architecture. A particular consideration is the profound reorganisation of the actin cytoskeleton and changes in the composition of the plasma membrane, which might also influence the rates of Cpn entry. It is very technically difficult to show that these effects are specifically due to the MT changes and consequently this experiment, while interesting might have many alternative interpretations.

      • *

      Answer to comment 10

      We agree with the reviewer that mitosis involves extensive cellular reorganization in addition to the replacement of the interphase MT network by the mitotic spindle, and that the experiment in Fig 3 cannot by itself attribute the reduced C. pneumoniae entry specifically to changes in MT architecture. Indeed, we already considered this issue in the Results section. We note that endocytosis is generally reduced during early mitosis and is reactivated from anaphase onwards. At the same time, receptor-specific internalization pathways can remain active during mitosis, including EGFR uptake, which is particularly relevant here because EGFR is utilized by C. pneumoniae for host-cell entry. In addition, Fig 3D shows the mitotic reorganization of the actin cytoskeleton by rhodamine-phalloidin staining.

      Thus, we agree that changes in actin organization, membrane trafficking and other mitosis-associated cellular properties may contribute to the reduced infection efficiency observed in mitotic cells. Our intention with this experiment was not to establish that the reduction in EB entry is caused exclusively by loss of the interphase MT array. Rather, we asked whether C. pneumoniae entry is altered in a physiological cellular state in which the interphase MT architecture is absent and replaced by the mitotic spindle. We find that mitotic cells remain permissive to EB entry, but infection efficiency is strongly reduced compared with interphase cells.

      The MT-specific conclusions of our study are therefore based primarily on the experiments in Figs 1 and 2, in which MT composition and stability are directly analysed or manipulated. The mitotic-cell experiment provides complementary evidence showing that a cellular state lacking the normal interphase MT architecture is associated with strongly reduced EB entry, but we agree that this experiment alone cannot distinguish the contribution of MT reorganization from other mitosis-associated changes.

      To make this limitation explicit, we have changed the final sentence of this part of the results section to (lines 276-278):" Thus, EB entry is strongly reduced in mitotic cells, a cellular state characterized by loss of the interphase MT architecture but also by broader changes in cytoskeletal organization and membrane trafficking."__ __

      Comment 11a (please note- we have divided this into several sub comments)

      While the overexpression experiments in cells and yeast are interesting, these come with caveats about the dose of the effector and the relevance of the system to the pathological process.

      Answer to comment 11a

      The S. pombe experiments were designed to analyse the effects of CPn0572 on MT dynamics under controlled expression conditions, rather than to reproduce the infection process. CPn0572-mCherry is expressed from a single genome-integrated copy under an inducible TetO promoter, and MT dynamics are analysed after only 1 h of induction. This minimizes dosage heterogeneity and allows early effects of CPn0572 on the MT cytoskeleton to be analysed in living cells. This rationale is already described in the manuscript (lines 384-391).

      The manuscript also explicitly acknowledges that TetO-driven expression cannot reproduce the spatially restricted delivery of an effector by the bacterial secretion system; rather, it provides a tractable system in which the consequences of CPn0572 appearance in a eukaryotic cell can be analysed (lines 577-583).

      The relevance of the S. pombe system for analysing MT dynamics is addressed in detail in our response to Comment 15. Importantly, CPn0572 has independently been shown to associate with and stabilize MTs in mammalian cells in the infection (Höhler et al., 2024, doi:10.1242/jcs.263450). Thus, the S. pombe experiments are used to resolve how CPn0572 alters MT behaviour, not as a surrogate for C. pneumoniae infection.

      Comment 11b

      *Could Chlamydia trachomatis TARP that interacts with actin but not MT be used as a control here? *

      Answer to comment 11b

      We do not consider C. trachomatis TarP an appropriate matched negative control for CPn0572. Although both proteins belong to the TarP family and both modulate actin, their activities toward the actin cytoskeleton are not equivalent. TarP and CPn0572 show distinct patterns of subcellular localization and F-actin association, and CPn0572 additionally binds preassembled F-actin and protects it from cofilin-mediated destabilization (Jewett et al., 2006, doi:10.1073/pnas.0603044103; Jewett et al., 2010, doi:10.1371/journal.ppat.1000997; Zrieq et al., 2017, doi:10.3389/fcimb.2017.00511). CPn0572 additionally associates with MTs (Höhler et al., 2024, doi:10.1242/jcs.263450).

      This distinction is important because the actin and MT cytoskeletons are functionally interconnected (Dogterom and Koenderink, 2019, doi:10.1038/s41580-018-0067-1). Consequently, differences in MT behaviour following expression of TarP and CPn0572 could not be attributed specifically to the presence or absence of MT-binding activity. TarP therefore would not constitute a control differing from CPn0572 only in its ability to target MTs.

      Comment 11c

      While there are interesting effects of CPn0572, which in part relate to the other phenotypes identified in the work, the link between the activities of Cpn0572 overexpression and the infection process are currently weak, beyond the fact that this is one of a number of effectors what have the capability of manipulating the actin and/or MT cytoskeletal networks. The current presentation is therefore speculative.

      Answer to comment 11c

      We believe that this concern reflects a misunderstanding of how the CPn0572 experiments are positioned within the manuscript. The study addresses three consecutive but distinct questions: (1) whether the pre-existing state of the host MT network influences C. pneumoniae entry; (2) whether C. pneumoniae itself alters the host MT network during early infection; and (3) how a chlamydial protein with MT-modulating activity can alter MT dynamics. CPn0572 is used for the third question as one experimentally tractable example of a chlamydial MT modulator. We do not propose that CPn0572 alone accounts for either the entry phenotype or the infection-induced increase in MT acetylation.

      This three-part logic is already stated in the original manuscript. In the Introduction, we first define the permissive host MT state, then describe the infection-induced increase in MT acetylation, and finally introduce controlled expression of CPn0572 to analyse its effect on MT stability (lines 96-104).

      The distinction between the first two parts is made particularly explicit in the Discussion: “The preferential infection of cells containing acetylated MTs needs to be distinguished from the increase in MT acetylation observed 1 hr after chlamydial infection.” The manuscript then states that the former represents a host-cell property present before infection, whereas the latter demonstrates that C. pneumoniae remodels the host MT cytoskeleton during infection (lines 531-536).

      Likewise, CPn0572 is explicitly introduced as one example with which to investigate how chlamydial proteins might alter MT dynamics. The relevant results sections states that several C. pneumoniae proteins are likely to jointly manipulate the MT cytoskeleton and that CPn0572 was analysed “to start to understand how MTs might be modulated by chlamydial proteins” (lines 374-381). We then show that CPn0572 reduces MT catastrophe and depolymerization and suppresses the normal catastrophe response at the cell cortex, thereby increasing MT persistence (lines 403-410).

      The manuscript furthermore explicitly argues against a single-effector model. We state that the infection-induced increase in MT acetylation is likely to result from “multiple EB-associated effectors that together remodel the host MT network” (lines 572-574).

      To strengthen this concept experimentally, we have added an experiment in the revised version of the manuscript. We now analysed the combined activity of two chlamydial MT-modulating proteins namely CPn0572 and CPn0443. Thus, if we have two independent MT modulations, we would expect a phenotype intermediate between those produced by either protein alone. CPn0443 was originally identified as a C. pneumoniae protein that strongly alters the interphase MT cytoskeleton by destabilizing it (Wevers et al., 2023, doi:10.3390/ijms24087618) which is opposite to the function of CPn0572.

      We therefore asked what happens when these two opposing chlamydial MT modulators are present in the same cell. CPn0572 increases MT occupancy, whereas CPn0443 strongly reduces MT occupancy and longitudinal MT organization. Importantly, simultaneous expression produces an intermediate phenotype: CPn0572 partially counteracts both the CPn0443-induced reduction in MT occupancy and the loss of longitudinal MT organization. These new data (Fig. 7) directly demonstrate that two chlamydial MT modulators can interact at the level of the same cellular MT network. They therefore provide additional experimental support for the concept that host MT remodelling may reflect the combined activities of multiple chlamydial proteins rather than the action of CPn0572 alone.

      Thus, the manuscript neither establishes nor claims a one-to-one causal relationship between CPn0572 and the infection-induced MT phenotype. Rather, a pre-existing host MT state affects bacterial entry, C. pneumoniae subsequently remodels the host MT network, and CPn0572 is used as one example to determine how a chlamydial MT-modulating protein can alter MT dynamics.

      Comment 12

      The Discussion is extensive, and could be reduced to deal with the key findings presented in the work and potentially to address some of the limitations.

      __ ____Answer to comment 12__

      We have shortened the discussion.

      The original discussion already discusses the limitations and boundaries. For example, we state that Taxol-induced MT stabilization may not fully recapitulate the properties of naturally acetylated MTs, that the proposed contribution of MT-dependent membrane trafficking to EB entry remains to be tested, and that the infection-induced MT phenotype is likely to reflect the combined activity of multiple chlamydial proteins rather than a single effector. We also explicitly note that TetO-driven ectopic expression of CPn0572 does not reproduce the spatially restricted delivery of an effector during infection.

      In addition, following the reviewer’s specific concern regarding the mitotic-cell experiment, we have clarified this in the revised results section (see comment 10).

      Reviewer #1 (Significance (Required)):

      * This is an interesting and potentially important study, which will be of interest to researchers studying Cpn, related Chlamydiae and obligate intracellular bacteria, and more generally to those studying the entry of bacterial pathogens into host mammalian cells. Bacterial effectors like Cpn0572 are also of interest to the cell biology community, as studying their activities can reveal novel insights into the regulation and dynamics of the cytoskeleton, relevant to fields including immunology, developmental biology and cancer biology.*

      * The manuscript addresses key unresolved questions - for example, it tries to reconcile the potential role for the MT cytoskeleton in bacterial entry, which has been suspected but overtaken by studies of the actin cytoskeleton, where cause and effect and more straightforward. The work investigates role for the posttranslational modification of MT and how this can be reprogrammed by pathogens. Finally, it offers an opportunity to study the interplay between the actin and MT networks and how this might be bridged. This is not well understood in mammalian cells.*

      *Notwithstanding the comments above, the individual experiments presented are largely well executed and support the individual conclusions drawn. The weakness of the study is that it is descriptive and correlative. It is an assembly of interesting, but potentially differentially related, experiments examining MT during Cpn infection, essentially in three separate sections i) stability of cellular MT being important for Cpn infection, ii) assessing changes to MT modifications during Cpn infection, iii) the effects of a particular effector amongst many on these processes. While broadly self-supporting in that they all address Cpn and MT, they are presented as cohesive, although the direct relationships between these different topics remains somewhat subjective.

      The reviewer actively researches interactions between bacterial pathogens and the host cytoskeleton.*

      Our answer to Reviewer 1 (significance)

      We appreciate the reviewer’s positive assessment of the interest and potential importance of the study. We would, however, like to clarify both the conceptual connection between the experimental sections and what we consider an important aspect of the novelty of the work.

      Bacterial entry into mammalian cells has overwhelmingly been studied as an actin-driven process. Although MTs have been implicated in several bacterial infection cycles, their functions have been studied much less extensively and mainly in post-entry trafficking and later stages of infection. A defined role for different MT states during bacterial entry has remained largely unexplored.

      Our central finding is therefore not simply that MTs contribute to C. pneumoniae infection. We show that, within the same mammalian cell population, cells with a particular pre-existing MT state are preferentially infected. Increasing amounts of acetylated/long-lived MTs correlate with increasing entry efficiency, whereas detyrosinated MTs do not, and the Taxol/Tubacin experiments further distinguish MT stability from acetylation itself. To our knowledge, a pre-existing MT state has not previously been identified as a determinant of differential host-cell permissiveness to bacterial entry.

      The subsequent experiments build directly on this finding. Having established that a long-lived interphase MT state favors entry, we ask whether C. pneumoniae itself modifies this state and show that early infection increases MT acetylation in a viability-dependent manner. We then use CPn0572 as one mechanistically tractable early effector to ask how a chlamydial MT stabilizer can generate increased MT persistence and show that it suppresses catastrophe and reduces depolymerization.

      Thus, while the study does not establish a single linear molecular pathway, the experiments are not an assembly of differentially related observations. Together, they identify a previously unrecognized host-cell MT state that determines permissiveness to bacterial entry, show that Chlamydia subsequently remodels this cytoskeletal system, and provide mechanistic insight into how an early chlamydial effector can generate a persistent MT state.


      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Evidence, reproducibility, and clarity

      * Summary

      The manuscript by Schenk et al examines how the microtubule state of U2OS cells affects the ability of Chlamydia pneumoniae to enter the host cell. The authors test whether two tubulin post-translational modifications, detyrosination and acetylation, affect bacterium entry and find that cells with higher levels of acetylation display more internalized Chlamydia particles. They also test whether the tubulin state or the post-translational modification is the important factor for Chlamydia entry and find that stabilization of microtubules with taxol treatment is sufficient for increasing the number of internalized particles. They show that entry is higher in interphase cells than mitotic cells. Finally, they show that the Chlamydia protein CPn0572, which was previously shown to alter microtubules in mammalian cells, can alter microtubule dynamics in yeast cells. Overall, this is a straight-forward set of experiments that add information about how the state of microtubules in cells impacts the entry step of Chlamydia infection.*

      Response to Summary

      We thank the reviewer for this accurate summary of the main findings of our study.

      Major comments

      Comment 13

      In general, the claims and the conclusions are supported by the data. The data in Figures 1–4 address very specific questions and are straightforward. The only issue is that the microtubule immunofluorescence does not look very good. Especially the total tubulin staining. In many cells, it doesn't even look filamentous. Generally, methanol fixation preserves microtubule structures much better than PFA.

      __Answer to comment 13 __

      We agree that methanol fixation can provide a sharper visualization of filamentous MTs. However, the choice of fixation also depends on the biological question being addressed. Importantly, a study specifically examining fixation effects in Chlamydia trachomatis-infected cells showed that alcohol-based fixation can induce cellular shrinkage and distortion, whereas formaldehyde fixation is used to better preserve overall cellular architecture and the spatial relationships between cellular components (Kokes and Valdivia, 2015; doi: 10.1371/journal.pone.0139153). This consideration was particularly important in our experiments, because our analyses required assessment of the spatial relationship between chlamydial EBs and the host-cell MT cytoskeleton during the early stages of infection.

      PFA fixation has also been used in previous studies examining early Chlamydia–host cell interactions, including studies in U2OS cells and during early C. pneumoniae infection (Nans et al., 2014; doi__: _10.1111/cmi.12310_; Mölleken and Hegemann, 2017; doi: 10.1371/journal.ppat.1006556__).

      We acknowledge that the total-tubulin staining appears less sharply filamentous in some cells. Nevertheless, MT structures relevant to our analyses are distinguishable under the experimental conditions used, and identical fixation, staining and imaging conditions were applied across the respective experimental groups. Importantly, as also noted by the reviewer, the quantitative data in Figures 1–4 support the conclusions drawn from these experiments.

      Comment 14

      Its not clear why the inside/outside staining was only used in Fig 2. How do the authors know that the particles in the other figures are inside vs outside the host cell?

      __Answer to comment 14 __

      Inside/outside staining was used in Fig 2 because these experiments were designed specifically to quantify EB internalization. In contrast, Fig 1 examines changes in MT post-translational modifications, while Fig 3 examines the organization of the MT and actin cytoskeletons. For these experiments, inside/outside staining was technically not feasible because the complete staining combination would require five fluorescence channels, whereas our microscopy setup allows a maximum of four.

      We therefore used a spatial approach to assign EB localization in Figs 1 and 3. We initially tested a plasma membrane marker as a means of defining the cell boundary. However, the permeabilization required for subsequent tubulin immunostaining resulted in additional intracellular staining of this marker, preventing an unambiguous identification of the plasma membrane in the final samples. We therefore used the outer boundary of the cytoplasmic α/β-tubulin signal to delineate the cellular area in interphase cells. For the mitotic cells analyzed in Fig 3, the prominent cortical F-actin signal provided a clear definition of the cell boundary.

      Each optical section of the complete confocal z-stack was examined individually, and EBs were classified according to their three-dimensional position relative to the delineated cellular area. Thus, whereas Fig 2 uses inside/outside staining to directly distinguish internalized from extracellular EBs, EB localization in Figs 1 and 3 was assigned on the basis of their spatial position within the cellular volume.

      Comment 15a (please note- we have divided this into several sub comments)

      The use of S. pombe to test the effects of CPn0572-mCherry on microtubule dynamics seems an odd choice. It is not clear whether these findings are relevant to the story since yeast cells are very different from mammalian cells.


      Answer to comment 15a

      We consider S. pombe a highly appropriate system for analysing the effect of CPn0572 on MT dynamics for four reasons: (1) its simple and exceptionally well-characterized interphase MT cytoskeleton allows changes in MT bundle dynamics to be resolved particularly clearly; (2) fundamental components of the MT system are evolutionarily ancient; (3) yeast-based approaches are established for identifying functions of chlamydial proteins; and (4) Chlamydiae are themselves an ancient lineage of intracellular bacteria, making conserved eukaryotic cellular processes plausible targets for their effectors.

      (1) S. pombe interphase cells contain only a small number of well-defined MT bundles whose dynamics and behaviour at the cell cortex can be followed directly and quantitatively in living cells (Drummond and Cross, 2000, doi:10.1016/S0960-9822(00)00570-4; Sawin and Tran, 2006, doi:10.1002/yea.1404). This makes changes in MT bundle dynamics considerably easier to resolve than within the dense MT network of mammalian cells.

      (2) Yeast model systems have been exceptionally successful in uncovering fundamental principles of eukaryotic cell biology, as exemplified by Nobel-Prize-for- Medicine winning work on cell-cycle control, vesicle trafficking and autophagy. The tubulin-based MT cytoskeleton is likewise evolutionarily ancient: α-, β- and γ-tubulins and diverse MT motors were already present in the last eukaryotic common ancestor, before diversification of the major eukaryotic lineages (Wickstead and Gull, 2011, doi:10.1083/jcb.201102065). Many years ago, our own work provided a direct example of functional conservation: S. pombe Mal3 belongs to the EB1 family of conserved MT plus-end-tracking proteins that regulate MT dynamics, and human EB1 can substitute for Mal3 in S. pombe (Beinhauer et al., 1997, doi:10.1083/jcb.139.3.717).

      __(3) __Several yeast-based approaches successfully investigated chlamydial proteins. A systematic Saccharomyces cerevisiae expression screen identified C. trachomatis proteins that affect yeast cellular functions or target eukaryotic organelles (Sisko et al., 2006, doi:10.1111/j.1365-2958.2006.05074.x), and subsequent yeast-based screening identified chlamydial proteins targeting lipid droplets (Kumar et al., 2006, doi:10.1016/j.cub.2006.06.060). In our own S. pombe screen, 13 of 116 tested C. pneumoniae proteins strongly altered the interphase MT cytoskeleton (Wevers et al., 2023, doi:10.3390/ijms24087618).

      (4) Chlamydiae have a long evolutionary history of interaction with eukaryotic cells. The last common ancestor of pathogenic and symbiotic Chlamydiae was already adapted to intracellular survival approximately 700 million years ago and possessed a type III secretion system (Horn et al., 2004, doi:10.1126/science.1096330). It is therefore plausible that chlamydial effectors exploit ancient, conserved features of eukaryotic cell biology, including the MT cytoskeleton.

      We therefore do not use S. pombe as a model for mammalian infection itself, but as a tractable system in which effects of CPn0572 on fundamental MT properties can be resolved clearly. Importantly, relevance to mammalian cells is independently supported by our previous demonstration that CPn0572 associates with and stabilizes MTs in mammalian cells (Höhler et al., 2024, doi:10.1242/jcs.263450).


      Comment 15b

      Furthermore, the Fleig group has already shown that CPn0572 binds to microtubules when ectopically expressed in mammalian cells and causes their stabilization and bundling. It would be useful to see if CPn0572 expression increases acetylation when expressed in mammalian cells

      Answer to comment 15b

      This experiment has already been performed. Ectopic expression of CPn0572 resulted in an approximately threefold increase in acetylated α-tubulin compared with control cells (Höhler et al., 2024, doi:10.1242/jcs.263450).

      Comment 15c

      And test whether it directly alters microtubule dynamics using reconstitution assays.

      Answer to comment 15c

      Reconstitution experiments with purified CPn0572 and tubulin could test whether CPn0572 is sufficient to alter MT dynamics in a minimal in vitro system. However, CPn0572 modulates both the actin and MT cytoskeletons, and such an assay would not establish how its MT effects arise in the cellular context, where additional host components or interactions between the two cytoskeletal systems may contribute. The aim of the present study was to determine whether CPn0572 affects MT organization and dynamics in living cells. Together with our previous demonstration that CPn0572 associates with and stabilizes MTs in mammalian cells (Höhler et al., 2024, doi:10.1242/jcs.263450), the S. pombe experiments establish that CPn0572 alters MT behaviour in a cellular context. Dissecting whether this activity is mediated by a direct interaction with tubulin/MTs or involves additional host factors will require a separate biochemical analysis and is beyond the scope of the present study.

      Comment 15d

      Furthermore, its role in Chlamydia infection could be tested by deleting the gene from the Chlamydia genome.

      Answer to comment 15d

      A CPn0572 deletion could address the contribution of this effector to C. pneumoniae infection. However, targeted gene-deletion approaches such as those available for C. trachomatis have not been established for C. pneumoniae (Shima et al., 2018, doi:10.1128/mSphere.00412-18; Wan et al., 2023, doi:10.3389/fimmu.2023.1209879). Thus, deletion of CPn0572 is currently not technically feasible.

      Moreover, CPn0572 is a TarP-family effector that modulates both the actin and MT cytoskeletons (Höhler et al., 2024, doi:10.1242/jcs.263450). Consequently, even if a CPn0572 deletion mutant were available, any resulting infection phenotype would reflect the combined loss of its cellular activities and would not by itself establish the specific contribution of its MT-modulating function.

      *Minor comments

      *

      Comment 16

      The exact antibodies used for immunofluorescence and western blot should be listed. Some tubulin antibodies are not very good and the reader needs to know that the results are reliable.

      __Answer to comment 16 __

      We have revised the Materials and Methods section to provide the exact antibodies used for all immunofluorescence and Western blot analyses, including the respective supplier, catalogue number and antibody dilution. In addition, for experiments in which different α-tubulin antibodies were used, we now specify the antibody and its host species for each individual experiment, allowing unambiguous identification of the antibody used.

      Comment 17

      Fig. 4 – it would be nice to validate the increase in acetylation by immunofluorescence.

      Answer to comment 17

      We thank the reviewer for this valuable suggestion. We have now independently validated the infection-induced increase in MT acetylation by immunofluorescence microscopy. Representative fluorescence images together with the corresponding quantitative analysis have been added to the revised Fig. 4G, H. Consistent with the Western blot analysis, immunofluorescence showed a significant MOI-dependent increase in acetylated α-tubulin 1 h post infection.

      The corresponding text has been added to the revised manuscript (lines 329–332):

      “To independently validate the infection-induced increase in MT acetylation observed at 1 hpi, we additionally analysed acetylated MT levels by immunofluorescence microscopy. Consistent with the Western blot results, immunofluorescence analysis showed a significant MOI-dependent increase in MT acetylation (Fig. 4G, H).”

      This independent analysis confirms the increase in MT acetylation observed by Western blotting and supports our conclusion that C. pneumoniae infection induces increased MT acetylation.

      Comment 18

      The term inside-out staining is confusing. I think the authors mean inside/outside staining.

      Answer to comment 18

      We agree that “inside/outside staining” more accurately describes the staining approach used in our experiments. We have therefore replaced “inside-out staining” with “inside/outside staining” throughout the revised manuscript.

      Reviewer #2 (Significance (Required)):

      * This study provides new information about how Chlamydia alters the microtubule cytoskeleton to enter mammalian cells. Previous work had shown that Chlamydia utilizes the actin cytoskeleton so this study expands our knowledge of the entry mechanisms. The insights would be more mechanistic if the effects of CPn0572 could be shown in reconstitution assays. The work will be of interest to researchers that study the basic mechanisms of pathogen entry into mammalian cells.*

      Our Response to Significance

      We thank the reviewer for this assessment. The point concerning a CPn0572 reconstitution assay is addressed in our response to Comment 15c above.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The manuscript by Schenk et al examines how the microtubule state of U2OS cells affects the ability of Chlamydia pneumoniae to enter the host cell. The authors test whether two tubulin post-translational modifications, detyrosination and acetylation, affect bacterium entry and find that cells with higher levels of acetylation display more internalized Chlamydia particles. They also test whether the tubulin state or the post-translational modification is the important factor for Chlamydia entry and find that stabilization of microtubules with taxol treatment is sufficient for increasing the number of internalized particles. They show that entry is higher in interphase cells than mitotic cells. Finally, they show that the Chlamydia protein CPn0572, which was previously shown to alter microtubules in mammalian cells, can alter microtubule dynamics in yeast cells. Overall, this is a straight-forward set of experiments that add information about how the state of microtubules in cells impacts the entry step of Chlamydia infection.

      Major comments

      In general, the claims and the conclusions are supported by the data. The data in Figures 1-4 address very specific questions and are straight-forward. The only issue is that the microtubule immunofluorescence does not look very good. Especially the total tubulin staining. In many cells, it doesn't even look filamentous. Generally, methanol fixation preserves microtubule structures much better than PFA.

      Its not clear why the inside/outside staining was only used in Fig 2. How do the authors know that the particles in the other figures are inside vs outside the host cell?

      The use of S pombe to test the effects of CPn0572-mCherry on microtubule dynamics seems an odd choice. It is not clear whether these findings are relevant to the story since yeast cells are very different from mammalian cells. Furthermore, the Fleig group has already shown that CPn0572 binds to microtubules when ectopically expressed in mammalian cells and causes their stabilization and bundling. It would be useful to see if CPn0572 expression increases acetylation when expressed in mammalian cells. And test whether it directly alters microtubule dynamics using reconstitution assays. Furthermore, its role in Chlamydia infection could be tested by deleting the gene from the Chlamydia genome.

      Minor comments

      The exact antibodies used for immunofluorescence and western blot should be listed. Some tubulin antibodies are not very good and the reader needs to know that the results are reliable.

      Fig 4 - it would be nice to validate the increase in acetylation by immunofluorescence

      The term inside-out staining is confusing. I think the authors mean inside/outside staining

      Significance

      This study provides new information about how Chlamydia alters the microtubule cytoskeleton to enter mammalian cells. Previous work had shown that Chlamydia utilizes the actin cytoskeleton so this study expands our knowledge of the entry mechanisms. The insights would be more mechanistic if the effects of CPn0572 could be shown in reconstitution assays. The work will be of interest to researchers that study the basic mechanisms of pathogen entry into mammalian cells.

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      Referee #1

      Evidence, reproducibility and clarity

      In this interesting manuscript the authors present experiments examining the relationship between the obligate intracellular bacterium Chlamydia pneumoniae (Cpn) and the microtubule (MT) cytoskeleton of the host eukaryotic cell using both mammalian cells and yeast as a model. They demonstrate that host microtubule stability contributes to the rate of entry of the bacteria into the cells. Interphase MT architecture is therefore important and correspondingly they show that mitotic cells are less permissive to bacterial entry. Other experiments show that Cpn entry is accompanied by changes to MT stability (measured indirectly via post-translational modifications). Finally they investigate the effects of overexpressing a Cpn virulence factor Cpn0572 in mammalian cells and yeast that they have previously shown to interact with both the actin and MT cytoskeletal networks. Cpn0572 expression induces MT acetylation (stability) which correlates with their previous observations and when expressed ectopically in yeast Cpn0572 suppresses force-dependent MT catastrophe.They propose that chlamydial effectors like Cpn0572 influence MT architecture and stability during Cpn entry, revealing a previously unappreciated role for MT in the bacterial entry process.

      Experimental points

      1. Fig 1A - to assist interpretation whole cell images similar to those in the supplementary file should be included. It is difficult to relate the images in the small immunofluorescence panels to the phenotypes depicted.
      2. Cold recovery assay: one wonders what happens to the actin and intermediate filament networks under these conditions and when treated with MT-targetted agents. Some data should be included to rule out that there are additional effects on these systems as off target effects on actin might also influence the data. This is an essential control to support the conclusions drawn.
      3. Is MT repolymerisation synchronous following cold recovery? Its difficult to assess from the included images and sample size. Why was this method selected in preference to nocodazole treatment and washout, where synchrony is easier to establish.
      4. Although EB are scored in Fig 1A, they are not shown alongside the microtubules and modified microtubules as described.
      5. Relating to point 2, what happens to actin/intermediate filaments following the treatments with taxol? Is this specifically targetting the MT under these conditions. How toxic are the treatments and how were they titrated - this does not seem to be included.
      6. line 156 - it is unclear what 'microtubule subsets' are referred to here and how the authors arrive at the fact that ~9% of MT are acetylated.
      7. Can the effects of taxol be modulated by changing the bacterial load (MOI)? The dose dependency of taxol is considered but not the reciprocal i.e. whether the effect can be suppressed by increasing the number of bacteria.
      8. Figure 1 shows limited co-localisation between EB and MT. Are the authors certain that this is not stochastic? How many EB align with F-actin stress fibres on intermediate filaments under similar conditions. This might be interesting and correct, but controls are lacking to demonstrate specificity, which would make the data more convincing.
      9. Figure 3. The cell cycle block relies upon RO-3306 which shifts the mitotic cell population from 5% to 36%. Would a thymidine block and release to synchronise the population yield a higher proportion of cells in mitosis? Did the authors consider this approach and exclude it for a defined reason?
      10. Many properties change in mitotic cells in addition to MT architecture. A particular consideration is the profound reorganisation of the actin cytoskeleton and changes in the composition of the plasma membrane, which might also influence the rates of Cpn entry. It is very technically difficult to show that these effects are specifically due to the MT changes and consequently this experiment, while interesting might have many alternative interpretations.
      11. While the overexpression experiments in cells and yeast are interesting, these come with caveats about the dose of the effector and the relevance of the system to the pathological process. Could Chlamydia trachomatis TARP that interacts with actin but not MT be used as a control here? While there are interesting effects of Cpn0572, which in part relate to the other phenotypes identified in the work, the link between the activities of Cpn0572 overexpression and the infection process are currently weak, beyond the fact that this is one of a number of effectors what have the capability of manipulating the actin and/or MT cytoskeletal networks. The current presentation is therefore speculative.
      12. The Discussion is extensive, and could be reduced to deal with the key findings presented in the work and potentially to address some of the limitations.

      Significance

      This is an interesting and potentially important study, which will be of interest to researchers studying Cpn, related Chlamydiae and obligate intracellular bacteria, and more generally to those studying the entry of bacterial pathogens into host mammalian cells. Bacterial effectors like Cpn0572 are also of interest to the cell biology community, as studying their activities can reveal novel insights into the regulation and dynamics of the cytoskeleton, relevant to fields including immunology, developmental biology and cancer biology.

      The manuscript addresses key unresolved questions - for example, it tries to reconcile the potential role for the MT cytoskeleton in bacterial entry, which has been suspected but overtaken by studies of the actin cytoskeleton, where cause and effect and more straightforward. The work investigates role for the posttranslational modification of MT and how this can be reprogrammed by pathogens. Finally, it offers an opportunity to study the interplay between the actin and MT networks and how this might be bridged. This is not well understood in mammalian cells.

      Notwithstanding the comments above, the individual experiments presented are largely well executed and support the individual conclusions drawn. The weakness of the study is that it is descriptive and correlative. It is an assembly of interesting, but potentially differentially related, experiments examining MT during Cpn infection, essentially in three separate sections i) stability of cellular MT being important for Cpn infection, ii) assessing changes to MT modifications during Cpn infection, iii) the effects of a particular effector amongst many on these processes. While broadly self-supporting in that they all address Cpn and MT, they are presented as cohesive, although the direct relationships between these different topics remains somewhat subjective.

      The reviewer actively researches interactions between bacterial pathogens and the host cytoskeleton.

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      Referee #3

      Evidence, reproducibility and clarity

      Microtubule dynamics depend on the concentration of soluble αβ-tubulins. When cells detect an increase in soluble αβ-tubulin, they trigger degradation of tubulin mRNAs via a process termed tubulin autoregulation. In this pathway, the ribosome-associated factor TTC5 recognizes nascent amino-terminal autoregulatory MREC and MREI motifs in αβ-tubulins. Upon recognition of the nascent tubulin chain, TTC5 recruits the adaptor protein SCAPER, which in turn engages the CCR4-NOT complex to promote mRNA decay. While this mechanism has been well characterized for α- and β-tubulin transcripts, how cells regulate the abundance of the core microtubule nucleator γ-tubulin remains poorly understood. Here, Assaf et al. show that γ-tubulin-encoding mRNAs are also downregulated through the same tubulin autoregulation pathway (the TTC5-SCAPER-CCR4-NOT axis) in response to elevated soluble αβ-tubulin. They demonstrated that disruption of this pathway, through knockout or mutation of TTC5, SCAPER, or CNOT11, leads to increased γ-tubulin mRNA levels following treatment with the microtubule destabilizer combretastatin A-4 (CA4). Furthermore, mutation of the autoregulatory MPREI motifs in TUBG1 and TUBG2 (TUBGR3H) results in a modest increase in γ-tubulin protein levels. This elevation enhances centrosomal γ-tubulin during mitosis, increases microtubule nucleation capacity (as measured by microtubule regrowth after cold treatment), and ultimately reduces mitotic fidelity.

      Major comments:

      1. The authors concluded that tubulin autoregulation-associated mitotic defects are largely driven by elevated γ-tubulin protein levels. However, it is somewhat surprising that such a modest increase in γ-tubulin protein level (1.10-, 1.18-, 1.23-fold in TTC5 KO, TUBGR3H, TTC5 KO+TUBGR3H cells, respectively) leads to chromosome alignment and segregation defects. Given that γ-tubulin is a relatively abundant protein, with only a small fraction localized at centrosomes [PMID: 27539480], it remains unclear whether this magnitude of increase is sufficient to account for the observed mitotic defects. To more directly test whether a modest increase in γ-tubulin is sufficient to impair mitotic fidelity, it would be informative to perform live-cell imaging of γ-tubulin-GFP and chromosomes in cells moderately overexpressing wild-type or R3H γ-tubulin (as in Figure 3A), in the presence and absence of siTUBG1. This approach would help determine whether a comparable increase in γ-tubulin levels is sufficient to induce chromosome missegregation. Alternatively, the authors should consider tempering or revising their conclusion.

      Minor comments:

      1. On page 4, the authors state that measuring TUBG pre-mRNA and mRNA levels allows for distinguishing transcriptional (pre-mRNA) from post-transcriptional (mRNA) regulation, citing reference [33: PMID: 15367667]. While this approach is appropriate, it is unclear why reference [33: PMID: 15367667] is cited here, as it does not appear to directly describe this methodology. Please provide a more relevant reference or clarify the rationale for this citation.
      2. For the immunoprecipitation shown in Fig. 3B, an appropriate negative control is needed. For example, a parental cell line lacking γ-tubulin-FLAG expression should be included to assess background binding.
      3. Comparing γ-tubulin localization at centrosomes between Fig. 3C, D and Fig. 4B, the difference between parental and mutant cell lines (TTC5 KO, TBUGR3H and TTC5KO + TBUGR3H) appears more pronounced in Fig. 4B. It would be helpful if the authors could quantify centrosomal γ-tubulin localization in Fig. 4B to facilitate a direct comparison. In addition, could the authors comment on whether the cold treatment used in Fig. 4B might influence the soluble αβ-tubulin levels? If so, this could potentially enhance tubulin autoregulation in parental cells, leading to reduced γ-tubulin mRNA levels, while this response would be impaired in the mutant cell lines. Such an effect might contribute to the increased difference in centrosomal γ-tubulin observed under these conditions.

      Significance

      The manuscript provides significant and new mechanistic insights into microtubule regulation by identifying γ-tubulin as a target of the microtubule autoregulation pathway. It further suggests a new model that cells coordinately adjust both microtubule building blocks and nucleation capacity in response to changes in soluble tubulin pools through a common molecular machinery. The data are clearly presented, the experiments are rigorous, including the well-controlled cell lines, and the manuscript is well written and easy to follow. Based on its quality, novelty, and significance, I strongly support publication.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Assaf et al. address an important question by investigating the post-transcriptional-regulation of gtubulin mediated by the tubulin autoregulation mechanism and its functional role in microtubule nucleation and chromosome segregation. Autoregulation of a/b-tubulin has been shown previously, including by the current authors, but whether other tubulin genes are regulated in a similar manner was unknown. Using cell culture models and treatment with microtubule-destabilising/stabilising drugs, they demonstrate that cells regulate gtubulin levels in response to changes in soluble a-b tubulin levels. This regulation depends on the tubulin autoregulation mechanism that the authors previously identified for a/b-tubulin and the authors identify a similar motif in the N-term of gtubulin that is recognized by TTC5. By using a R3H g-tubulin mutant, which specifically disrupts the binding of TTC5, they examine the consequences of deregulated gtubulin biosynthesis. They showed that loss of g-tubulin mRNA regulation moderately increases overall gtubulin levels, which is nevertheless sufficient to enhance microtubule nucleation and induce mitotic defect.

      Specific points:

      • As pointed out by the authors in the discussion, there needs to be an explanation for how regulating just the mRNA levels of g-tubulin (and not other complex components) can influence the overall protein levels of g-TuRC in order to achieve a functional output for the regulation. The authors provide a nice explanation for when g-tubulin levels drop - this would potentially expose Ubi sites on GCPs that would lead to their ubiquitination and degradation. First, can the authors show that the GCP protein levels are also decreased, like g-tubulin, when they increase the pool of a/b-tubulin dimers? Second, what do the authors think happens when g-tubulin levels are increased? How does this lead to an increase in GCP levels? I don't think this is essential to answer, and certainly not experimentally, but if there is no simple answer then the authors should at least acknowledge this in the discussion.
      • Statistics - In several cases (eg. Fig 3D), the authors compare multiple conditions to one control and use Mann-Whitney or t-tests. The need to use one-way ANOVA with correction for multiple comparisons. This is also true when they compare different conditions to each other, while also comparing to controls. The statistical analysis should be done in a single ANOVA analysis, not with multiple different individual tests. E.g. Fig 4C.
      • N numbers: In many of the experiments, the authors perform 3-4 biological replicates, plotting the value from each replicate e.g. only 3 or 4 values. In Fig 3D and 4C, however, where they examine g-tubulin levels at centrosomes and microtubule nucleation after cold treatment, they plot the individual centrosome values from each of the replicates. Given that there may be variability between the replicates, and the number of centrosomes are not equal between replicates, it would be better to plot the average value from each replicate, which would better match how data is plotted in other experiments. For example, in Figure 4C, perhaps there is not really a significant difference in microtubule nucleation between TTC5 KO and TTC5 KO + TUBGR3H. This result is a bit odd considering the levels of centrosomal g-tubulin are not different (Fig 3D). The authors try to address this in the discussion (without mentioning the result in the results), but I am not fully convinced by their arguments.
      • In the IP shown in Figure 3B, a negative control, such as a construct expressing the FLAG tag alone, is necessary to confirm the specificity of the interactions.
      • In Figure 4B, the authors should include an image that is representative of their quantification. Based on the current image, we would conclude that nucleation is reduced in the TTC5 + TubGR3H condition compared to TubGR3H alone, which is not consistent with the quantification shown in Figure 4C.
      • In Figure S7A, the authors show that all cell lines show similar timing; however, this is not evident from the examples shown in Fig 5B. This is likely because there is a lot of variation in cell division timing and the authors chose to show example images from cells that happened to have different timings. However, this may appear confusing to readers. I t may be better to include the timing graph in the main figure, along with single images to highlight phenotypes (rather than time series for each condition). The time series images could be moved to supplementary.
      • The authors conclude that « partial depletion of g-tubulin restored mitotic fidelity to levels comparable to those of control cells » however in fig 5E, and 5F they only statistically compare the siRNAi control to the siTUBG1 for each genotype (which shows a reduction). But to say that they go back down to control levels, they should also statistically test the difference with the parental siRNAi control.

      Minor comments:

      • Figure 1C : the line above "SCAPER KO" + should be only on the last 2 columns
      • Figure 1E : It would improve clarity if the authors indicated in the figure that the immunoprecipitation was performed using TTC5
      • Figure 5E-F : It is not clear from the graph whether the parental cells were also transfected with the siRNA, although this appears to be the case based on the figure legend.
      • In the legend of Figure S4E, the cell lines appear to be TTC5 KO and TUBGR3H, rather than mutant TTC5 as indicated in the legend

      Significance

      The manuscript is well written, the data is well presented and overall the data supports the conclusions being drawn. The results and conclusions are significant and will be of interest to a broad readership.

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      Referee #1

      Evidence, reproducibility and clarity

      Microtubule nucleation and dynamics are essential for proper microtubule organization and for diverse cellular functions, including cell division. Nucleation is templated by a ring of y-tubulins within the γ-tubulin ring complex (yTuRC) and therefore depends on the cellular availability of γ-tubulin. Nucleation rates are regulated not only by yTuRC activators but also by the pool of soluble αβ-tubulin available for microtubule polymerization. Cellular αβ-tubulin abundance is controlled by a previously identified autoregulatory pathway that fine-tunes αβ-tubulin mRNA stability in response to changes in soluble tubulin levels. Unexpectedly, y-tubulin transcripts were found to be downregulated in parallel with αβ-tubulin under conditions of elevated soluble tubulin, prompting Assaf et al. to investigate whether y-tubulin abundance is regulated by the canonical tubulin autoregulatory mechanism. By combining transcriptomic reanalysis, targeted genetic perturbation, biochemical interaction assays, and functional cell biological approaches, the authors show that γ-tubulin expression is regulated similarly to αβ-tubulin through a post-transcriptional mechanism in response to soluble tubulin levels, identifying the TTC5-SCAPER-CCR4-NOT axis as essential for the decay of γ-tubulin mRNA, as previously shown for αβ-tubulins. The authors also claim tha loss of γ-tubulin mRNA regulation leads to increased γ-tubulin protein levels and enhanced microtubule nucleation, which ultimately affects mitotic fidelity. Interestingly, they show that just subtle changes in γ-tubulin levels are sufficient to compromise mitotic fidelity, suggesting that γ-tubulin-mediated nucleation is a particularly sensitive control point for mitosis.

      Major comments:

      • Interpretation of TUBG2 transcript data (Fig. 1A, Fig. S1): the authors state that "a similar trend was observed for TUBG2, although the changes in transcript levels were more variable across cell lines." Given that TUBG2 mRNA is expressed at very low levels in non-neuronal cells, conclusions drawn from these datasets are inherently less reliable. This is also reflected by the fact that the authors do not pursue TUBG2 regulation further, in contrast to the detailed analysis of TUBG1. In addition, the trends observed for TUBG2 do not appear as consistent as for TUBG1. I therefore suggest toning down claims regarding TUBG2, or clearly stating that these observations are preliminary. If the authors wish to strengthen this point, experiments in neuronal cells - using microtubule-stabilizing or -destabilizing drugs, or cold-induced changes in soluble αβ-tubulin - would be more appropriate to assess TUBG2 regulation.
      • To improve clarity and focus, I suggest reorganizing Figure 1 and Supplementary Figure 1- move TUBG1 transcriptomic data from Fig. S1A to Fig. 1A and TUBG2 data from Fig. 1A to the Supplementary Figures. This would emphasize the main γ-tubulin analyzed throughout the manuscript under conditions of altered soluble αβ-tubulin, while relegating the less robust data to supplementary material. In this context, Fig. 1E could be moved to Fig. S1, while Fig. S1B could be promoted to Fig. 1, as the fold change observed for TUBG1 in rat heart myocardium is relatively large and appears biologically meaningful.
      • The final summary of results section 1concludes that "TUBG mRNA is regulated post-transcriptionally in response to changes in soluble αβ-tubulin levels." While the effects of the drugs used are well characterized, microtubule-targeting agents can differ in magnitude and kinetics across cell types. To fully support this statement, it would be important to directly show that soluble αβ-tubulin levels change under the conditions used, for example by biochemical fractionation (polymerized vs soluble tubulin). If this is not feasible, the conclusion should be softened to state that TUBG mRNA responds to microtubule-stabilizing and -destabilizing treatments, rather than inferred changes in soluble αβ-tubulin.
      • In Figure 2D, the fold change observed for CNOT11 is less pronounced than for other components of the autoregulation pathway, which is somewhat unexpected given its proposed role as the downstream effector. It would be helpful to clarify whether this regulation is specific to CNOT11, or whether other CCR4-NOT subunits might compensate or contribute. Relatedly, in Fig. S2C, the increase in TUBG1 pre-mRNA levels in CNOT11-KO cells - although not statistically significant - suggests that baseline transcription or mRNA stability may already be altred. This raises some uncertainty regarding the specificity of CNOT11 in regulating γ-tubulin mRNA decay. Additional discussion or clarification would strengthen the interpretation.
      • Quantification of centrosomal fluorescence in fgs. 3D, 4C, S5B-C: Centrosomal γ-tubulin and α-tubulin levels are quantified using integrated density measured within a fixed-size circular ROI. Because ROI area is constant, this approach effectively reflects mean fluorescence intensity, rather than total centrosomal content, unless the ROI fully encompasses all centrosomal signal. Given that centrosome size and γ-tubulin spatial distribution may vary between conditions, a brief justification of how the ROI size was chosen, or a control analysis demonstrating robustness to ROI size (beyond fig S6B), would strengthen the conclusions.
      • Fig. 3E: the change in γ-tubulin levels is modest; complementary measurements of global γ-tubulin levels would strengthen this conclusion.
      • In Fig. 4B, the representative TUBG-R3H cell appears not only to nucleate more microtubules, but also to display faster microtubule polymerization. This is unexpected, as this mutation is proposed to specifically affect γ-tubulin regulation rather than αβ-tubulin availability. In contrast, TTC5-KO cells, where both γ-tubulin and αβ-tubulin regulation may be affected, would more intuitively show such a phenotype. This discrepancy makes the quantification in Fig. 4C difficult to reconcile with the representative images, where microtubule regrowth (based on α-tubulin signal) appears highest in TUBG-R3H cells. Additional clarification or discussion would be helpful.

      Minor comments

      1. Introduction: while the authors thoroughly describe how cells respond to excess soluble αβ-tubulin through autoregulation, the manuscript does not address how cells initially sense changes in soluble tubulin levels. Even if this mechanism remains unresolved, briefly acknowledging this conceptual gap or discussing current hypotheses in the field would strengthen the Introduction and better frame the study.
      2. Figures and legends
        • Fig. 1 and Fig. S1 legends: reference 34 should be reference 36; ***p < 0.001 is mentioned but not shown; Fig. S1B should include a reference along with the GEO accession number.
        • The sentence "In line with previous results for TUBB transcripts (Fig. S1D)" is ambiguous. Please clarify whether this refers to previously published data only, newly generated data, or a combination. Similar clarification may be needed for Fig. S1C and S1E.
        • Fig. 1D: please briefly comment on why microtubule stabilization with PTX leads to a slight but significant decrease in pre-mRNA levels.
        • Replace "decay in tubulin autoregulation" with degradation of TUBA and TUBB mRNA.
        • Text where the call for Fig. 2E appears should read: "TUBG1 and TUBB mRNAs following CA4 treatment in TTC5-KO cells..."
        • Fig. 2F-H: use γ-tubulin and β-tubulin instead of TUBB abd TUBG; the positioning of the γ-tubulin nascent chain within the TTC5 pocket is not clearly illustrated based on author's claim: "the γ-tubulin nascent chain appears to be positioned deeper into the TTC5 pocket (Fig. 2F-G)", and an electrostatic interaction between γ-tubulin R3 and TCC5 D225 should be represented in 2H as in 2G to support the claim that "...the key electrostatic interactions (...) are predicted to be maintained...". Overall, these figures may need adjustement or clarification.
        • Fig. S3B: tubulin should be replaced by α-tubulin.
        • Fig. S3B-C: immunoblots should be accompanied by quantification of the five biological replicates.
        • Fig. S4F-G: these results are compelling and could be moved to the main figure.
        • The metaphase plates shown in Fig. 3C appear relatively homogeneous across conditions, which contrasts with the mitotic defects quantified later (e.g. Fig. 5C). Including representative examples with clearer chromosome alignment or segregation errors, particularly for TTC5-KO cells, would better illustrate the reported phenotypes.
        • In Figs. 3D, 4C, 5B-C, 6C, please specify whether each dot represents a single centrosome or the mean of both centrosomes per cell. Ideally, each dot should correspond to the mean value per cell.
        • Fig. 3E: the change in γ-tubulin levels is modest; complementary measurements of global γ-tubulin levels would strengthen this conclusion.
        • Fig. 4B y-axis should specify α-tubulin fluorescence intensity.
      3. Discussion
        • The Discussion would benefit from explicitly acknowledging limitations, such as the relatively - The statement "among γ-TuRC components, only γ-tubulin mRNA is subject to autoregulation" should be rephrased, or additional γ-TuRC subunits should be tested to support this claim.
      4. Methods
        • Consider separating imaging procedures from analysis into distinct sections (e.g., Immunofluorescence and Microscopy data analysis).
        • Correct 2.4 µm (not µM).
        • Clarify ROI selection and background subtraction strategy, as discussed above.
      5. Optional / stylistic
        • Use consistent placement of "n.s." and asterisks in bar plots.
        • Fig. 2A: if CA4 is included in the schematic, it should be mentioned in the legend. Alternatively, the model could depict a generic increase in soluble αβ-tubulin dimers.
        • Fig. S3A may be unnecessary; instead, consider summarizing homology percentages between αβ-tubulin and γ-tubulin, highlighting higher N-terminal and lower C-terminal conservation to further motivate shared autoregulation.

      Significance

      This study extends the concept of tubulin autoregulation beyond αβ-tubulin by identifying γ-tubulin as an additional target of the same post-transcriptional regulatory pathway. By doing so, it highlights a coordinated mechanism that links control of microtubule building blocks with regulation of microtubule nucleation capacity, which is central for maintaining proper microtubule organization and mitotic fidelity. The work therefore contributes to a more integrated view of how cells balance microtubule mass, number, and organization during cell division. The tubulin autoregulation pathway involving TTC5, SCAPER, and the CCR4-NOT complex has been well characterized for αβ-tubulin, and previous studies had already established that γ-tubulin levels are tightly controlled, with both overexpression and depletion leading to mitotic defects. While the extension of this regulatory mechanism to γ-tubulin is important, it builds on existing concepts rather than introducing a fundamentally new regulatory pathway. In this sense, the study refines and extends current knowledge by providing mechanistic insight into how γ-tubulin abundance is regulated. The finding that among γ-TuRC components only γ-tubulin mRNA appears to be subject to autoregulation raises interesting questions regarding the specificity and functional consequences of this selective regulation. Although the work does not introduce a completely novel concept, its detailed analysis of γ-tubulin autoregulation and its functional impact on microtubule nucleation and mitotic fidelity will be of interest to the microtubule and cell division research communities.

      My expertise lies in microtubule nucleation and minus-end regulation, with a focus on γ-TuRC function and the in vitro reconstitution of its regulation and activity.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Manuscript number: RC-2026-03654

      Corresponding author(s): Yusuke, Kishi

      1. General Statements [optional]

      Reviewer #1 (Evidence, reproducibility and clarity (Required))

      The manuscript by You et al. investigates changes in gene expression and histone modifications after juvenile social isolation (jSI) in the nucleus accumbens (NAc). They find many differentially expressed genes and find overlap with activating or repressive histone marks. They then go on to compare their data to other published datasets to support their findings. This is an interesting study, and the authors use creative approaches using unique and published data to identify epigenetic mechanisms underlying the changes in gene expression within the NAc. However, there are many points of clarification that are needed to fully evaluate the manuscript and several experimental details.

      Reviewer #1 (Significance (Required))

      This is an interesting study and the authors use creative approaches using unique and published data to identify epigenetic mechanisms underlying the changes in gene expression within the NAc. However there are many points of clarification that are needed to fully evaluate the manuscript and several experimental details are missing or unclear.

      We thank the reviewer for the positive assessment of our study and for the constructive comments, which have helped us to improve the manuscript considerably. The reviewer's points centered in particular on the framing of the Introduction, especially the conflation of adult and adolescent social isolation, the use of nominal rather than FDR-corrected p-values to define DEGs, and the lack of clarity in several parts of the Methods.

      We have addressed each of these points in our responses below, in part through revisions already made to the manuscript. Briefly, the Introduction and the Methods have been substantially revised, and we will carry out additional threshold-free analyses to support our conclusions. We are grateful to the reviewer for identifying the points that most needed clarification.

      Reviewer #2 (Evidence, reproducibility and clarity (Required))

      This paper profiles NeuN positive NAc nuclei after juvenile social isolation. The authors report RNA seq changes and CUT and Tag for H3K4me1, H3K4me3, H3K27ac, and H3K27me3. They then link DEGs to public datasets for Kdm6b, Brd4, and Setd1a. The neuron enriched design is useful. The main claim remains correlative. Causal support is thin.

      Reviewer #2 (Significance (Required))

      This study provides a useful neuron-enriched transcriptomic and histone modification resource from the nucleus accumbens following juvenile social isolation. The integration of RNA-seq and CUT&Tag data adds value for researchers studying epigenetic regulation and stress-related neurobiology. However, the advance is primarily descriptive rather than mechanistic, as the conclusions rely largely on correlative analyses without functional validation. The manuscript will be of interest to the neuroepigenetics and psychiatric neuroscience communities, but the conceptual advance is incremental, and the mechanistic claims should be moderated.

      We thank the reviewer for recognizing the value of our neuron-enriched dataset and for the constructive comments, which have helped us to improve the manuscript considerably. The reviewer's points centered in particular on the correlative nature of our findings and the need to moderate causal language, the permissive thresholds used to define DEGs and the absence of correction for multiple testing, and the interpretation of the comparisons with published datasets.

      We address each of these points in detail in our responses below, and have already implemented a substantial part of the revisions. Briefly, we have removed wording implying that the identified epigenetic factors mediate jSI-induced transcriptional changes and now describe these relationships as associations that remain to be tested, and the comparisons with published datasets are explicitly framed as exploratory. We will also carry out threshold-free analyses and apply correction for multiple testing. We are grateful to the reviewer for these suggestions, which we believe have made the manuscript more accurate about what our data can and cannot support.

      Reviewer #3 (Evidence, reproducibility and clarity (Required))

      Summary:

      In this study, the authors were investigating the effect of juvenile social isolation (jSI) on the nucleus accumbens (NAc) transcriptome in female mice. They used P21 wild type (C57BL6) female, isolated at P21 or group housed, and reunited at P35 to generate their samples for RNAseq on NAc punches. They also performed FACS sorting on the nuclei from NAc lysates to select only neuronal nuclei (NeuN staining). They studied the differentially expressed genes (DEGs) between group housed and jSI by RNAseq. Then, they studied the protein/protein interaction via stringDB on the DEGs identified (up or down) and perform GO analysis on them. They identified Ntrk2, Grin3a, Grik1 and Bcl2; associated with the neuronal function or transcription regulation terms. They also studied the histones modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) after jSI and identified neuronal function and transcription regulation terms again on the DDR (Cut and Tag method). They found that these histones modifications could play a role in jSI-induced adaptations and neuronal function. Finally, they reanalyzed public datasets of RNAseq data to identify histone modifications associated with their DEGs of interest, and compare their DEGs to differences between genotypes in the public datasets (in conditional KO models of some genes of interest, as Kdm6b cKO, BET inhibitor, or Setd1a +/- mice with 3 different mutations. They conclude that histone modification could be involved in jSI-induced gene expression alteration.

      Reviewer #3 (Significance (Required))

      Globally, this study is showing transcriptomic and histone modifications occurring after juvenile social isolation in female mice. The authors identified differentially expressed genes linked to neuronal development, regulation of transcription and chromatin remodeling. The reanalyzed public datasets to identify histone modification on the DEG identified and observed the impact of some already published mutations on the gene expression to compare it to their data. The limits of this issue are caused by the reanalysis parts, since the datasets used are cortical and cerebellum samples, in diverse development stage (embryonic, early juvenile, adults - both sexes) that is quite different compared to their paradigm (juvenile females). They also never display the name of the principal DEGs identified (text or plots) which leads to difficult understanding of the findings of this paper. A more focused analysis on NAc or striatal, female only, juvenile stage datasets would be more helpful in this situation. The text should be more precise sometimes and a specific explanation on the exclusion of male mice should be introduce early in the methods.

      This study finds its place in the current research on the role of NAc function/dysfunction in behavioral abnormalities induced by social isolation and try to understand the mechanisms behind the abnormal behaviors induced by separation. The audience could be composed of researchers from several domains where social isolation is the cause or the consequence of pathological behaviors, including studies on loss, depression, ASD, Alzheimer, Schizophrenia, etc). The context is quite broad. The results from this paper could help find new molecular targets to alleviate the effects of social isolation and perhaps ameliorate the behavior for mouse models of several diseases or later in patients. A better understanding of the effects of social isolation in female is interesting, but being able to compare both sexes would be even better: identifying sex-differences and common defect is of great interest nowadays in several domains.

      The present reviewer has expertise in behavior in mice (both male and female) from juvenile to adult stages, has studied neuronal circuits including prefrontal cortex and striatum (mainly NAc) in behavioral abnormalities in mice in a model of ASD and more recently in an addiction model. The reviewer is interested particularly in sex-differences in neuronal circuits defects and behavior expression in diseases. Finally, the reviewer has recently focused on spatial transcriptomic approaches in addiction models.

      We thank the reviewer for the detailed and constructive assessment of our study, and for the many specific suggestions, which have helped us to improve the manuscript considerably. The reviewer's points centered in particular on the discrepancy between the published datasets we reanalyzed and our own experimental paradigm, the absence of gene names in the text and figures, which made our findings difficult to follow, and the need for greater precision in the text, including an explicit explanation of why only female mice were used.

      We address each of these points in detail in our responses below, and have already implemented a substantial part of the revisions. Briefly, the rationale for using female mice is now stated in both the Introduction and the Methods, the text has been revised throughout for precision, and the comparisons with published datasets are explicitly framed as exploratory. We will also reanalyze the striatal dataset from Chen et al. (Sci. Adv., 2020), which is considerably closer to our samples than the prefrontal cortex datasets, and we will label the relevant genes in the text and figures. We are grateful to the reviewer for the care taken in reading the manuscript.

      2. Description of the planned revisions

      1-2

      The authors cite several studies from the Nestler lab on early life stress that link early life stress to histone modifications but failed to cite the manuscripts that investigated the transcriptional changes in response to jSI. These studies also highlight sex differences in jSI. This is important given that this study only uses females. Many of the effects described might not be comparable simply because of the sex of the animals.

      We thank the reviewer for this helpful comment. We agree that studies investigating transcriptional changes in response to jSI, including those reporting sex differences, should be cited, and we will add these references in the revised Introduction.

      The paragraph highlighted by the reviewer (the third paragraph of the Introduction) was intended to summarize the relationship between stress and epigenetic regulation in the NAc, which is why studies from the Nestler laboratory are prominently represented; transcriptional responses to social isolation in the NAc were summarized in the preceding paragraph. As the reviewer notes, however, other important studies have since been reported, and we will incorporate them accordingly.

      We also thank the reviewer for raising the issue of sex differences, which has been highlighted repeatedly by the other reviewers as well and is indeed an important point. We have revised the Introduction to specify the sex of the animals used in the rodent studies, and to make the reported sex differences explicit (lines 75–78).

      1-3

      Can the authors clarify if they used an adjusted p-value or nominal p-value. If they are using a nominal p-value the authors should explain their reasoning and provide information regarding if any of the transcripts survived a p-value correction. The addition of threshold-free approaches are more appropriate (GSEA) rather than focusing on transcripts with a nominal p-value. If they are going to present data using a nominal p-value, this should be justified and the cut off should be explained and every interpretation should include a caveat.

      We thank the reviewer for this important comment. The reviewer is correct that nominal p-values, rather than adjusted p-values (FDR), were used in this study, and we agree that this point requires both clarification and revision.

      In the revised manuscript, we will take the following steps. First, as the reviewer suggests, we will perform GSEA as a threshold-free approach and replace the current GO analysis of DEGs with the GSEA results. Second, for analyses that necessarily require a defined gene set, namely the PPI analysis, the ChIP-Atlas analysis, and the overlap analyses shown as Venn diagrams, we will continue to use gene sets defined by nominal p-values, but we will state explicitly in the text that these analyses are exploratory and hypothesis-generating rather than confirmatory.

      Regarding our original choice of threshold, we note that the number of biological replicates in this study (n = 4–6) is small relative to the number of genes tested, and that transcriptional responses to stress in the nervous system are typically of small effect size with substantial inter-individual variability. Under these conditions, FDR correction is highly conservative, and previous studies in this field have reported findings based on nominal p-values (e.g. Torres-Berrío et al., Nat. Neurosci., 2024). Since the aim of the present study was to generate hypotheses rather than to establish definitive causal factors, we adopted a permissive threshold. We will make this rationale explicit in the revised Methods and Results.

      Finally, we will add a statement to the Limitations section noting that the findings reported here require validation in future work. We hope that these revisions adequately address the reviewer's concern.

      1-4

      It is unclear how the authors confirmed that input RNA or neurons were similar across samples for the library prep. This is especially important given the top genes that are differentially expression. The finding that beta actin (Actb) is up in jSI vs GH animals. The results could be due to differences in input rather than actual differences in expression.

      We thank the reviewer for raising this important point. We understand the concern to be that the observed change in Actb, a gene commonly regarded as a housekeeping gene, may reflect differences in input material between samples rather than a genuine difference in expression.

      We would like to clarify that the same number of sorted nuclei was used for every sample, and we have stated this explicitly in the Methods section of the revised manuscript. Consistent with this, library quality assessed by fluorometry (Qubit) and capillary electrophoresis (TapeStation) was comparable across all samples, and the FACS profiles showed the similar pattern in every case; we will present these data in the point-by-point response accompanying the revised manuscript. In addition, we will report the TMM normalization factors calculated in edgeR, together with PCA and/or MDS analyses, to confirm that no sample deviated substantially from the others and that normalization was applied appropriately.

      Regarding housekeeping gene expression, Actb is known to undergo activity-dependent changes in expression in neurons and to contribute to synaptic function and plasticity. We consider this an interesting observation in its own right, particularly as another reviewer noted that the “actin reorganization” GO term among our differentially expressed genes warrants further description. In the revised manuscript, we will cite the relevant literature and discuss this point. We will also confirm that the expression of other housekeeping genes was largely unchanged, supporting the consistency of RNA-seq quality across samples.

      1-5

      There are aspects of the methods are difficult to understand. For example, under RNA-seq, the authors mention "frozen nuclei were thawed and centrifuged.....the supernatant was discarded and nuclei were centrifuged again under the same conditions" Can the authors clarify what was done here? Were the nuclei resuspended in STEM CellBANKER or something else? While this is a concrete example, there are many other places in the methods the are like this, meaning that steps seem to be skipped and it then becomes difficult to assess the approach. It is recommended that the authors work to clarify the methods. Another example, how the DNA was treated in the CUT&TAG and how much DNA was added to the library prep.

      We apologize for the lack of clarity in the Methods section. We will review and revise the entire Methods section to ensure that each step is described unambiguously.

      Regarding the specific example raised by the reviewer, sorted nuclei were resuspended in STEM CELLBANKER and frozen in this solution; after thawing, they were centrifuged directly without resuspension in any other buffer. We will make this explicit in the revised text.

      For the CUT&Tag experiments, the same number of nuclei was used for every sample, and we have stated this explicitly in the revised Methods. Regarding the amount of DNA used for library preparation, we did not quantify the DNA prior to PCR amplification, as the yield at this step is below the range of reliable quantification and measurement is not included in the original CUT&Tag protocol. Instead, all libraries were amplified with the same number of PCR cycles (12 cycles, as stated in the Methods), ensuring that they were prepared under identical conditions throughout.

      2-major-2

      DEG thresholds are loose. DEGs are defined as "p-value 1.2." There is no clear FDR cutoff. With ~1250 DEGs from n = 5 to 6, many hits may be noise. Please report FDR filtered lists or justify the uncorrected p value choice. Re run key GO and overlap tests on a stricter set.

      We thank the reviewer for this important comment, and we agree that the thresholds used to define DEGs require clarification and revision.

      In the revised manuscript, we will take the following steps. First, we will perform GSEA as a threshold-free approach and replace the current GO analysis of DEGs with the GSEA results, so that our functional conclusions do not depend on an arbitrary cutoff. Second, for analyses that necessarily require a defined gene set, namely the PPI analysis, the ChIP-Atlas analysis, and the overlap analyses shown as Venn diagrams, we will continue to use gene sets defined by nominal p-values, but we will state explicitly in the text that these analyses are exploratory and hypothesis-generating rather than confirmatory.

      Regarding our original choice of threshold, the number of biological replicates in this study (n = 4–6) is small relative to the number of genes tested, and transcriptional responses to stress in the nervous system are typically of small effect size with substantial inter-individual variability. Under these conditions, FDR correction is highly conservative, and previous studies in this field have reported findings based on nominal p-values (e.g. Torres-Berrío et al., Nat. Neurosci., 2024). Since the aim of the present study was to generate hypotheses rather than to establish definitive causal factors, we adopted a permissive threshold. We will make this rationale explicit in the revised Methods and Results, and we will add a statement to the Limitations section noting that the findings reported here require validation in future work.

      2-major-3

      Public data overlaps are hard to interpret. Kdm6b data are from cerebellum. Brd4 data are from cultured cortical neurons treated with JQ1. Setd1a data are mostly PFC. The authors note that "different brain regions, cell types, and experimental conditions... may contribute to... false negative or false positive results." That caveat is important. Overlaps should be framed as hypothesis generating only. Do not treat them as evidence that these enzymes act in NAc under jSI.

      We fully agree with the reviewer on this point, and we have revised the manuscript accordingly.

      First, we have explicitly framed all comparisons with published datasets as exploratory and hypothesis-generating, and we have removed any wording implying that these enzymes act as mediators of jSI-induced transcriptional changes in the NAc.

      Second, as pointed out by Reviewer #3, the study by Chen et al. (Sci. Adv., 2022) also performed scRNA-seq using the striatum of Setd1a heterozygous mice. Since striatal tissue is far closer to our NAc samples than the prefrontal cortex, we will reanalyze the striatal dataset and compare it with our jSI DEGs. Depending on the outcome of this analysis, we will reorganize the figures so that the most relevant comparison is presented in the main text and the remaining reanalyses are moved to the supplementary material.

      Third, we have explained our rationale for dataset selection in the revised text. The three factors examined here were nominated by our own data: Brd4 and Setd1a emerged from the ChIP-Atlas promoter analysis, and Kdm6b was itself upregulated in our RNA-seq data. We then searched for publicly available RNA-seq datasets in which these factors had been perturbed in the nervous system. No such dataset exists for the NAc or striatum for Kdm6b or Brd4, and we therefore selected the datasets that were closest to our system among those available. We have stated this limitation in the Limitation section, together with the differences in brain region, cell type, developmental stage, and sex between these datasets and our own.

      2-major-5

      CUT and Tag analysis is coarse for promoter claims. Signals are quantified in "all 5 kbp bins." That bin size can blur promoters, enhancers, and neighboring genes. Please add peak calling or TSS centered analyses for key loci such as Grik1, Bcl2, and Dkk3. Also show more browser tracks beyond one example.

      We thank the reviewer for this comment, and we agree that quantification in 5 kbp bins is too coarse to support claims about promoter-level regulation.

      In the revised manuscript, we will perform higher-resolution analyses centered on transcription start sites for the key loci highlighted by the reviewer, including Grik1, Bcl2, and Dkk3, so that promoter signals can be evaluated separately from those of surrounding regions. We will also try to perform peak calling to define regions of enrichment more precisely.

      We will also increase the number of browser tracks shown, so that examples are provided for each histone modification rather than for a single locus.

      2-major-6

      Multiple testing for overlaps needs attention. Many Fisher tests compare DEGs with DDRs and with several public DEG lists. Report whether p values were corrected across tests. Some reported overlaps are small in absolute numbers even when p values look significant.

      We thank the reviewer for this comment. The reviewer is correct that we did not correct for multiple testing across the repeated Fisher's exact tests.

      In the revised manuscript, we will apply the Benjamini-Hochberg procedure and report adjusted p-values in the figures. Correction will be performed within each analysis category rather than across all tests, namely the overlaps between DEGs and DDRs, and the overlaps between our jSI DEGs and each of the published datasets for Kdm6b, Brd4, and Setd1a. We note that these tests are not fully independent, since they share the jSI DEG list and involve mutually exclusive up- and down-regulated gene sets; the Benjamini-Hochberg procedure remains valid under such positive dependence.

      We also agree that p-values alone can be misleading when the absolute number of overlapping genes is small. For every comparison, we will additionally report the observed and expected numbers of overlapping genes together with the odds ratio or fold enrichment, so that the magnitude of each overlap can be assessed independently of its p-value.

      2-minor-2

      Down DEG GO terms include "Chondrocyte differentiation" and "Positive regulation of cartilage development." These look odd for NAc neurons. Check annotation quality and whether these terms survive stricter DEG filters.

      We thank the reviewer for this observation. Genes involved in developmental processes are frequently shared across tissues, and GO annotation assigns such pleiotropic genes to multiple terms, which can produce enrichment for categories that appear unrelated to the tissue under study.

      In our case, the genes driving the enrichment of "chondrocyte differentiation" and "positive regulation of cartilage development" include Sox5, Bmp4, and Nfib, all of which have established roles in nervous system development. Whether these genes are functionally important in the NAc remains unknown, but their appearance in these terms reflects annotation overlap between chondrocyte and neural developmental programs rather than an implausible result.

      We nevertheless acknowledge the concern regarding our DEG thresholds, as discussed in our response to the Reviewer #2's comment (#2-major-2). We will perform GSEA as a threshold-free approach and use it to confirm the functional categories identified by the current GO analysis.

      3-major-5(OPTIONAL)

      Datasets selection: The public datasets used through this study are far from the original experimental design proposed in this study (cerebellum at P14, cortex at E16.5, nonspecific inhibitor, whole adult PFC = 12-14weeks old). It is important to note that the dataset used, from Chen et al 2022 (whole PFC) also studied the striatum of heterozygous Setd1a mice in the same paper. Why did the author reanalyzed PFC data instead of striatum, which would probably look more like their NAc-restricted samples? Restrict the analysis of the public dataset on NAc data, if possible on females only, and/or try to obtain data from similar experimental design (social isolation, stressed mice). Note here, that the development stage during which the mice have been isolated/regrouped and sample taken will probably of importance. The reanalyzes mix embryonic, early juvenile and adult samples, none of which is consistent nor look like their set up (P31-35).

      We thank the reviewer for this thoughtful comment, and we agree that the discrepancy between the published datasets and our own experimental design is a substantial limitation.

      Regarding the Chen et al. (Sci. Adv., 2022) dataset, we are grateful to the reviewer for pointing out that the same study also profiled the striatum. Since the NAc is part of the striatum, this dataset is considerably closer to our samples than the prefrontal cortex, and we will reanalyze it and compare it with our jSI DEGs in the revised manuscript.

      We will also explain our rationale for dataset selection explicitly. The three factors examined here were nominated by our own data: Brd4 and Setd1a emerged from the ChIP-Atlas promoter analysis, and Kdm6b was itself upregulated in our RNA-seq data. We then searched for publicly available RNA-seq datasets in which these factors had been perturbed in the nervous system. To our knowledge, no dataset combines perturbation of these enzymes with the NAc or striatum (except for the Setd1a striatal dataset noted above), with female animals only, or with a social isolation or stress paradigm. Among the datasets available, we therefore selected those closest to our system, prioritizing perturbation of the factor of interest, since this was the specific question the analysis was designed to address. We will state this constraint clearly in the revised text, together with the differences in brain region, cell type, developmental stage, and sex between these datasets and our own.

      Finally, in line with the comments from this reviewer and from Reviewer #2 (#2-major-3), we will frame all of these comparisons as exploratory and hypothesis-generating, and will remove wording implying that these enzymes mediate jSI-induced transcriptional changes.

      3-major-7

      Assumptions are made based on 2 sets of reanalyses. These parts should be displayed in the supplementary to help the authors target some genes of interest rather than the principal figures. These analyses didn't seem convincing due to too much shift from the original issue of the paper (which is jSI in the NAc in female mice).

      We thank the reviewer for this comment, and we agree that the reanalyses of published datasets are exploratory in nature and are considerably removed from the central question of this study.

      As described in our response to the Reviewer #3's comment (#3-major-5), we will reanalyze the striatal dataset from Chen et al. (Sci. Adv., 2022), which is far closer to our NAc samples than the prefrontal cortex datasets used previously. Depending on the outcome of this analysis, we will reorganize the figures so that the most relevant comparison is retained in the main text and the remaining reanalyses are moved to the supplementary material.

      Throughout the revised manuscript, we will present these comparisons explicitly as a means of narrowing down candidate genes for future investigation, rather than as evidence that these enzymes act in the NAc under jSI. We have also added a statement at the beginning of this section noting that the datasets were obtained under conditions different from ours, and the specific differences will be described in the Limitations section.

      3-major-8

      Volcano plots throughout the study: Should display the genes names (at least top 10 up and top 10 down DEGs) on the graph, otherwise the volcano plots are unreadable.

      Thank you for your comment. We will present our top annotated genes in the figure.

      3-minor-intro-10

      General comment: Since the paper is focused on female, it could be of interest to state in the introduction if/how sex differences exist in relation to social isolation and human diseases showing isolation as a phenotype.

      We thank the reviewer for this suggestion, which we have adopted. We have added a statement to the Introduction describing what is known about sex differences in the effects of jSI in rodents, noting that while some outcomes are shared between sexes, others such as sociability and aggression differ. We will also describe what is known about sex differences in the human conditions in which isolation or loneliness features as a phenotype. We have also specified the sex of the animals used in the rodent studies we cite, as requested by Reviewer #1 (#1-2). We have kept this description focused on social isolation rather than surveying sex differences in psychiatric disease more broadly, so that it remains relevant to the present study.

      3-minor-results-1

      Section 1: Only 1 or 2 GO terms (down / up DEGs) are described, but top5 is represented in the figure. Description of the others would be of interest (for example actin reorganization could be particularly interesting). Also, citing some DEGs from the top UP and DOWN, representative of the GO terms, could be of huge interest here.

      We thank the reviewer for this comment. We agree that describing the GO terms in more detail would improve the readability of this section. In the revised manuscript, we will describe all of the top-ranked GO terms shown in the figure, rather than only one or two, including terms such as actin reorganization. We will also name representative differentially expressed genes belonging to these terms in the text, so that the reader can appreciate which genes underlie each enrichment without consulting the supplementary tables.

      3-minor-results-2

      Figure 1, E/F/G: Some genes in Fig1G are not from top10 nodes in DEGs (Slc17a8, Hcrtr2, Dkk3, Dact1, Nr4a1, Fosl2, Htr5a). They are stated as "potentially important genes" in the legends and are found later in the study as important using other methods than RNAseq. Since Fig1G is displaying RNAseq results, it would be better to stick to the Top10 genes displayed here and put in another figure the other "potentially important genes".

      What means "potentially important"? Why these? What are the criterion? Also, the fig1G is unclear visually: separate it in two for top10 down and top10 up, it would be easier to understand and navigate.

      Legends: Statistics used for 1G are not stated.

      Volcano plot: Top 10 genes UP/down could be displayed on the graph.

      We thank the reviewer for these comments.

      We agree that including genes in Fig. 1G that were not among the top nodes made the figure difficult to follow. These genes will be moved to the section corresponding to the previous Fig. 5, where they are first identified as candidates, and Fig. 1G will show only the top node genes.

      We also agree that the term "potentially important genes" was unclear. Since this panel shows the top nodes identified by the PPI analysis, we have replaced this wording with "top nodes" in the figure legend.

      Figure 1G will be divided into separate panels for up-regulated and down-regulated DEGs, as suggested.

      We will state the statistical method used in the figure legend, and we will label the top 10 genes on the volcano plot.

      3-minor-results-4

      Figure 2: A/B/C: only one mention of the NAc in the data; Fig D: 5/8 NAc datasets. The analysis here seems unbalanced. Why not take into account only NAc datasets? The composition of cortical area or retina is highly different than the NAc (mostly Glutamatergic vs GABAergic populations). If doable, the analysis focused on NAc datasets would be better.

      We thank the reviewer for this suggestion, which we agree would improve the specificity of this analysis.

      For the histone modification analysis, a sufficient number of NAc-derived datasets is available in ChIP-Atlas to support the analysis on its own, and we will therefore repeat this part using only NAc datasets in the revised manuscript.

      For the transcription factor analysis, however, the number of NAc-derived ChIP-seq datasets is too small for a comparable enrichment analysis, and restricting the analysis in this way would leave most candidate factors untested. We will therefore retain the "Neural" category for this part, and we will state explicitly that the underlying datasets derive from a range of neural tissues whose cellular composition differs from that of the NAc, as the reviewer notes. As described in our response to the Reviewer #3's comment (#3-minor-results-3), we will also make clear that this analysis was intended to generate candidates rather than to identify regulatory relationships operating in the NAc.

      3-minor-results-5

      Section 3: "First, neuronal development-related genes, such as 'nervous system development', were found in all DDRs of the four histone modifications" line 193-194: sentence is unclear, the author probably meant "term". No gene have been cited here in any histone modification experiment (nor visible in the figure, only dots without names, top 10 up/down could be displayed on the volcano plot). It would be of interest to state at least some of the genes identified here (DDRs, closest loci) and to see if/how many common genes from RNAseq data were found again here. GO terms: again, only one or two examples are described, but figure shows the top5. They all could be at least stated.

      The conclusion of the first paragraph states: "These results were consistent with the transcriptome analysis that neuronal function and transcription-related genes were affected, and with the transcription factor analysis that epigenetic regulators were predicted to bind to the promoter regions of these genes." line 197-199: Since the author did not state any genes, we can only believe that the result are consistent based on two vague GO terms "neuronal system development" and "regulation of transcription by RNApol II/chromatin remodeling". If the lector has to read itself every gene table to know which genes are dysregulated in jSI, this study will be really time consuming.

      We thank the reviewer for these comments, and we apologize that the description of "nervous system development" was inaccurate. We have rewritten this sentence so that it refers to genes functionally related to this GO term being enriched among the DDRs, rather than to the term itself being found among the DDRs (lines 218–219).

      We will also describe all of the top-ranked GO terms shown in the figure, rather than only one or two, and we will name the genes associated with the DDRs both in the text and on the volcano plots. In addition, we will state how many of these genes overlap with the DEGs identified in our RNA-seq analysis, so that the correspondence between the two datasets is apparent without consulting the supplementary tables.

      3-minor-results-6

      Figure 3: Volcano could display the top10 names of DDRs.

      "The results indicated that down-DEGs were associated with H3K4me1, H3K4me3, and H3K27ac." line 203: In which direction are altered H3K4me1, H3K4me3, and H3K27ac? This is important to know.

      "Consistent with their active roles in transcription, downregulation of H3K4me1 and H3K27ac was more relevant to down-DEGs than up-DEGs." line 205: Why? Unclear statement.

      "Considering the composite roles of H3K4me3 (an active histone modification) and H3K27me3 (a repressive histone modification), we hypothesize a major role of H3K27me3 in these up-DEGs, and the contribution of H3K4me3 to gene expression alteration by jSI might be small, though we cannot exclude the possibility that it regulates certain genes locally or plays a repressive role." line 208-211: It is very unclear here, why H3K27me3 should play a major role while H3K4me3 alteration "might be small". This has to be further discussed.

      "For top 10 nodes among up-DEGs, we didn't find any significant alterations in any of the four histone modifications around their gene loci, except for downregulated H3K4me3 around Aldh18a1, Lamp1, and Gnb4" line 217-219: Formulation is clumsy here, reformulate.

      "Some of these genes were marked by multiple altered histone modifications." Line 223: Which ones? Only Bcl2 displayed, but the authors state "some of these genes" right after writing "H3K27ac was found to be downregulated around Grin3a, Grik1, and Adgre1". Are these the other genes showing several histone modifications? It is unclear.

      We thank the reviewer for these comments, which have helped us to clarify this section.

      Regarding the direction of the histone modification changes, we have revised the text to state explicitly in which direction each modification was altered, rather than referring only to an association.

      Regarding the roles of H3K4me3 and H3K27me3 in up-DEGs, we agree that our reasoning was not adequately explained. We have rewritten this passage to make the logic explicit: the reduction of the repressive mark H3K27me3 is consistent with the upregulation of these genes, whereas the concurrent reduction of the active mark H3K4me3 is not, which is why we consider H3K27me3 the more likely contributor at these loci (lines 237–241).

      We have also rewritten the sentence describing the top 10 nodes among up-DEGs, which was awkwardly constructed, so that it now states positively which genes showed an alteration and in which direction (lines 243–253). Similarly, we have revised the sentence referring to genes marked by multiple altered histone modifications, so that it specifies which gene is being described rather than referring vaguely to "some of these genes".

      For the volcano plots, we will label the top-ranked DDRs, as we will also do for the volcano plots elsewhere in the manuscript.

      3-minor-results-7

      Figure 4B: only Grik1 as an example. Why only this one and not Bcl2 that moreover show several modifications? Could be helpful to show an example of each modification.

      Thank you for your comment. We will present the modification enrichment of specific genes that we mentioned.

      3-minor-results-8

      Section 4, Kdm6b: "To examine the possible contribution of Kdm6b to jSI, we re-analyzed the RNA-seq data from Kdm6b-knockout in the published study (Ramesh et al., 2023)." line 240-241: this study is about conditional Kdm6b KO in the cerebellum, on naive P14 male and female mice's neurons in culture. The authors extrapolate the results from a completely different neuronal population/region and sex to justify the potential effect jSI could have on their adolescent female mice. This sentence is misleading for the reader, since the model used (not stressed) and experimental conditions are far from what they are studying. This sentence needs some reformulation to better explain their goal. They show the DEGs (up/down) from reanalyzed data and overlap between these DEGs and the one from Figure1, but the conditions are far from each other here. One could ask what the specificity of their overlap demonstrated here.

      "Fosl2 and Nr4a1 are immediate early genes (IEGs) in response to neuronal activation in many brain regions (Dave et al., 2025; Shi et al., 2024), and these two genes have been reported to be involved in memory maintenance (McNulty et al., 2012; Mizuno et al., 2020) and Parkinson's disease (PD) (Fan et al., 2020; Rouillard et al., 2018). In addition, Htr5a, which encodes serotonin receptor 5A, was also upregulated in jSI and downregulated by Kdm6b KO (Fig. 1G, 5G, Table S1). And Htr5a has been reported to be a risk factor of human schizophrenia (Guan et al., 2016)." line 254-260: This part of the result paragraph is about introduction/discussion again. This should be moved appropriately.

      We thank the reviewer for these comments.

      Regarding the Ramesh et al. (Elife, 2023) dataset, we agree that the experimental conditions differ substantially from ours, and that our original wording did not make this clear. We have added a statement at the beginning of this section explaining how the datasets were selected and noting that they were obtained under conditions different from ours, so that the reader understands from the outset that these comparisons were intended to narrow down candidate genes rather than to test whether these enzymes act in the NAc after jSI. As described in our response to the Reviewer #3's comment (#3-major-5), we will also state the specific differences in brain region, cell type, developmental stage, and sex in the Limitations section, and we will reanalyze the striatal dataset from Chen et al. (Sci. Adv., 2022), which is considerably closer to our samples.

      Regarding the passage describing Fosl2, Nr4a1, and Htr5a, we agree that the discussion of their roles in memory and disease belongs in the Discussion rather than the Results. We have removed this material from the Results, retaining only the minimal information needed to follow why these genes were of interest, and we will incorporate the remainder into the Discussion. We have applied the same principle to the corresponding passage in the transcription factor section, as described in our response to the Reviewer #3's comment (#3-minor-results-3).

      3-minor-results-10

      Section 4, Setd1a: Here, they used 3 separate datasets: whole PFC of Setd1a heterozygous mice (exon 4 LacZ/Neo cassette insertion), whole PFC from loss of function Setd1a heterozygous mice and FoxP2+ nuclei from PFC of Setd1a +/- mice (frameshift in the 15th exon). These datasets are quite different between themselves and compared to NAc samples from jSI mice. The authors stated that the first two datasets had a low DEG overlap with their samples but continued with the third which showed a significant overlap for Hcrtr2, Dkk3 and Dact1.

      They finally conclude that: "Taken together, these results suggest that epigenetic factors, such as Kdm6b, Brd4, and Setd1a, may mediate jSI-induced gene expression alterations." Nothing in these datasets is comparable to what they want to prove here, it is a huge stretch to propose these genes as mediators of jSI. Reformulate. These results could be exploited as exploratory, to reduce the number of potential targets, but needs to be investigated on their own.

      We thank the reviewer for these comments, with which we largely agree.

      Regarding the differences between the three Setd1a datasets and our own samples, we have stated these in the Limitations section (lines 514–519), as described in our response to the Reviewer #3's comment (#3-major-5). We will also reanalyze the striatal dataset from the same study by Chen et al. (Sci. Adv., 2022), which is considerably closer to our NAc samples than the prefrontal cortex datasets, and we will reorganize this section accordingly.

      Regarding the difference in overlap between the three datasets, we would note that all three comparisons were performed and reported, and that the low overlap with the Mukai and Nagahama datasets was described in the original manuscript rather than omitted. One possible explanation for this difference is that the Chen dataset was generated from sorted Foxp2-positive nuclei, whereas the other two were derived from whole prefrontal cortex, in which signals from neurons may be diluted by non-neuronal cell types. We have added this to the text as a possible interpretation rather than a demonstrated explanation, and we have stated explicitly that all three datasets were compared in the same way (lines 318–322).

      Finally, we agree that proposing these enzymes as mediators of jSI-induced gene expression changes overstates what our data support. We have removed such wording and reframed these results as exploratory analyses that narrow down candidate genes for future investigation (lines 328–332), as described in our response to the Reviewer #2's comment (#2-major-1).

      3-minor-results-11

      Figure 5: volcano plots: top10 genes visible could be useful. This section of the results would fit better displayed in the supplementary, since they reanalyzed datasets far from their experimental conditions. These genes of interest should be further investigated in their jSI model.

      As described in our response to the Reviewer #3's comment (#3-major-7), we will reorganize this section in light of the reanalysis of the striatal dataset, retaining the most relevant comparison in the main text and moving the remaining reanalyses to the supplementary material. We have also revised the text so that each of these sections concludes by identifying the genes concerned as candidates requiring further investigation in our jSI model, rather than as established targets. We will label the top differentially expressed genes on the volcano plots, as described in our response to the Reviewer #3's comment (#3-minor-results-2).

      3-minor-discussion-7

      "Besides these two main shared functions affected by jSI, our results suggest that other biological processes are potentially mediated by one or more histone modifications. For example, some DDRs of H3K27ac and H3K27me3 are functionally enriched around cell adhesion-associated genes, and this is consistent with previous papers suggesting that cell adhesion is affected by isolation (Santiago et al., 2023; Wu et al., 2022)..." line 377-382: This GO term appeared in the figure, but has never been mentioned clearly in the results. The explanation goes on for a full paragraph. It could be better to introduce it before if it is of interest. Also, which cell-adhesion genes have been found in the RNAseq / cut&tag experiments for this family of genes (never stated)?

      We thank the reviewer for this comment. We agree that discussing cell adhesion at length in the Discussion is inappropriate when the corresponding GO term was never described in the Results, and that the genes underlying this enrichment were not identified.

      We will introduce this GO term in the Results section, where the functional enrichment of the DDRs is described, and we will name the cell adhesion-associated genes identified in our RNA-seq and CUT&Tag analyses both there and in the Discussion. This will be done together with the more comprehensive description of the top-ranked GO terms that we will add in response to the Reviewer #3's comments (#3-minor-results-1 and #3-minor-results-5).

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      1-1

      The text in the introduction conflates adult and adolescent social isolation which have very different effects on behavior. Additionally, there is evidence that isolation during adolescence can have permanent effects on behavior but the behavioral effects of adult isolation in rodents are transient. It is recommended that the authors restructure the intro to be more specific to describing the adolescent period and why epigenetic mechanisms would be expected to regulate changes induced by jSI.

      We thank the reviewer for pointing out that adult and adolescent social isolation are conflated in the Introduction. We agree that the effects of social isolation differ between adulthood and adolescence, and we have revised the Introduction to clearly distinguish between the two, with a specific focus on the adolescent period. Specifically, we now note that isolation during adolescence can produce lasting behavioral alterations, whereas the effects of adult isolation are largely transient, and we have added a statement explaining why the adolescent period may therefore be particularly susceptible to epigenetic reprogramming (lines 69–74). In addition, we now explain why epigenetic regulation is a plausible candidate mechanism for the changes induced by jSI: since the effects of jSI persist long after the isolation period has ended, environmental stress during this window is likely to leave a lasting molecular trace within affected cells, and epigenetic regulation can stably maintain altered transcriptional states (lines 93–96).

      1-6

      Can the authors please explain why only females were used for these experiments? In addition, can the authors please comment on potential caveats in the interpretation by only including females in the study?

      We thank the reviewer for raising this point, which was also noted by the other reviewers. We apologize that this rationale was not stated explicitly in the original manuscript, and that our previous work was not cited in this context.

      Our focus on female mice was not arbitrary but followed from our previous work using the same isolation paradigm as in the present study. In Sazhina et al. (Neuroimage, 2025), in which mice were isolated from P21 to P35 and regrouped from P35 to P49, we found that jSI produced a heightened fear response in female but not male mice. Since the NAc has been implicated in scaling fear responses to threat intensity, and since this region undergoes a critical period around P28, we hypothesized that isolation during this window disrupts NAc development in a manner that leads to inappropriate fear responses in adulthood, and that this underlies the female-specific phenotype we had observed. We therefore designed the present study to examine molecular changes in the NAc of female mice. We have stated this rationale explicitly in the Introduction and Methods of the revised manuscript (lines 73–74, 542-543), and added the relevant references.

      We also agree that restricting the study to females limits the interpretation of our findings. Because jSI is known to produce sex-dependent effects on both behavior and gene expression, the alterations reported here cannot be assumed to occur in males, and comparisons with published datasets derived from male or mixed-sex animals must be made with this in mind. We have discussed these caveats explicitly in the Limitations section (lines 524–527), noting that a parallel analysis in males would be required to distinguish shared mechanisms of jSI from sex-specific ones.

      1-7

      Were females shipped to the facility on P21? It is unclear.

      We apologize that this was not clearly described in the original manuscript. Female mice were shipped from the breeder and arrived at our animal facility at P21, at which point isolation was started directly. We have stated this explicitly in the revised Methods section (lines 540–541). Group-housed control animals were shipped and received on the same day and under the same conditions, so that both groups experienced identical transport.

      1-8

      Were any animals used for multiple endpoints or was each endpoint a separate cohort? Were any samples pooled?

      We apologize for not describing this clearly. Nuclei were isolated from the NAc of a single animal and divided into five aliquots, each of which was used as one sample for RNA-seq or for one of the four CUT&Tag experiments. Each biological replicate therefore corresponds to a single animal, and no samples were pooled; all endpoints were derived from the same set of animals rather than from separate cohorts. Both our RNA-seq and CUT&Tag protocols have been optimized for use with small numbers of nuclei, which allowed all five libraries to be prepared from a single animal. We have stated this explicitly in the revised Methods section (lines 554–563).

      2-major-1

      Causality is not shown. The Discussion states: "Although we didn't show the molecular mechanism of histone modification alteration regulating gene expression, we revealed the association between transcriptome and histone modifications." That limit should shape the Abstract and title more clearly. Phrases like "epigenetic alterations may also play a role" are fine. Stronger wording about mediation should be toned down until NAc specific perturbation is done.

      We agree with the reviewer that this study is hypothesis-generating and does not demonstrate causality. We were mindful of this in preparing the original manuscript, but we acknowledge that language implying mediation remained in several places. We have gone through the entire manuscript, including the Abstract, and revised the wording so that it accurately reflects the correlative nature of our findings. In particular, we have removed expressions implying that the identified epigenetic factors mediate jSI-induced transcriptional changes, and now describe these relationships as associations that remain to be tested by NAc-specific perturbation (lines 34-35, 40-42, 197-200, 330-332, 433-434, 465-467, 481-483, 493-496).

      Regarding the title, we would prefer to retain the current wording. The title states that jSI is accompanied by alterations in gene expression and in histone modifications, and does not assert that the latter mediates the former; we therefore believe it does not overstate our findings. We note that the title has been modified to specify the sex of the animals used, as requested by Reviewer #3.

      2-minor-1

      Only female mice were used. State this early and discuss sex limits. Juvenile isolation effects often differ by sex.

      We thank the reviewer for this comment, and we agree on both points.

      As described in our response to the Reviewer #1's comment (#1-6), our focus on female mice followed from our previous work using the same isolation paradigm, in which jSI produced a heightened fear response in female but not male mice. We have stated this rationale explicitly in the Introduction and at the beginning of the Methods (lines 524-527, 542-543), and we have made clear in the Abstract and the title that this study was performed in female mice (line 42).

      We also agree that the sex-specific limitations of our findings require explicit discussion. Since jSI is known to produce sex-dependent effects on both behavior and gene expression, our results cannot be assumed to generalize to males, and comparisons with published datasets derived from male or mixed-sex animals must be interpreted with this in mind. We have addressed these points in the Limitations section of the revised manuscript.

      2-minor-3

      Figure 1 lists "II2ra" in the top nodes table. That is likely Il2ra. Please correct.

      We thank the reviewer for catching this error. The reviewer is correct that the gene name in the top nodes table should read Il2ra rather than "II2ra", and we have corrected this in Figure 1.

      2-minor-4

      Sample sizes differ a lot across marks. H3K4me1 and H3K27me3 have n = 4 in jSI. Discuss power and why replicates differ.

      We thank the reviewer for this comment. In this study, 10 control and 9 jSI animals were prepared, and all analyses were performed on nuclei derived from each of these animals. However, a subset of CUT&Tag libraries failed to pass our post-sequencing quality criteria and was excluded from the analysis, which resulted in the differing numbers of replicates across histone modifications. We have described this in the revised Methods (line 554), together with the quality criteria used for exclusion, so that the basis for these differences is transparent.

      We also agree that the reduced number of replicates for H3K4me1 and H3K27me3 lowers the statistical power for these marks relative to the others, and that the number of differentially distributed regions detected for them may therefore be underestimated. We have stated this explicitly in the Limitations section.

      2-minor-5

      The isolation protocol includes regrouping from P35 to P49. Make clear that effects are lasting post isolation effects, not acute isolation effects.

      We thank the reviewer for this comment, which correctly identifies our intent. The regrouping period was included precisely because our interest is in the effects of juvenile isolation that persist into adulthood, rather than in the acute consequences of isolation itself. Our previous work using the same protocol demonstrated a lasting fear phenotype in female mice after the regrouping period (Sazhina et al., Neuroimage, 2025), and a central aim of the present study is to ask whether epigenetic regulation contributes to the persistence of such environmentally induced changes.

      We have stated this rationale explicitly in the Methods (lines 548–550), and the Introduction now notes that the behavioral effects of jSI persist long after the isolation period has ended (lines 69–72, 93–94).

      2-minor-6

      Methods say "GPT-5.4... and Claude Sonnet 4.6... was used." Fix subject verb agreement.

      We thank the reviewer for pointing this out. We have corrected the subject-verb agreement in this sentence of the Methods (lines 682–683), which now reads "GPT-5.4 ... and Claude Sonnet 4.6 and Opus 5 ... were used".

      2-minor-7

      Data Availability lists "GSE3508789." Confirm this accession. It looks malformed.

      We thank the reviewer for catching this. The accession number was indeed malformed; the correct accession is GSE123652, and we have corrected it in the Data Availability section (lines 672, 717).

      2-minor-8

      Abstract keywords include "Loneliness." The mouse work is social isolation. Keep that distinction clear, as the Introduction already does.

      We thank the reviewer for this comment. We agree that including "loneliness" as a keyword was inappropriate given that this study examines social isolation in mice, and we have removed it from the keyword list (lines 44–45).

      3-major-1

      Title: Add the sex: "in female mice" since it is specific.

      We agree with the reviewer and have revised the title to specify that this study was performed in female mice (lines 1–3).

      3-major-2

      Referencing: Biorender.com has been used to generate some schematics in this study, but it is never acknowledged or referenced.

      We thank the reviewer for pointing out this omission. The schematic in Figure 1 was created using BioRender, and we have added the citation to the figure legend in the format specified by BioRender, together with the corresponding publication license (line 737).

      3-major-6

      Wording: Formulation throughout the current study is vague, sometimes misleading, with some unclear sentences (see minor comments for the sentences showing problems). This issue needs to be checked again.

      Thank you for your comment. The responses could be checked in minor comments part.

      3-minor-intro-1

      "These negative effects are further supported by evidence from Covid-19 during the last few years" line 56/57: reformulate.

      We thank the reviewer for this comment. We agree that the original sentence was awkwardly phrased, since it referred to evidence "from Covid-19" rather than to the studies conducted during that period, and since "the last few years" was vague. We have rewritten it to state that studies conducted during the COVID-19 pandemic, when social contact was widely restricted, provided further evidence for these negative effects (lines 56–58).

      3-minor-intro-2

      "In addition, isolation contributes to severe social issues, such as increased human suicide risk" line 57/58: issues is plural, but only one example is given; moreover, the term "social issue" associated to suicide is poorly-worded.

      We thank the reviewer for this comment. We agree that the plural "issues" was not supported by the single example given, and that describing suicide as a "social issue" was poorly worded. We have rewritten the sentence so that social isolation is described as being associated with adverse outcomes, including the risk of suicide (lines 59–60).

      3-minor-intro-3

      "In the case of rodents, socially isolated animal models are proposed to be associated with various human diseases" line 59/60: clumsy sentence, the animal models are associated to human disease? This could be reformulated.

      We thank the reviewer for this comment. We agree that the original sentence was awkwardly constructed, since it stated that the animal models themselves were associated with human diseases. We have rewritten it so that the socially isolated animals are described as exhibiting a range of behavioral abnormalities, and these abnormalities, rather than the models themselves, are described as modelling aspects of human diseases such as depression and schizophrenia (lines 60–63).

      3-minor-intro-4

      "and the effects of juvenile social isolation (jSI) on motor, emotional, learning, and sociability-related behaviors in rodents have been widely reported (Li et al., 2021; Powell & Swerdlow, 2023; Walker et al., 2019), which further provides evidence of the pathogenesis and molecular mechanisms of human mental disorders." line 63-66: Examples of behavioral dysfunctions would be appreciated here.

      We thank the reviewer for this suggestion. We have added examples of the behavioral dysfunctions reported after jSI, namely hyperactivity, elevated anxiety, impaired spatial learning, and altered social play (lines 66–68). In the same paragraph we have also added a description of the sex differences reported for these effects, and a reference to our own previous work using the same isolation paradigm, as described in our responses to the Reviewer #1's comments (#1-2 and #1-6).

      3-minor-intro-5

      "The nucleus accumbens (NAc) is a critical component of the brain reward circuitry, and dysfunction of the NAc is associated with drug addiction (Zinsmaier et al., 2022), impaired social interaction (Pomrenze et al., 2022; Shan et al., 2022), and abnormal emotion expression (Gebara et al., 2021)" line 67-70: here, the statement reads as dysfunction of the NAc is responsible of abnormal behaviors (addiction, social behavior or emotional expression), but the articles show that NAc is dysregulated in models of these pathologies. Is the dysfunction in the NAc responsible of or a consequence of the pathologies? This could be better formulated.

      We thank the reviewer for this comment. We agree that the original wording could be read as asserting that NAc dysfunction causes these conditions, whereas the cited studies show that the NAc is dysregulated in models of them. We have rewritten the sentence so that NAc dysfunction is described as having been reported in animal models of these conditions, without implying a direction of causation (lines 81–85).

      3-minor-intro-6

      "An fMRI study showed that activity of the human NAc is associated with the sense of loss (Cooper et al., 2009; O'Connor et al., 2008)." line 70-72: Sentence says one study, but two references are used. The sentence refers to O'Connor only. Cooper is about reward/effort and NAc activity, not grief/loss, reformulate. Also, "activity" is unclear, the authors could be more precise with "hyperactivity of the NAc has been found in people suffering from loss".

      We thank the reviewer for pointing out these problems. We have removed the citation to Cooper et al. (2009), which concerns reward and effort rather than grief, so that the sentence now refers only to O’Connor et al. (Neuroimage, 2008) (lines 85–86). We have also replaced the vague reference to "activity" with a statement that hyperactivity of the NAc was reported in individuals experiencing loss, as the reviewer suggested.

      3-minor-intro-7

      "The NAc from lonely individuals showed key differentially expressed genes (DEGs) that are associated with both neurodegenerative and neuropsychiatric diseases" line 74-75: Unclear, give examples of the pathologies here to be consistent with the next sentence about female rats (Alzheimer, Parkinson, Huntington).

      We thank the reviewer for this suggestion. We have added the specific diseases identified in that study, namely Alzheimer's disease, Parkinson's disease, and major depression disorder, so that this sentence is consistent with the following sentence describing the findings in female rats (lines 88–92).

      3-minor-intro-8

      The next paragraph (line 79-95) about DNA methylation and histone modifications is missing a general conclusion: what is interesting or needs to be more studied? Also, H3K9 and H3K79 have been introduced but unused in the paper, while H3K27ac/me3 have not been introduced. What is known about them?

      We thank the reviewer for these comments. We agree that this paragraph lacked a conclusion and that the histone modifications discussed did not match those examined in this study.

      We have shortened the description of H3K79 methylation and removed the statement concerning H3K9, neither of which is examined here. In their place we have added a description of the four modifications we analyse, namely H3K4me1, H3K4me3, H3K27ac, and H3K27me3, together with what is known about their roles in the NAc (lines 108–118). The paragraph now concludes by noting that little is known about H3K4me3 and H3K27ac in the NAc under stress, and that how any of these modifications are altered after jSI remains unknown, which motivates the present study.

      3-minor-intro-9

      "How do epigenetic elements mediate gene expression dysfunction under jSI stress? In this study, we aimed to reveal the alterations in gene expression and histone modifications induced by jSI, and to elucidate their roles in the context of psychiatric disorders promoted by jSI" line 96-99: Statement is too general, it is missing the term "NAc" here.

      We thank the reviewer for this comment. We agree that our statement of aims was too general and omitted the brain region under study. We have rewritten both the question that opens this paragraph and the statement of aims so that they specify the NAc, and we have also indicated that the study was performed in female mice (lines 119–122). In addition, we have removed the wording implying that epigenetic elements mediate gene expression dysfunction, in line with the request from Reviewer #2 that causal language be moderated (#2-major-1).

      3-minor-methods-1

      Female mice only have been used in this study. It is never explained why so. Knowing that many diseases show sex differences in prevalence or symptom expression, it would have been helpful to include also male mice in the study, to identify common mechanism linked to jSI versus sex-specific alterations.

      We thank the reviewer for this comment, and we apologize that our rationale was not stated in the original manuscript. As described in our response to the Reviewer #1's comment (#1-6), our focus on female mice followed from our previous finding that the same isolation paradigm produced a behavioral phenotype in females but not males (Sazhina et al., Neuroimage, 2025). We have added a statement to this effect at the beginning of the Animals and sample collection section (lines 542–543), and we have discussed the resulting limitations in the Limitations section (lines 524–527).

      3-minor-methods-2

      Cut & Tag: Dilution of antibodies is not displayed ("1µL") and the reference for antibodies is unclear: "primary antibodies (H3K4me1, MABI, 536 MABI0302; H3K4me3, abcam, ab8580; H3K27me3, CST, 9733S; H3K27ac, CST, 537 8173S; 1 µL per reaction)". This should be adapted to look like the FACS antibody description: "anti-NeuN-488 conjugated antibody (Millipore, #MAB377X, 1:400 dilution)".

      We thank the reviewer for pointing this out. We have revised the description of the CUT&Tag antibodies so that it follows the same format as the FACS antibody description, specifying the host species and clonality, the supplier, the catalogue number, and the dilution for each antibody, in place of the volume per reaction given previously (lines 611–615).

      3-minor-methods-3

      "Frozen nuclei were thawed and bound to concanavalin A (ConA)-coated magnetic beads (BioMag®Plus Concanavalin A, 10 µL per reaction) for 10-60 min on a rotator at room temperature." Why so much difference in incubation time here?

      We thank the reviewer for pointing this out. We have checked our experimental records and confirmed that the incubation was performed for 10–20 min in all experiments reported here, and we have corrected the text accordingly (line 608). The wider range given in the original manuscript reflected our general protocol, in which we have confirmed that binding is satisfactory anywhere between 10 and 60 min, but this was not the range actually used in the present study.

      3-minor-methods-4

      Number of animals: While reading the manuscript, it was unclear that 5 different groups of mice have been used. The number of animals should be stated in the methods in the "nucleus extraction" part or "animals" section to facilitate understanding the methods.

      We thank the reviewer for this comment. We have renamed this section "Animals and sample collection" and added a paragraph stating the number of animals analysed in each group, that nuclei from each animal were divided between the RNA-seq and the four CUT&Tag experiments, and that no samples were pooled (lines 539, 554–563). We have also explained that the number of replicates is smaller than the number of animals for some datasets because a subset of libraries did not pass our quality criteria, and we note that the replicate number for each dataset is given in the corresponding figure legend.

      3-minor-methods-5

      Data analysis: "For RNA-seq data, p-value 1.2 were used as the threshold for identifying DEGs. For CUT&Tag data, p-value 2 were selected as the standard for identifying DDRs." Cut&Tag p-value and threshold is written in RNAseq section, move it to its proper part hereafter "CUT&Tag data analysis".

      Thank you for your comment. We revised it (lines 664–665).

      3-minor-methods-6

      Suggestion DDR: acronym is present in the methods but not explained. It is however explained in the results. This depends on the order in the publication, but if methods appear first, it would be helpful to understand what stands for DDR.

      Thank you for your comment. We revised it (line 665).

      3-minor-methods-7

      Cut&Tag data analysis: "The procedures of quality check and trimming were the same as RNA-seq data analysis." line 591; "and the removal of blacklisted regions was the same as RNA-seq" line 594; "GO analysis was the same as RNA-seq analysis." line 599: These parts could be ameliorated to avoid repetition. Since the preparation of nuclei and most of the analysis are the same, the methods could be more straightforwardly explained separating common preparation from specific analysis.

      Thank you for your comment. We revised it (lines 659–660).

      3-minor-methods-8

      Methods explaining how the authors performed the reanalysis of the public datasets is missing.

      We thank the reviewer for pointing out this omission. We have added a "Public data analysis" section to the Methods, describing how the raw data were retrieved from the DDBJ and GEO databases, and how they were processed (lines 667–671). Steps shared with our own datasets are indicated as such rather than repeated in full.

      3-minor-methods-9

      Animals have been separated at P21 and regrouped at P35: It is not stated if they were regrouped together or with a group of unstressed WT never separated, which could influence their behavior and stress levels.

      We thank the reviewer for this comment. We have clarified that the isolated mice were regrouped with other previously isolated animals, rather than with mice that had never been separated (lines 547–550). As described in our response to the Reviewer #2's comment (#2-minor-5), we have also stated why the regrouping period was included.

      3-minor-methods-10

      No behavioral test has been performed on these mice. It would have been appreciated to see that 2-weeks social isolation was efficient to generate stress in these animals (anxiety test, sociability at least). And if this protocol has been previously used in their lab, at least to explain briefly what behavior abnormalities the jSI was inducing.

      We thank the reviewer for this comment, and we apologize that this information was not included in the original manuscript.

      This isolation protocol has been used previously in our laboratory, and the resulting behavioral phenotype was reported in Sazhina et al. (Neuroimage, 2025), in which the same paradigm produced a heightened fear response in female mice. We have added this finding to the Introduction (lines 73–74), and we have also cited it in the Methods as the basis for our use of female animals (lines 542–543), so that the behavioral consequences of the paradigm are documented.

      As described in our response to the Reviewer #2's comment (#2-major-4), behavioral testing was not performed on the cohorts used for molecular analysis, in order to avoid introducing transcriptional and epigenetic changes unrelated to isolation.

      3-minor-results-3

      Section 2: The first paragraph here is about which Transcription Factor (TF) is predicted to participate in their DEG's expression. Half of this paragraph is introduction about the function of several TF. This is not part of results and should be moved appropriately in the introduction or discussion section, or shortened significantly, since it is now longer than the result part. Importantly here, the analysis is done on ChIP Atlas (public datasets).

      They state: "Taken together, promoter analysis of DEGs suggests that potential epigenetic mechanisms may act upstream of jSI-induced transcriptional dysregulation in the NAc." line 176, but the database has never been stated to be NAc-only data nor data from jSI animals. If not, this sentence has to be modified. It was unclear globally if this part was based on their work or data mining on a first read, it should be more clearly stated at the beginning that this is exploratory.

      We thank the reviewer for these comments.

      We agree that the introductory description of the transcription factors was disproportionately long for a Results section. We have shortened it substantially, retaining only the information required to follow why these factors were of interest, namely that Setd1a and Brd4 act through the histone modifications examined in this study (lines 184–190). The remaining background, including the association of these factors with neurological and psychiatric disease, has been moved to the Discussion.

      We also agree that the exploratory nature of this analysis was not stated clearly. The ChIP-Atlas database is compiled from published ChIP-seq experiments and does not contain data from the NAc of socially isolated animals. We have stated this at the outset of the section (lines 177–180), so that the reader understands from the beginning that the analysis was intended to generate candidates rather than to identify regulatory relationships operating in our system, and we have revised the concluding sentence so that it no longer implies that these mechanisms were demonstrated in the NAc under jSI (lines 199–200).

      3-minor-results-9

      Section 4, Brd4: Here, the authors reanalyzed data from E16.5 cortical neuronal culture treated with or without BET family inhibitor, which they state is not selective of Brd4 (even if it is part of the BET family). The crossover between this embryonic cortical neuronal population treated with nonspecific inhibitor and their model (juvenile Social Isolation, NAc) is a bit of a stretch. What do the overlap in DEGs really mean here?

      "We also examined the possible downstream Brd4 target genes within the gene sets of down-DEGs by jSI and down-DEGs by JQ1 treatment, and we identified Hcrtr2 and Dkk3 in these gene sets (Fig. 1G, 5H, Table S1). Hcrtr2 encodes an orexin receptor, and it has been reported to be involved in altered arousal levels through dopamine neurons (Bandarabadi et al., 2024). Dkk3 inhibits Wnt signaling and is reported to be related to anxiety and memory formation (X. Chen et al., 2025; Flores et al., 2024)." line 275-280: What is the conclusion on these results?

      We thank the reviewer for these comments.

      Regarding the Korb et al. (Nat. Neurosci., 2015) dataset, we have added a statement at the beginning of this section explaining how the datasets were selected and noting that they were obtained under conditions different from ours, so that the reader understands from the outset that these comparisons were intended to narrow down candidate genes rather than to test whether these enzymes act in the NAc after jSI (lines 261–265). As described in our response to the Reviewer #3's comment (#3-major-5), the specific differences are also stated in the Limitations section (lines 514–519).

      Regarding the meaning of the overlaps, we agree that our original wording did not convey this clearly, and in particular that opening with "as expected" was misleading given that we also observed a significant overlap in the opposite direction. We have rewritten this passage so that the overlap in the unexpected direction is stated as an independent observation rather than as a qualifying clause, and we now state explicitly that the two gene sets are related but that the direction of change does not correspond in a simple manner (lines 295–301).

      Regarding the conclusion of this section, we agree that none was previously given. We have removed the description of the roles of Hcrtr2 and Dkk3 in arousal, anxiety, and memory, which belongs in the Discussion, and have instead concluded the section by identifying these two genes as candidates whose expression may be regulated by Brd4 in the context of jSI (lines 304–306). We have applied the same principle to the corresponding passages in the Kdm6b and Setd1a sections, as described in our response to the Reviewer #3's comment (#3-minor-results-8).

      3-minor-discussion-1

      " For example, the expression of glutamate receptors is reduced in the NAc, prefrontal cortex, and hippocampus under isolation stress (Hermes et al., 2011; Mao et al., 2022; Sestito et al., 2011)." line 315-317: Which GluR are reduced here? It needs to be more precise for the reader here, and to state if some genes have been found in common between this literature and their DEGs.

      We thank the reviewer for this comment. We agree that the original sentence was too vague, and we have revised it to specify which glutamate receptors were reduced in the cited studies (lines 343–351), namely GluA1 and GluA3 in the NAc and caudate putamen under chronic social isolation stress.

      We now also state explicitly how these findings relate to our own data. Gria1 and Gria3, encoding GluA1 and GluA3, respectively, were not among our DEGs, but we identified other glutamatergic synapse-associated genes, including Grin3a and Grik1, and we note that isolation may therefore affect glutamatergic signaling in the NAc through a partly distinct set of genes under our conditions.

      3-minor-discussion-2

      "The NAc is a key component of the brain reward circuit, and it is involved in drug addiction and social behavior (Pomrenze et al., 2022; Zinsmaier et al., 2022). NAc neurons receive glutamatergic inputs from the PFC, basolateral amygdala (BLA), hippocampus, and ventral tegmental area (VTA) (Arrondeau et al., 2024; Dieterich et al., 2021; Elam et al., 2025; Le Borgne et al., 2025; Zinsmaier et al., 2022), and neurons in the NAc output the information to the ventral pallidum (VP) (Liu et al., 2022), VTA (Qi et al., 2022), and other areas of the basal ganglia (Lanciego et al., 2012)." line 318-324: These lines are describing the circuitry of the NAc, some of its inputs (no mention of dopamine afferences from the VTA) and outputs. No use of this information is used after, since they conclude the paragraph with: "Thus, deficits in glutamatergic synapses possibly mediate jSI-induced behavioral abnormalities, including impaired social interaction, anxiety, and an increased risk of substance abuse." line 325-326: What is the point of describing the circuit, if it is not interpreted regarding their results? What is their hypothesis on the circuit dysfunction in jSI female mice? They were discussing the DEGs from RNAseq result before this paragraph. What is the link/hypothesis between their DEGs and the glutamatergic circuits of the NAc? Is the NAc directly responsible of jSI-induced behavioral abnormalities for them or cortical/amygdal/hippocampal/VTA glutamatergic projection neurons are dysregulated, creating DEGs at the synapse in the NAc? This part of the discussion should be more specific on what they mean.

      We thank the reviewer for this comment. We agree that the description of the NAc circuitry was not connected to our own findings, and we have substantially shortened it, retaining only a single sentence summarizing the glutamatergic inputs to the NAc and its outputs to downstream regions (lines 351–356).

      We have also revised the concluding sentence of this paragraph so that it follows from the preceding discussion of our DEGs (lines 351–356). Since our data indicate that glutamatergic synapse-associated genes are downregulated in NAc neurons after jSI, and since the NAc integrates glutamatergic inputs from several regions implicated in social and emotional behavior, we now state that altered glutamatergic signaling at these synapses may contribute to the behavioral abnormalities induced by jSI, rather than asserting that such deficits mediate them.

      3-minor-discussion-3

      "Deficiencies in these proteins are associated with various behavioral abnormalities (Araujo et al., 2017; Chasse et al., 2024; Guo et al., 2020; Huang et al., 2021; Mukai et al., 2019)." line 334-335: what proteins and what behavioral abnormalities? This is not precise enough and needs reformulation/conclusions.

      We thank the reviewer for this comment. We agree that the original sentence was not sufficiently specific, since it referred to deficiencies in several proteins and to behavioral abnormalities without indicating which protein was associated with which phenotype.

      We have revised this passage to describe the reported phenotypes individually for each factor (lines 362–370). We have also incorporated here the background material on Setd1a and Brd4 that we removed from the Results section, as described in our response to the Reviewer #3's comment (#3-minor-results-3), so that the association of these factors with neurological and psychiatric conditions is presented in the Discussion rather than interrupting the presentation of our findings.

      3-minor-discussion-4

      "A previous report suggests that histone modifications such as H3K4me3 in the hippocampus respond to an enriched environment (Schaffner et al., 2023), and our results indicate that these histone modifications may influence gene expression in the NAc under jSI stress as well." line 342-344: In which direction is the modification in the hippocampus in enriched environment? Is it opposite to what the authors have found in jSI (which would be interesting, since one could see a more social environment as an enriched condition too)? The idea behind this sentence needs to be precised.

      We thank the reviewer for this comment. We have revised this sentence to describe the reported finding more precisely (lines 376–379), namely that the loss of H3K4me1 observed in SNCA transgenic mice was partially dampened by environmental enrichment.

      Regarding the reviewer's suggestion that enrichment might be viewed as the converse of isolation, we agree this is an interesting possibility, but we have chosen not to develop the comparison in the text. Social isolation and environmental enrichment are not straightforwardly opposite conditions, since the absence of social contact is not simply the inverse of enrichment relative to standard housing, and the two may engage distinct circuits and cell populations. We therefore refer to this study only as evidence that these histone modifications are responsive to the housing environment, rather than drawing a directional comparison with our own data.

      3-minor-discussion-6

      "Since neural development relies on the regulation of gene expression (Jain et al., 2001; Xiang et al., 2020), we hypothesize that these terms reflect altered gene expression regulation mechanisms under jSI stress." "However, these hypotheses need to be further validated by additional experiments." line 369-371 & 375-377: The authors are not integrating their results, they are being cautious, but the message stays unclear to the reader. What is the message here?

      We thank the reviewer for this comment. We agree that our original wording was cautious to the point of leaving the message unclear, and we have rewritten this passage.

      We now state explicitly what we wish to propose: that genes involved in transcriptional and chromatin regulation carried altered histone modifications, that changes at such loci may have consequences extending beyond the genes themselves through their downstream targets, and that this may be particularly relevant during the developmental window examined here (lines 404–416). The paragraph now concludes by presenting this as a hypothesis, namely that histone modification changes at regulatory genes act as an upstream event after jSI whose consequences are amplified through the targets of those regulators, together with a statement that this remains to be tested experimentally, rather than ending with a general remark that further validation is required.

      3-minor-discussion-8

      "To determine whether histone modifications regulate specific genes, we focused on potentially important genes. Grik1, for example, exhibits reduced H3K27ac levels. It encodes a subunit of ionotropic glutamate receptors, and its deficiency has been found in mental diseases, such as schizophrenia and ADHD (Chatterjee et al., 2022; Hirata et al., 2012). The inactivation of Grik1 in rodents promotes anxiety-like behaviors via glutamatergic transmission (Englund et al., 2021)." line 387-392: What is the conclusion/hypothesis on Grik1's role?

      We thank the reviewer for this comment. We agree that the original passage described what is known about Grik1without stating what we ourselves wished to conclude.

      We have added a statement of our hypothesis at the end of this passage: that the reduction in H3K27ac around the Grik1 locus contributes to the downregulation of Grik1 after jSI, which in turn may contribute to the anxiety-like phenotypes associated with isolation (lines 432–437). We also indicate what would be required to test this, namely manipulating H3K27ac at the Grik1 locus and assessing the resulting transcriptional and behavioral changes.

      3-minor-discussion-9

      "Bcl2, for example, has downregulated H3K4me1, H3K4me3, and upregulated H3K27me3 levels. Bcl2 is an apoptosis-related gene that determines neuronal survival under stress. A previous study suggests that chronic social defeat stress decreases the Bcl-2/Bax ratio in NeuN+ neurons in the hippocampus (Zhu et al., 2024). Our data suggest that jSI is another type of stress that suppresses Bcl2 expression, and that the epigenetic factors are possible upstream regulatory mechanisms." line 393-399: No links or clear hypothesis have been made here. The authors proposed to go deeper in this direction later. It would be interesting to conclude on the hypothesis on these two genes (Grik1/Bcl2) in their model.

      We thank the reviewer for this comment. We agree that this passage describes our observations concerning Bcl2 without stating what we conclude from them.

      We have revised it to present our hypothesis explicitly, namely that the coordinated reduction of H3K4me1 and H3K4me3 together with the increase in H3K27me3 around the Bcl2 locus contributes to its downregulation after jSI, and that this may in turn affect neuronal survival under stress (lines 443–446). We have also indicated what would be required to test this, in the same way as for Grik1, as described in our response to the Reviewer #3's comment (#3-minor-discussion-8).

      3-minor-discussion-10

      "We found that Kdm6b may be at least partially involved in the gene expression changes in jSI mice, especially for potentially important genes like Nr4a1. Nr4a1 encodes a transcription factor that regulates dopamine metabolism, and it is reported to be involved in drug addiction, which is probably mediated by histone modification alterations." line 408-411: The authors are very cautious about their conclusion, maybe too much. Since they base their hypothesis on P14 cerebellum neurons in culture, more work is needed here to establish clearly the role of Kdm6b. It feels like the authors are dropping clues for the reader, without concluding themselves on their hypothesis.

      We thank the reviewer for this comment, which we found particularly helpful. We recognize that our repeated use of hedging language left the reader uncertain as to what we were actually proposing.

      We have restructured these passages so that our hypothesis is stated explicitly, followed by a statement of what would be required to test it (lines 455–469). For Kdm6b, we now propose that Kdm6b-mediated H3K27 demethylation contributes to the upregulation of Nr4a1 in the NAc after jSI, and that testing this hypothesis will require NAc-specific manipulation of Kdm6b together with assessment of both transcriptional and behavioral outcomes. The limitations of the published dataset from which this hypothesis derives, including its origin in cerebellar tissue at P14, are stated in the Limitations section, as described in our response to the Reviewer #3's comment (#3-major-5).

      We believe this approach addresses the reviewer's concern without overstating our findings, and it is consistent with the request from Reviewer #2 that causal language be moderated (#2-major-1).

      3-minor-discussion-11

      "Our experiments suggest that Dkk3 is regulated by jSI stress, and this is probably mediated by Brd4." / "Our analysis also implies that Dkk3 and Hcrtr2 are probably regulated by other epigenetic regulators such as Setd1a." line 429-430 & 431-432: "Probably". No proof is given on that statement. Everything is "potential" or "possible" or "probable" here.

      We agree with the reviewer. As described in our response to the Reviewer #3's comment (#3-minor-discussion-10), we have gone through the Discussion and removed or replaced the repeated use of "probably", "possible", and "potential", restructuring the relevant passages so that each hypothesis is stated explicitly, together with a statement of what would be required to test it (lines 476–483, 484-496).

      3-minor-discussion-12

      "To summarize, we revealed the changes in gene expression and histone modification levels under jSI stress." line 444: Here they are missing the term "in the NAc" and "in female mice" and maybe need to add "some changes" in the sentence.

      We thank the reviewer for this comment. We have revised the summary sentence to specify both the brain region and the sex of the animals studied, and to indicate that we identified a subset of changes rather than a comprehensive account (lines 498–499). It now reads: "To summarize, we revealed some changes in gene expression and histone modification levels in the NAc of female mice under jSI stress."

      3-minor-discussion-13

      Major issue of this study is stated as "Another limitation related to this is that we inferred candidate epigenetic factors based on previously published public data. However, different brain regions, cell types, and experimental conditions between our analyses and public datasets may contribute to the gene expression differences, leading to false negative or false positive results in this study." line 456-460: Indeed, as they highlight here, the differences between the datasets chosen and their initial context (jSI, NAc) is huge. Maybe the authors could explain better why did they choose these datasets instead of more similar ones.

      Also, in the RNAseq / cut&tag analysis, they "were unable to separate these subtypes (D1+ or D2+) for this current analysis". This analysis could be interesting to see in the supplementary or a brief description of what they have tried in the discussion, since the composition of the NAc is made of about 90-95% of MSNs and a plethora of interneurons (Parvalbumin, Cholinergic, etc), leading to different circuit connectivity and function. The striatum also contains a third population of D1/D2 hybrid neurons, maybe this population (about 5-10% of the whole striatum) made the identification of the neuronal subtypes harder.

      We thank the reviewer for these comments.

      Regarding the choice of public datasets, we have revised the Limitations section to state that these datasets were selected as the closest available to our system, since no dataset in which these factors had been perturbed exists for the NAc or striatum, and we have added developmental stage and sex to the list of differences between those datasets and our own (lines 506–527).

      Regarding the separation of neuronal subtypes, our nuclei were sorted using an anti-NeuN antibody, which labels neuronal nuclei broadly and does not distinguish D1- from D2-positive medium spiny neurons. We did not attempt to separate these subtypes, and we have now stated this reason explicitly in the Limitations section. As the reviewer notes, this is a meaningful limitation, since changes restricted to one subtype, or occurring in opposite directions between subtypes, would be underestimated or missed entirely in our analysis. We agree that subtype-resolved approaches will be required to address this in future work.

      4. Description of analyses that authors prefer not to carry out

      2-major-4

      Behavior is missing from this study. The Introduction says jSI affects "motor, emotional, learning, and sociability-related behaviors." This manuscript does not show that the molecular changes track those phenotypes in the same cohort. Without that link, the psychiatric disease framing stays speculative.

      We thank the reviewer for this comment, and we apologize that our reasoning was not made clear in the original manuscript.

      Behavioral phenotypes produced by this isolation paradigm have been characterized in our previous study (Sazhina et al., Neuroimage, 2025), which used exactly the same protocol as the present work, with isolation from P21 to P35 and regrouping from P35 to P49. We have described these findings in the Introduction so that the behavioral consequences of our paradigm are explicit (lines 554–557), rather than referring only to the literature in general terms.

      We did not perform behavioral testing on the animals used for molecular analysis, because behavioral testing itself constitutes a substantial stimulus. Exposure to a novel environment, handling, learning experience, and aversive stimuli such as the foot shock used in fear conditioning all induce transcriptional and epigenetic changes in the brain, including the induction of immediate early genes such as Nr4a1 and Fosl2, which are among the candidate genes discussed in this study. Had the same animals been subjected to behavioral testing, we would not have been able to attribute the observed changes to jSI rather than to the testing procedure. We therefore used dedicated cohorts for molecular profiling, and we have stated this rationale explicitly in the revised Methods.

      We acknowledge, nevertheless, that this design means we cannot demonstrate a correspondence between molecular changes and behavioral phenotypes within the same individuals. We have stated this limitation in the Limitation section and have moderated the framing of our findings in relation to psychiatric disease accordingly (lines 524–527), as described in our response to the Reviewer #2's comment (#2-major-1).

      3-major-3(OPTIONAL)

      Males: Experiments are only focused on females here. The authors didn't state why. These experiments (RNAseq, Cut&Tag) could be performed independently in male mice. The introduction refers to several pathologies that show sex-bias in human or animal models and even articles with sex differences. If the authors had a reason to select female only, it should be clearly stated. This proposition would take the same amount of resources and time that for this issue but would increase the knowledge about the (sex differences in the) effect of juvenile social isolation greatly.

      We thank the reviewer for this suggestion, and we agree that a parallel analysis in male mice would substantially extend the value of this work.

      As described in our response to the Reviewer #1's comment (#1-6), our focus on female mice was not arbitrary. In our previous study using the same isolation paradigm (Sazhina et al., Neuroimage, 2025), jSI produced a heightened fear response in female but not male mice, and the present study was designed to examine the molecular basis of that female-specific phenotype in the NAc. We apologize that this rationale was not stated in the original manuscript, and we have described it explicitly in the Introduction and Methods of the revised version (lines 73–74, 524-527, 542-543).

      We would nevertheless like to explain why we are unable to perform the proposed experiments within the scope of this revision. Generating a comparable dataset in males would require a new cohort of animals, isolation and regrouping over four weeks, nuclear isolation and sorting, and the preparation and sequencing of five libraries per animal, followed by the full analysis pipeline. As the reviewer notes, this would require resources and time comparable to those invested in the present study, and it is not feasible within the revision period. We have stated in the Limitations section that a parallel analysis in males is required to distinguish shared mechanisms of jSI from sex-specific ones (lines 524–527), and we intend to pursue this in future work.

      3-major-4(OPTIONAL)

      Behavior abnormalities: Showing behavior abnormalities after jSI of female mice or, if it has been published somewhere else previously, a general description of the phenotypes observed in juvenile and adult female mice. This experiment could be performed in one batch of female separated at P21 and regrouped at P35, with anxiety (openfield or elevated plus maze, 1 day each), sociability (direct or 3-Chambers, 1 day each) and eventually depressive-like behaviors/anhedonia (sucrose preference test, 1 week including habituation to the two bottles and/or tail suspension/forced swimming, 1 day) tested. Same tests would be performed in juveniles or in adults.

      We thank the reviewer for this suggestion, and for noting that a description of previously published phenotypes would be an acceptable alternative to new behavioral experiments.

      Behavioral phenotypes produced by this isolation paradigm have been characterized in our previous study (Sazhina et al., Neuroimage, 2025), which used exactly the same protocol as the present work, with isolation from P21 to P35 and regrouping from P35 to P49. In that study, jSI produced a heightened fear response in female but not male mice. We have described these findings explicitly in the Introduction (lines 73–74), so that the behavioral consequences of our paradigm are stated rather than left to the reader to infer from the general literature.

      As described in our response to the Reviewer #2's comment (#2-major-4), we deliberately did not perform behavioral testing on the animals used for molecular profiling, since behavioral testing itself induces transcriptional and epigenetic changes in the brain and would have confounded the changes attributable to isolation. We have stated this rationale explicitly in the revised Methods (lines 554–557).

      3-major-9(OPTIONAL)

      Include more mechanistic experiments (as suggested in the discussion) on some of the factors identified (Kdm6b, Brd4, Setd1a) to better confirm their involvement in the jSI-induced changes in transcriptome (and behavior). Conditional KO in the NAc with viral infection (mouse line Kdm6b-flox or Brd4-flox or Setd1a-flox existing + AAV-cre) or AAV Crispr for specific knockdown could hardly be performed in the time window used in this study (at least 3week expression of the Cre/Cas9). But systemic or intracerebral pharmacological approach (ip injection of an inhibitor or cannula implantation, local intra-accumbens injection) could be performed in juvenile (1 week recovery post-surgery only, can be done at P21 before isolation).

      We thank the reviewer for this thoughtful suggestion, and we agree that functional analysis of Kdm6b, Brd4, and Setd1a would substantially strengthen the conclusions of this study.

      As the reviewer notes, conditional knockout approaches requiring viral expression are not feasible within the developmental window used here. Regarding the pharmacological alternatives proposed, systemic administration would not allow us to attribute any resulting changes specifically to the NAc, and local intra-accumbens administration, while addressing this point, would require establishing a new surgical and behavioral pipeline in juvenile animals alongside the molecular analyses. Neither is achievable within the revision period.

      We have therefore stated explicitly in the Discussion that functional validation of these candidate factors is required to test the hypotheses raised here (lines 509–513), and we intend to pursue this in future work. We are grateful to the reviewer for these constructive suggestions, which we will take up in designing those experiments.

      3-minor-discussion-5

      "Since our mice were isolated from P21 to P35, a period critical for the maturation of neurons (Makinodan et al., 2012; Walker et al., 2019; Yamaguchi et al., 2024), it is possible that the neuronal development process is affected by the isolated housing environment." line 351-354: This sentence states that juvenile social isolation during development could impair development. It is probable, since early life stress (even maternal stress) can have prolonged effect on the offspring behaviors. It is probable that the time-window of jSI is important for development.

      OPTIONAL: The authors would benefit of more experiments here: 1) They could perform SI in the same conditions but in adult females and compare the DEGs observed in that case (less development related genes probably). 2) "Neurons during adolescence mainly experience synaptic pruning and elimination (Afroz et al., 2016; Germann et al., 2021; Watanabe & Kano, 2024), and isolation stress probably impairs such processes." Here, the author could check in the NAc of their jSI female mice the state of dendritic arborization of MSNs (number, length, etc) on NAc brain slices.

      We thank the reviewer for these suggestions, both of which we agree would strengthen the study.

      Regarding social isolation in adult females, a comparison with adult-isolated animals would indeed help to establish whether the changes we observed are specific to the adolescent period. This would require a new cohort, a full isolation and regrouping schedule, nuclear isolation and sorting, and library preparation and sequencing, and is not feasible within the revision period. We will note this as a direction for future work. We have, however, revised the Introduction to cite studies defining P21 to P35 as a critical period for maturation in this system (lines 64–65), so that the rationale for focusing on this window is better supported.

      Regarding dendritic morphology, we agree that examining the arborization of NAc MSNs would provide a useful structural correlate of the developmental processes discussed in this section. We did not collect tissue suitable for morphological analysis from these animals, since the entire NAc punch was used for nuclear isolation, and this analysis would therefore also require a new cohort. We will likewise note this as a direction for future work, and we have revisedthe corresponding statements in the Discussion so that they are presented as hypotheses rather than as established consequences of isolation (lines 389–391).

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this study, the authors were investigating the effect of juvenile social isolation (jSI) on the nucleus accumbens (NAc) transcriptome in female mice. They used P21 wild type (C57BL6) female, isolated at P21 or group housed, and reunited at P35 to generate their samples for RNAseq on NAc punches. They also performed FACS sorting on the nuclei from NAc lysates to select only neuronal nuclei (NeuN staining). They studied the differentially expressed genes (DEGs) between group housed and jSI by RNAseq. Then, they studied the protein/protein interaction via stringDB on the DEGs identified (up or down) and perform GO analysis on them. They identified Ntrk2, Grin3a, Grik1 and Bcl2; associated with the neuronal function or transcription regulation terms. They also studied the histones modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) after jSI and identified neuronal function and transcription regulation terms again on the DDR (Cut and Tag method). They found that these histones modifications could play a role in jSI-induced adaptations and neuronal function. Finally, they reanalyzed public datasets of RNAseq data to identify histone modifications associated with their DEGs of interest, and compare their DEGs to differences between genotypes in the public datasets (in conditional KO models of some genes of interest, as Kdm6b cKO, BET inhibitor, or Setd1a +/- mice with 3 different mutations. They conclude that histone modification could be involved in jSI-induced gene expression alteration.

      Major comments:

      • Title: Add the sex : "in female mice" since it is specific.
      • Referencing: Biorender.com has been used to generate some schematics in this study, but it is never acknowledged or referenced.
      • OPTIONNAL: Males: Experiments are only focused on females here. The authors didn't state why. These experiments (RNAseq, Cut&Tag) could be performed independently in male mice. The introduction refers to several pathologies that show sex-bias in human or animal models and even articles with sex differences. If the authors had a reason to select female only, it should be clearly stated. This proposition would take the same amount of resources and time that for this issue but would increase the knowledge about the (sex differences in the) effect of juvenile social isolation greatly.
      • OPTIONNAL: Behavior abnormalities: Showing behavior abnormalities after jSI of female mice or, if it has been published somewhere else previously, a general description of the phenotypes observed in juvenile and adult female mice. This experiment could be performed in one batch of female separated at P21 and regrouped at P35, with anxiety (openfield or elevated plus maze, 1 day each), sociability (direct or 3-Chambers, 1 day each) and eventually depressive-like behaviors/anhedonia (sucrose preference test, 1 week including habituation to the two bottles and/or tail suspension/forced swimming, 1 day) tested. Same tests would be performed in juveniles or in adults.
      • OPTIONNAL: Datasets selection: The public datasets used through this study are far from the original experimental design proposed in this study (cerebellum at P14, cortex at E16.5, nonspecific inhibitor, whole adult PFC = 12-14weeks old). It is important to note that the dataset used, from Chen et al 2022 (whole PFC) also studied the striatum of heterozygous Setd1a mice in the same paper. Why did the author reanalyzed PFC data instead of striatum, which would probably look more like their NAc-restricted samples? Restrict the analysis of the public dataset on NAc data, if possible on females only, and /or try to obtain data from similar experimental design (social isolation, stressed mice). Note here, that the development stage during which the mice have been isolated/regrouped and sample taken will probably of importance. The reanalyzes mix embryonic, early juvenile and adult samples, none of which is consistent nor look like their set up (P31-35).
      • Wording: Formulation throughout the current study is vague, sometimes misleading, with some unclear sentences (see minor comments for the sentences showing problems). This issue needs to be checked again.
      • Assumptions are made based on 2 sets of reanalises. These parts should be displayed in the supplementary to help the authors target some genes of interest rather than the principal figures. These analyses didn't seem convincing due to too much shift from the original issue of the paper (which is jSI in the NAc in female mice).
      • Volcano plots throughout the study: Should display the genes names (at least top 10 up and top 10 down DEGs) on the graph, otherwise the volcano plots are unreadable.
      • OPTIONNAL: Include more mechanistic experiments (as suggested in the discussion) on some of the factors identified (Kdm6b, Brd4, Setd1a) to better confirm their involvement in the jSI-induced changes in transcriptome (and behavior). Conditional KO in the NAc with viral infection (mouse line Kdm6b-flox or Brd4-flox or Setd1a-flox existing + AAV-cre) or AAV Crispr for specific knockdown could hardly be performed in the time window used in this study (at least 3week expression of the Cre/Cas9). But systemic or intracerebral pharmalogical approach (ip injection of an inhibitor or cannula implantation, local intra-accumbens injection) could be performed in juvenile (1 week recovery post-surgery only, can be done at P21 before isolation).
      • Some of the top genes differentially expressed should be clearly cited in the results.

      Minor comments:

      Introduction:

      • Throughout the introduction, some parts could be ameliorated, since some sentences are unclear or confusing. Here are some examples and explanations on the issue detected (find in red comments/suggestions from the reviewer): "These negative effects are further supported by evidence from Covid-19 during the last few years » line 56/57 : reformulate.

      "In addition, isolation contributes to severe social issues, such as increased human suicide risk » line 57/58: issues is plural, but only one example is given; moreover, the term "social issue" associated to suicide is poorly-worded.

      "In the case of rodents, socially isolated animal models are proposed to be associated with various human diseases » line 59/60: clumsy sentence, the animal models are associated to human disease? This could be reformulated.

      "and the effects of juvenile social isolation (jSI) on motor, emotional, learning, and sociability-related behaviors in rodents have been widely reported (Li et al., 2021; Powell & Swerdlow, 65 2023; Walker et al., 2019), which further provides evidence of the pathogenesis and 66 molecular mechanisms of human mental disorders. » line 63-66: Examples of behavioral dysfunctions would be appreciated here.

      "The nucleus accumbens (NAc) is a critical component of the brain reward 68 circuitry, and dysfunction of the NAc is associated with drug addiction (Zinsmaier et al., 69 2022), impaired social interaction (Pomrenze et al., 2022; Shan et al., 2022), and 70 abnormal emotion expression (Gebara et al., 2021) » line 67-70: here, the statement reads as dysfunction of the NAc is responsible of abnormal behaviors (addiction, social behavior or emotional expression), but the articles show that NAc is dysregulated in models of these pathologies. Is the dysfunction in the NAc responsible of or a consequence of the pathologies? This could be better formulated.

      "An fMRI study showed that activity of the human NAc is associated with the sense of loss (Cooper et al., 2009; O'Connor et al., 2008). » line 70-72: Sentence says one study, but two rfereneces are used. The sentence refers to O'Connor only. Cooper is about reward/effort and NAc activity, not grief/loss, reformulate. Also, "activity" is unclear, the authors could be more precise with "hyperactivity of the NAc has been found in people suffering from loss".

      "The NAc from lonely individuals showed key differentially expressed genes (DEGs) that are associated with both neurodegenerative and neuropsychiatric diseases » line 74-75: Unclear, give examples of the pathologies here to be consistent with the next sentence about female rats (Alzheimer, Parkinson, Huntington). The next paragraph (line79-95) about DNA methylation and histone modifications is missing a general conclusion: what is interesting or needs to be more studied? Also, H3K9 and H3K79 have been introduced but unused in the paper, while H3K27ac/me3 have not been introduced. What is known about them?

      « How do epigenetic elements mediate gene expression dysfunction under jSI stress? In this study, we aimed to reveal the alterations in gene expression and histone modifications induced by jSI, and to elucidate their roles in the context of psychiatric disorders promoted by jSI » line 96-99: Statement is too general, it is missing the term "NAc" here. - General comment: Since the paper is focused on female, it could be of interest to state in the introduction if/how sex differences exist in relation to social isolation and human diseases showing isolation as a phenotype.

      Methods:

      • Female mice only have been used in this study. It is never explained why so. Knowing that many diseases show sex differences in prevalence or symptom expression, it would have been helpful to include also male mice in the study, to identify common mechanism linked to jSI versus sex-specific alterations.
      • Cut & Tag: Dilution of antibodies is not displayed ("1µL") and the reference for antibodies is unclear: "primary antibodies (H3K4me1, MABI, 536 MABI0302; H3K4me3, abcam, ab8580; H3K27me3, CST, 9733S; H3K27ac, CST, 537 8173S; 1 µL per reaction) ». This should be adapted to look like the FACS antibody description: "anti-NeuN-488 conjugated antibody (Millipore, #MAB377X, 1:400 dilution) ».
      • "Frozen nuclei were thawed and bound to concanavalin A (ConA)-coated magnetic beads (BioMag®Plus Concanavalin A, 10 µL per reaction) for 10-60 min on a rotator at room temperature. » Why so much difference in incubation time here?
      • Number of animals: While reading the manuscript, it was unclear that 5 different groups of mice have been used. The number of animals should be stated in the methods in the "nucleus extraction" part or "animals" section to facilitate understanding the methods.
      • Data analysis: "For RNA-seq data, p-value < 0.05 and Fold Change > 1.2 were used as the threshold for identifying DEGs. For CUT&Tag data, p-value < 0.05 and Fold Change > 2 were selected as the standard for identifying DDRs. » Cut&Tag p-value and threshold is written in RNAseq section, move it to its proper part hereafter "CUT&Tag data analysis ».
      • Suggestion DDR: acronym is present in the methods but not explained. It is however explained in the results. This depends on the order in the publication, but if methods appear first, it would be helpful to understand what stands for DDR.
      • Cut&Tag data analysis: "The procedures of quality check and trimming were the same as RNA-seq data analysis. » line 591; "and the removal of blacklisted regions was the same as RNA-seq » line 594; "GO analysis was the same as RNA-seq analysis. » line 599 : These parts could be ameliorated to avoid repetition. Since the preparation of nuclei and most of the analysis are the same, the methods could be more straightforwardly explained separating common preparation from specific analysis.
      • Methods explaining how the authors performed the reanalysis of the public datasets is missing.
      • Animals have been separated at P21 and regrouped at P35: It is not stated if they were regrouped together or with a group of unstressed WT never separated, which could influence their behavior and stress levels.
      • No behavioral test has been performed on these mice. It would have been appreciated to see that 2-weeks social isolation was efficient to generate stress in these animals (anxiety test, sociability at least). And if this protocol has been previously used in their lab, at least to explain briefly what behavior abnormalities the jSI was inducing.

      Results:

      • Section 1: Only 1 or 2 GO terms (down / up DEGs) are described, but top5 is represented in the figure. Description of the others would be of interest (for example actine reorganization could be particularly interesting). Also, citing some DEGs from the top UP and DOWN, representative of the GO terms, could be of huge interest here.
      • Figure 1, E/F/G: Some genes in Fig1G are not from top10 nodes in DEGs (Slc17a8, Hcrtr2, Dkk3, Dact1, Nr4a1, Fosl2, Htr5a). They are stated as "potentially important genes" in the legends and are found later in the study as important using other methods than RNAseq. Since Fig1G is displaying RNAseq results, it would be better to stick to the Top10 genes displayed here and put in another figure the other "potentially important genes". What means "potentially important" ? Why these ? What are the criterion? Also, the fig1G is unclear visually: separate it in two for top10 down and top10 up, it would be easier to understand and navigate. Legends: Statistics used for 1G are not stated. Volcano plot: Top 10 genes UP/down could be displayed on the graph.
      • Section 2: The first paragraph here is about which Transcription Factor (TF) is predicted to participate in their DEG's expression. Half of this paragraph is introduction about the function of several TF. This is not part of results and should be moved appropriately in the introduction or discussion section, or shortened significantly, since it is now longer than the result part.

      Importantly here, the analysis is done on ChIP Atlas (public datasets). They state : "Taken together, promoter analysis of DEGs suggests that potential epigenetic mechanisms may act upstream of jSI-induced transcriptional dysregulation in the NAc. » line 176, but the database has never been stated to be NAc-only data nor data from jSI animals. If not, this sentence has to be modified. It was unclear globally if this part was based on their work or data mining on a first read, it should be more clearly stated at the beginning that this is exploratory. - Figure2 : A/B/C: only one mention of the NAc in the data; Fig D: 5/8 NAc datasets. The analysis here seams unbalanced. Why not take into account only NAc datasets ? The composition of cortical area or retina is highly different than the NAc (mostly Glutamatergic vs GABAergic populations). If doable, the analysis focused on NAc datasets would be better. - Section 3: "First, neuronal development-related genes, such as "nervous system development", were found in all DDRs of the four histone modifications » line 193-194: sentence is unclear, the author probably meant "term".

      No gene have been cited here in any histone modification experiment (nor visible in the figure, only dots without names, top 10 up/down could be displayed on the volcano plot). It would be of interest to state at least some of the genes identified here (DDRs, closest loci) and to see if / how many common genes from RNAseq data were found again here.

      GO terms: again, only one or two examples are described, but figure shows the top5. They all could be at least stated.

      The conclusion of the first paragraph states : "These results were consistent with the transcriptome analysis that neuronal function and transcription-related genes were affected, and with the transcription factor analysis that epigenetic regulators were predicted to bind to the promoter regions of these genes. » line 197-199: Since the author did not state any genes, we can only believe that the result are consistent based on two vague GO terms "neuronal system development" and "regulation of transcription by RNApol II/chromatin remodeling". If the lector has to read itself every gene table to know which genes are dysregulated in jSI, this study will be really time consuming. - Figure3: Volcano could display the top10 names of DDRs.

      "The results indicated that down-DEGs were associated with H3K4me1, H3K4me3, and H3K27ac. » line 203: In which direction are altered H3K4me1, H3K4me3, and H3K27ac ? This is important to know. "Consistent with their active roles in transcription, downregulation of H3K4me1 and 205 H3K27ac was more relevant to down-DEGs than up-DEGs. » line 205: Why ? Unclear statement.

      "Considering the composite roles of H3K4me3 (an active histone modification) and H3K27me3 (a repressive histone modification), we hypothesize a major role of H3K27me3 in these up-DEGs, and the contribution of H3K4me3 to gene expression alteration by jSI might be small, though we cannot exclude the possibility that it regulates certain genes locally or plays a repressive role. » line 208-211: It is very unclear here, why H3K27me3 should play a major role while H3K4me3 alteration "might be small". This has to be further discussed.

      "For top 10 nodes among up-DEGs, we didn't find any significant alterations in any of the four histone modifications around their gene loci, except for downregulated H3K4me3 around Aldh18a1, Lamp1, and Gnb4 » line 217-219: Formulation is clumsy here, reformulate.

      "Some of these genes were marked by multiple altered histone modifications." Line 223: Which ones? Ony Bcl2 displayed, but the authors state "some of these genes" right after writing "H3K27ac was found to be 222 downregulated around Grin3a, Grik1, and Adgre1 ». Are these the other genes showing several histone modifications? It is unclear.<br /> - Figure 4B : only Grik1 as an example. Why only this one and not Bcl2 that moreover show several modifications? Could be helpful to show an example of each modification. -Section 4:

      Kdm6b:

      "To examine the possible contribution of Kdm6b to jSI, we re-analyzed the RNA-seq data from Kdm6b-knockout in the published study (Ramesh et al., 2023). » line 240-241: this study is about conditional Kdm6b KO in the cerebellum, on naive P14 male and female mice's neurons in culture. The authors extrapolate the results from a completely different neuronal population/region and sex to justify the potential effect jSI could have on their adolescent female mice. This sentence is misleading for the reader, since the model used (not stressed) and experimental conditions are far from what they are studying. This sentence needs some reformulation to better explain their goal. They show the DEGs (up/down) from reanalyzed data and overlap between these DEGs and the one from Figure1, but the conditions are far from each other here. One could ask what the specificity of their overlap demonstrated here.

      "Fosl2 and Nr4a1 are immediate early genes (IEGs) in response to neuronal activation in many brain regions (Dave et al., 2025; Shi et al., 2024), and these two genes have been reported to be involved in memory maintenance (McNulty et al., 2012; Mizuno 257 et al., 2020) and Parkinson's disease (PD) (Fan et al., 2020; Rouillard et al., 2018). In addition, Htr5a, which encodes serotonin receptor 5A, was also upregulated in jSI and downregulated by Kdm6b KO (Fig. 1G, 5G, Table S1). And Htr5a has been reported to be a risk factor of human schizophrenia (Guan et al., 2016). » line 254-260: This part of the result paragraph is about introduction/discussion again. This should be move appropriately.

      Brd4:

      Here, the authors reanalyzed data from E16.5 cortical neuronal culture treated with or without BET family inhibitor, which they state is not selective of Brd4 (even if it is part of the BET family). The crossover between this embryonic cortical neuronal population treated with nonspecific inhibitor and their model (juvenile Social Isolation, NAc) is a bit of a stretch. What do the overlap in DEGs really means here? "We also examined the possible downstream Brd4 target genes within the gene sets of down-DEGs by jSI and down-DEGs by JQ1 treatment, and we identified Hcrtr2 and Dkk3 in these gene sets (Fig. 1G, 5H, Table S1). Hcrtr2 encodes an orexin receptor, and it has been reported to be involved in altered arousal levels through dopamine neurons (Bandarabadi et al., 2024). Dkk3 inhibits Wnt signaling and is reported to be related to anxiety and memory formation (X. Chen et al., 2025; Flores et al., 2024). » line 275-280: What is the conclusion on these results?

      Setd1a:

      Here, they used 3 separate datasets: whole PFC of Setd1a heterozygous mice (exon 4 LacZ/Neo cassette insertion), whole PFC from loss of function Setd1a heterozygous mice and FoxP2+ nuclei from PFC of Setd1a +/- mice (frameshift in the 15th exon). These datasets are quite different between themselves and compared to NAc samples from jSI mice. The authors stated that the first two datasets had a low DEG overlap with their samples but continued with the third which showed a significant overlap for Hcrtr2, Dkk3 and Dact1. They finally conclude that: "Taken together, these results suggest that epigenetic factors, such as Kdm6b, Brd4, and Setd1a, may mediate jSI-induced gene expression alterations. » Nothing in these datasets is comparable to what they want to prove here, it is a huge stretch to propose these genes as mediators of jSI. Reformulate. These results could be exploited as exploratory, to reduce the number of potential targets, but needs to be investigated on their own.

      Figure 5: volcano plots: top10 genes visible could be useful. This section of the results would fit better displayed in the supplementary, since they reanalyzed datasets far from their experimental conditions. These genes of interest should be further investigated in their jSI model.

      Discussion:

      "For example, the expression of glutamate receptors is reduced in the NAc, prefrontal cortex, and hippocampus under isolation stress (Hermes et al., 2011; Mao et al., 2022; Sestito et al., 2011). » line 315-317: Which GluR are reduced here ? It needs to be more precise for the reader here, and to state if some genes as been found in common between this literature and their DEGs.

      « The NAc is a key component of the brain reward circuit, and it is involved in drug addiction and social behavior (Pomrenze et al., 2022; Zinsmaier et al., 2022). NAc neurons receive glutamatergic inputs from the PFC, basolateral amygdala (BLA), hippocampus, and ventral tegmental area (VTA) (Arrondeau et al., 2024; Dieterich et al., 2021; Elam et al., 2025; Le Borgne et al., 2025; Zinsmaier et al., 2022), and neurons in the NAc output the information to the ventral pallidum (VP) (Liu et al., 2022), VTA (Qi et al., 2022), and other areas of the basal ganglia (Lanciego et al., 2012). » line 318-324: These lines are describing the circuitry of the NAc, some of its inputs (no mention of dopamine afferences from the VTA) and outputs. No use of this information is used after, since they conclude the paragraph with: "Thus, deficits in glutamatergic synapses possibly mediate jSI-induced behavioral abnormalities, including impaired social interaction, anxiety, and an increased risk of substance abuse ». line 325-326: What is the point of describing the circuit, if it is not interpreted regarding their results? What is their hypothesis on the circuit dysfunction in jSI female mice? They were discussing the DEGs from RNAseq result before this paragraph. What is the link/hypothesis between their DEGs and the glutamatergic circuits of the NAc? Is the NAc directly responsible of jSI-induced behavioral abnormalities for them or cortical/amygdal/hippocampal/VTA glutamatergic projection neurons are dysregulated, creating DEGs at the synapse in the NAc? This part of the discussion should be more specific on what they mean.

      "Deficiencies in these proteins are associated with various behavioral abnormalities (Araujo et al., 2017; Chasse 335 et al., 2024; Guo et al., 2020; Huang et al., 2021; Mukai et al., 2019). » line 334-335: what proteins and what behavioral abnormalities? This is not precise enough and needs reformulation/conclusions. "A previous report suggests that histone modifications such as H3K4me3 in the hippocampus respond to an enriched environment (Schaffner et al., 2023), and our results indicate that these histone modifications may influence gene expression in the NAc under jSI stress as well. » line 342-344: In which direction is the modification in the hippocampus in enriched environment? Is it opposite to what the authors have found in jSI (which would be interesting, since one could see a more social environment as an enriched condition too)? The idea behind this sentence needs to be precised.

      "Since our mice were isolated from P21 to P35, a period critical for the maturation of neurons (Makinodan et al., 2012; Walker et al., 2019; Yamaguchi et al., 2024), it is possible that the neuronal development process is affected by the isolated housing environment. » line 351-354: This sentence states that juvenile social isolation during development could impair development. It is probable, since early life stress (even maternal stress) can have prolonged effect on the offspring behaviors. It is probable that the time-window of jSI is important for development. OPTIONNAL: The authors would beneficiate of more experiments here: 1) They could perform SI in the same conditions but in adult females and compare the DEGs observed in that case (less development related genes probably). 2) "Neurons during adolescence mainly experience synaptic pruning and elimination (Afroz et al., 2016; Germann et al., 2021; Watanabe & Kano, 2024), and isolation stress probably impairs such processes. ". Here, the author could check in the NAc of their jSI female mice the state of dendritic arborization of MSNs (number, length, etc) on NAc brain slices.

      ". Since neural development relies on the regulation of gene expression (Jain 370 et al., 2001; Xiang et al., 2020), we hypothesize that these terms reflect altered gene expression regulation mechanisms under jSI stress » "However, these hypotheses need to be further validated by additional experiments. » line 369-371 & 375-377: The author are not integrating their results, they are being cautious, but the message stays unclear to the reader. What is the message here ?

      « Besides these two main shared functions affected by jSI, our results suggest that other biological processes are potentially mediated by one or more histone modifications. For example, some DDRs of H3K27ac and H3K27me3 are functionally enriched around cell adhesion-associated genes, and this is consistent with previous papers suggesting that cell adhesion is affected by isolation (Santiago et al., 2023; Wu et 382 al., 2022)... » line 377-382: This GO term appeared in the figure, but has never been mentioned clearly in the results. The explanation goes on for a full paragraph. It could be better to introduce it before if it is of interest. Also, which cell-adhesion genes have been found in the RNAseq / cut&tag experiments for this family of genes (never stated)?

      "To determine whether histone modifications regulate specific genes, we focused on potentially important genes. Grik1, for example, exhibits reduced H3K27ac levels. It encodes a subunit of ionotropic glutamate receptors, and its deficiency has been found in mental diseases, such as schizophrenia and ADHD (Chatterjee et al., 2022; Hirata et al., 2012). The inactivation of Grik1 in rodents promotes anxiety-like behaviors via glutamatergic transmission (Englund et al., 2021). » line 387-392: What is the conclusion/hypothesis on Grik1's role ?

      "Bcl2, for example, has downregulated H3K4me1, H3K4me3, and upregulated H3K27me3 levels. Bcl2 is an apoptosis-related gene that determines neuronal survival under stress. A previous study suggests that chronic social defeat stress decreases the Bcl-2/Bax ratio in NeuN+ neurons in the hippocampus (Zhu et al., 2024). Our data suggest that jSI is another type of stress that suppresses Bcl2 expression, and that the epigenetic factors are possible upstream regulatory mechanisms » line 393-399: No links or clear hypothesis have been made here. The authors proposed to go deeper in this direction later. It would be interesting to conclude on the hypothesis on these two genes (Grik1/Bcl2) in their model.

      "We found that Kdm6b may be at least partially involved in the gene expression changes in jSI mice, especially for potentially important genes like Nr4a1. Nr4a1 encodes a transcription factor that regulates dopamine metabolism, and it is reported to be involved in drug addiction, which is probably mediated by histone modification alterations » line 408-411: The authors are very cautious about their conclusion, maybe too much. Since they base their hypothesis on P14 cerebellum neurons in culture, more work is needed here to establish clearly the role of Kdm6b. It feels like the authors are dropping clues for the reader, without concluding themselves on their hypothesis.

      "Our experiments suggest that Dkk3 is regulated by jSI stress, and this is probably mediated by Brd4.» / "Our analysis also implies that Dkk3 and Hcrtr2 are probably regulated by other epigenetic regulators such as Setd1a. "line 429-430 & 431-432: "Probably". No proof is given on that statement. Everything is "potential" or "possible" or "probable" here.

      Summary paragraph: "To summarize, we revealed the changes in gene expression and histone modification levels under jSI stress. » line 444: Here they are missing the term "in the NAc" and "in female mice" and maybe need to add "some changes" in the sentence.

      Limitations paragraph: Major issue of this study is stated as "Another limitation related to this is that we inferred candidate epigenetic factors based on previously published public data. However, different brain regions, cell types, and experimental conditions between our analyses and public datasets may contribute to the gene expression differences, leading to false negative or false positive results in this study. » line 456-460: Indeed, as they highlight here, the differences between the datasets chosen and their initial context (jSI, NAc) is huge. Maybe the authors could explain better why did they choose these datasets instead of more similar ones. Also, in the RNAseq / cut&tag analysis, they "were unable to separate these subtypes (D1+ or D2+) for this current analysis ». This analysis could be interesting to see in the supplementary or a brief description of what they have tried in the discussion, since the composition of the NAc is made of about 90-95% of MSNs and a plethora of interneurons (Parvalbumin, Cholinergic, etc), leading to different circuit connectivity and function. The striatum also contains a third population of D1/D2 hybrid neurons, maybe this population (about 5-10% of the whole striatum) made the identification of the neuronal subtypes harder.

      Significance

      Globally, this study is showing transcriptomic and histone modifications occurring after juvenile social isolation in female mice. The authors identified differentially expressed genes linked to neuronal development, regulation of transcription and chromatin remodeling. The reanalyzed public datasets to identify histone modification on the DEG identified and observed the impact of some already published mutations on the gene expression to compare it to their data. The limits of this issue are caused by the reanalysis parts, since the datasets used are cortical and cerebellum samples, in diverse development stage (embryonic, early juvenile, adults - both sexes) that is quite different compared to their paradigm (juvenile females). They also never display the name of the principal DEGs identified (text or plots) which leads to difficult understanding of the findings of this paper. A more focused analysis on NAc or striatal, female only, juvenile stage datasets would be more helpful in this situation. The text should be more precise sometimes and a specific explanation on the exclusion of male mice should be introduce early in the methods.

      This study finds its place in the current research on the role of NAc function/dysfunction in behavioral abnormalities induced by social isolation and try to understand the mechanisms behind the abnormal behaviors induced by separation. The audience could be composed of researchers from several domains where social isolation is the cause or the consequence of pathological behaviors, including studies on loss, depression, ASD, Alzheimer, Schizophrenia, etc). The context is quite broad. The results from this paper could help find new molecular targets to alleviate the effects of social isolation and perhaps ameliorate the behavior for mouse models of several diseases or later in patients. A better understanding of the effects of social isolation in female is interesting, but being able to compare both sexes would be even better: identifying sex-differences and common defect is of great interest nowadays in several domains.

      The present reviewer has expertise in behavior in mice (both male and female) from juvenile to adult stages, has studied neuronal circuits including prefrontal cortex and striatum (mainly NAc) in behavioral abnormalities in mice in a model of ASD and more recently in an addiction model. The reviewer is interested particularly in sex-differences in neuronal circuits defects and behavior expression in diseases. Finally, the reviewer has recently focused on spatial transcriptomic approaches in addiction models.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      This paper profiles NeuN positive NAc nuclei after juvenile social isolation. The authors report RNA seq changes and CUT and Tag for H3K4me1, H3K4me3, H3K27ac, and H3K27me3. They then link DEGs to public datasets for Kdm6b, Brd4, and Setd1a. The neuron enriched design is useful. The main claim remains correlative. Causal support is thin.

      Major points

      Causality is not shown. The Discussion states: "Although we didn't show the molecular mechanism of histone modification alteration regulating gene expression, we revealed the association between transcriptome and histone modifications." That limit should shape the Abstract and title more clearly. Phrases like "epigenetic alterations may also play a role" are fine. Stronger wording about mediation should be toned down until NAc specific perturbation is done.

      DEG thresholds are loose. DEGs are defined as "p-value < 0.05 and Fold Change (FC) > 1.2." There is no clear FDR cutoff. With ~1250 DEGs from n = 5 to 6, many hits may be noise. Please report FDR filtered lists or justify the uncorrected p value choice. Re run key GO and overlap tests on a stricter set.

      Public data overlaps are hard to interpret. Kdm6b data are from cerebellum. Brd4 data are from cultured cortical neurons treated with JQ1. Setd1a data are mostly PFC. The authors note that "different brain regions, cell types, and experimental conditions... may contribute to... false negative or false positive results." That caveat is important. Overlaps should be framed as hypothesis generating only. Do not treat them as evidence that these enzymes act in NAc under jSI.

      Behavior is missing from this study. The Introduction says jSI affects "motor, emotional, learning, and sociability-related behaviors." This manuscript does not show that the molecular changes track those phenotypes in the same cohort. Without that link, the psychiatric disease framing stays speculative.

      CUT and Tag analysis is coarse for promoter claims. Signals are quantified in "all 5 kbp bins." That bin size can blur promoters, enhancers, and neighboring genes. Please add peak calling or TSS centered analyses for key loci such as Grik1, Bcl2, and Dkk3. Also show more browser tracks beyond one example.

      Multiple testing for overlaps needs attention. Many Fisher tests compare DEGs with DDRs and with several public DEG lists. Report whether p values were corrected across tests. Some reported overlaps are small in absolute numbers even when p values look significant.

      Minor points

      Only female mice were used. State this early and discuss sex limits. Juvenile isolation effects often differ by sex. Down DEG GO terms include "Chondrocyte differentiation" and "Positive regulation of cartilage development." These look odd for NAc neurons. Check annotation quality and whether these terms survive stricter DEG filters.

      Figure 1 lists "II2ra" in the top nodes table. That is likely Il2ra. Please correct. Sample sizes differ a lot across marks. H3K4me1 and H3K27me3 have n = 4 in jSI. Discuss power and why replicates differ.

      The isolation protocol includes regrouping from P35 to P49. Make clear that effects are lasting post isolation effects, not acute isolation effects.

      Methods say "GPT-5.4... and Claude Sonnet 4.6... was used." Fix subject verb agreement. Data Availability lists "GSE3508789." Confirm this accession. It looks malformed.

      Abstract keywords include "Loneliness." The mouse work is social isolation. Keep that distinction clear, as the Introduction already does.

      Overall

      Solid descriptive resource on NAc neuron transcriptome and histone marks after jSI. Not yet strong enough for firm mechanistic claims about Kdm6b, Brd4, or Setd1a. Tighten statistics, soften causal language, and add locus level epigenomic detail. Functional tests in NAc would raise impact a lot. At present I see this as useful but preliminary.

      Significance

      This study provides a useful neuron-enriched transcriptomic and histone modification resource from the nucleus accumbens following juvenile social isolation. The integration of RNA-seq and CUT&Tag data adds value for researchers studying epigenetic regulation and stress-related neurobiology. However, the advance is primarily descriptive rather than mechanistic, as the conclusions rely largely on correlative analyses without functional validation. The manuscript will be of interest to the neuroepigenetics and psychiatric neuroscience communities, but the conceptual advance is incremental, and the mechanistic claims should be moderated.

      My expertise: Single-cell and bulk transcriptomics, epigenomics, neuropsychiatric disorders, and computational genomics.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by You et al. investigates changes in gene expression and histone modifications after juvenile social isolation (jSI) in the nucleus accumbens (NAc). They find many differentially expressed genes and find overlap with activating or repressive histone marks. They then go on to compare their data to other published datasets to support their findings. This is an interesting study, and the authors use creative approaches using unique and published data to identify epigenetic mechanisms underlying the changes in gene expression within the NAc. However, there are many points of clarification that are needed to fully evaluate the manuscript and several experimental details.

      1) The text in the introduction conflates adult and adolescent social isolation which have very different effects on behavior. Additionally, there is evidence that isolation during adolescence can have permanent effects on behavior but the behavioral effects of adult isolation in rodents are transient. It is recommended that the authors restructure the intro to be more specific to describing the adolescent period and why epigenetic mechanisms would be expected to reguate changes induced by jSI.

      2) The authors cite several studies from the Nestler lab on early life stress that link early life stress to histone modifications but failed to cite the manuscripts that investigated the transcriptional changes in response to jSI. These studies also highlight sex differences in jSI. This is important given that this study only uses females. Many of the effects described might not be comparable simply because of the sex of the animals.

      3) Can the authors clarify if they used an adjusted p-value or nominal p-value. If they are using a nominal p-value the authors should explain their reasoning and provide information regarding if any of the transcripts survived a p-value correction. The addition of threshold-free approaches are more appropriate (GSEA) rather than focusing on transcripts with a nominal p-value. If they are going to present data using a nominal p-value, this should be justified and the cut off should be explained and every interpretation should include a caveat.

      4) It is unclear how the authors confirmed that input RNA or neurons were similar across samples for the library prep. This is especially important given the top genes that are differentially expression. The finding that beta actin (Actb) is up in jSI vs GH animals. The results could be due to differences in input rather than actual differences in expression.

      5) There are aspects of the methods are difficult to understand. For example, under RNA-seq, the authors mention "frozen nuclei were thawed and centrifuged.....the supernatant was discarded and nuclei were centrifuged again under the same conditions" Can the authors clarify what was done here? Were the nuclei resuspended in STEM CellBANKER or something else? While this is a concrete example, there are many other places in the methods the are like this, meaning that steps seem to be skipped and it then becomes difficult to assess the approach. It is recommended that the authors work to clarify the methods. Another example, how the DNA was treated in the CUT&TAG and how much DNA was added to the library prep.

      6) Can the authors please explain why only females were used for these experiments? In addition, can the authors please comment on potential caveats in the interpretation by only including females in the study?

      7) Were females shipped to the facility on P21? It is unclear.

      8) Were any animals used for multiple endpoints or was each endpoint a separate cohort? Were any samples pooled?

      Significance

      This is an interesting study and the authors use creative approaches using unique and published data to identify epigenetic mechanisms underlying the changes in gene expression within the NAc. However there are many points of clarification that are needed to fully evaluate the manuscript and several experimental details are missing or unclear.

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      Reply to the reviewers

      Response to the Reviewers

      Manuscript number: RC-2026-03632

      Corresponding authors: Olivier Sperandio and Eugénie Romero

      We thank the Editor and the three reviewers for their careful and constructive assessment of our manuscript. We are grateful for the positive feedback on the concept of a pocketome framework for comparing local binding environments across amyloid fibrils. The comments have also helped us clarify an important distinction that was not sufficiently explicit in the original manuscript: our analysis characterizes recurrent structural and physicochemical pocket environments, but does not by itself predict ligand affinity or selectivity. In the revised manuscript, we therefore narrow the interpretation of the pocketome and strengthen the study through experimental ligand-site validation, repeatability and calibration analyses, sensitivity analyses, and an external validation on TDP-43, TMEM106B and transthyretin. We also clarify that the reference pocketome is constructed from amyloid-β, tau and α-synuclein and represents the sampled ordered-core surface environments rather than an exhaustive catalogue of all amyloid ligand-binding modes.

      Reviewer #1

      1. __ Although this is a computational study, there is a lack of validation. This paper is mainly focused on alpha-synuclein however it would be useful if the authors could validate on tau where ligands such as APN-1607 and MK-6240 have been found to bind to AD PHFs and SFs by cryo-EM. They could also test AV1451 which binds to CTE filaments by cryo-EM. __We thank the reviewer for this helpful and important comment. We have assembled 38 ligand-bound amyloid fibril structures, including tau structures containing APN-1607, MK-6240 and flortaucipir/AV-1451. For each structure, the ligand is removed before pocket detection and the ligand-bound structure is used only afterwards to identify whether a detected pocket corresponds to the experimentally occupied site. We will report site recovery, spatial overlap and filter survival, including which filter removes experimentally occupied sites when they are not retained. This provides a direct validation of the pocket-detection procedure without retuning the detector to individual ligands.
      2. __ Robustness of the pocket detection pipeline. If you change the rotamer of a side chain where the ligand has been observed to bind or the charge state, does the pocket remain stable? I suppose one could test this on the high redundancy of the same in vitro structures that reappear in the PDB of alpha-synuclein (as these models have all been built in different cryo-EM maps with different resolutions) - is the same pocket always identified and if not, what is causing that? __We thank the reviewer for this relevant comment. Rather than selecting rotamers or charge states post hoc, we will assess repeatability using corresponding pockets in symmetry-related copies, independent structures belonging to the same structural groups, and repeated experimentally occupied sites. Correspondence will be defined independently of PSI, after which pocket properties and PSI variability will be quantified. We will also examine whether pocket number, volume or similarity depend on structural/model quality.
      3. __ The manuscript implies that similar pockets will bind to similar ligands which makes sense. However it would be helpful to describe that with specifics: i.e. electrostatics, solvent accessibility, water molecules nearby, induced fit etc. An overall conclusion Figure that describes pocket architecture would be useful. We will explicitly describe the geometric and physicochemical descriptors used in the pocket representation and distinguish these from factors not currently captured, such as detailed hydration, induced fit and ligand–ligand interactions. We will also add a conceptual figure summarizing the structural features represented by the pocketome and those that remain outside its scope. __Minor comments:

      4. __ There is no reference to Figure 1 in the main text. Please include MK6240 as well as a tau pet tracer.__ We will add the missing reference to Figure 1 and incorporate MK-6240 and relevant tau PET tracers into the introductory discussion. We will also correct the terminology concerning the PET tracers, including the spelling of florbetaben and the characterization of ACI-12589.

      5. __ Have the authors tried to group filaments based on Scheres amyloid packing algorithm (APD)? __We agree that amyloid packing difference provides a useful orthogonal structural comparison. We will therefore examine the relationship between pocket similarity and APD where technically appropriate, while retaining the existing structural grouping for redundancy control and representative selection rather than replacing the entire classification procedure.
      6. __ Figure 5 legend: L298 Please add that this pathological phenotype (i.e. the inclusions formed in cell and mouse models) do not necessarily recapitulate what is observed in disease. This may or may not be due to the structures formed in these model systems. __We will clarify that pathological phenotypes and inclusions observed in cellular or mouse models do not necessarily reproduce the structures observed in human disease and that our structural analysis should not be interpreted as establishing such equivalence.
      7. __ L390 - Implications for in vitro models: It should be mentioned that it depends on the question. Distinct structures of alpha-synuclein form in synucleinopathies, as such, studying the disease (e.g. in mouse), it is important to study this within that context. Indeed if a binding site is identical, which they are in many of the greek-key like fold of alpha synculein then yes this can be used in the development of a ligand. However, it should always be validated with brain derived filaments.__ We will revise this section to emphasize that the relevance of an in vitro fibril depends on the biological question. In particular, a conserved local binding environment may support the use of an in vitro fibril as a ligand-development model, but this does not establish that the complete structure reproduces the disease-associated filament. We will therefore emphasize the importance of validation against brain-derived filaments when these are available.
      8. __ L408: Missing references to the filament structures. The relevant filament structures and corresponding references will be added at this location and throughout the manuscript where appropriate. __Reviewer #2

      __1, A clear definition of a pocket should be included. How deep can it be, what volume, how solvent accessible? This was not at all clear to me and as shown in Figure S15c, if a larger definition of a pocket is used amyloid-specific ligands are found. This might be expected: small pockets are more likely to be shared in common compared with larger ones. Hence, how does the analysis perform if pockets of larger size are considered? Such an analysis would really improve the article and be of immense use for the field. __We agree that the operational definition of a pocket should be made substantially clearer. The revised Methods will provide the VolSite parameters, volume and accessibility criteria, layer and interface filters, symmetry rule, descriptor definitions and complete filter attrition. We will also perform a sensitivity analysis of the pocket-size criterion using the existing detections and examine how pocket counts and similarity patterns change with pocket size. The 38 ligand-bound structures will provide an independent empirical benchmark for determining whether experimentally occupied sites are represented by the retained pockets or by sites excluded by the current filters.

      __ Abeta was considered in the work, yet there is little discussion of its pockets in the latter parts of the Results section. This fibril type is especially interesting as it does not have a fuzzy coat. Please add this detail. __We will expand the comparison of amyloid-β pockets and explicitly discuss how its structural organization differs from the other two proteins considered in the reference dataset. __ Regarding the fuzzy coat, this could occlude some of the pockets. Have the authors considered this fact? A pocket may not be solvent exposed in the context of the full fibril and not just the fibril core. __We agree that the fuzzy coat may influence the biological accessibility of pockets. Our analysis is based on the experimentally resolved ordered fibril core and therefore does not reconstruct unresolved or dynamically disordered regions. We will make this limitation explicit and distinguish accessibility in the resolved structural model from accessibility in the complete biological fibril. __ A unique feature of the amyloid fold is its repeating beta strands and the twist. So how does this impact a pocket? Surely many pockets will repeat along the fibril axis creating grooves rather than pockets? Please explain. And regarding the twist, how does this affect the pockets defined? The polymorph analysis in Calypso uses only a very few layers so the twist is not considered in their analysis. __We will clarify that our pocket representation describes local environments within a finite number of fibril layers and does not necessarily capture the complete recognition surface of a ligand spanning several rungs. This limitation will also be discussed in relation to the ligand-bound validation analysis. __ As the field will be especially interested in finding polymorph-specific ligands for disease related amyloids, I would appreciate adding a section that compare the pockets in those fibrils for Abeta, Alpha-synuclein and tau with detailed figures to assist the analysis and clarity. __We will strengthen the cross-protein comparison of amyloid-β, tau and α-synuclein, including representative examples of recurrent and more restricted pocket environments. We will also make the scope explicit throughout the manuscript so that conclusions concerning shared environments are clearly understood as applying to the three-protein reference dataset unless independently supported by the external validation analysis.

      Reviewer #3

      1. __ The filters may exclude experimentally observed ligand-binding site classes . __We agree that the current filters may exclude experimentally occupied site classes and that this limits the interpretation of the original conclusions. We have therefore initiated an analysis of 38 ligand-bound fibril structures in which ligands are removed before pocket detection, allowing us to determine whether experimentally occupied sites are detected and, if not, which filter excludes them. We will also distinguish surface, enclosed and interface-related site classes in the revised analysis. Importantly, we will revise the conclusions so that the reported recurrence of pockets applies to the sampled ordered-core surface pocket class and is not presented as evidence that selectiv sites are generally rare across all amyloid ligand-binding modes.
      2. __ The dataset is restricted to three proteins without justification . __We agree that the restriction to amyloid-β, tau and α-synuclein needed to be stated more explicitly. The revised manuscript will clearly define these three proteins as the reference dataset and will qualify the corresponding conclusions accordingly. To test transferability without compromising the independence of the validation, we have additionally completed representative selection and pocket detection for TDP-43, TMEM106B and transthyretin. These proteins will be treated as an external hold-out set and projected into the frozen three-protein reference space rather than being used to redefine the reference metric.
      3. __ The central claim is not yet supported by an explicit test. __We will quantify within- and between-protein similarity using structure-balanced analyses and blocked/hierarchical resampling, accounting for the fact that multiple pockets can originate from the same structure. We will compare the observed mixing with appropriate structure-level null models and report both pocket-weighted and structure-balanced results.
      4. __ The metric is unvalidated and the counts behind it are unreported__ __Major comment 4a : __We agree that the PSI scale requires empirical calibration. We will report the full and nearest-neighbour distance/PSI distributions and interpret individual values relative to these empirical distributions rather than treating a fixed PSI threshold as equivalent to uniqueness. We will also explicitly state which dataset defines the reference scale and retain this scale unchanged when projecting the external validation datasets.

      Major comment 4b : We will provide the exact mathematical definition of the descriptor vector and distance calculation, including the descriptor index set, units, preprocessing and scaling. We will additionally assess the contribution of descriptor families and test whether the principal conclusions are robust to standardization and feature-family sensitivity analyses.

      Major comment 4c : We will quantify similarity and variability for symmetry-related pocket copies, closely related independent structures and repeated experimentally occupied sites, defining correspondence independently of PSI. We will report dispersion and matching failures rather than relying only on average similarity.

      Major comment 4d : We will add a complete structure-to-pocket flow showing the number of structures at each stage, the structural groups, representatives, raw cavities, filter attrition and final retained pockets. We will explicitly distinguish the approximately 400 structures used for structural classification from the representative structures subjected to the pocket analysis and from the final pocket dataset.

      __Major comment 4e : __We agree that the use of one representative per structural group limits the statistical power to establish how frequent polymorph-specific pockets are. We will therefore qualify the absence of polymorph-specific clusters as an observation within the current representative sampling rather than evidence that such environments are intrinsically rare.

      __Major comment 4f : __We agree that model quality is particularly relevant because the pocket descriptors depend on side-chain placement. We will report resolution and available model-quality information for the representatives and test whether pocket properties and similarity correlate with structural quality. We will also perform targeted quality sensitivity analyses and examine the effect of retaining non-identical second protofilaments where appropriate.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors ask whether amyloid fibril polymorphism produces enough diversity in local surface pockets to allow protein-selective or polymorph-selective ligands. They treat the fibril surface as a catalogue of cavities rather than a set of folds. From 400 cryo-EM structures of α-synuclein, tau and amyloid-β they keep one representative per fold group, detect surface cavities, filter to those judged accessible to a small molecule, encode each as a vector of geometric and physicochemical descriptors, and compare all pairs with a single similarity index. The resulting map is the amyloid pocketome.

      They find it largely continuous. Pockets recur across polymorphs and across the three proteins, they do not track fold classification, and the commonest class is a small cavity formed by a few charged or polar side chains. The Concluding perspective states the objective plainly: the work "argues for a shift from a fold-centric to pocket-centric paradigm in amyloid ligand discovery" (l. 480), reports that "many pockets are shared across proteins and polymorphs, whereas truly isolated pockets are rare" (l. 483-485), and holds that this landscape "helps explain why selective amyloid ligands have been difficult to obtain" and "defines the structural limits and opportunities for selective amyloid targeting" (l. 487-492). The study is computational throughout: no new structures and no binding measurements are reported.

      Major comments

      1. The filters may exclude experimentally observed ligand-binding site classes Ligand-bound amyloid structures include at least two recurrent geometries, defined by the orientation of the ligand relative to the helical axis. The present filters appear to retain exposed surface-groove sites while removing several enclosed or interface-bound sites. Perpendicular (columnar). The ligand lies across the fibril and copies stack into a column along the axis. MK-6240 adopts "a stacked arrangement perpendicular to the fibril axis" and spans about two tau rungs [R2]; GTP-1 stacks across three rungs [R1]; flortaucipir sits at about 46{degree sign} to the helical axis [R3] and F0502B at about 50{degree sign} [R6]. Burial is substantially ligand-ligand: 243 Ų against 208 Ų of protein contact for MK-6240 [R2]. EGCG is the flat limit of this mode, in register with the 4.7 to 4.8 Šrise at 1:1 stoichiometry [R5]; separating flat from tilted cases is secondary to the point made here. Parallel. The ligand's long axis runs along the fibril axis, lying lengthwise in a surface groove and spanning several rungs. APN-1607 binds tau this way at sites 1, 2a and 2b, "parallel to the long helical axis", in both paired helical and straight filaments [R4]. PM-PBB3 is modelled the same way on TMEM106B, "paralleling to fibril axis and spanning four rungs" [R12]. Filter 3 removes cavities at the protofilament interface, and filter 4, together with the criterion illustrated in Fig. S9E for pockets "localized within the adopted monomeric fold", removes cavities buried within the fibril or enclosed by a single fold. Several perpendicular or cleft-bound ligands occupy these site classes. MK-6240 and APN-1607 site 3 are resolved within the C-shaped cavity of the tau fold [R2, R4]; EGCG binds tau at "the polar cleft at the intersection of the two protofilaments" [R5]; F0502B occupies a site at "the protofilamental interface of WT polymorph 5a" on 8ZMY [R7], one of the manuscript's own representatives. What survives filtering appears dominated by shallow surface grooves and by the recurrent 100 to 150 ų cavities defined by two or three residues that the paper calls non-discriminatory (l. 216-222; l. 269-271). State whether the pipeline detects and retains the C-shaped cavity of the Alzheimer tau fold and, if not, which filter removes it. Under either objective stated in the Concluding perspective, this materially limits the claimed scope. If the aim is to describe the amyloid pocketome, a map that omits empirically occupied site classes is incomplete. If the aim is to guide selective ligand design, excluding sites used by several of the best structurally characterised tracers leaves the central question only partially addressed. Retain the excluded classes as separate strata and analyse them alongside the retained surface grooves rather than discarding them before analysis. Isolated pockets are then counted in what remains. Concluding from that count that selective sites are rare in general (l. 385), and that this explains why selective ligands have been hard to obtain (l. 487-488; also l. 355-360, l. 414-419), selects on the predictor. Fold-restricted sites do exist: flortaucipir gives clear density on chronic traumatic encephalopathy Type I filaments while the same study was "unable to visualize additional cryo-EM density for flortaucipir for AD paired helical or straight filaments" [R3]. Fig. S10 does not settle this. VolSite finds a 511 ų cavity coincident with F0502B in 7WMM, so detection works. But 7WMM is holo, so a cavity recovered after deleting the ligand is a cast of it; the test is whether it appears in the apo form, since the representatives are largely apo. And 511 ų is set partly by the four-layer model and the tip filter, so comparing it with a 100 to 150 ų per-rung trough in one unscaled Euclidean space compares different objects. Requested (essential; existing data). Perform a sensitivity analysis of the pocket definition rather than treating the 80 ų minimum (l. 524) as sufficient evidence of ligand relevance. The one cavity shown here to accommodate a real fibril ligand is 511 ų (Fig. S10), whereas the recurrent class called non-discriminatory is 100 to 150 ų (l. 217-218) and cross-amyloid cluster 4 is "approximately 150 ų or less" (l. 266-267). Because a ligand may engage a shallow subpocket or extend beyond the detected volume, this example does not establish one correct cutoff. Re-run the analysis across a justified range anchored to the dimensions and contact footprints of published ligand sites; report how the pocketome, intermingling and isolated-pocket counts change; and report buried contact area per pocket alongside volume. Also compare apo and holo forms of the same polymorph. Separately, recast the conclusion as an explanation of ligand promiscuity rather than of the absence of selectivity, scope the negative-design proposal to the site classes actually sampled, and qualify or remove l. 355-360, l. 385 and l. 487-488. Report how many cavities each filter removes, per protein, and reinstate interface and enclosed classes as separate strata so that empirically occupied binding-site classes are represented. The expected outcome is itself the interesting result. A site able to host a ligand across several rungs is an extended groove or cleft, and not every fold presents one: the C-shaped cleft of the Alzheimer paired helical filament does, whereas flatter folds such as the Pick's disease filament (6GX5, a representative here) are unlikely to. If raising the threshold leaves pockets concentrated on a subset of folds, that is fold-level discrimination emerging from the authors' own data, and it bears directly on the conclusion drawn at l. 385.
      2. The dataset is restricted to three proteins without justification The Methods inclusion criteria (l. 497-501) specify cryo-EM structures from the Amyloid Atlas and exclude ssNMR assemblies, monomers and peptides. They never state a restriction to amyloid-β, tau and α-synuclein. The three proteins first appear at the classification step (l. 504-506) as an assumption, so the criteria as written do not reproduce the dataset, and the restriction is absent from the limitations at l. 441-470. The Atlas already holds cryo-EM fibril structures for TDP-43, TMEM106B, transthyretin, IAPP, serum amyloid A, β2-microglobulin, immunoglobulin light chain and prion protein. Extending the map would be a direct test of the central claim, although it would require additional curation, representative selection and filtering rather than being purely mechanical. If convergence reflects backbone-lined grooves that any cross-β spine presents, these folds should intermingle with the three already included; if the map instead separates once more proteins are present, the claim changes. The restriction also sets the scale of the whole metric, since σ is the standard deviation of distances within this three-protein set (l. 543-544). "Isolated in the amyloid pocketome" therefore currently means "isolated among these three proteins", while the title, the term "cross-amyloid pockets" (l. 265) and the Concluding perspective generalise to amyloid as such. TMEM106B is the most pointed omission. It forms amyloid filaments in aged and diseased human brain [R9], and it is a documented off-target of tau PET tracers: both [¹⁸F]PM-PBB3 and [¹⁸F]flortaucipir bind TMEM106B-containing choroid plexus homogenate with high affinity, and PM-PBB3 co-localises with TMEM106B-immunoreactive Biondi ring structures in the choroid plexus epithelium [R10]. Cross-protein binding by clinical tracers is the phenomenon this manuscript sets out to explain, in a protein it excludes. The comparative literature the manuscript relies on is in any case already broader than three proteins: ref. 78 compares disease-associated folds of prion protein, tau, α-synuclein, TDP-43 and TAF15 [R8]. Requested (essential scope correction; expansion strongly recommended). State and justify the three-protein restriction in Methods and add it to the limitations. Revise the title, Abstract and conclusions so that the present map is explicitly a pocketome of the sampled site classes in amyloid-β, tau and α-synuclein, or broaden the analysis sufficiently to support the general terminology. A strongly recommended extension is to add other Atlas proteins, at minimum TDP-43, TMEM106B and transthyretin, and report whether intermingling, the σ-normalised outlier set and the clusters change. If distinguishing sites emerge, describe them and outline how they might be targeted.
      3. The central claim is not yet supported by an explicit test "Extensively intermingled" (l. 160-162) and "did not systematically co-localize" (l. 183-184) are inferred from the colours in Figs. 5, 6 and S11 to S13; no test is reported. A minimum spanning tree connects every node by construction (l. 549-551), so connectivity itself cannot establish mixing or non-isolation. A terminal node joined by a long edge can still represent geometric isolation, but the figures provide no interpretable PSI or edge-length scale. Fig. 5 also shows long single-colour runs, which the authors concede as "local enrichments" (l. 162-163). Requested. Compare PSI within proteins against PSI between proteins using structure-balanced summaries. Count how often neighbouring pockets share a label and compare the result with a null obtained by permuting labels at the representative-structure or fold level, preserving all pockets from the same structure. Several pockets are nested within one model, and every pocket contributes to many pairwise PSI values; pocket-level shuffling or treating all PSI pairs as independent would inflate the effective sample size and create impossible mixed labels within a structure. Use blocked permutations or a hierarchical bootstrap with uncertainty intervals, report both pocket-weighted and structure-balanced estimates, and show that the result is not driven by structures yielding unusually many cavities. Repeat for polymorph and for fibril source, which Fig. 2B records but no analysis uses. The second claim, that pockets are decoupled from fold, compares two rulers that measure different things. Pockets are defined by side chains (l. 565-566); polymorph groups come from a Cα RMSD (l. 506-508), which is blind to side-chain packing, since Cα RMSDs "can lead to relatively low values for structures that share similar backbone conformations but differ in their side-chain packing interactions" [R8]. Two structures can therefore share a group and still present different pockets, which is precisely the reported observation (l. 185-186). Requested. Drop the hand-cut groups and correlate PSI against pairwise fold distance directly, computed both as Cα RMSD and as the amyloid packing difference [R8], which counts differing side-chain packing contacts instead of superposing coordinates. If PSI tracks the packing difference but not Cα RMSD, the decoupling is an artefact of the comparator. RMSD also assumes a common superposition, and the α-synuclein models do not share one: ordered spans run 42 to 102 residues and about a quarter of entries contain internal chain breaks (Fig. S1), so equal RMSD values do not describe equal differences. Tau is largely exempt (Fig. S2); amyloid-β mixes Aβ40 and Aβ42 constructs on one axis (Fig. S3). Requested. State the superposed residue range for every comparison, and report whether the α-synuclein and amyloid-β groupings survive restriction to a common core.
      4. The metric is unvalidated and the counts behind it are unreported (a) No scale. PSI = exp(−d²/2σ²) restates distance in units of σ: 0.624 is 0.97σ, 0.148 is 1.95σ, and 0.005 is 3.3σ. No distribution is shown, so no value can be judged typical or extreme. The kernel strongly compresses the tail beyond about 3σ, and the "PSI below 0.005" rule operationally groups a broad range of large distances into the same outlier category. It therefore does not by itself separate unusual from unique, yet it defines the selective-targeting opportunities (l. 277-281; l. 320-325). σ is computed within the dataset analysed (l. 543-544), so global and protein-specific values sit on different scales while being quoted together, and every added structure changes every PSI, complicating the claim that new structures can be placed in an unchanged existing map (l. 464-470). Requested. Publish the full and nearest-neighbour PSI distributions and define "similar" and "isolated" against them; rank outliers on raw distance in σ; state which matrix each quoted value comes from; and either give a dataset-independent normalisation or drop the extensibility claim. (b) Ambiguous distance, unscaled descriptors. "Non-zero descriptor values only" (l. 540-541) names no index set. Dropping globally zero columns, or keeping descriptors non-zero in either pocket, equals the full-vector distance; keeping only those non-zero in both compares each pair in a subspace of different size and discards the largest differences, since a descriptor present in one pocket and absent in the other is deleted rather than counted (Fig. 4D: OD1 = 32.2 against 0). Small simple cavities carry the most zeros, so this rule could bias the analysis toward the reported convergence. No scaling is specified, so volume in ų shares a sum with bounded percentages and may dominate. Requested. Give the rule as one equation, recompute under the standardized full-vector alternative and report the correlation between PSI and volume difference. With 109 descriptors and only on the order of 10² retained pockets, correlated descriptor families can overweight one physical property and Euclidean distances can concentrate. Report descriptor correlations or effective rank and the nearest-to-farthest distance contrast, and show that nearest neighbours and DBSCAN assignments are stable after standardization, correlation pruning or PCA, and feature-family ablation. (c) No repeatability benchmark. Two controls sit in the authors' own data. Symmetry-related copies of one pocket are detected twice and the smaller discarded (l. 532-534); their similarity distribution would provide an empirical repeatability envelope or expected upper range, and Fig. S9F already shows five clouds against four on supposedly identical protofilaments. Second, structures sharing a fold group at different resolutions can be run against each other to test whether the same surface yields the same pockets. Until one is reported, observed PSI differences cannot be separated from detection and model variability. Requested. Report both controls, including matching failures and dispersion rather than only a mean PSI. (d) Counts. The Abstract and Significance statement attribute the analysis to 400 structures (l. 5-6; l. 21-22), but pocket detection is performed on roughly 50 representatives selected after classification of those structures. No pocket total is given, in any breakdown, so the DBSCAN result and the claim that "most pockets were not assigned to any cluster" (l. 249-251) cannot be checked. The two headline families are seven pockets from six tau structures and only three pockets spanning two proteins, so the cross-protein example is very small. Requested. Add a flow table from retrieved structures through grouping, representative selection and each filter to retained pockets, and distinguish clearly between the approximately 400 structures used for classification and the approximately 50 representatives subjected to pocket analysis. (e) Limited power for polymorph-specific clusters. No cluster composed only of pockets from a single polymorph was found (l. 275-276), but only one representative structure per group entered the analysis. Several pockets from that representative could in principle form a cluster, but the design supplies no independent within-polymorph replication and has little power to establish that polymorph-specific environments are rare. Requested. Qualify the conclusion as a limitation of the sampling, or test several members of each group. (f) Model quality. Selecting the best-resolved member of each group (l. 125) removes redundancy and is sensible, but it is a relative criterion with no quality floor: a group whose best member is poor still contributes a poor model. The α-synuclein representatives span 1.93 Å (9EUU) to 4.8 Å (9D5C), with 7L7H at 4.0 Å. Global resolution is not identical to local side-chain certainty, but these values raise a material concern for a side-chain-based comparison. One such model carries a reported result: the Lewy-fold match at PSI 0.307 (Fig. 7B) compares a pocket from 9D5C at 4.8 Å against one from 8A9L at 2.2 Å. Separately, five of the 18 α-synuclein representatives contain a second non-identical protofilament, including 6XYO, 8ZMY and 9OBP, so keeping unit 1 only discards a different surface rather than a redundant copy. Requested. Report the global resolution and, where available, local map and model-validation measures for every representative; test whether pocket count, volume and PSI correlate with model quality; apply a justified quality sensitivity analysis; and include non-identical second protofilaments where they were dropped. Bearing on the Concluding perspective (l. 478-492) Read against the analysis, one of the claims made there survives intact.
      5. "Ligand selectivity is ultimately governed by the local binding environments exposed on fibril surfaces" (l. 482-483) is the premise rather than a result, and it sits awkwardly with the paper's own material: for a stacked ligand most of the buried surface is ligand-ligand rather than protein, 243 Ų against 208 Ų for MK-6240 [R2], so selectivity there is not governed by the local protein environment alone.
      6. "Many pockets are shared across proteins and polymorphs, whereas truly isolated pockets are rare" (l. 483-485) rests on an untested visual reading (point 3), a rarity criterion that saturates and so cannot separate unusual from unique (point 4a), a σ fixed by the three-protein dataset (point 2), and a filtered set that excludes the enclosed sites where ligands are actually observed (point 1).
      7. "This constrained pocket landscape helps explain why selective amyloid ligands have been difficult to obtain" (l. 487-488) is not supported. No binding or selectivity measurement enters the analysis at any point, and the ligands being explained bind by a mode the Limitations concede the descriptors do not capture (l. 449-454).
      8. "Avoid recurrent cross-amyloid pockets that are structurally predisposed to off-target recognition" (l. 490-491) rests on a cross-amyloid cluster of only three pockets spanning two proteins. Those cavities are 100 to 150 ų, well below the 511 ų of the one site shown here to accommodate a ligand, and whether they constitute complete ligand-binding sites has not been established.
      9. "Use in vitro fibrils when they reproduce the relevant local binding environment" (l. 489-490) is the recommendation the analysis does support, and the refinements listed under Patient-derived pockets would strengthen it further. Accordingly, "defines the structural limits and opportunities for selective amyloid targeting" (l. 492) overstates what was done. "Maps the cavity landscape of the sampled site class" is defensible on the present analysis, and would still be a useful contribution. A constructive extension (OPTIONAL): two catalogues this analysis could deliver As run, the analysis chiefly produces a negative result: shared pockets are common and isolated pockets are rare. Two concise catalogues would make the framework more useful and give each result a testable prediction. Broadening them beyond the present three proteins would strengthen their scope but is an optional extension rather than a prerequisite for a properly scoped paper. A catalogue of genuinely selective sites. Rank pockets by similarity to the nearest pocket from another fold. Candidates whose nearest out-of-fold neighbour is distant should be reported with lining residues, volume, accessibility, putative binding mode and the folds in which no counterpart was found. The prediction is direct: ligands designed against these sites should discriminate among a defined fibril panel. A catalogue of minimally selective sites. The converse list is also useful and is closer to what the present data support. Pockets recurring across many folds or proteins are candidate targets for pan-amyloid, pan-tauopathy or other coverage-oriented applications. Report each recurrent class and the proteins and folds in which it occurs; here recurrence is a design specification rather than only a liability. Anchoring makes both lists interpretable. Run published amyloid-ligand cryo-EM complexes through the same pipeline after ligand removal and use the recovered sites as empirical anchors. These include enclosed tau-fold cavities (MK-6240 [R2], flortaucipir [R3]), extended grooves (APN-1607 [R4], F0502B [R6]) and a polar protofilament cleft (EGCG [R5]). If sites occupied by related chemotypes are neighbours, the map gains predictive content; if not, the descriptor set requires revision. A pass/fail test is available now. GTP-1 and MK-6240 independently occupy the same Alzheimer tau site involving Gln351, Lys353, Asp358 and Ile360 [R1, R2]. This provides two checks with known structural answers: whether the pipeline detects and retains that site, and whether it separates the site from tau folds that do not present the cavity, including Pick's disease (6GX5), corticobasal degeneration (6TJX) and progressive supranuclear palsy (7P65). Recovery and discrimination on this case would be a much stronger validation than the single holo example in Fig. S10. One list says where to aim for specificity, the other where to aim for coverage. Both follow from work already done, and together they would convert the pocketome from a catalogue of cavities into an instrument for choosing targets, which is closer to what the Concluding perspective claims.

      Minor comments

      Patient-derived pockets

      • The comparison of patient-derived and in vitro pockets (l. 304-335; Fig. 7) already reports both matches and non-matches, which the surrounding text undersells. Two MSA pockets are matched to in vitro fibrils at PSI 0.624 and 0.335, three Lewy-fold pockets to other polymorphs at 0.154, 0.148 and 0.307, and two Lewy-fold cavities are reported as having no close neighbour at 0.005 or below, with the buried and unassigned-density caveats stated by the authors. The metric therefore produces non-matches as well as matches.
      • Three refinements would let the section carry the weight the Discussion places on it. Report all pockets of every ex vivo structure with ranked nearest in vitro matches rather than a selection. Interpret the values against the background distribution requested in point 4a, without which 0.624 and 0.148 cannot be ranked against one another. And use F0502B as a positive control: its site is in the dataset twice (7WMM, Fig. S10; 8ZMY, a representative) and it is roughly tenfold selective for α-synuclein over tau and amyloid-β by direct affinity measurement [R6], so recovering it as selective by PSI would validate the framework on a ligand of known behaviour.
      • State how MSA's two protofilaments were handled. The text discusses only "protofilament IA" (l. 310) while Figs. S6 and S8 state the representative unit "were always number 1". Published packing differences for this comparison are 8 to 11% for one protofilament against 60% for the other [R8], so the answer depends on which is used.
      • Pocket detection is validated on one example, with no overlap metric and no denominator (l. 141-143; Fig. S10). Tabulate the published amyloid-ligand cryo-EM complexes: cavity detected at the ligand site, numerical overlap, and survival of each filter. The set covers tau [R1-R5], α-synuclein [R6, R7] and TMEM106B [R12]. Internal consistency
      • Fig. 6 caption says the legend shows pocket volume; the in-figure legend reads "Polymorph" with entries "-" and 1.0 to 11.0.
      • Text gives "7V4C pocket 7" at PSI 0.624 (l. 313); the Fig. 7 legend gives pocket 9. The second MSA pocket at l. 314-316 does not appear in Fig. 7A.
      • "Approximately 15 groups per amyloid family" (l. 123) against 18 (Fig. 3), 14 (Fig. S4) and 20 (Fig. S5).
      • The dashed cut in Fig. 3 measures 6.72 {plus minus} 0.02 Å against the stated 5.3 Å (l. 507), with 19 to 20 branches crossing rather than 18. The lines in Figs. S4 and S5 measure 4.80 and 3.31 Å and match their legends. Regenerate Fig. 3 or correct the threshold, and confirm which value the 18-group set used.
      • Fig. S7 shows 13 tau representatives against 14 groups in Fig. S4; the missing one is explained only in the S4 legend, where 9GG6 is merged with 9GG0 and 6GX5.
      • Polymorph labels reach 11 for α-synuclein and 9 for tau, with gaps at amyloid-β 3 and 6, against 18/14/20 groups, plus an unexplained "-" category. Explain the mapping and what "-" denotes.
      • Fig. 4C says six filters; Methods number five plus a symmetry rule (l. 523-534). Fig. S15C is titled "Polymorph-specific pockets" although none were found. Typographic: duplicate titles on the title page, "polymorphs.." (l. 329), "Tha alpha-synuclein pocketome" (Fig. S14), "fibil" (Fig. S10).

      Figures

      • The main and supplementary split does not match where the information is. Fig. 1 contains no data. Fig. 3 shows only the α-synuclein dendrogram, so 55% of structures and 65% of groups are classified in the supplement, and the one dendrogram in the main text is the one that disagrees with its legend. Three supplementary figures are load-bearing: S9, the only place the six filters are shown; S14, on which the isolated-pockets claim rests; S15, the only view of the pocket families. Move Fig. 1 to the supplement, combine the three dendrograms into one main figure, and promote condensed forms of S9, S14 and S15. Fig. S15's image and legend are on separate pages.
      • Figs. 5, 6 and S11 to S14 are screenshots of the interactive TMAP page, with an HTML dropdown visible in the legend box, and Fig. 5 labels proteins by PFAM name ("Tubulin-binding"). Regenerate as vector figures with a PSI scale. The 29-category palette of Fig. S11 is unreadable. A volume-coloured pocketome should be shown, given point 4b.
      • The most useful missing figure: a PSI distribution with internal anchors on one axis (symmetry-mate duplicates, within-structure pairs, cross-polymorph pairs, cross-protein pairs, permuted null), beside the mixing statistic against that null. If two copies of the same pocket do not score near 1, the labels in Fig. 7 cannot be interpreted.
      • Fig. 1: "[¹⁸F]Florbetapen" should read florbetaben. "Clinically-validated" overstates [¹⁸F]ACI-12589, which separates MSA from controls, PD and DLB but shows no increased retention in sporadic PD or DLB [R11]. Second-generation tau tracers are absent although MK-6240 and APN-1607 are central to point 1.

      Methods and reproducibility

      Not reproducible as written. Absent: VolSite parameters (given as values "previously validated for protein-protein interaction cavities", l. 521-522); DBSCAN eps and min_samples, the definition of the PSI-derived distance matrix, and cluster and noise counts, noting that Fig. S14A shows one chained cluster holding roughly 54 of about 75 assigned pockets across every arm of the tree; the 109 descriptors with units and scaling; MOE version and QuickPrep settings, including pH and whether the default minimisation was disabled, since l. 515 says none was applied; how the four layers were chosen; whether ligands, ions and waters were removed before detection and how many holo structures contribute, since 8ZMY and 9QYL are ligand- or cofactor-bound; TMAP parameters and software versions; the 400 PDB IDs with retrieval date; and analysis code. Supplementary Files 1 to 3 are announced on SI p. 1 but are absent from the supplied review packet; provide them and describe their contents. Only two of the six filters are stated operationally, volume and layer position; give a numeric criterion and per-filter attrition count for each. Manual interventions appear only in SI legends. "Repeated along the fibril axis" (l. 134-135; l. 384; l. 445) is never measured. Useful sensitivity analyses include the RMSD cut, layer count, representative choice, and inclusion versus exclusion of interface and buried pockets. Prior literature Ref. 78 is cited as a preprint but is published [R8]; check refs. 77 and 80 likewise. The ligand-bound amyloid literature is otherwise absent although it bears on point 1: the published classification of binding geometries on α-synuclein polymorphs [R7] is uncited although one of its structures is used as a representative, and no ligand-bound tau or amyloid-β structure is discussed. The FibrilSite preprint (ref. 80) reaches the opposite conclusion on cross-protein site sharing and is answered at l. 421-427 only by appeal to observed ligand non-selectivity, which this manuscript does not test; compare the two methods on a shared subset of α-synuclein structures.

      References

      [R1] Merz GE, et al. Nat Commun 14:3048 (2023). GTP-1; PDB 8FUG.

      [R2] Kunach P, et al. Nat Commun 15:8497 (2024). MK-6240; PDB 8UQ7.

      [R3] Shi Y, et al. J Mol Biol 435:168025 (2023). Flortaucipir, CTE filaments; PDB 8BYN.

      [R4] Shi Y, et al. Acta Neuropathol 141:697-708 (2021). APN-1607; PDB 7NRV, 7NRX.

      [R5] Seidler PM, et al. Nat Commun 13:5451 (2022). EGCG on tau; PDB 7UPG.

      [R6] Xiang J, et al. Cell 186:3350-3367 (2023). F0502B; PDB 7WMM.

      [R7] Liu K, et al. Proc Natl Acad Sci USA 121:e2321633121 (2024). Binding geometries on α-synuclein polymorphs; PDB 8ZMY, 8X7B, 8X7O, 8X7L, 8X7Q, 8ZLI.

      [R8] Scheres SHW. Structure 34:1061-1071 (2026). Amyloid packing difference.

      [R9] Schweighauser M, et al. Nature 605:310-314 (2022). TMEM106B filaments.

      [R10] Yokoyama Y, Harada R, Kudo K, et al. Transmembrane protein 106B amyloid is a potential off-target molecule of tau PET tracers in the choroid plexus. Nucl Med Biol 142-143:108986 (2025). Postmortem autoradiography and binding assays; PM-PBB3 co-localises with TMEM106B-immunoreactive Biondi ring structures, and both PM-PBB3 and flortaucipir bind TMEM106B-containing choroid plexus homogenate with high affinity.

      [R11] Smith R, et al. Nat Commun 14:6750 (2023). [¹⁸F]ACI-12589.

      [R12] Zhao Q, et al. Cell Discov 10:50 (2024). PM-PBB3 on TMEM106B; PDB 8J7N. Density weak and also present in apo and PiB maps; pose low-confidence. APN-1607, PM-PBB3 and florzolotau are one compound; flortaucipir is AV-1451 or T807.

      Significance

      General assessment

      The framing is the strongest aspect. The gap between the growth of amyloid cryo-EM structures and the absence of selective ligands is real, and locating the bottleneck at pocket discriminability rather than fold classification is productive. The distinction between a pocket being ligandable and being discriminable is useful, and using a pocketome as a negative filter, to exclude sites where selectivity is implausible before chemistry is invested, is the most valuable idea here. The filtering logic reflects real thought about what a fibril-bound ligand can reach, the limitations section is candid, and the data are deposited. The principal weakness is conceptual. The pocketome pools site classes that the ligand-bound literature treats separately, and because the filters remove interface and enclosed cavities, the reported continuum may be a property of the sampled subpopulation rather than of amyloid surfaces. The work supports the claim that certain ligand classes are promiscuous. It does not establish how rare selective sites are across amyloids, and published fold-discriminating and protein-selective examples constrain any broader negative inference. Beyond that, no hierarchy-aware statistic or repeatability control is reported; the similarity index is under-specified and uncalibrated; and the scope wording blurs the distinction between approximately 400 structures used for classification and approximately 50 representatives used for pocket analysis. Most requested corrections are re-analyses or clearer reporting of data already in hand. One structural feature of the manuscript deserves comment. The Limitations section (l. 441-470) already concedes several of the points on which the central conclusions depend: that detecting a cavity does not demonstrate binding, affinity or selectivity (l. 443-447); that ligands binding "in repeated arrays along the fibril axis" and stabilised by "both fibril-ligand and ligand-ligand interactions" are "not fully captured by pocket descriptors based on local cavity geometry" (l. 449-454); and that the analysis is bounded by the structures available. Each is accurate. The difficulty is that they are offered as caveats on precision while the Discussion draws conclusions that require them to be false. A catalogue of detected cavities is not undermined by the fact that stacking is unrepresented; an explanation of why amyloid ligands are non-selective is, because the ligands in question bind by stacking. Likewise, a map of cavities is unaffected by the caveat that detection is not binding, whereas the claim that isolated pockets are rare, and that selective targeting is therefore structurally constrained, requires detected cavities to stand in for targetable sites. As written, the paper is a legitimate descriptive resource carrying an interpretive layer its own Methods disclaims. Either scope the conclusions to what the analysis supports, or address the limitations rather than acknowledge them.

      Advance

      Conceptual and technical rather than mechanistic or clinical. It reframes amyloid ligand design from fold-centric to pocket-centric and ports pocketome methodology to filamentous assemblies, which required non-trivial decisions about what counts as a pocket on a helical polymer. The closest work is the FibrilSite preprint (ref. 80), which reaches the opposite conclusion on cross-protein site sharing; the framework is complementary to the packing-difference metric [R8]. Against a ligand-bound literature that has characterised sites one structure at a time [R1-R7], a population-level view is new and is the right instrument for the question. The most useful specific contributions are the identification of recurrent Lys/Arg/Gln/Tyr cavities as poorly discriminating, and the demonstration that some patient-derived α-synuclein pockets have in vitro counterparts.

      Audience

      Specialised: amyloid structural biologists working on cryo-EM of tau, α-synuclein and amyloid-β filaments; computational and structure-based design groups working on shallow or interface-like surfaces; and PET tracer programmes in neurodegeneration, for whom the negative-design idea and the in vitro model-selection argument are directly relevant. With the statistics and the geometric stratification in place, the general message, that polymorphism at the fold level need not imply polymorphism at the pocket level, would reach a broader biophysics and drug-discovery readership. The framework transfers to TDP-43, TMEM106B, hnRNPA1/A2 and transthyretin.

      Reviewer expertise

      Cryo-EM of amyloid fibrils and helical reconstruction; structural biology of tau and α-synuclein polymorphs; ligand-bound filament structures and PET tracer binding modes; computational structure-based design; clustering methodology on structural datasets.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript describes an interesting analysis of the presence of absence of binding pockets displayed on the surface of all know amyloid fibril structures of Abeta, tau and alpha-synuclein. Using their definition of a 'pocket' the authors show that fibrils of the same protein type of different polymorph type (defined by the Calypso algorithm developed by Connor et al) or those from different proteins can share common pockets with a few examples of fibril type specific pockets.

      The work will be of broad interest to those seeking to develop amyloid-specific binders for discovery research and for its translation into the clinic in neurodegenerative disorders. I have comments that I hope will improve the understanding and impact of this analysis:

      1. A clear definition of a pocket should be included. How deep can it be, what volume, how solvent accessible? This was not at all clear to me and as shown in Figure S15c, if a larger definition of a pocket is used amyloid-specific ligands are found. This might be expected: small pockets are more likely to be shared in common compared with larger ones. Hence, how does the analysis perform if pockets of larger size are considered? Such an analysis would really improve the article and be of immense use for the field.
      2. Abeta was considered in the work, yet there is little discussion of its pockets in the latter parts of the Results section. This fibril type is especially interesting as it does not have a fuzzy coat. Please add this detail.
      3. Regarding the fuzzy coat, this could occlude some of the pockets. Have the authors considered this fact? A pocket may not be solvent exposed in the context of the full fibril and not just the fibril core.
      4. A unique feature of the amyloid fold is its repeating beta strands and the twist. So how does this impact a pocket? Surely many pockets will repeat along the fibril axis creating grooves rather than pockets? Please explain. And regarding the twist, how does this affect the pockets defined? The polymorph analysis in Calypso uses only a very few layers so the twist is not considered in their analysis.
      5. As the field will be especially interested in finding polymorph-specific ligands for disease related amyloids, I would appreciate adding a section that compare the pockets in those fibrils for Abeta, Alpha-synuclein and tau with detailed figures to assist the analysis and clarity.

      Referee cross-commenting

      All three referees appeared to like the concept of the manuscript and all three ask for more detail and to include more proteins to test the generality of the claims made. I agree with all the suggestions and I hope the authors can address the comments with the requested details and analyses added. If so, this would then make an excellent reference for those working in the amyloid field.

      Significance

      This manuscript describes an interesting analysis of the presence of absence of binding pockets displayed on the surface of all know amyloid fibril structures of Abeta, tau and alpha-synuclein. Using their definition of a 'pocket' the authors show that fibrils of the same protein type of different polymorph type (defined by the Calypso algorithm developed by Connor et al) or those from different proteins can share common pockets with a few examples of fibril type specific pockets.

      The work will be of broad interest to those seeking to develop amyloid-specific binders for discovery research and for its translation into the clinic in neurodegenerative disorders.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript presents a computational framework for characterizing ligand binding pockets for amyloid filaments. They construct a "pocketome" and the authors describe binding site similarities across different amyloid proteins. They identify conserved and polymorph-specific pockets (which is important for specificity of ligands) and discuss implications for selective ligand design.

      The work does address an important problem in that amyloid filaments typically have limited sites in which ligands can bind to and these can be shared across different amyloid polymorphs which is a strength and conceptually interesting. This will be of specialized interest to the ligand-amyloid field. I have a few concerns:

      Major comments:

      1. Although this is a computational study, there is a lack of validation. This paper is mainly focused on alpha-synuclein however it would be useful if the authors could validate on tau where ligands such as APN-1607 and MK-6240 have been found to bind to AD PHFs and SFs by cryo-EM. They could also test AV1451 which binds to CTE filaments by cryo-EM.
      2. Robustness of the pocket detection pipeline. If you change the rotamer of a side chain where the ligand has been observed to bind or the charge state, does the pocket remain stable? I suppose one could test this on the high redundancy of the same in vitro structures that reappear in the PDB of alpha-synuclein (as these models have all been built in different cryo-EM maps with different resolutions) - is the same pocket always identified and if not, what is causing that?
      3. The manuscript implies that similar pockets will bind to similar ligands which makes sense. However it would be helpful to describe that with specifics: i.e. electrostatics, solvent accessibility, water molecules nearby, induced fit etc. An overall conclusion Figure that describes pocket architecture would be useful.

      Minor comments:

      1. There is no reference to Figure 1 in the main text. Please include MK6240 as well as a tau pet tracer.
      2. Have the authors tried to group filaments based on Scheres amyloid packing algorithm (APD)?
      3. Figure 5 legend: L298 Please add that this pathological phenotype (i.e. the inclusions formed in cell and mouse models) do not necessarily recapitulate what is observed in disease. This may or may not be due to the structures formed in these model systems.
      4. L329: typo error with two ".."
      5. L390 - Implications for in vitro models: It should be mentioned that it depends on the question. Distinct structures of alpha-synuclein form in synucleinopathies, as such, studying the disease (e.g. in mouse), it is important to study this within that context. Indeed if a binding site is identical, which they are in many of the greek-key like fold of alpha synculein then yes this can be used in the development of a ligand. However, it should always be validated with brain derived filaments.
      6. L408: Missing references to the filament structures.

      Significance

      This manuscript presents a computational framework for characterizing ligand binding pockets for amyloid filaments. They construct a "pocketome" and the authors describe binding site similarities across different amyloid proteins. They identify conserved and polymorph-specific pockets (which is important for specificity of ligands) and discuss implications for selective ligand design.

      The work does address an important problem in that amyloid filaments typically have limited sites in which ligands can bind to and these can be shared across different amyloid polymorphs which is a strength and conceptually interesting. This will be of specialized interest to the ligand-amyloid field.

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      Reply to the reviewers

      1. General Statements [optional]

      On behalf of the authors, I thank the reviewers for their critical reading of the manuscript. We really appreciate the care and attention they have applied to their reading and reports.

      An initial comment may add some context - in accordance with German law, I (AFS) had to retire from my position at Dresden University in October 2023. I am still working however have very little capacity to add new experiments to the manuscript. Consequently my response to the reviewers is somewhat more critical than the normal concessions and acquiescence that are usually adopted.

      2. Point-by-point description of the revisions

      *Reviewer #1 Summary *

      This manuscript describes functional characterization of Bod1 family proteins (particularly Bod1L) and their interaction with COMPASS family histone methyltransferase complexes. Bod1 proteins are conserved through evolution and related to yeast Shg1, which binds to the yeast COMPASS via a conserved domain and negatively regulates H3K4me3 levels. Here the authors performed IP-MS of Bod1L, Bod1, Setd1a, and Setd1b in engineered mouse embryonic stem cells and confirmed presence of Bod1 and Bod1L in both Setd1a and Setd1b-containing COMPASS complexes. AlphaFold modeling predicted an interaction between the Bod1/L Shg domain and a conserved helix in Setd1a/b which was validated in stable Setd1a deletion ESC lines and with isolated Sed1a/b fragments. Functional analysis of Bod1L and Setd1a deletion lines confirmed that Bod1L negatively regulated H3K4me3 levels. Moreover, the knockouts caused parallel effects on the expression of DNA repair genes and both apparently enhanced levels of baseline DNA damage, in agreement with findings in leukemia cell lines. This argues that Setd1a/Bod1L regulates DNA repair gene expression independently of H3K4me3.

      Major comments Figures 2D, 2E, 3C: there are no panels showing the efficiency of Bod1 or Bod1L immunoprecipitation. The immunoblots seem to indicate that either Bod1 or Bod1L precipitate a substantial fraction of Setd1a and a much smaller fraction of Setd1b, although it is impossible to tell without the blots of Bod1/Bod1L. The idea that Setd1a is primarily associated with Bod1L vs Bod1 is presumed in the rest of the manuscript (likely based on previous results in other cell lines) but is not strongly supported by these figures.

      Response: The protein complex and interaction data is based on AP-MS (affinity purification-mass spectrometry) and the results are presented as Volcano plots, which is the standard and most accessible format. AP-MS acquires candidate data because some bona-fide interactions will be missed and spurious interactions will be included, especially when the threshold of significance is lowered. To validate the candidate data from tagged SETD1A and SETD1B we used

      (a) reciprocal AP-MS with tagged BOD1L, BOD1 and CXXC1. These results secured the primary conclusion regarding the associations of BOD1L and BOD1 with SETD1A and SETD1B (as well as other subunits). I should add that we show – for the first time – functional evidence that BOD1L is a subunit of the SETD1A complex (Figure 5).

      (b) immunoprecipitations to confirm selected interactions. As stated in the legend of Figure 2, 10% total extract are shown as controls. This control allows the reader to evaluate the efficiency of the associated protein in the IP. For example, (Fig. 2D), clearly BOD1L associates much more with SETD1A than with SETD1B. Nevertheless, the association with SETD1B was detected and reciprocally confirmed (Fig. 3C). Another example, (Fig. 2E), clearly BPTF interacts with BOD1L at notable efficiency but not with SETD1A. There are also additional IPs in the supplement that add further confidence.

      Additionally, AP-MS with three SETD1A deletion mutants (Fig. 4) adds supporting evidence to the conclusions drawn from Figures 2 and 3. The implication that BOD1L and BOD1 interact with X3, which in Figure 4 is a negative result and therefore – because negative results in AP-MS cannot be used to draw conclusions – we explicitly tested the proposition that BOD1L and BOD1 interact with X3 to secure the conclusion (Fig 4D).

      Consequently, with respect, we do not agree with the following comment by reviewer 1 -

      - although it is impossible to tell without the blots of Bod1/Bod1L. The idea that Setd1a is primarily associated with Bod1L vs Bod1 is presumed in the rest of the manuscript (likely based on previous results in other cell lines) but is not strongly supported by these figures.

      __ T__he data presented clearly allows reasonable evaluation of yield and consequently conclusions about the BOD1L and SET1A interactions. The primary association of BOD1L with SETD1A is established by the data presented, as is the secondary associations between BOD1 and SETD1A, as well as BOD1L and SETD1B.

      Figure 4: the SETD1A-X1 and X3 internal deletion lines show many interesting interactions not detected in the wild-type line, notably with CPSF components. What is the significance of this?

      AP-MS explores the proteome and a number of intriguing associations can be found in addition to the most robust biochemical interactions. In this manuscript, we present some exploration of the intriguing extras but remain focused on the SETD1A and B complexes. We and others have published on the connection between the yeast Set1C and yeast CPSF, but exploring that issue connection is beyond the experimental and conceptual themes of this manuscript, especially considering other comments by the reviewers about reducing the manuscript.

      Related to Figure 4: Why were none of the functional genomics experiments described in Figures 5-7 performed on the Setd1a-X3 deletion line given that the authors had it in hand? This would have been a logical complement to the Bod1L deletion experiment and further addressed the issue of functional partnership between Setd1a and Bod1/L. One could make a similar point regarding Bod1-it is unclear why (given the co-IP and AP-MS data in Figures 2 and 3) a deletion line of Bod1 was not analyzed in parallel. There was convincing rationale in the Hoshii 2024 paper to focus on Setd1a/Bod1L in that system; the rationale for doing this here is less clear.

      We thank the reviewer for this suggestion. Indeed this is a good experiment that emerges from the data we are presenting (and not from Hoshii et al 2024). We take this excellent suggestion as an indication that our manuscript has presented the evidence sufficiently well to permit the reviewer to make this suggestion, which is worth pursing in a new project. However the manuscript is already replete with progress. It is worth mentioning that in response to an earlier round of reviewing elsewhere, we added the experiment that is now Figure 7. This process of adding further experiments in response to thoughtful reviewing comes at the risk of promoting further good suggestions, which are constructive and welcome but at some point progress should be published.

      Figure 5G: This figure does not seem to include a control in which wild-type ESCs are treated with tamoxifen in parallel with the Flp Bod1L line.

      There is no published or conceptual reason to include a control for the induction of DNA damage by tamoxifen in wild type cells. My lab pioneered ligand inducible conditional mutagenesis (Logie C and Stewart AF. 1995 Ligand-regulated site-specific recombination. PNAS 92, 5940-5944) including tamoxifen inducible conditional mutagenesis in mice (Schwenk F, Kühn R, Angrand P-O, Rajewsky K and Stewart AF. 1998 Temporally and spatially regulated somatic mutagenesis in mice. Nucleic Acids Res. 26, 1427-1432) and we have published advice for the appropriate controls for tamoxifen induced conditional mutagenesis (Anastassiadis et al 2010, Methods Enzymol, 477, 109-23). All experiments involving tamoxifen induction of conditional mutagenesis were thoroughly accompanied by appropriate controls. In the case of Fig. 5G, administration of tamoxifen to ESCs had no detectable effect on DNA damage as evaluated by p-H2AX or p-ATM staining – as expected and therefore not shown.

      Figure 6: This figure should include Venn diagrams that clearly show the overlap between genes affected by Bod1L removal compared to Setd1a.

      In Figure 6B, the overlap is clearly illustrated in a colour presentation that we think is superior to presenting these data as a Venn diagram. These data are also presented in different formats - in the Supplement Fig. 6D and the most significant DNA repair genes are listed in Table 1 again presented in an overlapping format.

      Related to Figure 6/7: These figures should include analysis of the H3K4me3 ChIP-seq data in Figure 5 specifically at Bod1L-regulated DEGs.

      We now present a new Supplemental Figure 6E and include the statement - ‘Increased H3K4me3 peaks were also observed at the promoters of the DNA repair genes that showed decreased expression after loss of BOD1L (Supplemental Figure 6E).’ in the text. In other words, elevated H3K4me3 is also observed on the DNA repair genes that show decreased expression.

      Discussion: The authors should note that the direct role of Setd1a at DNA breaks is proposed to rely on enzymatic activity (Higgs et al 2018, Bayley et al 2022).

      A sentence regarding SETD1A enzyme activity in DNA repair has been included in the Discussion.

      Minor comments

      __ Figure 5: panel arrangement is confusing. __The arrangement of Figure 5 has been improved.

      __Hoshii et al, 2024 is not listed in the references. __This omission has been corrected

      __Reviewer #1 (Significance (Required)): ____*

      The key advance in this work is demonstration that Bod1L removal affects expression of DNA repair genes and increases DNA damage in ES cells. This seems to occur independently of changes in H3K4me3 (although specific analysis of H3K4me3 at these genes was not performed). *__

      We now include analysis of H3K4me3 at promoters of genes downregulated after loss of Bod1L (new Supplemental Figure 6E). As with all active promoters, H3K4me3 is also elevated on these genes.

      Removal of Setd1a had similar effects. The work seems to support previous reports in leukemia cell lines for specific effects of Setd1a/Bod1L on DNA repair that is not related to Setd1a enzymatic activity (Hoshii et al 2018, 2024 in the manuscript)(and contrasts with findings in U2Os cells that do implicate enzymatic activity and emphasize a direct role at replication forks rather than in transcription; Higgs et al 2018 and Bayley et al 2022 in the manuscript). This is a modest but important conceptual advance in thinking about Bod1L and COMPASS complex functions in transcription and DNA damage repair. Whether these functions are specific to Bod1L vs Bod1 in ESCs, or to Setd1a over Setd1b, in ESCs, was not addressed.

      In the Introduction we highlighted the difference between Setd1a and b in ESCs. We previously published that Setd1a is essential whereas Setd1b is not, and Setd1b fails to rescue the loss of Setd1a in ESCs when (over)expressed from the Setd1a promoter (Bledau et al, 2014). Furthermore the preferential association of BOD1 with SETD1B rather than SETD1A can be concluded from the data provided. These considerations support the conclusion that SETD1A and BOD1L are specifically regulating DNA repair gene expression.

      The audience for this work will be chromatin/epigenetics experts. My expertise is in the field of chromatin and epigenetics, and I have studied histone modification function extensively in the context of transcription.

      As a final comment to reviewer 1 – thankyou for your thoughtfulness but please allow a comment on the term ‘COMPASS’, which is not used in our manuscript. COMPASS is a confusing and ambiguous term. It means ‘COMPlex ASsociated with yeast Set1’ and was first used to describe the incomplete yeast Set1 complex. Concomitantly, my group published the complete complex, termed Set1C, along with the first biochemical proof that it was an H3K4 methyltransferase (the first bona fide H3K4 methyltransferase, and the second bona fide histone methyltransferase). The following year, the second COMPASS publication reported the full complex by including the missing subunit and also biochemical proof of H3K4 methyltransferase enzyme activity. Subsequently the term ‘COMPASS’ has been applied to the Trithorax and MLL complexes, which share half of the Set1C/COMPASS complex – these four subunits are highly conserved in eukaryotes – but also include another 4+ subunits unrelated to the other half of Set1C/COMPASS, which are also are different between the Trithorax/MLL1,2 and Trithorax related/MLL3,4 complexes. So it is imprecise and confusing – especially for the majority of bioscientists who do not have histone methylation expertise - to name these partially related but distinct complexes, ‘COMPASS’. The highly conserved 4 subunits of these complexes have been more precisely termed ‘WRAD’ (after the mammalian names, WDR5, RBBP5, ASH2L, DPY30). WRAD, as opposed to COMPASS, is unambiguous and does not require specialist insider knowledge to unravel the confusion. Therefore the term ‘WRAD’ is used to refer to the conserved quartet in the manuscript.

      __*Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This manuscript investigates H3K4me-methylating complexes in mouse embryonic stem cells with a specific focus on the SETD1A complex and its binding partners. The authors define the interaction between SETD1A and BOD1L and characterize a requirement for BOD1L in maintaining the expression of DNA repair genes in mESCs. BOD1L has previously been implicated in regulating the replication fork (Bayley et al., Mol Cell, 2022 and others), however the authors propose another role for BOD1L in restraining H3K4me3 at TSS-proximal nucleosomes which impacts gene expression of DNA repair genes. *__

      With respect, we do not suggest that the restraining action of BOD1L on H3K4me3 has any impact on gene expression. In contrast, we note that elevated H3K4me3 at TSS-proximal nucleosomes does not correlate with changes in gene expression.

      However, a number of aspects of this model require additional support. Furthermore, while there are some new insights gained from the experiments performed, the data presentation makes the impact of the studies difficult to interpret. Specific concerns are outlined in further detail below: ____ 1. A key approach used through the manuscript is AP-MS experiments to determine protein interactors of SETD1A, SETD1B, and other complex subunits. It appears these experiments were generally performed in triplicate, however, there should be more discussion of what thresholds were used to quantify interactors. The methods states that if 2 unique peptides were identified, though it is very difficult to tell from the volcano plots why some proteins are labelled and named as interactors and others are not discussed. Furthermore, the volcano plots are generally difficult to read and do not lend themselves well to comparisons between different experiments. Another format in addition to potential volcano plots, such as a heatmap, would improve the readability and provide a better method of visualizing the quantitative results of these experiments.

      Our presentation of AP-MS data in Volcano plots is conventional. As mentioned above (reviewer 1, response 1), AP-MS analyses present candidate data that requires further support, which we supplied for the conclusions we draw, as detailed above.

      There is very little discussion of the additional interactors identified, outside of expected components, in the AP-MS experiments described in Figures 2 and 3. The authors state that they pursued additional experiments but that there was not productive data. It is unclear what this means as to whether these are not legitimate interactors or if there were other technical challenges. There is some discussion of OGT and BPTF, but it is not particularly informative. I think further clarification and/or characterization on the other interactors would be useful to be able to interpret the validity of the data presented in the AP-MS volcano plots____.

      The candidate data obtained by AP-MS analyses include a core of reliable interactions as well as other less robust identifications that may be true or false positives. In this manuscript, we focused on the reliable and verified interactions, and mentioned notable additions, which we hope will assist further investigations. Further proteomic exploration is beyond the scope of this manuscript.

      In Figure 4, AP-MS is used to characterize interactors of different deletion mutants of SETD1A. The expression of the deletion mutants should be shown by western or another approach to see how these compare to wildtype. In addition, the interaction is further probed in cells by a co-IP approach using an overexpression construct of the X3 region of SETD1A. Since the authors have the deletion mutant, this could be used in a co-IP experiment in addition to exogenous expression of just the X3 fragment. This would allow a direct comparison with WT SETD1A and other mutants. Also, to further support the specificity of the interaction show in Fig 4D, a similar experiment could be performed with other regions of SETD1A (or B), such as a the X1 or X2 regions.

      We are not certain about these comments. The deletion mutants were examined by AP-MS, which is effectively superior to a co-IP, and retrieved most of the expected proteins. If we had pursued unexpected proteins identified in the mutant AP-MS, then Western (or similar) analysis of expression levels of the SETD1A mutants would be important. However we obtained reciprocal confirmation of the primary result, which is better than a co-IP with Western. Detailed analyses with other X regions are beyond the focus of this work.

      Figure 5C and this H3K4me3 chip results in the BOD1L mutant cells would be further supported by showing the levels of SETD1A (and other H3K4 methylating enzymes) in these cells lines to better support the conclusion that BOD1L is directly restricting SETD1A activity at chromatin____.

      In both yeast and in vitro, elevated H3K4me3 by Set1C without Shg1 is not due to elevated Set1 expression or changes of the Set1 complex, (other than loss of Shg1; Roguev et al, 2001, Kim et al, 2013). Concordantly, we show that SETD1A without X3 (i.e. without BOD1L) still retrieves the rest of the SETD1A-Complex (Figure 4B). Furthermore, Setd1a is expressed from its endogenous promoter to ensure physiological expression level.

      Figure 5F shows growth curves of WT and BOD1 mutant ESCs. However, this data requires statistical analysis to make an accurate comparison between cell lines. Furthermore, the mutant cell lines in particular would benefit from showing at least one additional time point if feasible. In addition, the authors state that this is likely representative of increased cell death in the mutant cells, however this is not directly tested in this experiment.

      Figure 5F shows straightforward growth curves of ESCs wt, heterozygous or homozygous Bod1l mutants. Please note that the figure includes the growth curves of two independent heterozygous and homozygous Bod1l ES cell lines thereby presenting reproducibility for the impaired growth.

      The volcano plots in Figure 6 for the RNA-seq analysis are also difficult to interpret. Another data presentation method should also be used to be able to compare between experiments- this is not that feasible with the method and labeling of the data here, and the quantitative aspect of this data is not fully realized using this approach.

      Figure 6A represents the RNA-seq data in Volcano plots, which is complemented by the dot plots of Figure 6B and the listing of genes in Table 1. RNA-seq data is difficult to present in visually accessible figures and we think that the presentations in Figure 6 are effective, because this visualization allows the estimation of effect size as well as p-values. These data are also presented in a different format in the Supplement Figure 6D.

      The authors propose that the role of BOD1L DNA damage repair in ESCs is two fold in ESCs-one is a direct role at replication forks, and a second is its role in regulation of DNA repair genes with SETD1A. This may be the case, but the data provided here do not show that there is a direct role for BOD1L in regulating these genes. Additional experiments showing chIP or CUT&RUN of BOD1L and or SETD1A-dependent H3K4methyl species would provide more evidence that these DNA repair gene expression changes are directly due to BOD1L's role. It is also possible the gene expression changes are an indirect consequence of it's role at replication forks, but this is not really addressed. Furthermore, the model proposed in Figure 8 is difficult to understand and does not clearly represent the data in places (for example, the impact on H3K4methylation).

      Our conclusion regarding the two-fold role of BOD1L is based on (a) the data of others regarding protection of the replication fork; (b) our verification that BOD1L is a component of the SETD1A complex; (c) the known role of SETD1A as the major H3K4 trimethyltransferase at active promoters; (d) the observation that conditional mutagenesis of Bod1l predominantly leads to decreased mRNAs that encode for various components of DNA repair pathways. If the loss of BOD1L led only to DNA damage and not gene expression changes due to compromised action of the SETD1A complex, then a loss of expression of DNA damage genes would not be expected. In particular, double strand DNA damage elevates the expression of Xrcc4, Xrcc5 and Rad51c however these mRNAs are strongly down-regulated when Bod1l (or Setd1a) is lost.

      These points constitute a strong basis for our conclusion of a two-fold role for BOD1L and these points have been strengthened in the revised manuscript.

      Regarding the reviewers comments –

      This may be the case, but the data provided here do not show that there is a direct role for BOD1L in regulating these genes. Additional experiments showing chIP or CUT&RUN of BOD1L and or SETD1A-dependent H3K4methyl species would provide more evidence that these DNA repair gene expression changes are directly due to BOD1L's role.

      • it is extremely difficult to demonstrate a direct role for BOD1L in gene regulation using ChIP/CUT&RUN,because SETD1A and B, and subunits of their complexes, are found on all active promoters – (for example, Cxxc1; Fig. 3, Denissov et al 2014). The question of target gene specificity, which emerges from RNA-seq analyses, is a notable problem for the H3K4 methyltransferases because their widespread and overlapping chromatin occupancy on promoters does not facilitate conclusions about specificities. Stated differently, yes we find BOD1L on the affected promoters, but we also find BOD1L on all active promoters, so it’s location on affected promoters is indecisive.

      The discussion section could be significantly streamlined and focused more directly on the content of the manuscript. There are a number of different areas covered in detail that go beyond what is needed for the discussion of the paper and are distracting and confusing. For example, the discussion of the work of Hoshii et al on page 13 is highly relevant, but it could be shortened to focus on the most relevant data from the 2024 paper. (Although I could not find this paper in the reference list, but assume it is this one:https://pubmed.ncbi.nlm.nih.gov/38989615/). Other areas that seems somewhat tangential include the discussion of the potential PP2A interaction on page 14, which is not focused on in the manuscript. Also, the broader question of the role of H3K4 methylation in transcription is covered in some detail and this could be shortened to discuss in a more straightforward manner the potential implications of this study on our understanding of H3K4methyl marks in transcription.

      With due respect, we disagree. A shorter, less informative and less thoughtful discussion would probably have been criticized as insufficient. The reviewer is expressing an opinion. We think that we have succinctly presented the complexities of H3K4 methylation in transcription and our brief comment about PP2A may assist further research.

      Reviewer #2 (Significance (Required)): There are some new insights provided into the relationship between SETD1A/SETD1B and the BOD1 and BOD1L components of the complex, however, the overall advances of this manuscript are relatively limited. A number of additional experiments are required to advance this work beyond what is already known in the field, and the specificty of their conclusions needs additional support. This limits the overall impact of this study* *

      We thank the reviewer for acknowledging that there are some new insights. We have a different opinion regarding their impact on current knowledge. The SET1Complex and H3K4 methylation lies at the centre of epigenetic. Any progress is vitally important.

      __*Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      This manuscript reveals that BOD1L, as a subunit of the SETD1A complex, plays a key role in embryonic stem cell survival by maintaining the expression of DNA repair genes to protect cells from the accumulation of DNA damage. The study finds that BOD1L interacts with the X3 helix of SETD1A through its Shg1 homology region, and that loss of BOD1L leads to elevated H3K4me2/3 levels (consistent with the conserved function of Shg1 in yeast), downregulation of DNA repair gene expression, accumulation of DNA damage, and cell death. While the findings possess a certain degree of novelty, there are some logical issues that require further revision.__ ** 1.The authors conclude that "BOD1L maintains DNA repair gene expression through the SETD1A complex," but the current evidence is merely correlational. However, does the downregulation of DNA repair genes directly lead to DNA damage accumulation and cell death? Does BOD1L's own function in replication fork protection (Higgs et al., 2015) also contribute to this phenotype? The authors mention this point in the discussion, but the experiments do not distinguish between these two functions. *

      Please see the comments above responding to reviewer 2, point 7. The manuscript presents strong evidence that the conclusion is not ‘merely coincidental’. We addressed the question ____Does BOD1L’s own function ____in replication fork protection (Higgs et al., 2015) also contribute to this phenotype? in the experiment of Figure 7 and not just mentioned in the discussion.

      It is recommended to supplement with rescue experiments: in BOD1L-deficient cells, complement with wild-type BOD1L and mutants (e.g., lacking the X3 binding domain) to examine DNA repair gene expression, DNA damage accumulation, and cell death.

      As noted in our response to reviewer 1 point 3, we thank the reviewer for this constructive suggestion for further experiments that ideally will be in another manuscript.

      2.Figure 5C shows that BOD1L deletion leads to increased H3K4me3, whereas SETD1A deletion results in decreased H3K4me3. However, RNA-seq reveals substantial overlap in the downregulated genes between the two conditions (Figure 6B). Based on this, the authors infer that "H3K4me3 is not essential for DNA repair gene expression." This inference is reasonable, but one possibility needs to be excluded: whether the increase in H3K4me3 caused by BOD1L deletion occurs at non-target genes (specifically, DNA damage repair genes). It is recommended to perform H3K4me3 ChIP-qPCR in BOD1L-deficient cells to validate changes at the promoter regions of key DNA repair ____genes.

      This comment is similar to a point made by reviewer 1 point 6. The analysis is now included in Supplement Figure 6E.

      3.Figure 5G uses γH2AX and pATM staining to detect DNA damage, but the type of DNA damage (double-strand breaks, single-strand breaks, replication fork stalling, etc.) and its extent have not been quantified. It is recommended to supplement with: (1) a neutral comet assay to detect double-strand breaks, or an alkaline comet assay to detect total DNA damage; (2) an analysis of replication fork stability (e.g., a DNA fiber assay) to distinguish between BOD1L's replication fork protection function and its transcriptional regulatory role.

      With respect, further DNA damage assays will not distinguish between BOD1L's replication fork protection function and its transcriptional regulatory role.

      4.Figure 2E shows that BOD1L interacts with BPTF independently of SETD1A. However, the functional significance of this interaction has not been further explored. BPTF is a subunit of the NURF chromatin remodeling complex, and its interaction with BOD1L may be involved in DNA repair or transcriptional regulation. It is recommended to supplement with: (1) examining changes in BPTF chromatin binding in BOD1L-deficient cells (BPTF ChIP-seq); (2) investigating whether BPTF knockdown affects DNA repair gene expression or the DNA damage response.

      The manuscript is not about BPTF, rather we present a complementary observation to assist further research.

      5.The study demonstrates that BOD1L binding to the X3 helix inhibits the methylation activity of the SET domain, but the molecular mechanism remains unclear. The authors propose hypotheses in the Discussion, such as "monomer vs dimer" or "allosteric regulation," but direct biophysical evidence to explain how this long-range regulation is achieved is lacking.

      The interaction between BOD1L and SETD1A is presented and the implications are discussed in the context of existing information on the Set1 complexes. Further work – indeed a completely new project - is required to explore the implications of the findings we report.

      6.The experiment observed that BOD1 can also bind to the X3 site of SETD1B, and the two proteins share structural similarity. Although the text mentions that BOD1L is the major subunit in ESCs, it does not sufficiently explore whether BOD1 exerts partial compensatory effects in the absence of BOD1L, or the logic underlying their specific switching in different tissues.

      We agree – the manuscript does not explore whether BOD1 exerts partial compensatory effects in the absence of BOD1L. That would also be another project. Also we have not included speculations about ‘the logic underlying their specific switching in different tissues. ____‘

      Minor suggestion________

      1.The RNA-seq experiments were performed with biological duplicates (two samples per condition), it is generally recommended to have at least three biological replicates to ensure statistical power.

      As specified in the M&M, the primary RNA-seq experiments were performed with biological duplicates in parallel in three closely related ESC culture conditions and the conclusions are drawn from these six overlapping datasets. The other RNA-seq experiment was performed in triplicates, as was an RNA-seq experiment using ESCFCS conditions, that was not included but delivered the same results as presented here.

      2.In the co-IP experiments shown in Figure 2D and 2E, there is a lack of quantification or internal controls.

      As mentioned in response to reviewer 1, point 1, the controls are included and quantification can be estimated from the figures. Further data are provided in Supplement Figure 1.__3.The peak calling parameters and statistical methods for the ChIP-seq analysis were not described in detail. __Now included in the M&M.

      4.The description of the BOD1L-BPTF interaction results (Figure 2E) in the main text is too brief, and the conditions and controls for the IP experiment are not specified.

      The text referring to BPTF has been expanded and is now –

      Therefore, we examined the interaction with BPTF in more detail. By immunoprecipitation using BOD1L-VENUS expressed from a Bod1l BAC transgene, the interaction between BOD1L and BPTF was confirmed. However, immunoprecipitation using BPTF-VENUS expressed from a Bptf BAC transgene retrieved the NURF subunit SNF2L/SMARCA1 (Supplemental Fig. S1) but failed to retrieve SETD1A (Fig. 2E) indicating that BOD1L independently interacts with both SETD1A-C and BPTF, and that BPTF interacts with BOD1L independently of its interaction with NURF.

      5.The discussion section is somewhat lengthy and contains speculative content (such as the discussion on OGT and MLL complexes). Although interesting, these points are not strongly related to the core findings of this study and could be streamlined.

      The Discussion is a little less than 1300 words.

      6.Page8 "Bod1 esiRNAi knock-down" should be ""Bod1 esiRNA knock-down".

      Corrected.

      Reviewer #3 (Significance (Required)): Should be revised.

      Revisons suggested by the reviewers have been incorporated.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript reveals that BOD1L, as a subunit of the SETD1A complex, plays a key role in embryonic stem cell survival by maintaining the expression of DNA repair genes to protect cells from the accumulation of DNA damage. The study finds that BOD1L interacts with the X3 helix of SETD1A through its Shg1 homology region, and that loss of BOD1L leads to elevated H3K4me2/3 levels (consistent with the conserved function of Shg1 in yeast), downregulation of DNA repair gene expression, accumulation of DNA damage, and cell death. While the findings possess a certain degree of novelty, there are some logical issues that require further revision.

      1.The authors conclude that "BOD1L maintains DNA repair gene expression through the SETD1A complex," but the current evidence is merely correlational. However, does the downregulation of DNA repair genes directly lead to DNA damage accumulation and cell death? Does BOD1L's own function in replication fork protection (Higgs et al., 2015) also contribute to this phenotype? The authors mention this point in the discussion, but the experiments do not distinguish between these two functions. It is recommended to supplement with rescue experiments: in BOD1L-deficient cells, complement with wild-type BOD1L and mutants (e.g., lacking the X3 binding domain) to examine DNA repair gene expression, DNA damage accumulation, and cell death. 2.Figure 5C shows that BOD1L deletion leads to increased H3K4me3, whereas SETD1A deletion results in decreased H3K4me3. However, RNA-seq reveals substantial overlap in the downregulated genes between the two conditions (Figure 6B). Based on this, the authors infer that "H3K4me3 is not essential for DNA repair gene expression." This inference is reasonable, but one possibility needs to be excluded: whether the increase in H3K4me3 caused by BOD1L deletion occurs at non-target genes (specifically, DNA damage repair genes). It is recommended to perform H3K4me3 ChIP-qPCR in BOD1L-deficient cells to validate changes at the promoter regions of key DNA repair genes. 3.Figure 5G uses γH2AX and pATM staining to detect DNA damage, but the type of DNA damage (double-strand breaks, single-strand breaks, replication fork stalling, etc.) and its extent have not been quantified. It is recommended to supplement with: (1) a neutral comet assay to detect double-strand breaks, or an alkaline comet assay to detect total DNA damage; (2) an analysis of replication fork stability (e.g., a DNA fiber assay) to distinguish between BOD1L's replication fork protection function and its transcriptional regulatory role. 4.Figure 2E shows that BOD1L interacts with BPTF independently of SETD1A. However, the functional significance of this interaction has not been further explored. BPTF is a subunit of the NURF chromatin remodeling complex, and its interaction with BOD1L may be involved in DNA repair or transcriptional regulation. It is recommended to supplement with: (1) examining changes in BPTF chromatin binding in BOD1L-deficient cells (BPTF ChIP-seq); (2) investigating whether BPTF knockdown affects DNA repair gene expression or the DNA damage response. 5.The study demonstrates that BOD1L binding to the X3 helix inhibits the methylation activity of the SET domain, but the molecular mechanism remains unclear. The authors propose hypotheses in the Discussion, such as "monomer vs dimer" or "allosteric regulation," but direct biophysical evidence to explain how this long-range regulation is achieved is lacking. 6.The experiment observed that BOD1 can also bind to the X3 site of SETD1B, and the two proteins share structural similarity. Although the text mentions that BOD1L is the major subunit in ESCs, it does not sufficiently explore whether BOD1 exerts partial compensatory effects in the absence of BOD1L, or the logic underlying their specific switching in different tissues.

      Minor suggestion

      1.The RNA-seq experiments were performed with biological duplicates (two samples per condition), it is generally recommended to have at least three biological replicates to ensure statistical power. 2.In the co-IP experiments shown in Figure 2D and 2E, there is a lack of quantification or internal controls. 3.The peak calling parameters and statistical methods for the ChIP-seq analysis were not described in detail. 4.The description of the BOD1L-BPTF interaction results (Figure 2E) in the main text is too brief, and the conditions and controls for the IP experiment are not specified. 5.The discussion section is somewhat lengthy and contains speculative content (such as the discussion on OGT and MLL complexes). Although interesting, these points are not strongly related to the core findings of this study and could be streamlined. 6.Page8 "Bod1 esiRNAi knock-down" should be ""Bod1 esiRNA knock-down".

      Significance

      Should be revised.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates H3K4me-methylating complexes in mouse embryonic stem cells with a specific focus on the SETD1A complex and its binding partners. The authors define the interaction between SETD1A and BOD1L and characterize a requirement for BOD1L in maintaining the expression of DNA repair genes in mESCs. BOD1L has previously been implicated in regulating the replication fork (Bayley et al., Mol Cell, 2022 and others), however the authors propose another role for BOD1L in restraining H3K4me3 at TSS-proximal nucleosomes which impacts gene expression of DNA repair genes. However, a number of aspects of this model require additional support. Furthermore, while there are some new insights gained from the experiments performed, the data presentation makes the impact of the studies difficult to interpret. Specific concerns are outlined in further detail below:

      1. A key approach used through the manuscript is AP-MS experiments to determine protein interactors of SETD1A, SETD1B, and other complex subunits. It appears these experiments were generally performed in triplicate, however, there should be more discussion of what thresholds were used to quantify interactors. The methods states that if 2 unique peptides were identified, though it is very difficult to tell from the volcano plots why some proteins are labelled and named as interactors and others are not discussed. Furthermore, the volcano plots are generally difficult to read and do not lend themselves well to comparisons between different experiments. Another format in addition to potential volcano plots, such as a heatmap, would improve the readability and provide a better method of visualizing the quantitative results of these experiments.
      2. There is very little discussion of the additional interactors identified, outside of expected components, in the AP-MS experiments described in Figures 2 and 3. The authors state that they pursued additional experiments but that there was not productive data. It is unclear what this means as to whether these are not legitimate interactors or if there were other technical challenges. There is some discussion of OGT and BPTF, but it is not particularly informative. I think further clarification and/or characterization on the other interactors would be useful to be able to interpret the validity of the data presented in the AP-MS volcano plots.
      3. In Figure 4, AP-MS is used to characterize interactors of different deletion mutants of SETD1A. The expression of the deletion mutants should be shown by western or another approach to see how these compare to wildtype. In addition, the interaction is further probed in cells by a co-IP approach using an overexpression construct of the X3 region of SETD1A. Since the authors have the deletion mutant, this could be used in a co-IP experiment in addition to exogenous expression of just the X3 fragment. This would allow a direct comparison with WT SETD1A and other mutants. Also, to further support the specificity of the interaction show in Fig 4D, a similar experiment could be performed with other regions of SETD1A (or B), such as a the X1 or X2 regions.
      4. Figure 5C and this H3K4me3 chip results in the BOD1L mutant cells would be further supported by showing the levels of SETD1A (and other H3K4 methylating enzymes) in these cells lines to better support the conclusion that BOD1L is directly restricting SETD1A activity at chromatin.
      5. Figure 5F shows growth curves of WT and BOD1 mutant ESCs. However, this data requires statistical analysis to make an accurate comparison between cell lines. Furthermore, the mutant cell lines in particular would benefit from showing at least one additional time point if feasible. In addition, the authors state that this is likely representative of increased cell death in the mutant cells, however this is not directly tested in this experiment.
      6. The volcano plots in Figure 6 for the RNA-seq analysis are also difficult to interpret. Another data presentation method should also be used to be able to compare between experiments- this is not that feasible with the method and labeling of the data here, and the quantitative aspect of this data is not fully realized using this approach.
      7. The authors propose that the role of BOD1L DNA damage repair in ESCs is two fold in ESCs-one is a direct role at replication forks, and a second is its role in regulation of DNA repair genes with SETD1A. This may be the case, but the data provided here do not show that there is a direct role for BOD1L in regulating these genes. Additional experiments showing chIP or CUT&RUN of BOD1L and or SETD1A-dependent H3K4methyl species would provide more evidence that these DNA repair gene expression changes are directly due to BOD1L's role. It is also possible the gene expression changes are an indirect consequence of it's role at replication forks, but this is not really addressed. Furthermore, the model proposed in Figure 8 is difficult to understand and does not clearly represent the data in places (for example, the impact on H3K4methylation).
      8. The discussion section could be significantly streamlined and focused more directly on the content of the manuscript. There are a number of different areas covered in detail that go beyond what is needed for the discussion of the paper and are distracting and confusing. For example, the discussion of the work of Hoshii et al on page 13 is highly relevant, but it could be shortened to focus on the most relevant data from the 2024 paper. (Although I could not find this paper in the reference list, but assume it is this one: https://pubmed.ncbi.nlm.nih.gov/38989615/). Other areas that seems somewhat tangential include the discussion of the potential PP2A interaction on page 14, which is not focused on in the manuscript. Also, the broader question of the role of H3K4 methylation in transcription is covered in some detail and this could be shortened to discuss in a more straightforward manner the potential implications of this study on our understanding of H3K4methyl marks in transcription.

      Significance

      There are some new insights provided into the relationship between SETD1A/SETD1B and the BOD1 and BOD1L components of the complex, however, the overall advances of this manuscript are relatively limited. A number of additional experiments are required to advance this work beyond what is already known in the field, and the specificty of their conclusions needs additional support. This limits the overall impact of this study

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This manuscript describes functional characterization of Bod1 family proteins (particularly Bod1L) and their interaction with COMPASS family histone methyltransferase complexes. Bod1 proteins are conserved through evolution and related to yeast Shg1, which binds to the yeast COMPASS via a conserved domain and negatively regulates H3K4me3 levels. Here the authors performed IP-MS of Bod1L, Bod1, Setd1a, and Setd1b in engineered mouse embryonic stem cells and confirmed presence of Bod1 and Bod1L in both Setd1a and Setd1b-containing COMPASS complexes. AlphaFold modeling predicted an interaction between the Bod1/L Shg domain and a conserved helix in Setd1a/b which was validated in stable Setd1a deletion ESC lines and with isolated Sed1a/b fragments. Functional analysis of Bod1L and Setd1a deletion lines confirmed that Bod1L negatively regulated H3K4me3 levels. Moreover, the knockouts caused parallel effects on the expression of DNA repair genes and both apparently enhanced levels of baseline DNA damage, in agreement with findings in leukemia cell lines. This argues that Setd1a/Bod1L regulates DNA repair gene expression independently of H3K4me3.

      Major comments

      Figures 2D, 2E, 3C: there are no panels showing the efficiency of Bod1 or Bod1L immunoprecipitation. The immunoblots seem to indicate that either Bod1 or Bod1L precipitate a substantial fraction of Setd1a and a much smaller fraction of Setd1b, although it is impossible to tell without the blots of Bod1/Bod1L. The idea that Setd1a is primarily associated with Bod1L vs Bod1 is presumed in the rest of the manuscript (likely based on previous results in other cell lines) but is not strongly supported by these figures. Figure 4: the SETD1A-X1 and X3 internal deletion lines show many interesting interactions not detected in the wild-type line, notably with CPSF components. What is the significance of this? Related to Figure 4: Why were none of the functional genomics experiments described in Figures 5-7 performed on the Setd1a-X3 deletion line given that the authors had it in hand? This would have been a logical complement to the Bod1L deletion experiment and further addressed the issue of functional partnership between Setd1a and Bod1/L. One could make a similar point regarding Bod1-it is unclear why (given the co-IP and AP-MS data in Figures 2 and 3) a deletion line of Bod1 was not analyzed in parallel. There was convincing rationale in the Hoshii 2024 paper to focus on Setd1a/Bod1L in that system; the rationale for doing this here is less clear. Figure 5G: This figure does not seem to include a control in which wild-type ESCs are treated with tamoxifen in parallel with the Flp Bod1L line. Figure 6: This figure should include Venn diagrams that clearly show the overlap between genes affected by Bod1L removal compared to Setd1a. Related to Figure 6/7: These figures should include analysis of the H3K4me3 ChIP-seq data in Figure 5 specifically at Bod1L-regulated DEGs.<br /> Discussion: The authors should note that the direct role of Setd1a at DNA breaks is proposed to rely on enzymatic activity (Higgs et al 2018, Bayley et al 2022)

      Minor comments

      Figure 5: panel arrangement is confusing Hoshii et al, 2024 is not listed in the references

      Significance

      The key advance in this work is demonstration that Bod1L removal affects expression of DNA repair genes and increases DNA damage in ES cells. This seems to occur independently of changes in H3K4me3 (although specific analysis of H3K4me3 at these genes was not performed). Removal of Setd1a had similar effects. The work seems to support previous reports in leukemia cell lines for specific effects of Setd1a/Bod1L on DNA repair that is not related to Setd1a enzymatic activity (Hoshii et al 2018, 2024 in the manuscript)(and contrasts with findings in U2Os cells that do implicate enzymatic activity and emphasize a direct role at replication forks rather than in transcription; Higgs et al 2018 and Bayley et al 2022 in the manuscript). This is a modest but important conceptual advance in thinking about Bod1L and COMPASS complex functions in transcription and DNA damage repair. Whether these functions are specific to Bod1L vs Bod1 in ESCs, or to Setd1a over Setd1b, in ESCs, was not addressed. The audience for this work will be chromatin/epigenetics experts. My expertise is in the field of chromatin and epigenetics, and I have studied histone modification function extensively in the context of transcription.

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      Reply to the reviewers

      __Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

      *Mitochondrial network morphology constantly adapts to the physiological conditions of the cell. Fusion and fission play a well established role in remodeling of the mitochondrial network. In their present study, Kasmaie et al. focused on pull-out events that generate novel mitochondrial tubules emanating from the side of existing tubules and show that this is an important mechanism contributing to network morphology. Using live cell microscopy combined with pharmacological treatments and/or knock-down of key components of mitochondrial dynamics they show that this process is dependent on the machinery of mitochondrial fusion and fission and mitochondria-associated actin. Furthermore, they claim that ER mitochondria contact sites (ERMCS) may be important. The authors have made some interesting observations, and most parts of the manuscript are sound. However, some of their conclusions should be supported by additional experimentation, as outlined below. *

      __Major comments __

      1*. On page 5 the authors claim that cells grown in galactose medium showed an increased fusion but not an increased fission rate. Excessive fusion in the presence of unchanged fission is expected to result in hyperfused networks. After adaptation to the new medium cells should reach a steady state, where fusion and fission activities are balanced. Why was this not observed? *

      We think that this is because the cells have not attained yet a new equilibrium. To show this, we will repeat the starvation experiment at different time points up to 24h and measure PO, fusion and fission. We have preliminary data showing that connectivity does not further increase beyond 4 hours, so we expect that fusion and fission will reach a new equilibrium past 4h.

      2*. The authors propose on p. 6 that the overall mitochondrial area increases upon growth in galactose medium, while the amount of mitochondrial proteins and mitochondrial mass remain unchanged. This would mean that mitochondrial proteins become more dilute in the organelle. Is this what they think is happening? Is there a loss of cristae to compensate for the overall growth of the organelle under conditions where mitochondrial mass remains unchanged? Would this affect the diffusion of metabolites in the matrix? These are central questions that should be addressed experimentally. *

      We have started to reanalyse mitochondrial structure using Lightning super-resolution. We found that, in MEFs, mitochondria become thinner in galactose. We therefore think that what is happening is that, consistent with dynamic tubulation, mitochondria stretch out without changing their mass in galactose. We still need to do the same analysis in primary fibroblasts and complement this with electron microscopy.

      3. Fig. 5A and B show the association of mitochondrial pull-out events with ERMCS. Unfortunately, the field of view that is shown is very crowded and ER is almost everywhere. How was the association of ER and mitochondria defined? To support their statement the authors should provide some functional evidence. For example, they could test whether a reduction of ERMCS results in a reduction of pull-outs.

      While manipulating functional ERMCS would be interesting, it would be a very challenging experiment as this would affect other mitochondrial functions that could indirectly affect pull-outs. There are also many ERMCS tethers in mammalian cells that can potentially compensate for each other, further complicating the interpretation of the data. However, it is not surprising that pull-outs would be associated with ERMCS as fission/fusion and dynamic tubulation were shown to occur at ERMCS using similar methods. To us, this is thus not a key part of the current work and thus, given the difficulties associated with the proposed experiment, we are not planning on doing it.

      However, given that some issues with the ER data were also raised by reviewer 4, we will redo the imaging and analysis. First, we will use a better ER construct that we have (mCherry-Cyb5) and reimage using Lightning super resolution on our new Leica Stellaris microscope, which will greatly improve image quality. We will also do as suggested by reviewer 4 and used images turned 90 degrees as a negative control (see also R4 Point 3).

      4*. In Fig. 5E the authors show that MFN2 is enriched at pull-out sites. However, in Fig. 6 they analyzed a knock-down of MFN1, rather than MFN2. Is MFN1 also enriched at pull-out sites, and does a knock-down of MFN2 result in altered pull-out activity? *

      We originally looked at MFN2 because we had technical issues with the MFN1 construct that significantly altered mitochondrial structure, making the analysis unreliable. To address the reviewer’s point, we will measure pull-outs in cells where MFN2 is knocked down. This will allow us to determine whether both MFNs are involved in pull-outs.

      __Minor comments __

      5*. The third paragraph of the introduction refers to an "original study" and "more recent work", however, both cite the same paper (ref. 10). Please clarify. *

      We have reworked the introduction to clarify the relationship between pull-outs and dynamic tubulation (pull-outs are a type of dynamic tubulation). This sentence has thus been removed.

      6*. Fig. 1 quantifies the pull-out events per cell. Please also indicate the time of observation, i.e. number of events per cell and minute. *

      The length of the videos (5 minutes) and the fact that each data point represent the total number of events within this 5 minute is now clearly stated in the legend of each figure where pull-outs were quantified.

      7*. Page 4, the text refers to Fig. 1F (Bottom, white arrows) for successful fusion events. However, these are shown in Fig. 1E and highlighted by arrowheads. Also the reference to Fig. 1F for stable branches is not correct. *

      This has been corrected.

      8*. Please indicate in Fig. 4B what was loaded on the gels. Total cell extracts? Also add molecular weight markers. *

      It is now mentioned in the figure legend that these are whole cell lysates and the MW markers have been added to the blots.

      9*. I'm not sure whether appropriate statistical analysis was performed in Fig. 6D and G. Please double-check. Please note that I am not an expert on this. *

      T-tests are appropriate here.

      10*. Please briefly describe the DeAct-mito tool in the text to help the reader to understand the experiment without having to look it up in the cited paper. Also, please add a reference for CK666 in the results section. *

      This is now added in the text.

      __Reviewer #1 (Significance (Required)): __

      *The paper provides some conceptual advances for the understanding of mitochondrial network formation, which will be of interest to an audience interested in mitochondrial dynamics. It offers initial insights into the molecular machinery involved in generating mitochondrial pull-outs, but does not go into much detail regarding the molecular aspects of these processes. *

      Pull-outs were previously identified as a form of dynamic tubulation, but their regulation and whether they represent the same process as tip elongation remained unclear. The goal of the manuscript was thus to identify pull-outs as a distinct type of dynamic tubulation that is metabolically regulated. In this sense, we think that a detailed molecular analysis of pull-outs is outside the scope of our manuscript.

      Nevertheless, to provide more molecular details of pull-outs, we have added data showing that pull-outs that fuse have higher MFN2 intensity (New Figure 5H) and that retracting pull-outs are short-lived and shorter in length (New Supplemental Figure 1C)(see also R2 Minor comments Point 5). Retracting pull-outs were however not associated with differences in MFN2 or DRP1 enrichment, but we showed that actin plays a role in pull-outs. We will thus address whether the presence of actin affect pull-out fate by doing the same analysis on our AC probe data present in Figure 7.

      MFNs have previously been shown to oligomerize/activate in response to change in the redox balance (Shutt el al. (2012) EMBO Rep.). we will thus use MFN1 oligomerization as a readout to measure MFN1 activation in response to starvation. Our preliminary data suggests that MFN1 oligomerizes/activates during glucose starvation, which would provide a mechanistic link between starvation and increased pull-outs. Finally, as Miro participate in the pulling of mitochondrial tubules during dynamic tubulation, we will determine its role using siRNA.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

      *Mitochondria must constantly adapt their structure and function in response to changes in cellular physiology. For example, starvation conditions result in a more highly connected mitochondrial network, which increases ATP production and protects the organelle from mitophagy. Yet how the network achieves the rapid increase in connectivity is not fully understood. In this manuscript, live cell imaging captures mitochondrial pull-outs, a feature of all cell lines that is regulated by mitochondrial function. Quantification of mitochondrial fusion, fission, and pullouts indicates that pullouts occur at the greatest frequency and that most of these events result in reversal at steady state but can also lead to fusion events or stable branches in the network. These events increase when physiological conditions change mitochondrial function, either when cells are grown in galactose or deprived of glucose. The frequency of mitochondrial pullouts increased in both conditions, which correlates with increased connectivity and number of branches. Inhibiting beta-oxidation of fatty acids and thus the production of acetyl coA to fuel the TCA cycle in glucose starvation conditions prevented the increased frequency of mitochondrial pullouts, indicating that these events are regulated by mitochondrial function. Modeling predicts that the increased connectivity of the network facilitates rapid diffusion of metabolites. Live cell imaging also indicates that the sites of mitochondrial pullouts are marked by ER, DRP1, MFN2, and actin. Consistent with this, depletion of DRP1 or MFN1 by siRNA reduces the frequency of mitochondrial pullouts, as does depletion of mitochondrial actin or cellular actin ARP2/3-dependent polymerization. In sum, the data document a novel aspect of mitochondrial dynamics that contributes to remodeling organellar structure in response to changes in cellular physiology. *

      __Major Comments: __

      1*. In Figure 3, fatty acid oxidation is blocked to prevent production of acetyl coA, which fuels the TCA cycle and production of reducing equivalents required for oxidative phosphorylation. The data convincingly demonstrate that inhibition of FAO blocks the increase in mitochondrial pullouts resulting from glucose starvation. However, it remains unclear if this is specifically due to reduced oxidative phosphorylation or reduced FOA or specific blocks in the TCA cycle. Why not use inhibitors of the electron transport chain for these experiments? *

      *What does the mitochondrial network look like under these conditions? Representative images would be useful in addition to the connectivity score. *

      *Also in this figure, the number of mitochondrial pull-outs is different from the original data presented in Figure 1D (10 vs 17). Is there a difference in growth conditions or time of the video? *

      To address the effect of ETC inhibition, we will add pull-out data for cells incubated with oligomycin, which blocks the ATP synthase.

      We also added images for conditions where there were no representative images in the original manuscript (New Figure 3A).

      As for the difference in pull-outs between Figure 1 and 3, we do not know. All these experiments were conducted in the same way with the same acquisition parameters (same duration, same frame rate). As these are primary cells, it is possible that there is some batch-to-batch variation or that, although we keep our cells in culture for a limited number of passages, there was some variation in passage number that caused that.

      2*. The data in Figure 5 show that DRP1 and MFN2 are present at pull-out events. It is not clear why a rather complicated/convoluted normalization strategy was used to present this data. It would be more useful to readers to see the raw numbers for each condition (X number of pull-out events also had DRP1 or MFN2 present). Why was MFN1 not used in this experiment (especially given that this is the target of siRNA knockdown in the next figure). *

      *It may be too technically challenging, but it would be interesting to know if both mitochondrial dynamins are present at pull-out events or if it's either/or. Perhaps if only DRP1 is present, the pull-out is more likely to retract or form a stable branch while the presence of MFN1/2 would increase the probability that the pull-out goes on to fuse? *

      We originally did as the reviewer suggested and performed a binary quantification of the presence of DRP1 and MFN2 at pull-out sites. However, as the foci were sometimes more diffuse (especially for MFN2 that is always on mitochondria), this was challenging to do in an unbiassed manner. We used this alternative method have a more objective evaluation of the recruitment of MFN and DRP1 at pull-out sites. As suggested by the reviewer, we have now added this original quantification that also shows that DRP1 and MFN2 are present at pull-out sites (New Figure 5E).

      We used MFN2 for this experiment as we were not able to transfect our tagged MFN1 construct without causing major issues to mitochondrial networks, issues that compromised the experiment. To address this question, we will instead provide pull-out data for MFN2 knockdown.

      As for the suggestion, the experiments were in fact done with both proteins expressed. We thus went back and correlated MFN2 and DRP1 expression with pull-out fate. Most pull-out sites had both dynamins present and the absence of DRP1 did not change the fate of the pull-out. However, the pull-out that went on to fuse had higher MFN2 levels than those that retracted or stayed without fusing (New Figure 5H). We also clarified in the text that both proteins were expressed at the same time.

      __Minor comments: __

      *1a. The Western blot in Figure 4 should include molecular weight markers. *

      They are now added to the figure

      *b. The search time is a little hard to interpret with only micrometer squared values - could these be correlated to molecules of approximately similar size? *

      Membrane-associated proteins have a lower diffusion speed than soluble molecules, whether proteins or small solutes. As stated in the text, we estimate that membrane proteins diffuse slower (around the 0.3 µm2/s estimate of the figure) compared to soluble molecules (3-30 µm2/s).

      1. This figure also refers to end-to-end fission rates in the graph axis labels and figure legend text - this should be fusion rates.

      This is actually end-to-end fission. To minimize the number of variables, all fusion and fission rates were calculated as a function of end-to end fission.

      *2. The Western blot in Figure 6 should also include molecular weight markers. *

      They have been added.

      *3. The duration of the CK666 treatment used in Figure 7 is not noted in the figure legend or materials and methods. *

      It has been added to the figure legend.

      *4. It would be convenient for readers to find the duration of the image acquisition used to quantify the number of pull-out events in the figure legends rather than having to dig into the materials and methods. *

      This has been added

      5. Could more information about the pull-outs that retract be shared? Is there anything notable about these pull-outs compared to those that are stable or go on to fuse? Are they shorter or more dynamic or lack actin?

      As stated above (R2 point 2), reanalysing the DRP1/MFN2 expression data, we found that on average, the pull-outs that go on to fuse have more MFN2 signal, that often remains with the tip of the pull-out (New Figure 5H). There was however no difference in DRP1 and MFN2 between the pull-outs that retract or stay without fusing. In addition, pull-outs that retracted were on average shorter and usually retracted within a short time (__Reviewer #2 (Significance (Required)): __

      *The manuscript reports a novel phenomenon that the authors name mitochondrial pull-outs. While most of these events simply retract, some are stable and some result in a fusion event, which seems to contribute to increasing mitochondrial network connectivity under conditions that rely on oxidative phosphorylation for ATP production. Importantly, acute loss of DRP1, MFN1, or actin results in a decreased frequency of pull-out events, suggesting that the core machinery required for mitochondrial dynamics also supports mitochondrial pull-outs. While the imaging and quantification data are convincing, the study lacks a functional assay to demonstrate that mitochondrial pull-outs are required for the adaptation of mitochondrial functions in galactose and/or glucose starvation conditions, which somewhat limits the significance of the study. *

      We already showed (Figure 7G) that blocking pull-outs using the Arp2/3 complex inhibitor CK666 inhibits the increase in oxygen consumption rates (OCR) caused by glucose starvation, suggesting that pull-outs re required for this.

      Loss of MFN1 also prevents pull-outs (Figure 6). We now have preliminary data suggesting that although loss of MFN1 does not decrease OCR under basal conditions, it prevents the increase in OCR caused by glucose starvation. We will complete these experiments add survival data showing that MFN1 survival is impaired by long-term starvation.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

      *Kasmaie et al. present a manuscript in which they claim to have identified a new form of mitochondrial dynamic tubulation. A major concern is that this kind of dynamic tubulation has already been described by other authors--up to 11 years ago. Although the authors of this manuscript cite prior publications, they do not adequately contextualize their own work in relation to what has been shown previously by others in peer-reviewed publications.The manuscript presents this phenomenon as a distinct entity, although similar observations have previously been reported.. Moreover, throughout this work there is a disregard for employing appropriate methodologies for the visualization and quantification of mitochondrial fusion events. Diffraction-limited confocal microscopy is not an appropiate method for the assessment of mitochondrial fusion. Apparent stable continuity between diffraction-limited mitochondrial signals is by no means the same as mitochondrial fusion, thus the fusion-frequency measurements cannot support the authors' conclusions. This concern is especially important because the same methodological assumption underlies recent related work from this group (Gatti et al., 2025), which the present manuscript cites as precedent rather than recognizing as a limitation. Overall, both the conceptual and methodological basis of this work are fundamentally flawed. Therefore, I would strongly recommend rejection. *

      Throughout their initial assessment (above) and the text that follows below, Reviewer 3 has two main arguments against our manuscript:

      We did not properly cite Wang et al (2015) and Qin et al (2020) relative to dynamic tubulation. We did not use PA-GFP for quantifying fusion. Contrary to the reviewer, we do not think that these arguments are sufficient to reject the manuscript. We will summarise our point of view here and refer to it when addressing specific comments from the reviewer.

      __1. Distinction between dynamic tubulation, pull-outs and tip elongation. __

      First, we recognise that the original version of the abstract was not too clear and could have read as we thought that dynamic tubulation and pull-outs were distinct. We are sorry for the confusion it created. However, in the result section, we clearly stated that pull-out is a form of dynamic tubulation.

      Our view is that pull-outs and tip elongation are distinct types of dynamic tubulation that were not previously recognised as such because the original papers (which we cited) did not separate them. While example of both were provided in these papers, they were lumped together for all quantifications. Here, we specifically separate the two types of dynamic tubulation while making it clear that these are both types of dynamic tubulation. Importantly, the novelty of our work does not reside in the description of these processes but rather in the demonstration that pull-outs, but not tip elongation, are regulated by mitochondrial activity and actin.

      We have reworked the abstract and introduction to make this clearer and will update the discussion when we are done with the revisions.

      2. Use of PA-GFP to quantify fusion.

      The reviewer brings an important point about the limitations of our approach for quantifying fusion. This is something that we previously addressed in response to reviewers in Gatti et al. (to the reviewer’s satisfaction). We compared our fusion criterion (fusion must last 100 sec) to PA-GFP and found that 94% of all apparent fusion events that lasted 100 sec were actual fusion events as measured with PA-GFP. To us, this provided a very good approximation to the PA-GFP experiments (although it might miss some transient fusion events) without the drawback of not being able to quantify all fusion events within a cell. We thus proceeded the same way for the current study.

      We also want to point out that, as the other reviewers mentioned, this study is not about mitochondrial fusion but about dynamic tubulation. We used fission and fusion as a comparison point as they are better understood than dynamic tubulation. In this context, the use of PAGFP is challenging as it does not allow to measure all mitochondrial dynamics events occurring in one cell (it measures only fusion between PAGFP-negative and PAGFP-positive mitochondria) and the spread of PAGFP will vary between control and starved cells (the latter being more connected).

      We will revise the text to state the caveats of our quantification method (mainly that it might miss some transient fusion events). If the reviewer insists, we could provide a panel showing that starvation increases fusion using PAGFP but, as the other reviewers stated, would not bring much new to our study.

      __Major comments: __

      1*. In their abstract, the authors define "dynamic tubulation" as "Rapid elongation of the tip of mitochondrial tubules." However, the previous literature regarding this phenomenon does not define it, as the authors here indicate, as only emerging from mitochondrial tips1,2. Indeed, both of these previously published articles provide examples of dynamic tubulation events not only from mitochondrial tips but also from mitochondrial sides-i.e., "lateral" surfaces. In Wang et al., 2015, the HeLa cell time-lapse images in Figure S3 provide an unambiguous example of a lateral extrusion of a dynamic tubulation event; and in Qin et al., 2020, there is a clear example of this phenomenon in Figure 1a, where the dynamic tubulation event is extruded from the lateral face of the organelle, rather than the tip. It is essential that the authors of this manuscript align their definition of "dynamic tubulation" with that of previous studies, which played key roles in establishing the very dynamics and mechanistic underpinnings of this phenomenon. *

      *Continuing on in their abstract, the authors say, "However, these mechanisms alone cannot fully account for the formation of highly interconnected mitochondrial networks that are required for rapid distribution of mitochondria material." This sentence purports that the other authors of previous work argued that dynamic tubulation from tips could account for the highly interconnected nature of mitochondrial networks, but as I have highlighted above, prior publications did not define tubulation events in this narrow way. It is important to note, too, for instance, Wang et al., 2015, discussed the dependency of pulling tubules out of mitochondria as a basic mechanism for forming complex mitochondrial reticula: *

      *"However, fusion of individual mitochondria is not sufficient to form mitochondrial networks in vitro (Meeusen et al., 2004), indicating that other unknown mechanisms are essential for mitochondrial network formation. The basic module for network formation is the tubule. Tubules connect to form a lattice, and the network is a series of interconnected lattices. Hypothetically, a network can be constructed by pulling tubules out of existing membrane compartments and then connecting these tubules by fusion to give a lattice; by repeating this process, a network can be created." *

      *Given the above, it is critical that the authors appropriately re-contextualize their study in terms of the existing literature, which has not only laid broad foundations for this topic but has been around for more than a decade. *

      As mentioned above, the original abstract was not clear about dynamic tubulation and pull-outs. We had also missed that an example of pull-out was shown in the supplementary data of Wang et al., which did not assess any potential distinction between tip elongation and pull-outs. Also, the quote from Wang et al. provided by the reviewer is purely hypothetical and does not really address pull-outs.

      The abstract has been changed to address this point.

      2*. The authors of the present manuscript then say in the abstract, "Here, we identify a distinct type of dynamic tubulation, mitochondrial pull-outs characterized by the lateral extrusion from pre-existing mitochondrial tubules, as metabolically regulated determinants of mitochondrial network formation." The claim that this represents a distinct type of dynamic tubulation is difficult to reconcile with previous reports describing similar events. In the next sentence of the abstract, the authors allude to additional mechanistic insight: "Pull-outs are distinct from the tip elongation form of dynamic tubulation as they are modulated by the mitochondrial dynamins MFN1 and DRP1. . . ." The extent to which this provides novel information is open to question, as Wang et al., 2015, and Qin et al., 2020, both examined the potential roles of Mitofusins and DRP1 in this process. Although they do not rigorously quantify the effects of suppressing these genes on the dynamic tubulation process, they nevertheless indicate that "In Mfn-null MEF cells, dynamic tubulation can still occur (Figure 6F and Supplementary information, Movie S21), but the tubules fail to fuse into networks," and "in these Drp1-depleted cells, we were still able to observe frequent MDT events. . . ." The authors of the present manuscript suggest that the suppression of at least MFN1 and DRP1 leads to a significant decrease in the frequency of dynamic tubulation events, but, to determine whether these factors are essential for the process or not would require complete knockout (KO) of the respective genes. *

      It is a distinct type of dynamic tubulation in the sense that it is distinct from tip elongation, which we show in our manuscript. This does not mean that we think that pull-outs have never been reported before, sorry if our wording seemed to imply this. The novelty here is the fact that pull-outs are responsive to metabolic changes.

      As for the effect of knocking down MFN1 or DRP1, we show that it prevents pull-outs but never claimed that it blocks dynamic tubulation as a whole, as this would imply that it also affects tip elongation. As stated in the result section of our manuscript, we agree with Qin et al. that loss of DRP1 does not affect tip elongation. We also never claimed that mitochondrial dynamins are essential for pull-outs, so we do not understand the last part of the reviewer’s comment, especially since the knockdowns show a phenotype.

      The abstract has been changed to address the reviewer’s point.

      3*. In their introduction, the authors indicate: "In fact, most fusion events occur on the side of a mitochondrial tubule rather than at its end, creating new mitochondrial junctions that increase network connectivity (8, 9)." It is essential that the authors contextualize their comments in terms of well-established literature that laid the foundations for their specific topics of focus. In this case, they mention frequencies of types of mitochondrial fusion events, but they fail to cite a landmark paper, namely, Liu et al., 2009, which is important in any assessment of the types of fusion events that exist3. Notably, one of the recent papers that they do cite here, Gatti et al., 2025, did not account for the importance of using PAGFP to monitor individual fusion events, particularly when attempting to score such events with diffraction-limited confocal microscopy, so this work was unable to distinguish between transient (i.e., "kiss-and-run") versus complete mitochondrial fusion types. It is worth carefully noting that it is intrinsically unfeasible to systematically quantify and categorize fusion events using conventional confocal microscopy (in the absence PAGFP), because it is unfeasible to know whether a genuine fusion event has actually occurred or whether the membranes are simply adjacent to one another, engaged in an inter-mitochondrial contact4. As it turns out, a good example of this intrinsically ambiguous situation is on display in Fig. 1c of Gatti et al., 2025. Given that transient fusion events3 can occur within as short a time as 4 s, the frequency calculation of mitochondrial fusion events from Gatti et al., 2025, using an indirect measure of 100 s as a "validated" criterion invariably missed the large category of fusion events described in the much earlier publication3. Thus, Gatti et al, 2025, should not be used as a reference for the frequency of different types of mitochondrial fusion, since it did not directly employ PAGFP in its frequency analyses of confocal time-lapse imaging, and thus it neither accounted for the existence of transient fusion events nor the unfeasibility of distinguishing between stable fusion and inter-mitochondrial contacts. In other words, the frequency measurements from Gatti et al, 2025, which rely on confocal microscopy alone, cannot possibly be reliable. *

      We are well aware of Liu et al. However, that paper does not address tip-to-tip versus tip-to-side fusion, instead focussing on transient versus stable fusion events. The concept of tip vs side fusion was established by the Voeltz lab and we got similar results. The two papers we cited are the ones usually cited when discussing this (see Preminger and Schuldiner (2024) PLoS Biology). We could add the reference to satisfy the reviewer, but this does not add anything to our manuscript.

      As for the PAGFP argument, most of the fusion events we counted in Gatti et al were tip to side, and we know from the PAGFP experiment we provided in that paper, that these represent stable fusion events. It is possible that transient fusion events (which we rarely observed) behave differently but it remains that a large fraction of fusion events occur tip-to side as originally shown by the Voeltz lab.

      *4. In the introduction, the authors say, "While the original study focussed [sic] on mitochondria tip elongation(10), more recent work included events arising from discrete protrusions on the side of mitochondrial tubules as part of dynamic tubulation events(10). Nevertheless, these pull-out events (previously reported in yeast (12)) were not fully described and their relationship to mitochondrial tip elongation (the original dynamic tubulation definition(10)) remained unclear." It is concerning that, even though the authors are aware of Wang et al., 2015, and Qin et al., 2020, they do not accurately represent what is inside these published articles. As indicated above, both papers provide examples of dynamic tubulation events originating from the sides of mitochondria, thus these particular events were also represented within the data sets of these papers. For this reason, it is concerning thatthe manuscript introduces the term "pull-outs" for events that appear similar to those described previously.. This section of the introduction should be removed or heavily edited to provide an accurate representation of the manner in which prior publications accounted for and defined these phenomena. *

      As stated above, we reworked the introduction and yes, we missed the pull-out present in a supplementary figure of Wang et al. We do not think however that we did not properly represent what is inside these published articles: while the two types of dynamic tubulation are shown in these papers, they are lumped together in the analysis as if they are all the same thing. We used the terms pull-out and tip elongation to describe the two types of dynamic tubulation and address their differences.

      The introduction has nevertheless been reworked to clarify this.

      *5. Likewise, the last paragraph of the introduction needs to be removed or rewritten to provide an accurate and balanced framing of where the current study sits within the context of the existing literature. This necessitates giving an accurate account of prior publications and not treating "pull-outs" as if this were a novelty, which is patently untrue. *

      We did not intend to claim that pull-outs are a novelty, and we indeed cite the original publication stating the name “pull-out”. If the reviewer refers to the use of the term distinct (R3 Point 2), it is there to state that pull-outs are different from tip elongation, not that they are a novel mitochondrial behaviour. Beyond the semantics, we do not think that there anything in that paragraph that is not supported by data.

      *6. The images in Figure 1A appear quite artifactual. What is a "schematic representation," according to the authors? Is this a cartoon or a micrograph? The authors must specify. For example, the tip of the mitochondrion in the top left box appears like it has been cut with a straight edge. This is the first time I have seen a mitochondrion that, instead of having a rounded end, looks completely flat. This is the same for the so-called "pull-out" part of the mitochondrion in the lower right box. How is it possible that the very end of the mitochondrion is at a right angle? What's more, the mitochondria in these images are so saturated, that it is impossible to see anything resembling their natural contours. The authors present only two frames, as well, so it is impossible to assess the tubulation in any meaningfully dynamic way. *

      As stated in the figure legend, this is a schematic representation, a drawing to help the reader understand the concept that pull-out and tip elongation are two types of dynamic tubulation.

      7. The authors say, "Pull-outs, but not tip elongation, result in the formation of branched mitochondria that are typical of hyperfused networks associated with cell survival and increased bioenergetics."[first paragraph of result section] On what basis could the authors possibly make this claim? If either a tip elongation or "pull-out" led to fusion with another mitochondrion, there is a strong chance that this fusion event would lead to a branched, rather than linear, mitochondrial morphology. This sentence should be removed unless it is actually supported by rigorous data and analysis.

      The process of pull-out itself creates a three-way junction, a branching point. As the reviewer points out, any subsequent fusion would also create a junction. As a pull-out that fuses creates 2 junctions while a tip elongation that fuses only creates 1, this still means that pull-outs create more junctions than tip elongation. As the sentence could nevertheless be somewhat misleading, we modified it to “Pull-outs, but not tip elongation, result in the formation of a new mitochondrial branch, increasing mitochondrial connectivity which is typical of hyperfused networks associated with cell survival and increased bioenergetics.”

      8. The authors say, "Thus, these two modes of dynamic tubulation affect mitochondrial network topology in distinct manners and could thus have distinct roles and regulation. We thus set out to characterise mitochondrial pull-outs." The evidence presented up to this point does not provide a sufficiently strong basis for the subsequent analyses. The conclusions rely primarily on a limited number of images without quantitative support, making it difficult to justify the proposed framework for the experiments that follow.

      This is still the first introductory paragraph of the result section. We are not claiming anything at this point, only stating that this is a question that we think is worth pursuing, hence the characterisation of pull-outs. We think that our subsequent data support the idea that these experiments were worth doing.

      9. It is not clear what Figure 1C is supposed to represent, based on the ambiguous description in the legend or the y axis. The main text suggests that you are trying to quantify relative pull-out frequency. This has nothing to do with being OMM-IMM-positive, per se. That is not the point. What is the frequency of tubulation from the tip versus the side?

      This was indeed unclear. This graph shows the quantification of the proportion of pull-outs (per cell) that are positive for the outer membrane marker mCherry-Fis1 and the matrix marker CCO-GFP. This has been clarified in the text and the figure legend.

      10. Figure 1D compares the frequency of pull-outs to mitochondrial fusion and fission events. This is not really a meaningful comparison. What is the point of doing a statistical analysis when comparing different categories of phenomena? A relevant comparison would be the frequency of tubulation from the tip versus the side. Where is that, along with statistics?

      The main point of the panel was to show that pull-outs are frequent, not necessarily that that they are more frequent than fusion and fission, but this nonetheless provides a point of reference for the reader.

      We did not show data for tip elongation in Figure 1 as this figure is about characterizing pull-outs. However, we did analyse pull-outs in relation to tip elongation in the starvation experiments in Figure 2. Overall, pull-outs tend to be somewhat more frequent than tip elongation in control cells, although the difference is more pronounced in primary fibroblasts than U2OS cells. Importantly, starvation significantly increases the number of pull-outs relative to tip elongation, supporting the conclusion that tip elongation and pull-outs are distinct types of dynamic tubulation.

      *11. In Figures 1E-G, the authors present data involving fusion events. It is critical that the authors appreciate that it is unfeasible to score mitochondrial fusion events using diffraction-limited confocal microscopy alone, because as the membranes converge, it is impossible to visualize what is happening below around 250 nm with any confidence. This is the reason that you need to employ PAGFP and/or super-resolution microscopy to be able to minimize both false-positives and false-negatives. Unfortunately, the authors did not address this limitation in their previous work and the same issue remains in the current manuscript. All data regarding fusion that is not using PAGFP or super-resolution to evaluate each specific event should be discarded, because it will inevitably lead to erroneous measurements. *

      As stated above, while we acknowledge the caveats of our approach, the fusion events that we count as fusion events (100 sec of apparent fusion) are almost certainly actual fusion events (94% accuracy in Gatti et al.). All this means is that we might underestimate the number of pull-outs and tip elongation that lead to fusion. Also, fusion resulting from tip elongation or pull-out is much more frequent than from any random end, using the same criteria.

      *12. The images in Figure 1G appear to be taken somehow from Figure 1A. As mentioned above, it is not clear whether these images represent microscopy data or schematic illustrations. This information should be made explicit in the figure itself, rather than requiring the reader to infer it or locate it in the Methods section. It is essential that the authors of this manuscript attempt to be more thoughtful about providing key information in the figures that clarify obvious questions, such as, Is this a cartoon or not? What type of microscope was used? Etc. *

      It is indeed a drawing at the top of the table in Figure 1G. This has been clarified in the figure legend. The type of microscope that was used is the same for most experiments and is stated in the method section.

      13. In all data, the authors need to report the number of events analyzed, not just the number of cells and independent experiments.

      All pull-out/fission/fusion graphs report the number of events as the data in the graph. Total numbers are also found in the tables. For connectivity and mitochondrial area, each point represents a cell but there are no “events” as such. For the few graphs where % were shown, the average number of pull-outs/cell was added to the figure legend.

      14. The data in Figure 2, relating to mitochondrial morphology, length, etc. need to reflect direct measurements and quantifications. For example, the units in the length measurement in 2B are opaque. It is insufficient to base conclusions upon a probabilistic algorithm, where the authors lose contact with the ground truth of the images. This should be re-analyzed in a standard, direct way.

      Out method for calculating mitochondrial length was thoroughly validated in our original publication (Ouellet et al (2017) PLoS Comp Biol). The quantification reflects our raw images (see Figure 2A) In any case, length is not the key point, but connectivity is. Our readout for connectivity is the ratio of branch points to mitochondrial tips, which has also been validated in Ouellet et al. We could do a branch length analysis in Fiji if this is what the reviewer is alluding to, but we do not think that this would bring anything new to our manuscript.

      15. All the data in Figure 2 relating to purported fusion events must be removed, because it derives from the unacceptable use of diffraction-limited microscopy without PAGFP. I have already explained why these data are insufficient.

      We have already addressed this concern this above.

      16. Again: the data in Figure 3, relating to mitochondrial fusion should be removed and redone with proper methodologies.

      See above

      16. In Figure 4B, it is not enough to show an immunoblot and conclude that there was no change. This should be quantified over at least 3 experiments. Also, using the total OXPHOS antibody cocktail is not adequate for making a broad claim about mitochondrial mass, particularly when it is contradicted by the data in the previous panel. It is important to note that a larger mitochondrial area would likely correspond to an increased mitochondrial mass. The authors should note that proteins are not the only feature of the organelle that possesses mass. Therefore, even if all the mass of all the proteins remained equal among the different treatments, this does not account for changes in the mass of other components, such as membranes, etc. The authors must include images along with their quantifications in Figure 4A. Also, they should clarify how they have accurately measured mitochondrial area, particularly in the context of a more branched mitochondrial reticulum, which leads to difficulties in segmentation.

      The western blot is now quantified in New Figure 4C. The images are in Figure 2A.

      As the reviewer states, it is difficult to accurately measure mitochondrial area/length from the images as they are crowded in the perinuclear area. It is also unlikely that there would be a significant change in mitochondrial mass within the short frame of the experiment (4 hours), especially at the level of protein content. The most likely explanation for the discrepancy is not that the western blot contradicts the area measurements, but rather than the area measurements did not tell the full story. We think that, consistent with dynamic tubulation, mitochondria stretch in response to starvation. We are currently looking at this using Lightning super-resolution and will add the data to the final manuscript. See also R1 Point 2.

      17. I would recommend that the authors refrain from using simulations, as they do not provide direct measurements, which are essential, and they rely on oversimplifications of complex cellular behavior, which make their conclusions not convincing.

      We think that modelling of biological systems is an important field of research that has provided valuable insights into complex biological processes. We do not think that we should remove this data from the manuscript. The stimulation is not meant to provide experimental data but rather to show that our data is consistent with an increase in diffusibility.

      18. For Figure 5, the authors say, "Strikingly, 95% of pull-out events were localized at ER-mitochondria interfaces (Figure 5B), suggesting that these events occur at ERMCS." This observation is less compelling than suggested, given the widespread distribution of the ER throughout the cell.. It would be more striking if the tubulations happened 95% of the time where there was no ER at all. To provide clarity, the authors should perform this analysis exclusively on mitochondria in the periphery of the cell, where the ER is not so super-abundant. Also, it is important to note that Qin et al., 2020, already defined the relationship between tubulation and ER. To be transparent, the authors should reference their work, which undoubtedly encompassed what they have here re-branded as "pull-outs."

      We will redo the imaging and analysis as suggested by Reviewer 4 (R4 Point 3). We would also like to reiterate that we did not re-brand anything as 1) the term pull-out was already in use and 2) we are studying a subset of dynamic tubulation events, which also include tip elongation.

      19. Assuming that the images in Figure 5 were obtained with Airyscan, examining fusion should be feasible. However, despite their quantifications of fusion in Figure 5D and F, they provide no examples of it, which is not acceptable. Moreover, the data should not be normalized in this fashion. It is important to provide the raw frequencies, with respect to mitochondrial area and time. I would also note that 6 s time intervals is too long to follow these events accurately. One or 2 s intervals would be optimal.

      The key message here (as with the rest of the manuscript) is about pull-outs, not fusion. This is why we did not include examples of fusion and fission in the figure. However, as requested by the reviewer, we will provide this data in the revised version.

      In terms of quantification, as explained to Reviewer 2 (Point 2), we used the relative signal intensity because the determination of recruitment in a yes/no manner was challenging, especially for MFN2 that is already on mitochondria. We nevertheless added this quantification in New Figure 5E.

      20. In Figure 7A, it is not surprising that most pull-out events were marked by the AC-mito signal, because the AC-mito signal appears almost ubiquitously across the whole contour of the organelle. You could just as well inquire what is the frequency of the mCherry-Fis1 signal at pull-out sights. It would be better to label the general mitochondrial population in this experiment with a dye like MitoTracker, so that there is a generalized label, which has nothing to do with a specific protein, or its targeting sequence.

      We will refer the reviewer to the original publication on AC probes and their validation (Schiavon (2020) Nat Methods). The concept of the AC probe is that it clusters where actin is present on mitochondria. mCherry-fis1 is used as a control as it has the same transmembrane domain but fails to cluster around actin. Mitotracker is not the proper control for this experiment as it is not on the outer membrane and cannot take into account diffusion along the outer membrane.

      21. In Figure 7C, the authors are not accounting for variables that affect the accuracy of the frequencies that they are reporting. It is not enough to quantify events per cell. Indeed it is not even clear what this means. Did they authors really examine the whole cell for each analysis or was it an ROI within the cell? One way or the other, it is essential that the authors are obtaining frequencies where there is the same amount of mitochondria over the same amount of time. If one cell has twice as many mitochondria, for example, then there will be twice as many chances, on average, to find a tubulation event. There is no indication here that the authors have accounted for this key variable anywhere in this manuscript. If so, it must be clearly written in the y axis as "events/micron^2/s."

      As stated in the graphs and figure legends, we counted all the events occurring within each cell. We did not normalise to mitochondrial area due to the limitations stated above (R3 Point 16). However, as requested by the reviewer, we will also provide the data normalised to mitochondrial area in the new version of the manuscript.

      22. The Discussion is overly long and should be revised in light of the comments above. In particular, any proposed new terminology or conceptual distinction should be carefully justified and discussed in the context of the existing literature.. Any potential novelty in this work, such as the role of mitochondrial actin in tubulation, must be contextualized with respect to the existing literature, rather than a frontier that the authors have re-drawn to make it seem as if they were exploring uncharted territory.

      The discussion will be reworked in the light of the new data and all reviewers’ comments, but we are unsure of what would constitute a proper length for a discussion, as it is currently only just over 1.5 pages long.

      23. Lastly, the term "pull-out" is poorly chosen, for different reasons. Perhaps foremost among these, it implies that a mitochondrial tubule is physically pulled out from a pre-existing mitochondrion, yet the authors do not demonstrate a pulling force, identify a force-generating mechanism, or exclude alternative explanations such as local membrane expansion, pushing, or mitochondrial movement. Thus, the terminology prematurely converts an observed morphology into a mechanistic process. A term that does not presuppose mechanistic insight, such as "lateral mitochondrial tubulation," would be more appropriate.

      The main claim of the reviewer is that our study should not be published because we are studying pull-outs, a type of dynamic tubulation that is already known. The reviewer should thus agree with us that these tubulation events occur because membranes are pulled out of an existing tubule with the help of Miro and Kif5. It should also be noted that this is the term originally used to describe these events in yeast (Osman et al, (2012) PNAS).

      As suggested by Reviewer 4 (Point 1), we will nevertheless provide data for Miro knockdown that should address this point.

      __Minor comments: __

      *24. In the title "events" should be "event" *

      Changed

      25. There are various typographic and grammatical errors that should be addressed.

      We will thoroughly revise the text.

      __**Referees cross-commenting** __

      __*This section contains comments from different reviewers* __

      __*Reviewer 3* __

      *I note that referees 1, 2, and 4 do not refer to the limitations of attempting to quantify mitochondrial fusion events with diffraction-limited microscopy. In my opinion, any study purporting to investigate mechanisms of mitochondrial fusion need not only to discuss PAGFP and super-resolution imaging techniques but actually employ them in their experiments, as well. *

      __*Reviewer 1* __

      *Reviewer 3 has raised some important points that should be carefully considered by the authors and editors. However, I do not share his/her view that super-resolution microscopy and PAGFP are mandatory tools for studying mitochondrial fusion. Having worked in the field of mitochondrial dynamics for almost 30 years, I have witnessed groundbreaking discoveries on mitochondrial fusion that were made long before either super-resolution microscopy or PAGFP became available. I continue to believe that mitochondrial network dynamics can be studied effectively using conventional microscopy. Nevertheless, the apparent discrepancies in the data presented by Kasmaie et al. should be resolved (see, for example, my point 1). *

      __*Reviewer 3* __

      *It is unclear how one could hope to accurately quantify rates of individual fusion events using standard diffraction-limited microscopy without PAGFP or some variant of it. The reason for this is that it is impossible to distinguish between mitochondria that are simply touching and next to each other and ones that have truly undergone fusion. This is particularly complicated when one considers transient or "kiss-and-run" fusion events. While it is not impossible to see a fusion a event with standard confocal microscopy, it is quite unfeasible to obtain an accurate frequency of fusion events, under different conditions and experimental groups, as this manuscript was purporting to do, unless one uses appropriate tools, such as PAGFP, to enable one to definitively say whether a fusion event has occurred.. I am fairly certain that this reasoning is sound. Of course, I do not mean to be dismissive, if I am in error. Perhaps there is another tool that has escaped my notice. A PEG assay might work, but it is quite unnatural, and it is not usually employed for looking at individual fusion events but rather whole mitochondrial network fusion capacity. *

      __*Reviewer 4* __

      *While I agree that additional validation using complementary methods could strengthen some of the conclusions, I also do not share the view that these tools are necessary for the scope of this work. Fusion dynamics are not the primary focus of this study, and the authors present important findings using well-established methodologies that, in my opinion, will advance the field and make the manuscript suitable for publication. *

      __*Reviewer 3* __

      *Reviewer #4 indicates that "Fusion dynamics are not the primary focus of this study. . . ." Nevertheless, it is clear that this manuscript focuses on the role of "pull-outs" in mediating increased mitochondrial interconnectivity. It is clear that "interconnectivity," in the context of this study, refers to mitochondrial fusion. The abstract quite clearly emphasizes mitochondrial fusion and the interconnection of mitochondrial networks. Moreover, 5 out of 7 of the main figures contain panels that purport to directly measure individual fusion events, as a method of evaluating certain conclusions as to "pull-outs." Therefore, it does not seem accurate to conclude that "Fusion dynamics are not the primary focus of this study. . . ." I am not convinced that the frequency of individual fusion events can be quantified using diffraction-limited microscopy without PAGFP (or comparable variant).. Inasmuch it is impossible to physically resolve the mitochondrial membranes, and mitochondria frequently touch without undergoing fusion, and they also engage in rapid transient fusion events, without the aid of a probe that unambiguously reports on fusion, it is indeed a matter of great uncertainty whether a fusion event may or may not have occurred. *

      __*Reviewer 1* __

      *I think it is clear by now that PAGFP and, with some limitations, super-resolution microscopy are superior methods for studying individual mitochondrial fusion events. However, transient 'kiss-and-run' type interactions do not result in an overall change in network topology. Increased network interconnectivity is driven by bona fide fusion events, which can be assessed using conventional microscopy. The authors may wish to elaborate on this point in the discussion section. *

      It should perhaps be acknowledged that opinions on these methodological issues may differ

      Our response:

      In response to the various comments in the cross-commenting section, Reviewer 4 is right that “Fusion dynamics are not the primary focus of this study”. We are not “purporting to investigate mechanisms of mitochondrial fusion” as suggested by Reviewer 3 and contrary to what the reviewer seems to assume, it is not “clear that "interconnectivity," in the context of this study, refers to mitochondrial fusion”. Stable pull-outs increase connectivity (the number of junctions and branches) without fusion.

      We have previously shown (Gatti et al.) that our criterion for fusion (apparent fusion lasting 100 sec) accurately identifies fusion events. It is possible, as suggested by Reviewer 3, that some transient events are missed by this analysis, which we will acknowledge in the revised manuscript, but this will not change the conclusion that pull-outs are metabolically regulated. In addition, the fact that PAGFP diffuses at different rates in control and starved cells and the fact that it cannot identify all fusion events within a cell greatly limits its use in the current manuscript.

      As stated above, we can provide data showing that PAGFP-positive fusion is increased in starved cells if the editor thinks that it is required.

      __Reviewer #3 (Significance (Required)): __

      *This study provides, at best, only an incremental advance in the field of mitochondrial dynamics and membrane remodeling. Generally, there is a significant concern that it does not appropriately contextualize the aims and conclusions of the research with respect to previous publications, which laid the foundations for the phenomenon of dynamic tubulation. Moreover, although this study attempts to quantify mitochondrial fusion events in relation to dynamic tubulation, the reliance on diffraction-limited confocal microscopy is not suited for investigations of mitochondrial fusion. While there is some indication that mitochondrial actin may be important for the formation of these lateral tubulations, significant additional work will need to be performed to support this conclusion. *

      *Background of referee: *

      *I have many years of experience in mitochondrial research, including imaging mitochondria, from confocal microscopy with PAGFP to various super-resolution imaging techniques. *

      We have reworked the introduction and will rework the discussion considering our new data and the comments of the 4 reviewers. In our opinion, disagreement about how the study should be introduces should not be ground to reject the paper if the science is sound (the reviewer had only a few data-related comments that were also raised by the other reviewers and have been or will be addressed in the revised manuscript).

      And, lastly, we are not studying fusion here. We are showing that a specific form of dynamic tubulation (pull-outs) is regulated by nutrient availability. Fusion and fission are used as comparison points since they are better known and understood than dynamic tubulation.

      __Reviewer #4 (Evidence, reproducibility and clarity (Required)): __

      __Summary: __

      Kasmaie et al. demonstrate a mechanism of mitochondrial shaping by tubule pullout. The authors propose this mechanism and its regulation is distinct from previously described dynamic tubulation processes, and propose the involvement of mitochondrial dynamins and actin in pullout events.

      Before acceptance, it will be important to address the points below to ensure the conclusions are fully supported and accurate.

      __Major comments: __

      *1. Throughout the manuscript, the authors distinguish between the mechanism described by Qin et al. (2020) and Wang et al. (2015) and the pullout mechanism described here. However, I do not believe this distinction is entirely proven, and consequently the proposed knowledge gap is not well defined. In Qin et al. (2020), the term dynamic tubulation encompasses both tip elongation and pullout/branching events. Therefore, how mitochondria can create the 3-way junction morphology does not appear to represent the primary distinction between the two processes or the relevant knowledge gap. In contrast, as the authors note, Osman et al (2015) describe an actin-dependent form of tubulation whose mechanism remains unresolved, a more appropriate knowledge gap to emphasize. Alternatively, the authors could highlight the limited understanding of tubulation beyond the cell periphery. *

      Furthermore, the original dynamic tubulation studies thoroughly characterized the roles of KIF5B, Miro1, and microtubules. Since pullout events are consistently compared throughout the manuscript with dynamic tubulation/tip elongation events and are proposed as mechanistically distinct, it is required to show what is the dependency of pullouts on these factors as well.

      *Another option to strengthen the distinction would be the propensity of pullouts in the cell periphery compared to perinuclear regions, as dynamic tubulation was shown to happen exclusively in the periphery. *

      We agree with the reviewer that we did not properly frame our study. We have now reworked the introduction to better explain that pull-outs are a type of dynamic tubulation is regulated in a manner distinct from tip elongation. We will also thoroughly revise the discussion once we have all the new data within the manuscript and take into consideration the pertinent suggestions that the reviewer made.

      As suggested by the reviewer, we will also test the effect of Miro knockdown on pull-outs and assess whether there is a particular spatial distribution of pull-outs.

      2. The extensive use of different cell lines throughout the manuscript is concerning. At least four distinct cell lines, including lines from different organisms, are used across the study, with the cell line changing between figures and, in some cases, even within the same figure. On the one hand it is reassuring to see that the findings seem sto be independent of cell iine and this is very good. On the other, this makes it difficult to draw a coherent mechanistic framework, especially since conclusions based on one cell line are later used to support conclusions drawn from experiments in another.

      Most of the experiments were done in primary fibroblasts with some in MEFs or U2OS cells, the latter because they are easier to transfect with siRNA. We had tried to set up the experiments so that there was an overlap between the cell lines, but we agree that this was not the case for the U2OS in the second part of the manuscript. To bridge this gap, we have repeated the starvation experiments in U2OS and found similar results as for MEFs and primary fibroblasts (New Supplemental Figure 2). As repeating all the U2OS experiments in primary fibroblasts (or the reverse) would be time consuming and would not provide additional insights, we think that this provides a good compromise.

      3. While the results regarding the involvement of the ER, DRP1, MFN1/2, and actin are important for the characterization of pullouts and suggest an interesting and distinct mechanism, they require better controls to fully support the stated claims.

      *For instance, regarding the ER-positive pullout sites - could this observation simply reflect the overall extent of ER-mitochondria contact? If a large fraction of mitochondrial area is overlapping with ER in 2D microscopy, it would be expected that nearly all pullouts would also occur at ER-positive regions. A useful control would be to quantify the fraction of total mitochondrial area associated with the ER, or to perform a random-overlap measurement, as described by Abrisch et al. (JCB, 2020), by rotating one image 90{degree sign} with respect to the other image and measuring random overlap. The same measurement (% of X-positive pullouts) and controls should be done for the other components - DRP1, MFN1/2 and actin. *

      *More generally, the current data demonstrate localization of these factors at pullout sites and the effects of their depletion on pullouts, but do not establish their direct mechanistic involvement. Given the broad roles of these factors and their effect on a variety of mitochondrial and cellular processes, additional evidence is required to support such a conclusion. Until then, the claims regarding their role in pullout events should be toned down. *

      We will redo the imaging and analysis. First, we will use a better ER construct that we have (mCherry-Cytb5) and reimage using Lightning super resolution on our new Leica Stellaris microscope, which will greatly improve image quality. We will also use images turned 90 degrees as a negative control for the colocalization analysis (see also R1 Point 3 and R3 Point 18).

      As mentioned in R1 Significance section and in response to R2 Minor comment #5, we have added data showing that pull-outs that fuse have higher MFN2 intensity (New Figure 5H) and that retracting pull-outs are short-lived and shorter in length (New Supplemental Figure 1C). We will analyse our actin data in Figure 7 in the same way and determine whether MFN1 activates/oligomerizes in response to starvation (for which we have some preliminary data).

      4. "These results indicate that the mitochondrial ends generated by dynamic tubulation, including pull-outs, are selectively used for future fusion events."

      *It is not clear how the data support this conclusion. Figure 1G shows 42% of fusion events are of pullout tips - a substantial fraction, but not the majority. Based on this, the probability of fusion at pullout ends does not appear to differ substantially from that at non-pullout ends. If the authors instead mean that pullout tips are more likely to undergo fusion than fission, then the statements should be corrected accordingly, including the legend of Figure 1G and the discussion. *

      What we meant is that most fusion events occurred following tip elongation or a pull-out, both forms of dynamic tubulation. This behaviour was not exclusive to pull-outs. The text was not very clear and the table in __Figure 1G__mentioned pull-outs and dynamic tubulation, not tip elongation. This has now been clarified in the text and the figure. We think that the sentence mentioned by the reviewer is correct given the clarification that this also include tip elongation.

      *5. Figure 4C-D should clearly indicate that the presented data and the associated conclusions are based on model predictions rather than experimental data. This is currently not made sufficiently clear in the figure panels, title, or legend. *

      *In addition, the presentation of these panels is unusual. While the axes are rendered at high resolution, the plotted data appear to be low-resolution images. The authors should provide the original high-resolution graphs. *

      We have clarified this in the figure legend. The resolution of the panels was also fixed.

      *6. The authors claim that pullouts are more frequent than fission/fusion. The tracking of the events is biased in that sense, since the criteria for a fission/fusion event to be counted require it to be stable, whereas pullouts are counted even if they are short-lived and retract, likely inflating pullout counts relative to fusion and fission. Moreover, they are counted using a cell ine potentially sub-optimal in its dynamics due to tagging of Fis1. *

      Our main point here was only to show that pull-outs are frequent, with the comparison with fission and fusion providing a baseline that is known to the readers. Thus, we quantified mitochondrial dynamics relative to total events to address the overall frequencies. This is why retracting pull-out are included here. We nevertheless agree that fast retracting pull-outs will not have the same effect on overall network structure. We will add a figure separating stable pull-outs from retracting ones along with individual numbers for fission and fusion (which are pooled in Figure 1D).

      Concerning the statement on Fis1, we are not sure of what the reviewer means. In the majority of the experiments, we use mitotracker to label mitochondria, thus Fis1 is not tagged. The reviewer might be referring to our mCherry-Fis1 construct that was used in a few experiments (Figure 1B-C, actin experiments in Figure 7)? This construct only contains the transmembrane domain of Fis1 at the c-terminus of mCherry. This construct does not affect mitochondrial structure. This has been clarified in the result section.

      __Minor comments: __

      *1. Please provide quantification for the Western blot shown in Figure 4B and indicate whether the experiment was performed in triplicates. *

      Now in New Figure 4C.

      2. The authors show an increase in pullouts and fusion under galactose and glucose-starvation conditions, while tip elongation and fission remain unchanged. Frequencies are reported per cell, and not normalized to mitochondrial length. The authors show that mitochondrial area is higher in these conditions, and higher counts of these events could rise simply due to a larger network. Normalizing event counts to mitochondrial length or area during the metabolic manipulations would directly test whether pullouts and fusion are truly upregulated per network unit.

      We will normalise data to mitochondrial length.

      3. The authors could consider including a schematic model summarizing the proposed mechanism.

      We will add a schematic model as suggested by the reviewer.

      4. Please include timestamps in the videos and indicate the scale bar size in all microscopy panels. Please specify in the figure legends the time intervals over which the measurements were collected.

      We will add the appropriate information

      5. Why does Arp2/3 inhibition have no apparent effect under normal conditions, whereas DeAct does? Why was DeAct not measured under glucose starvation? This should be addressed.

      First, it is not surprising that DeAct would have a stronger phenotype that CK666 as the former removes all mitochondrial actin while the latter only affect Arp2/3-dependent actin. As for why Arp2/3 inhibition would not have an effect in control cells, we think that it has to do with the amount and stability of actin on mitochondria. We have some preliminary data suggesting that there is less actin on glucose-starved which could potentiate the effect of CK666. As actin is not the main focus of this manuscript, we wanted to keep the data for another story.

      We will add the DeAct glucose starvation data to the manuscript.

      *6. If I understand correctly, the mCherry-Fis1 construct contains only the targeting sequence (tail anchor) of Fis1. If so, please clarify that in the text. *

      Yes, it is the case. It has been clarified.

      *7. Please clarify the rationale for using yeast mitochondria parameters for the simulations, particularly when these are combined with parameters obtained from experimental data in mammalian cells. *

      This is the model that was available to us. It should not affect our conclusions.

      *8. The manuscript would benefit from addressing physical properties of the tubules that might reflect mechanical requirements for pullouts. It would be informative to compare tubule diameters across the different event types. This parameter was analyzed in the original dynamic tubulation studies and would provide an additional point of comparison between the mechanisms described here. *

      This is a good point. We will measure pull-out and tip elongation diameters using Lightning super-resolution.

      __Minor textual comments (no need to respond in the point-by-point letter): __

      *1. The first results section is missing a section title. *

      Added

      *2. References to the panels in Figure 1 appear in the wrong order in the text. The same for Video 1 and 2. *

      Indeed it was. This has been fixed.

      *3. Supplementary Figure 1 appears to be mislabeled. I assume that panels A-B depict pullouts, whereas panels C-D depict tip elongation. Additionally, please clarify what the blue regions represent. *

      Yes it was. This has been fixed.

      *4. For consistency, consider replacing "DMEM" with "Ctrl" in all relevant panels. *

      We will make the necessary adjustments.

      *5. Consider replacing "Energy production" (in Abstract and introduction) with "Energy conversion" which is the correct term (Energy is not produced at any point in cells). *

      This has been changed

      __Reviewer #4 (Significance (Required)): __

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      Referee #4

      Evidence, reproducibility and clarity

      Summary:

      Kasmaie et al. demonstrate a mechanism of mitochondrial shaping by tubule pullout. The authors propose this mechanism and its regulation is distinct from previously described dynamic tubulation processes, and propose the involvement of mitochondrial dynamins and actin in pullout events. Before acceptance, it will be important to address the points below to ensure the conclusions are fully supported and accurate.

      Major comments:

      1. Throughout the manuscript, the authors distinguish between the mechanism described by Qin et al. (2020) and Wang et al. (2015) and the pullout mechanism described here. However, I do not believe this distinction is entirely proven, and consequently the proposed knowledge gap is not well defined. In Qin et al. (2020), the term dynamic tubulation encompasses both tip elongation and pullout/branching events. Therefore, how mitochondria can create the 3-way junction morphology does not appear to represent the primary distinction between the two processes or the relevant knowledge gap. In contrast, as the authors note, Osman et al (2015) describe an actin-dependent form of tubulation whose mechanism remains unresolved, a more appropriate knowledge gap to emphasize. Alternatively, the authors could highlight the limited understanding of tubulation beyond the cell periphery. Furthermore, the original dynamic tubulation studies thoroughly characterized the roles of KIF5B, Miro1, and microtubules. Since pullout events are consistently compared throughout the manuscript with dynamic tubulation/tip elongation events and are proposed as mechanistically distinct, it is required to show what is the dependency of pullouts on these factors as well. Another option to strengthen the distinction would be the propensity of pullouts in the cell periphery compared to perinuclear regions, as dynamic tubulation was shown to happen exclusively in the periphery.
      2. The extensive use of different cell lines throughout the manuscript is concerning. At least four distinct cell lines, including lines from different organisms, are used across the study, with the cell line changing between figures and, in some cases, even within the same figure. On the one hand it is reassuring to see that the findings seem sto be independent of cell iine and this is very good. On the other, this makes it difficult to draw a coherent mechanistic framework, especially since conclusions based on one cell line are later used to support conclusions drawn from experiments in another.
      3. While the results regarding the involvement of the ER, DRP1, MFN1/2, and actin are important for the characterization of pullouts and suggest an interesting and distinct mechanism, they require better controls to fully support the stated claims. For instance, regarding the ER-positive pullout sites - could this observation simply reflect the overall extent of ER-mitochondria contact? If a large fraction of mitochondrial area is overlapping with ER in 2D microscopy, it would be expected that nearly all pullouts would also occur at ER-positive regions. A useful control would be to quantify the fraction of total mitochondrial area associated with the ER, or to perform a random-overlap measurement, as described by Abrisch et al. (JCB, 2020), by rotating one image 90{degree sign} with respect to the other image and measuring random overlap. The same measurement (% of X-positive pullouts) and controls should be done for the other components - DRP1, MFN1/2 and actin. More generally, the current data demonstrate localization of these factors at pullout sites and the effects of their depletion on pullouts, but do not establish their direct mechanistic involvement. Given the broad roles of these factors and their effect on a variety of mitochondrial and cellular processes, additional evidence is required to support such a conclusion. Until then, the claims regarding their role in pullout events should be toned down.
      4. "These results indicate that the mitochondrial ends generated by dynamic tubulation, including pull-outs, are selectively used for future fusion events." It is not clear how the data support this conclusion. Figure 1G shows 42% of fusion events are of pullout tips - a substantial fraction, but not the majority. Based on this, the probability of fusion at pullout ends does not appear to differ substantially from that at non-pullout ends. If the authors instead mean that pullout tips are more likely to undergo fusion than fission, then the statements should be corrected accordingly, including the legend of Figure 1G and the discussion.
      5. Figure 4C-D should clearly indicate that the presented data and the associated conclusions are based on model predictions rather than experimental data. This is currently not made sufficiently clear in the figure panels, title, or legend. In addition, the presentation of these panels is unusual. While the axes are rendered at high resolution, the plotted data appear to be low-resolution images. The authors should provide the original high-resolution graphs.
      6. The authors claim that pullouts are more frequent than fission/fusion. The tracking of the events is biased in that sense, since the criteria for a fission/fusion event to be counted require it to be stable, whereas pullouts are counted even if they are short-lived and retract, likely inflating pullout counts relative to fusion and fission. Moreover, they are counted using a cell ine potentially sub-optimal in its dynamics due to tagging of Fis1.

      Minor comments:

      1. Please provide quantification for the Western blot shown in Figure 4B and indicate whether the experiment was performed in triplicates.
      2. The authors show an increase in pullouts and fusion under galactose and glucose-starvation conditions, while tip elongation and fission remain unchanged. Frequencies are reported per cell, and not normalized to mitochondrial length. The authors show that mitochondrial area is higher in these conditions, and higher counts of these events could rise simply due to a larger network. Normalizing event counts to mitochondrial length or area during the metabolic manipulations would directly test whether pullouts and fusion are truly upregulated per network unit.
      3. The authors could consider including a schematic model summarizing the proposed mechanism.
      4. Please include timestamps in the videos and indicate the scale bar size in all microscopy panels. Please specify in the figure legends the time intervals over which the measurements were collected.
      5. Why does Arp2/3 inhibition have no apparent effect under normal conditions, whereas DeAct does? Why was DeAct not measured under glucose starvation? This should be addressed.
      6. If I understand correctly, the mCherry-Fis1 construct contains only the targeting sequence (tail anchor) of Fis1. If so, please clarify that in the text.
      7. Please clarify the rationale for using yeast mitochondria parameters for the simulations, particularly when these are combined with parameters obtained from experimental data in mammalian cells.
      8. The manuscript would benefit from addressing physical properties of the tubules that might reflect mechanical requirements for pullouts. It would be informative to compare tubule diameters across the different event types. This parameter was analyzed in the original dynamic tubulation studies and would provide an additional point of comparison between the mechanisms described here.

      Minor textual comments (no need to respond in the point-by-point letter):

      1. The first results section is missing a section title.
      2. References to the panels in Figure 1 appear in the wrong order in the text. The same for Video 1 and 2.
      3. Supplementary Figure 1 appears to be mislabeled. I assume that panels A-B depict pullouts, whereas panels C-D depict tip elongation. Additionally, please clarify what the blue regions represent.
      4. For consistency, consider replacing "DMEM" with "Ctrl" in all relevant panels.
      5. Consider replacing "Energy production" (in Abstract and introduction) with "Energy conversion" which is the correct term (Energy is not produced at any point in cells).

      Significance

      This study addresses an understudied yet highly important aspect of mitochondrial shaping, further expanding our understanding of mitochondrial tubulation, its different forms and their functional context.

      The discovery of a new shaping mechanism for mitochondria is of great interest to the cell biology community specifically and the biology community at large, and hence I strongly recommend the publication of this manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      Kasmaie et al. present a manuscript in which they claim to have identified a new form of mitochondrial dynamic tubulation. A major concern is that this kind of dynamic tubulation has already been described by other authors--up to 11 years ago. Although the authors of this manuscript cite prior publications, they do not adequately contextualize their own work in relation to what has been shown previously by others in peer-reviewed publications.The manuscript presents this phenomenon as a distinct entity, although similar observations have previously been reported.. Moreover, throughout this work there is a disregard for employing appropriate methodologies for the visualization and quantification of mitochondrial fusion events. Diffraction-limited confocal microscopy is not an appropiate method for the assessment of mitochondrial fusion. Apparent stable continuity between diffraction-limited mitochondrial signals is by no means the same as mitochondrial fusion, thus the fusion-frequency measurements cannot support the authors' conclusions. This concern is especially important because the same methodological assumption underlies recent related work from this group (Gatti et al., 2025), which the present manuscript cites as precedent rather than recognizing as a limitation. Overall, both the conceptual and methodological basis of this work are fundamentally flawed. Therefore, I would strongly recommend rejection.

      Major comments:

      In their abstract, the authors define "dynamic tubulation" as "Rapid elongation of the tip of mitochondrial tubules." However, the previous literature regarding this phenomenon does not define it, as the authors here indicate, as only emerging from mitochondrial tips1,2. Indeed, both of these previously published articles provide examples of dynamic tubulation events not only from mitochondrial tips but also from mitochondrial sides-i.e., "lateral" surfaces. In Wang et al., 2015, the HeLa cell time-lapse images in Figure S3 provide an unambiguous example of a lateral extrusion of a dynamic tubulation event; and in Qin et al., 2020, there is a clear example of this phenomenon in Figure 1a, where the dynamic tubulation event is extruded from the lateral face of the organelle, rather than the tip. It is essential that the authors of this manuscript align their definition of "dynamic tubulation" with that of previous studies, which played key roles in establishing the very dynamics and mechanistic underpinnings of this phenomenon. Continuing on in their abstract, the authors say, "However, these mechanisms alone cannot fully account for the formation of highly interconnected mitochondrial networks that are required for rapid distribution of mitochondria material." This sentence purports that the other authors of previous work argued that dynamic tubulation from tips could account for the highly interconnected nature of mitochondrial networks, but as I have highlighted above, prior publications did not define tubulation events in this narrow way. It is important to note, too, for instance, Wang et al., 2015, discussed the dependency of pulling tubules out of mitochondria as a basic mechanism for forming complex mitochondrial reticula: "However, fusion of individual mitochondria is not sufficient to form mitochondrial networks in vitro (Meeusen et al., 2004), indicating that other unknown mechanisms are essential for mitochondrial network formation. The basic module for network formation is the tubule. Tubules connect to form a lattice, and the network is a series of interconnected lattices. Hypothetically, a network can be constructed by pulling tubules out of existing membrane compartments and then connecting these tubules by fusion to give a lattice; by repeating this process, a network can be created." Given the above, it is critical that the authors appropriately re-contextualize their study in terms of the existing literature, which has not only laid broad foundations for this topic but has been around for more than a decade. The authors of the present manuscript then say in the abstract, "Here, we identify a distinct type of dynamic tubulation, mitochondrial pull-outs characterized by the lateral extrusion from pre-existing mitochondrial tubules, as metabolically regulated determinants of mitochondrial network formation." The claim that this represents a distinct type of dynamic tubulation is difficult to reconcile with previous reports describing similar events.In the next sentence of the abstract, the authors allude to additional mechanistic insight: "Pull-outs are distinct from the tip elongation form of dynamic tubulation as they are modulated by the mitochondrial dynamins MFN1 and DRP1. . . ." The extent to which this provides novel information is open to question, as Wang et al., 2015, and Qin et al., 2020, both examined the potential roles of Mitofusins and DRP1 in this process. Although they do not rigorously quantify the effects of suppressing these genes on the dynamic tubulation process, they nevertheless indicate that "In Mfn-null MEF cells, dynamic tubulation can still occur (Figure 6F and Supplementary information, Movie S21), but the tubules fail to fuse into networks," and "in these Drp1-depleted cells, we were still able to observe frequent MDT events. . . ." The authors of the present manuscript suggest that the suppression of at least MFN1 and DRP1 leads to a significant decrease in the frequency of dynamic tubulation events, but, to determine whether these factors are essential for the process or not would require complete knockout (KO) of the respective genes. In their introduction, the authors indicate: "In fact, most fusion events occur on the side of a mitochondrial tubule rather than at its end, creating new mitochondrial junctions that increase network connectivity (8, 9)." It is essential that the authors contextualize their comments in terms of well-established literature that laid the foundations for their specific topics of focus. In this case, they mention frequencies of types of mitochondrial fusion events, but they fail to cite a landmark paper, namely, Liu et al., 2009, which is important in any assessment of the types of fusion events that exist3. Notably, one of the recent papers that they do cite here, Gatti et al., 2025, did not account for the importance of using PAGFP to monitor individual fusion events, particularly when attempting to score such events with diffraction-limited confocal microscopy, so this work was unable to distinguish between transient (i.e., "kiss-and-run") versus complete mitochondrial fusion types. It is worth carefully noting that it is intrinsically unfeasible to systematically quantify and categorize fusion events using conventional confocal microscopy (in the absence PAGFP), because it is unfeasible to know whether a genuine fusion event has actually occurred or whether the membranes are simply adjacent to one another, engaged in an inter-mitochondrial contact4. As it turns out, a good example of this intrinsically ambiguous situation is on display in Fig. 1c of Gatti et al., 2025. Given that transient fusion events3 can occur within as short a time as 4 s, the frequency calculation of mitochondrial fusion events from Gatti et al., 2025, using an indirect measure of 100 s as a "validated" criterion invariably missed the large category of fusion events described in the much earlier publication3. Thus, Gatti et al, 2025, should not be used as a reference for the frequency of different types of mitochondrial fusion, since it did not directly employ PAGFP in its frequency analyses of confocal time-lapse imaging, and thus it neither accounted for the existence of transient fusion events nor the unfeasibility of distinguishing between stable fusion and inter-mitochondrial contacts. In other words, the frequency measurements from Gatti et al, 2025, which rely on confocal microscopy alone, cannot possibly be reliable. In the introduction, the authors say, "While the original study focussed [sic] on mitochondria tip elongation(10), more recent work included events arising from discrete protrusions on the side of mitochondrial tubules as part of dynamic tubulation events(10). Nevertheless, these pull-out events (previously reported in yeast (12)) were not fully described and their relationship to mitochondrial tip elongation (the original dynamic tubulation definition(10)) remained unclear." It is concerning that, even though the authors are aware of Wang et al., 2015, and Qin et al., 2020, they do not accurately represent what is inside these published articles. As indicated above, both papers provide examples of dynamic tubulation events originating from the sides of mitochondria, thus these particular events were also represented within the data sets of these papers. For this reason, it is concerning thatthe manuscript introduces the term "pull-outs" for events that appear similar to those described previously.. This section of the introduction should be removed or heavily edited to provide an accurate representation of the manner in which prior publications accounted for and defined these phenomena. Likewise, the last paragraph of the introduction needs to be removed or rewritten to provide an accurate and balanced framing of where the current study sits within the context of the existing literature. This necessitates giving an accurate account of prior publications and not treating "pull-outs" as if this were a novelty, which is patently untrue. The images in Figure 1A appear quite artifactual. What is a "schematic representation," according to the authors? Is this a cartoon or a micrograph? The authors must specify. For example, the tip of the mitochondrion in the top left box appears like it has been cut with a straight edge. This is the first time I have seen a mitochondrion that, instead of having a rounded end, looks completely flat. This is the same for the so-called "pull-out" part of the mitochondrion in the lower right box. How is it possible that the very end of the mitochondrion is at a right angle? What's more, the mitochondria in these images are so saturated, that it is impossible to see anything resembling their natural contours. The authors present only two frames, as well, so it is impossible to assess the tubulation in any meaningfully dynamic way. The authors say, "Pull-outs, but not tip elongation, result in the formation of branched mitochondria that are typical of hyperfused networks associated with cell survival and increased bioenergetics." On what basis could the authors possibly make this claim? If either a tip elongation or "pull-out" led to fusion with another mitochondrion, there is a strong chance that this fusion event would lead to a branched, rather than linear, mitochondrial morphology. This sentence should be removed unless it is actually supported by rigorous data and analysis. The authors say, "Thus, these two modes of dynamic tubulation affect mitochondrial network topology in distinct manners and could thus have distinct roles and regulation. We thus set out to characterise mitochondrial pull-outs." The evidence presented up to this point does not provide a sufficiently strong basis for the subsequent analyses. The conclusions rely primarily on a limited number of images without quantitative support, making it difficult to justify the proposed framework for the experiments that follow.It is not clear what Figure 1C is supposed to represent, based on the ambiguous description in the legend or the y axis. The main text suggests that you are trying to quantify relative pull-out frequency. This has nothing to do with being OMM-IMM-positive, per se. That is not the point. What is the frequency of tubulation from the tip versus the side? Figure 1D compares the frequency of pull-outs to mitochondrial fusion and fission events. This is not really a meaningful comparison. What is the point of doing a statistical analysis when comparing different categories of phenomena? A relevant comparison would be the frequency of tubulation from the tip versus the side. Where is that, along with statistics? In Figures 1E-G, the authors present data involving fusion events. It is critical that the authors appreciate that it is unfeasible to score mitochondrial fusion events using diffraction-limited confocal microscopy alone, because as the membranes converge, it is impossible to visualize what is happening below around 250 nm with any confidence. This is the reason that you need to employ PAGFP and/or super-resolution microscopy to be able to minimize both false-positives and false-negatives.Unfortunately, the authors did not address this limitation in their previous work and the same issue remains in the current manuscript.. All data regarding fusion that is not using PAGFP or super-resolution to evaluate each specific event should be discarded, because it will inevitably lead to erroneous measurements. The images in Figure 1G appear to be taken somehow from Figure 1A. As mentioned above, it is not clear whether these images represent microscopy data or schematic illustrations. This information should be made explicit in the figure itself, rather than requiring the reader to infer it or locate it in the Methods section.. It is essential that the authors of this manuscript attempt to be more thoughtful about providing key information in the figures that clarify obvious questions, such as, Is this a cartoon or not? What type of microscope was used? Etc. In all data, the authors need to report the number of events analyzed, not just the number of cells and independent experiments. The data in Figure 2, relating to mitochondrial morphology, length, etc. need to reflect direct measurements and quantifications. For example, the units in the length measurement in 2B are opaque. It is insufficient to base conclusions upon a probabilistic algorithm, where the authors lose contact with the ground truth of the images. This should be re-analyzed in a standard, direct way. All the data in Figure 2 relating to purported fusion events must be removed, because it derives from the unacceptable use of diffraction-limited microscopy without PAGFP. I have already explained why these data are insufficient. Again: the data in Figure 3, relating to mitochondrial fusion should be removed and redone with proper methodologies. In Figure 4B, it is not enough to show an immunoblot and conclude that there was no change. This should be quantified over at least 3 experiments. Also, using the total OXPHOS antibody cocktail is not adequate for making a broad claim about mitochondrial mass, particularly when it is contradicted by the data in the previous panel. It is important to note that a larger mitochondrial area would likely correspond to an increased mitochondrial mass. The authors should note that proteins are not the only feature of the organelle that possesses mass. Therefore, even if all the mass of all the proteins remained equal among the different treatments, this does not account for changes in the mass of other components, such as membranes, etc. The authors must include images along with their quantifications in Figure 4A. Also, they should clarify how they have accurately measured mitochondrial area, particularly in the context of a more branched mitochondrial reticulum, which leads to difficulties in segmentation. I would recommend that the authors refrain from using simulations, as they do not provide direct measurements, which are essential, and they rely on oversimplifications of complex cellular behavior, which make their conclusions not convincing. For Figure 5, the authors say, "Strikingly, 95% of pull-out events were localized at ER-mitochondria interfaces (Figure 5B), suggesting that these events occur at ERMCS." This observation is less compelling than suggested, given the widespread distribution of the ER throughout the cell.. It would be more striking if the tubulations happened 95% of the time where there was no ER at all. To provide clarity, the authors should perform this analysis exclusively on mitochondria in the periphery of the cell, where the ER is not so super-abundant. Also, it is important to note that Qin et al., 2020, already defined the relationship between tubulation and ER. To be transparent, the authors should reference their work, which undoubtedly encompassed what they have here re-branded as "pull-outs." Assuming that the images in Figure 5 were obtained with Airyscan, examining fusion should be feasible. However, despite their quantifications of fusion in Figure 5D and F, they provide no examples of it, which is not acceptable. Moreover, the data should not be normalized in this fashion. It is important to provide the raw frequencies, with respect to mitochondrial area and time. I would also note that 6 s time intervals is too long to follow these events accurately. One or 2 s intervals would be optimal. In Figure 7A, it is not surprising that most pull-out events were marked by the AC-mito signal, because the AC-mito signal appears almost ubiquitously across the whole contour of the organelle. You could just as well inquire what is the frequency of the mCherry-Fis1 signal at pull-out sights. It would be better to label the general mitochondrial population in this experiment with a dye like MitoTracker, so that there is a generalized label, which has nothing to do with a specific protein, or its targeting sequence. In Figure 7C, the authors are not accounting for variables that affect the accuracy of the frequencies that they are reporting. It is not enough to quantify events per cell. Indeed it is not even clear what this means. Did they authors really examine the whole cell for each analysis or was it an ROI within the cell? One way or the other, it is essential that the authors are obtaining frequencies where there is the same amount of mitochondria over the same amount of time. If one cell has twice as many mitochondria, for example, then there will be twice as many chances, on average, to find a tubulation event. There is no indication here that the authors have accounted for this key variable anywhere in this manuscript. If so, it must be clearly written in the y axis as "events/micron^2/s."The Discussion is overly long and should be revised in light of the comments above. In particular, any proposed new terminology or conceptual distinction should be carefully justified and discussed in the context of the existing literature.. Any potential novelty in this work, such as the role of mitochondrial actin in tubulation, must be contextualized with respect to the existing literature, rather than a frontier that the authors have re-drawn to make it seem as if they were exploring uncharted territory. Lastly, the term "pull-out" is poorly chosen, for different reasons. Perhaps foremost among these, it implies that a mitochondrial tubule is physically pulled out from a pre-existing mitochondrion, yet the authors do not demonstrate a pulling force, identify a force-generating mechanism, or exclude alternative explanations such as local membrane expansion, pushing, or mitochondrial movement. Thus, the terminology prematurely converts an observed morphology into a mechanistic process. A term that does not presuppose mechanistic insight, such as "lateral mitochondrial tubulation," would be more appropriate.

      Minor comments:

      In the title "events" should be "event" There are various typographic and grammatical errors that should be addressed. References: 1. Wang C, Du W, Su QP, Zhu M, Feng P, Li Y, Zhou Y, Mi N, Zhu Y, Jiang D, Zhang S, Zhang Z, Sun Y, Yu L. Dynamic tubulation of mitochondria drives mitochondrial network formation. Cell Res. 2015 Oct;25(10):1108-20. doi: 10.1038/cr.2015.89. Epub 2015 Jul 24. PMID: 26206315; PMCID: PMC4650629.

      1. Qin J, Guo Y, Xue B, Shi P, Chen Y, Su QP, Hao H, Zhao S, Wu C, Yu L, Li D, Sun Y. ER-mitochondria contacts promote mtDNA nucleoids active transportation via mitochondrial dynamic tubulation. Nat Commun. 2020 Sep 8;11(1):4471. doi: 10.1038/s41467-020-18202-4. PMID: 32901010; PMCID: PMC7478960.

      2. Liu X, Weaver D, Shirihai O, Hajnóczky G. Mitochondrial 'kiss-and-run': interplay between mitochondrial motility and fusion-fission dynamics. EMBO J. 2009 Oct 21;28(20):3074-89. doi: 10.1038/emboj.2009.255. Epub 2009 Sep 10. PMID: 19745815; PMCID: PMC2771091.

      3. Wong YC, Peng W, Krainc D. Lysosomal Regulation of Inter-mitochondrial Contact Fate and Motility in Charcot-Marie-Tooth Type 2. Dev Cell. 2019 Aug 5;50(3):339-354.e4. doi: 10.1016/j.devcel.2019.05.033. Epub 2019 Jun 20. PMID: 31231042; PMCID: PMC6726396.

      Referees cross-commenting

      This section contains comments from different reviewers

      Reviewer 3 I note that referees 1, 2, and 4 do not refer to the limitations of attempting to quantify mitochondrial fusion events with diffraction-limited microscopy. In my opinion, any study purporting to investigate mechanisms of mitochondrial fusion need not only to discuss PAGFP and super-resolution imaging techniques but actually employ them in their experiments, as well.

      Reviewer 1 Reviewer 3 has raised some important points that should be carefully considered by the authors and editors. However, I do not share his/her view that super-resolution microscopy and PAGFP are mandatory tools for studying mitochondrial fusion. Having worked in the field of mitochondrial dynamics for almost 30 years, I have witnessed groundbreaking discoveries on mitochondrial fusion that were made long before either super-resolution microscopy or PAGFP became available. I continue to believe that mitochondrial network dynamics can be studied effectively using conventional microscopy. Nevertheless, the apparent discrepancies in the data presented by Kasmaie et al. should be resolved (see, for example, my point 1).

      Reviewer 3 It is unclear how one could hope to accurately quantify rates of individual fusion events using standard diffraction-limited microscopy without PAGFP or some variant of it. The reason for this is that it is impossible to distinguish between mitochondria that are simply touching and next to each other and ones that have truly undergone fusion. This is particularly complicated when one considers transient or "kiss-and-run" fusion events. While it is not impossible to see a fusion a event with standard confocal microscopy, it is quite unfeasible to obtain an accurate frequency of fusion events, under different conditions and experimental groups, as this manuscript was purporting to do, unless one uses appropriate tools, such as PAGFP, to enable one to definitively say whether a fusion event has occurred.. I am fairly certain that this reasoning is sound. Of course, I do not mean to be dismissive, if I am in error. Perhaps there is another tool that has escaped my notice. A PEG assay might work, but it is quite unnatural, and it is not usually employed for looking at individual fusion events but rather whole mitochondrial network fusion capacity.

      Reviewer 4 While I agree that additional validation using complementary methods could strengthen some of the conclusions, I also do not share the view that these tools are necessary for the scope of this work. Fusion dynamics are not the primary focus of this study, and the authors present important findings using well-established methodologies that, in my opinion, will advance the field and make the manuscript suitable for publication.

      Reviewer 3 Reviewer #4 indicates that "Fusion dynamics are not the primary focus of this study. . . ." Nevertheless, it is clear that this manuscript focuses on the role of "pull-outs" in mediating increased mitochondrial interconnectivity. It is clear that "interconnectivity," in the context of this study, refers to mitochondrial fusion. The abstract quite clearly emphasizes mitochondrial fusion and the interconnection of mitochondrial networks. Moreover, 5 out of 7 of the main figures contain panels that purport to directly measure individual fusion events, as a method of evaluating certain conclusions as to "pull-outs." Therefore, it does not seem accurate to conclude that "Fusion dynamics are not the primary focus of this study. . . ." I am not convinced that the frequency of individual fusion events can be quantified using diffraction-limited microscopy without PAGFP (or comparable variant).. Inasmuch it is impossible to physically resolve the mitochondrial membranes, and mitochondria frequently touch without undergoing fusion, and they also engage in rapid transient fusion events, without the aid of a probe that unambiguously reports on fusion, it is indeed a matter of great uncertainty whether a fusion event may or may not have occurred.

      Reviewer 1 I think it is clear by now that PAGFP and, with some limitations, super-resolution microscopy are superior methods for studying individual mitochondrial fusion events. However, transient 'kiss-and-run' type interactions do not result in an overall change in network topology. Increased network interconnectivity is driven by bona fide fusion events, which can be assessed using conventional microscopy. The authors may wish to elaborate on this point in the discussion section. It should perhaps be acknowledged that opinions on these methodological issues may differ

      Significance

      This study provides, at best, only an incremental advance in the field of mitochondrial dynamics and membrane remodeling. Generally, there is a significant concern that it does not appropriately contextualize the aims and conclusions of the research with respect to previous publications, which laid the foundations for the phenomenon of dynamic tubulation. Moreover, although this study attempts to quantify mitochondrial fusion events in relation to dynamic tubulation, the reliance on diffraction-limited confocal microscopy is not suited for investigations of mitochondrial fusion. While there is some indication that mitochondrial actin may be important for the formation of these lateral tubulations, significant additional work will need to be performed to support this conclusion.

      Background of referee:

      I have many years of experience in mitochondrial research, including imaging mitochondria, from confocal microscopy with PAGFP to various super-resolution imaging techniques.

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      Referee #2

      Evidence, reproducibility and clarity

      Mitochondria must constantly adapt their structure and function in response to changes in cellular physiology. For example, starvation conditions result in a more highly connected mitochondrial network, which increases ATP production and protects the organelle from mitophagy. Yet how the network achieves the rapid increase in connectivity is not fully understood. In this manuscript, live cell imaging captures mitochondrial pull-outs, a feature of all cell lines that is regulated by mitochondrial function. Quantification of mitochondrial fusion, fission, and pullouts indicates that pullouts occur at the greatest frequency and that most of these events result in reversal at steady state but can also lead to fusion events or stable branches in the network. These events increase when physiological conditions change mitochondrial function, either when cells are grown in galactose or deprived of glucose. The frequency of mitochondrial pullouts increased in both conditions, which correlates with increased connectivity and number of branches. Inhibiting beta-oxidation of fatty acids and thus the production of acetyl coA to fuel the TCA cycle in glucose starvation conditions prevented the increased frequency of mitochondrial pullouts, indicating that these events are regulated by mitochondrial function. Modeling predicts that the increased connectivity of the network facilitates rapid diffusion of metabolites. Live cell imaging also indicates that the sites of mitochondrial pullouts are marked by ER, DRP1, MFN2, and actin. Consistent with this, depletion of DRP1 or MFN1 by siRNA reduces the frequency of mitochondrial pullouts, as does depletion of mitochondrial actin or cellular actin ARP2/3-dependent polymerization. In sum, the data document a novel aspect of mitochondrial dynamics that contributes to remodeling organellar structure in response to changes in cellular physiology.

      Major Comments:

      1. In Figure 3, fatty acid oxidation is blocked to prevent production of acetyl coA, which fuels the TCA cycle and production of reducing equivalents required for oxidative phosphorylation. The data convincingly demonstrate that inhibition of FAO blocks the increase in mitochondrial pullouts resulting from glucose starvation. However, it remains unclear if this is specifically due to reduced oxidative phosphorylation or reduced FOA or specific blocks in the TCA cycle. Why not use inhibitors of the electron transport chain for these experiments? What does the mitochondrial network look like under these conditions? Representative images would be useful in addition to the connectivity score. Also in this figure, the number of mitochondrial pull-outs is different from the original data presented in Figure 1D (10 vs 17). Is there a difference in growth conditions or time of the video?
      2. The data in Figure 5 show that DRP1 and MFN2 are present at pull-out events. It is not clear why a rather complicated/convoluted normalization strategy was used to present this data. It would be more useful to readers to see the raw numbers for each condition (X number of pull-out events also had DRP1 or MFN2 present). Why was MFN1 not used in this experiment (especially given that this is the target of siRNA knockdown in the next figure). It may be too technically challenging, but it would be interesting to know if both mitochondrial dynamins are present at pull-out events or if it's either/or. Perhaps if only DRP1 is present, the pull-out is more likely to retract or form a stable branch while the presence of MFN1/2 would increase the probability that the pull-out goes on to fuse?

      Minor comments:

      1. The Western blot in Figure 4 should include molecular weight markers. The search time is a little hard to interpret with only micrometer squared values - could these be correlated to molecules of approximately similar size? This figure also refers to end-to-end fission rates in the graph axis labels and figure legend text - this should be fusion rates.
      2. The Western blot in Figure 6 should also include molecular weight markers.
      3. The duration of the CK666 treatment used in Figure 7 is not noted in the figure legend or materials and methods.
      4. It would be convenient for readers to find the duration of the image acquisition used to quantify the number of pull-out events in the figure legends rather than having to dig into the materials and methods.
      5. Could more information about the pull-outs that retract be shared? Is there anything notable about these pull-outs compared to those that are stable or go on to fuse? Are they shorter or more dynamic or lack actin?

      Significance

      The manuscript reports a novel phenomenon that the authors name mitochondrial pull-outs. While most of these events simply retract, some are stable and some result in a fusion event, which seems to contribute to increasing mitochondrial network connectivity under conditions that rely on oxidative phosphorylation for ATP production. Importantly, acute loss of DRP1, MFN1, or actin results in a decreased frequency of pull-out events, suggesting that the core machinery required for mitochondrial dynamics also supports mitochondrial pull-outs. While the imaging and quantification data are convincing, the study lacks a functional assay to demonstrate that mitochondrial pull-outs are required for the adaptation of mitochondrial functions in galactose and/or glucose starvation conditions, which somewhat limits the significance of the study.

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      Referee #1

      Evidence, reproducibility and clarity

      Mitochondrial network morphology constantly adapts to the physiological conditions of the cell. Fusion and fission play a well established role in remodeling of the mitochondrial network. In their present study, Kasmaie et al. focused on pull-out events that generate novel mitochondrial tubules emanating from the side of existing tubules and show that this is an important mechanism contributing to network morphology. Using live cell microscopy combined with pharmacological treatments and/or knock-down of key components of mitochondrial dynamics they show that this process is dependent on the machinery of mitochondrial fusion and fission and mitochondria-associated actin. Furthermore, they claim that ER mitochondria contact sites (ERMCS) may be important. The authors have made some interesting observations, and most parts of the manuscript are sound. However, some of their conclusions should be supported by additional experimentation, as outlined below.

      Major comments

      1. On page 5 the authors claim that cells grown in galactose medium showed an increased fusion but not an increased fission rate. Excessive fusion in the presence of unchanged fission is expected to result in hyperfused networks. After adaptation to the new medium cells should reach a steady state, where fusion and fission activities are balanced. Why was this not observed?
      2. The authors propose on p. 6 that the overall mitochondrial area increases upon growth in galactose medium, while the amount of mitochondrial proteins and mitochondrial mass remain unchanged. This would mean that mitochondrial proteins become more dilute in the organelle. Is this what they think is happening? Is there a loss of cristae to compensate for the overall growth of the organelle under conditions where mitochondrial mass remains unchanged? Would this affect the diffusion of metabolites in the matrix? These are central questions that should be addressed experimentally.
      3. Fig. 5A and B show the association of mitochondrial pull-out events with ERMCS. Unfortunately, the field of view that is shown is very crowded and ER is almost everywhere. How was the association of ER and mitochondria defined? To support their statement the authors should provide some functional evidence. For example, they could test whether a reduction of ERMCS results in a reduction of pull-outs.
      4. In Fig. 5E the authors show that MFN2 is enriched at pull-out sites. However, in Fig. 6 they analyzed a knock-down of MFN1, rather than MFN2. Is MFN1 also enriched at pull-out sites, and does a knock-down of MFN2 result in altered pull-out activity?

      Minor comments 5. The third paragraph of the introduction refers to an "original study" and "more recent work", however, both cite the same paper (ref. 10). Please clarify. 6. Fig. 1 quantifies the pull-out events per cell. Please also indicate the time of observation, i.e. number of events per cell and minute. 7. Page 4, the text refers to Fig. 1F (Bottom, white arrows) for successful fusion events. However, these are shown in Fig. 1E and highlighted by arrowheads. Also the reference to Fig. 1F for stable branches is not correct. 8. Please indicate in Fig. 4B what was loaded on the gels. Total cell extracts? Also add molecular weight markers. 9. I'm not sure whether appropriate statistical analysis was performed in Fig. 6D and G. Please double-check. Please note that I am not an expert on this. 10. Please briefly describe the DeAct-mito tool in the text to help the reader to understand the experiment without having to look it up in the cited paper. Also, please add a reference for CK666 in the results section.

      Significance

      The paper provides some conceptual advances for the understanding of mitochondrial network formation, which will be of interest to an audience interested in mitochondrial dynamics. It offers initial insights into the molecular machinery involved in generating mitochondrial pull-outs, but does not go into much detail regarding the molecular aspects of these processes.

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      Reply to the reviewers

      Manuscript number: RC-2024-02737

      Corresponding author(s): King, Megan

      1. General Statements

      We thank all reviewers for their comments and recommendations.

      In this revised submission we outline compelling evidence that the critical window for polymerase theta (Pol theta) activity is just prior to the G2/M transition, downstream of PLK1 activation but upstream of full CDK1 activation; we arrive at this new focus through our mechanistic dissection of how loss of the DNA repair and replication factor DNA2 compromises Pol theta-mediated end joining (TMEJ). Although a direct role for PLK1 in licensing TMEJ was recently reported, the interpretation of prior studies was that TMEJ repair is restricted to mitosis. Instead, our results highlight that the window in which TMEJ drives most repair is in late G2 during the period of mitotic commitment when PLK1 activity begins to build. In this revision, we provide further evidence to support this key observation, as we now demonstrate that the Aurora A kinase is likewise required for efficient TMEJ. We now propose that during the period of mitotic commitment (but crucially prior to mitosis) Aurora A (not CDK1) primes PLK1 to become active, driving TMEJ competence. This finding is important because although it reinforces that TMEJ is tied to mitosis, it highlights that most TMEJ repair likely occurs prior to mitotic entry itself. We provide further evidence that a minor subset of TMEJ repair events require CDK1 activation and occur in mitosis, but this is largely restricted to complex repair outcomes called templated insertions. Mechanistically, we suspect that these templated insertions events require Pol theta’s ATPase activity (in addition to polymerase activity) and may constitute as second, delayed stage of TMEJ function that drives more complex DSB repair products during mitosis.

      In preparing this revision we have made major changes to the manuscript necessary to provide clarity; this includes the removal of data using genetic or pharmacological perturbations to the BLM helicase. Although we observed similar consequences of BLM knock-down or inhibitors on the cell cycle (leading to primarily a G1 arrest) on TMEJ proficiency, we were unable to rescue the genetic effect with a complementation construct while the pharmacological inhibitors may not be sufficiently specific. We therefore felt it best to remove this aspect of the manuscript, which now instead focuses on the cell cycle regulation of TMEJ.

      2. Point-by-point description of the revisions

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

      *Summary *

      *DNA double-strand breaks can be repaired by NHEJ, TMEJ, or HR. While there have been many studies of the factors that regulate double-strand break repair pathway choice, the exact mechanisms by which TMEJ is regulated remain under active study. In this study, McBrine and colleagues conducted experiments in two human cell lines to investigate the contribution of DNA resection to TMEJ. After induction of Cas9 breaks at a single locus, they used NGS amplicon sequencing and a pre-existing computational pipeline to quantify repair by inaccurate NHEJ or TMEJ. Their initial data suggested that loss/inhibition of the BLM DNA helicase or DNA2 nuclease inhibit resection and thereby decrease TMEJ repair. Probing further, they conducted carefully controlled cell cycle analysis and determined that loss of either BLM or DNA2 slows progression to the G2/M transition, when TMEJ is activated through PLK1 phosphorylation of polymerase theta. Based on these findings, they conclude that both BLM and DNA2 promote TMEJ repair, likely by promoting efficient replication and timely progression into G2/M. *

      *Major comments *

      Overall, the authors' conclusions are well-supported by their data. They used multiple approaches to validate their findings, including the use of siRNAs and small molecule inhibitors that largely produce similar results. However, three major additions/experiments should be included.

        • First, most of the data from the NGS sequencing results are displayed as TMEJ/NHEJ ratios. While this makes it easy to compare between different experimental conditions, it could mask differences in overall repair efficiency. I recommend that the authors include the initial percentages of TMEJ-like and NHEJ-like repair junctions in supplemental information, so that readers can determine the efficiencies of the different repair pathways in each condition. * We agree with the Reviewer that there is additional information that can be gleaned from the NGS sequencing approach. In the revised manuscript, we now include the full percent of outcomes interpreted as TMEJ and NHEJ (as percent of total sequences) as side-by-side bar graphs in the Supplemental Material for all data (Supplemental Figure S4). As appreciated by the Reviewer, we have retained expressing the TMEJ:NHEJ ratio in the main text for clarity.
      1. Second, the DSB repair spectrum (as displayed in Figure 1B) should be provided for each TMEJ:NHEJ plot in supplemental information, at the very least the for knockdown/inhibitor experiments where a significant difference in TMEJ:NHEJ ratio is observed. *

      We agree with the Reviewer and now include a representative DSB repair spectrum for each experiment in the Supplemental Material of the revision (Supplemental Figures S1, S2), selecting an experiment that falls in the middle of the distribution from all biological replicates.

      • Finally, the conclusion that transition to the G2 stage (where polymerase theta becomes activated through phosphorylation by PLK1) is necessary for TMEJ is logical, based on the data and previously published studies. However, to provide additional support for this model, the authors should consider repeating the experiments from Figure 5B/5C using cells with the PLK1 phosphorylation sites on polymerase theta mutated (e.g., using the 10A construct from Gelot et al., doi.org/10.1038/s41586-023-06506-6). The expectation is that the TMEJ reduction observed with BLM or DNA2 inhibition should not decrease further, if indeed the decrease is largely due to an inability to progress to the point in the cell cycle where polymerase theta is activated by phosphorylation. This experiment should be able to be completed within 1-2 months, if the appropriate cell line can be obtained. *

      While we appreciate the intent of the Reviewer’s question, given the consensus discussion between all three reviewers that this was a challenging yet unessential experiment, we have declined to take on this line of questioning as part of this study. We do, however, now provide further evidence (and depth) for this model by demonstrating that Aurora A inhibitors also compromise TMEJ (Figure 5).

      *Minor comments *

      • The cell cycle plots in Figures 4 and 5 are difficult to interpret given the current color scheme (especially the difference between S and G2). I suggest changing the color scheme and arranging the legend so that the key is in the same order as the plots (G1 on the bottom, S in the middle, G2 on the top).*

      We have re-ordered the cell cycle legend and adjusted the colors as suggested.

      • In Figure 3B, the resection time course is interesting, but I wondered why they are no time points between 12 and 24 hours? Also, if the time course was performed multiple times, it would be nice to see these data represented in the plot, along with standard error bars. *

      The resection time course was performed as a pilot experiment to determine the earliest reasonable time to harvest gDNA and observe sufficiently high resected, ssDNA to make conclusions about relative resection efficiency across the various siRNA-treatment conditions. It was also performed to verify that after ~ 24 hours (the time at which we harvest genomic DNA for endpoint indel scoring) DSBs have been largely repaired. As a pilot experiment, however, we did not collect any timepoints between 12 -24 hours, or multiple replicates for all timepoints. We agree with the Reviewer’s concern about reproducibility and given that these other timepoints are not necessary to support the conclusions of the Figure we have now moved this panel to Supplemental Material (as it may be of interest to readers) in Supplemental Figure S5.

      ***Referees cross-commenting** *

      *I agree with Reviewer #2 that "The primary issue with this manuscript is that it does not present the experimental conclusions in a straightforward manner. Instead of focusing on what was learned, it reads more like a journey." Based on the abstract, I had expected to learn more about the role of BLM and DNA2 in regulating TMEJ via resection, but the crux of the story is about cell cycle effects impacting TMEJ when the levels of these proteins are reduced. Rewriting would help address this. *

      Reviewer 2 also asked that that representative spectra of all relevant experiments are presented as supplementary information instead of flattening the entire spectrum in one number. This seems critical in a revised manuscript.

      We have endeavored to rewrite the manuscript in a more straightforward manner that focuses on the cell cycle regulation of TMEJ. We now also include representative spectra in the Supplemental Material.

      *Reviewer #1 (Significance (Required)): *

      *I am a basic scientist studying DNA repair mechanisms and have a long-standing interest in the mechanism of TMEJ. *

      *In my opinion, the results from this study will be interesting to scientists studying double-strand break repair pathway choice and those thinking about how inhibition of different DSB repair proteins can be used in cancer treatments. The strongest aspect of the study is the way in which the authors investigated the effect of BLM, DNA2, and EXO1 knockdown/inhibition on cell cycle progression, and used the data to make conclusions about the most important factors influencing TMEJ repair. The experiments are carefully controlled, and the most important conclusions are supported by multiple lines of experimental reasoning. As stated above in the summary, the study can be improved by the inclusion of more of the primary data in the supplemental information, and by the addition of a key experiment that will further support the authors' model. *

      *The study supports the recent findings from the Sfier and Ceccaldi labs that the main driver of TMEJ, polymerase theta, operates primarily during mitosis, and its activation depends on PLK1. Further, it presents initial data on the intriguing finding that BRCA1 deficiency is synthetic lethal with BLM deficiency, and this can be enhanced with Olaparib treatment, which will be of interest to translational and clinical scientists. *

      *Overall, the study is notable because of its careful attention to cell cycle progression on DSB repair outcomes, and why caution needs to be taken when interpreting how genetic perturbation of DNA repair proteins impacts repair mechanisms. *

      We appreciate the Reviewer’s appreciation for our rigorous approach and statement of the context and impact of our work.

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      *In this manuscript, the authors use two cell lines, U2OS and RPE1, to investigate the effects of impeding long-range end resection on alternative end-joining (TMEJ). They monitor repair of a CRISPR-induced DSB, presenting their findings as the ratio of TMEJ to cNHEJ outcomes. Initially, the authors observe that inhibiting BLM and DNA2 produces a TMEJ-deficient phenotype similar to that resulting from pharmacological inhibition of PolQ, suggesting a potential role for long-range end resection. However, this inference is challenged by evidence showing that genetically or pharmacologically perturbed cells fail to progress through the cell cycle, not reaching the previously reported cell cycle phase in which TMEJ typically occurs. Although it remains an open question whether TMEJ acts exclusively in M phase (given MMEJ manifestations in G0/G1 cells), the authors attempt to pinpoint the cell cycle stage of TMEJ-mediated DSB repair by using cell cycle inhibitors. However, in my opinion, these assays and experiments fall short of providing conclusive evidence. I therefore feel that the data is best presented focusing on the cellular consequences of impeding BLM/DNA2, and not on TMEJ, which is basically used as a marker/proxy for mitosis/mitotic cells. In the discussion, the authors themselves suggest several additional experimental approaches that would strengthen their conclusions or deepen their investigations. I believe that implementing such strategies would broaden the impact and interest; in their absence (I do not suggest the authors to start performing these time and resource consuming experimentation to be included in the current manuscript) the study has a more specific target audience. *

      We appreciate that the author felt that our initial submission was unsuccessful at straddling two aspects: one related to BLM/DNA2 function and the other to the cell cycle. In this revision, we have chosen to focus entirely on the latter context including through the addition of new experiments and analysis. The revised manuscript highlights a role for Aurora A-PLK1 in activating “simple” TMEJ repair in late G2 with complex TMEJ-driven repair occurring in mitosis. We would argue that these findings indeed provide novel insight into TMEJ regulation.

      *Major comments *

      1. *i) The primary issue with this manuscript is that it does not present the experimental conclusions in a straightforward manner. Instead of focusing on what was learned, it reads more like a "journey." This issue is apparent from the abstract: 13 out of 16 lines are devoted to describing the authors' initial intent to investigate the role of long-range end resection enzymes in TMEJ, only to reveal that the observed effect is actually an indirect consequence of cell cycle perturbations. The manuscript uses TMEJ as a proxy for cells not reaching mitosis, thus supporting the conclusions reached from the more direct cell cycle analyses. While the majority of the experiments address the effects of inhibiting BLM and DNA2, the abstract suggests that these findings reveal new insights about TMEJ, which is doubtful or at most limited. The only potential novelty regarding TMEJ, aside from confirming its regulation by the cell cycle, is the narrowing down of the specific substage in which it operates. However, this conclusion is inferred from indirect effects rather than direct measurement, and also somewhat debatable by being dependent on a very explicit interpretation of the cell cycle inhibitors that are being used (whose action in my experiences are not as strict/narrow as here considered). * In response to this point and the critiques of the other reviewers, we have now extensively edited the manuscript. Given concerns raised about the interpretation of our data on knock-down of the BLM helicase given our inability to rescue TMEJ by complementation as described above, we have rewritten the manuscript to have a clear focus on: 1) what can be learned from the NGS approach to interrogating repair outcomes in response to the perturbations we explore; and 2) the effect of cell cycle progression on TMEJ proficiency, particularly in response to inhibitors of PLK1 and Aurora A compared to CDK1 and nocodazole blocks. We agree with the Reviewer that our initial description of the cell cycle inhibitors was overly binary. However, given that inhibitors of these kinases are actively being pursued in the clinic, and effects on TMEJ could contribute to their efficacy, we would argue that our insights will be important to the field.

      2. *ii) I do not have an immediate solution to this concern but feel the data itself is valuable. I also appreciate the authors' thorough approach in conclusively establishing that the observed disruption of TMEJ upon BLM/DNA2 depletion is a downstream consequence of cellular phenotypes, rather than an anticipated resection issue at CRISPR-induced DNA breaks. I would thus recommend rewriting the manuscript to reflect this perspective. * We appreciate that the reviewer feels that the data is valuable and highlights the need to take cell cycle effects into account when assessing the genetic or pharmacological inhibition of DNA repair factors.

      *iii) Some conclusions are overinterpreted, especially regarding the timing of DNA repair based on mutant spectra analysis. For instance, in lines 257-258, the authors conclude, "by the same timepoints, we find over 65% of DSBs have been repaired with mutagenic indels, indicating high transfection efficiency and rapid DSB induction." This statement is inaccurate for several reasons: (1) Given the iterative cycles of DSB induction, error-free repair, and recutting, it's possible that, for example, only 15% of DSBs have actually been repaired with mutagenic indels; (2) The assay detects only intact molecules, so unrepaired DSBs/broken molecules are not accounted for, especially those that are not resected and thus escape detection in Figure 3; and (3) The assay captures only deletions that retain the primer binding site. A more accurate statement would thus be something like, "65% of intact alleles at this timepoint contain mutations indicative of error-prone repair, indicating high transfection efficiency and robust (rather than rapid) DSB induction." The use of "rapid" is questionable here, as I consider 24 hours a long time, given that NHEJ typically occurs within an hour. *

      We agree with the Reviewer. This sentence was meant to indicate that the ‘final’ repair product (i.e. a mutagenic indel) was robustly observed and indicates that the transfection/induction was successful, rather than establishing a value reflecting the outcome of a single DNA cutting and repair event. As perfect repair is indistinguishable from uncut chromosomes in our assay, we can infer that the total percentage of DSBs induced per genome is likely higher than 65%. Moreover, persistent DSBs or repaired DSBs that deleted enough flanking sequence to lose the primer binding region would also be invisible in our assay. Given this, we have now edited this statement to more accurately state: “65% of intact alleles at this timepoint contain mutations indicative of error-prone repair, indicating high transfection efficiency and robust DSB induction” as suggested by the Reviewer.

      1. *iv) While I'm hesitant to request additional experiments, I believe the HR measurements of siDNA2 through the mClover assay, alongside the siNT condition, require additional replicates. Currently, these results are marked as non-significant in Figure 3D but labeled with a "+" instead of "+++" in Figure 3E. Additionally, the combination siBLM+siEXO1 is marked with a "-" in Figure 3E, though a statistical comparison of single siDNA2 to the double combination would likely result in a non-significant outcome. This makes it difficult to draw firm conclusions, and further biological replicates would help clarify the data; two of the current data points are wildtype, while one aligns with siBLM levels. * We appreciate the concern of the Reviewer and agree. We now include additional biological replicates of the LMNA-mClover assay with siDNA2 treatment to reinforce this result.

      2. *v) Another major concern is the potential off-target effects of siRNA, as the reported phenotypes might partly stem from this issue. None of the experiments have been validated using complementing constructs resistant to targeting. Although the inhibitor data is useful, not all experiments have been validated with this method. Adding these controls would be beneficial but is ultimately "optional". In their absence, I recommend the authors temper their conclusions regarding siRNA-only data. * We took this concern of this and the other reviewers to heart and attempted to carry out key experiments of siBLM conditions with rescue constructs that are insensitive to the siRNA. As described above, we were unable to achieve a rescue of TMEJ. While this could be a consequence of effects of BLM under- or over-expression, as it is challenging to match to the endogenous levels, or a potential complication from different BLM splice variants, we are not sufficiently confident at this point to include this data and have therefore removed it from the manuscript. This revision instead focuses on the cell cycle regulation of TMEJ. We remain confident in the results of the siDNA2 and DNA2i given our ability to recapitulate the expected delays in S-phase upon this perturbation and the alignment of these changes with the DNA repair outcome.

      *Minor comments *

      • *

      *1) The title is unclear: "its" refers to a repair pathway, which is here not demonstrated to be "sensitive" but instead not employed in cells where BLM-DNA2 is inhibited and/or downregulated. It is to me also somewhat off point: it turns out that TMEJ is a proxy for reading out a cell cycle stage, and I feel that the title should reflect the more direct consequences induced by loss of BLM-DNA. Otherwise one could substitute TMEJ for any other mitotic process creating a "true" statement. *

      Given the shift in focus of the manuscript, the manuscript has been retitled: “Activation of Pol theta-mediated end joining begins during mitotic commitment”.

      *2) To me, the readability of the manuscript would be improved by shortening the text: Especially the intro is very lengthy and reads like a review instead of focusing on providing the information necessary to understand the experiments/logic of the result section. Additional information that is relevant for discussing the outcomes of the experiments can be introduced at that stage. I realize this is also a matter of style but I felt that I had to go through a lot of context before getting to the actual data, which did not necessarily brought new insight into all what was discussed before. *

      As described above, we have endeavored to rewrite the manuscript in more direct and concise fashion.

      *3) Typo's: Line 242, missing "on" (Based on...); line 590, missing "to" (important to test) *

      Thank you, we have corrected this error.

      *4) I suggest to have the y-axis of the histograms in the figures to be identical. For example Figure 1 C en D have three histograms with Y-axis going to 4, but one going to 3. For consistency and comparison it is more useful to have all of them going to 4. Idem for Figure 2 where D deviates from A-C (I understand that e.g. has no datapoint exceeding the arbitrary mark 3 but it allows for a better comparison of D to A-C if the Y-axes are identical. *

      We agree and have made all y-axes consistent across the Figures for each relevant comparison.

      *5) Please specify, upon first usage, e.g. in the legend of a Figure how the ratio TMEJ:NHEJ is defined (instead of only in the method section). Also mention in the text that this is an arbitrary and subjective unit used for clarity which is indicative but not an absolute measure for the underlying biology: all product that have a MH of 2 and higher are binned under TMEJ, but surely non-sequence guided NHEJ will also produce deletions that, just by pure chance, have junctions in which 2 bp overlap. Also, not all TMEJ outcomes have 2bp of MH. NHEJ outcomes are thus in the TMEJ subclass and vice versa. This is not problematic to me but it should be kept in mind, especially in interpreting mutant backgrounds. *

      We agree with the Reviewer and now discuss our categorization scheme in far greater detail including the rationale and caveats in the revised manuscript.

      *6) Related to the above I also want to propose that representative spectra of all relevant experiments are presented as supplementary information instead of flattening the entire spectrum in one number, in which all nuance but also details may be lost, which can be informative to the TMEJ and NHEJ afficionados. *

      We agree that this is likely one aspect of our work that will be useful to experts. We therefore now include a representative DSB mutation spectrum for each experimental condition in the Supplemental Material (Supplemental Figures S1, S2).

      ***Referees cross-commenting** *

      *I agree with all the points brought up by the other 2 reviewers (and glad to see so much overlap). And thus feel that the authors need to address all them. *

      *I initially was somewhat reserved on asking for additional experiments addressing potential off-targets of the siRNAs underlying some of the phenotypes (no experience myself using si's as opposed to sg's and clean knock-out models) wanting to see how the other reviewers potentially more experienced with this technology would weigh this concern. Having read their reports I thus als underwrite reviewer #3's remark: 2. I would strongly recommend that the authors confirm their BLM siRNA results (experiments in Fig. 1C, 3C and 4A) by examining whether add-back of exogenous, siRNA-resistant BLM can rescue the phenotypes they see on cell cycle progression and TMEJ. Alternatively, they could also make use of at least one of the many BLM knockout cell models that have been generated previously by many labs (including in both RPE-1 (Tsukada et al., Mol. Cell 2024) and U2OS (Jiang et al., Mol. Cell 2024) backgrounds), where exogenous BLM and helicase-dead BLM have been added back. This would be especially important to do if the authors cannot confirm the potency and selectivity of BLM-IN-1, as suggested above." *

      Final comment from my end: I perhaps was somewhat unclear or went too fast: I've read concern #3 of reviewer 1 in the light of "the authors should consider..." thinking it was "optional". I agree with reviewer #3 that it is not essential.

      Again, we thank the Reviewer for their expert advice and have addressed these issues in the the point-by-point responses above.

      *Reviewer #2 (Significance (Required)): *

      *This study is of interest to those studying the long range end resection enzymes including DNA2, BLM and EXO1, and to researchers with in depth knowledge and interest into the regulation of TMEJ. The study is limited in providing very concrete new information - it is somewhat descriptive (e.g. the study doesn't provide insight into why cells arrest at a given stage in the absence of BLM or DNA2 functionality). Hence, I feel that the outcomes are of interest to specialist as I feel that the individual observations are of relevance but there is not a impactful overarching gain of knowledge. The data can be used by others to further design experiments aimed to elucidate the roles of specific DNA repair enzymes. *

      *Of note and as requested, my field of interest is repair of DNA breaks: EJ, cNHEJ, altEJ. *

      We would suggest that we have enhanced the impact of our work in this revision through the addition of new data highlighting our evidence for a privileged late G2 window for TMEJ prior to mitotic entry while complex, TMEJ-driven templated insertions occur in mitosis itself.

      *Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

      *Summary and significance: *

      • *

      *In this manuscript, the authors describe mutational outcomes at a single site-specific DNA double-strand break induced by Cas9 in RPE-1 and U2OS cells. They find that depletion of BLM or DNA2 using siRNA or treatment of cells with small molecule inhibitors causes a shift from DNA polymerase theta (POLQ)-dependent repair outcomes to classical non-homologous end-joining (NHEJ)-mediated ones. In contrast, EXO1 depletion has no effect. They go on to show that this is not due to any potential role for BLM and DNA2 in DNA-end resection, but rather because under their experimental conditions, loss of BLM or DNA2 leads to cell cycle disruption such that fewer cells make it into mitosis, where POLQ is most active. *

      *The key conclusions the authors draw are in large part convincing, as most experiments in the manuscript are replicating previously published findings. There is no reason to believe that long-range DNA-end resection would be required for POLQ-mediated end-joining (TMEJ), because TMEJ does not need longer overhangs than those MRE11 and CtIP can already provide via initial end-processing (50-100 nt). The fact that BLM and EXO1 do not promote microhomology-mediated end-joining was already shown in 2013 by Truong et al., PNAS. *

      *The manuscript reads a bit like a warning that cell cycle progression defects must be taken into account when measuring POLQ-mediated end-joining (TMEJ) repair events; but this is not really a novel insight, given the recent reports that POLQ is most active in mitosis (work from Löbrich, Sfeir and Ceccaldi labs). *

      We appreciate that the Reviewer found our work largely convincing. We have substantially edited the manuscript to make it more straightforward. In addition, taken together our data make an important addition to prior reports by highlighting the concept that TMEJ becomes fully activated in late G2 prior to mitosis (given that CDK1 inhibition, which blocks mitosis but not the Aurora A-PLK1 circuit, leads to TMEJ that is as efficient as in unperturbed cells).

      *The manuscript would be suitable for publication if the authors could address the points below. *

      *Major comments: *

        • Major conclusions are drawn about effects of BLM loss on cell cycle progression based on the use of only a single siRNA, and the use of reported chemical inhibitors of BLM DNA-binding activity. Unfortunately, the ML216 inhibitor the authors use was found not to be a selective or potent inhibitor of BLM (Oliver lab, eLife 2021), and BLM-IN-1 has not been sufficiently well-characterised in cells yet as far as I am aware. The authors need to remove the ML216 data, as any effects they see are likely just due to the toxic effects of this compound rather than any effect on BLM specifically. Have the authors checked SCEs in cells treated with BLM-IN-1? If they do not see the characteristic 5-10x increase in SCEs that is observed in all BLM-deficient cells, then they can also not trust any results they get with that compound either. * As highlighted above, we agree with the Reviewer and endeavored to further validate our siBLM knock-down results through a variety of means. Ultimately, as described above, we were unable to achieve a rescue of TMEJ. We suspect that this is a consequence of effects of BLM under- or over-expression, as it is challenging to match to the endogenous levels, or a potential complication from different BLM splice variants. We also agree about the toxicity of the BLM inhibitors. Given these concerns, we have now removed this data from the manuscript, which has been refocused to directly address the cell cycle regulation of TMEJ.
      1. I would strongly recommend that the authors confirm their BLM siRNA results (experiments in Fig. 1C, 3C and 4A) by examining whether add-back of exogenous, siRNA-resistant BLM can rescue the phenotypes they see on cell cycle progression and TMEJ. Alternatively, they could also make use of at least one of the many BLM knockout cell models that have been generated previously by many labs (including in both RPE-1 (Tsukada et al., Mol. Cell 2024) and U2OS (Jiang et al., Mol. Cell 2024) backgrounds), where exogenous BLM and helicase-dead BLM have been added back. This would be especially important to do if the authors cannot confirm the potency and selectivity of BLM-IN-1, as suggested above. *

      Please see the response to Point 1.

      • How do the authors know whether their EXO1 depletion is working in Figure S4C? EXO1 loss is synthetic lethal with BRCA1 deficiency (van de Kooij et al., Mol. Cell 2024 and Tsukada et al., Mol. Cell 2024), so I would have expected to see a drop in cell viability when EXO1 is depleted in BRCA1-deficient cells. Also, western blots for RPE-1 in Fig. 1C are missing; as are ones for Figures S4A-C. In Figure S4D, blots are not of sufficient quality, alternative ones should be provided. What does "stain-free" mean here? *

      Given the removal of data involving siBLM treatments, this Western blot has been removed. Instead, we validate target transcript knockdown by qRT-PCR. In the case of DNA2, the effects of an siRNA knockdown are corroborated with a small molecule inhibitor.

      *Minor comments: *

        • I find the title of the manuscript somewhat lacking clarity/precision. It is not clear what the word "its" is referring to. * Given the shift in focus of the manuscript, the manuscript has been retitled: “Activation of Pol theta-mediated end joining begins during mitotic commitment”.
      1. Lines 23-24: TMEJ is not just a back-up for HR, but also for NHEJ. *

      We have edited this text in the revised manuscript and now focus on TMEJ’s ability to act on resected, unresolved DSBs that persist to the G2/M transition .

      Lines 27-28: who has proposed this? Consider re-phrasing this (see second paragraph of summary above).

      We have rephased this section in the revised manuscript (paragraph beginning at line 71). Our rationale was not based on long-range resection being a strict, biochemical requirement for TMEJ, but rather was based on the observation that many of the proposed substrates for TMEJ (overly resected DSBs that cannot be ligated by NHEJ, or aborted HR intermediates) are downstream of long-range resection. For example, in Vergara, et al. 2023, Nat Comm loss of BLM (or RMI2) suppresses MMEJ repair in an MMEJ vs NHEJ reporter assay. Indeed, Truong et al. 2013 PNAS indicates that BLM loss actually promotes MMEJ, but notably this assay only measures competition between HR and MMEJ outcomes, rather than MMEJ vs NHEJ.

      • Line 32: DNA should be DNA2. *

      Thank you, this error has been corrected.

      • Line 71: Sgs1 should be in italics. *

      Thank you, this error has been corrected.

      • Line 147: NHEJ is not active throughout the cell cycle; it is inhibited during mitosis. *

      Thank you, this has been corrected to “throughout interphase” (Line 92).

      • Line 242: the word "on" is missing after the word "Based". *

      Thank you, this error has been corrected.

      • Line 282: replace resolution with dissolution. *

      Thank you, this text was removed in revision.

      • Line 482: it is already known that BRCA-deficient tumours accumulate mutational signatures attributed to TMEJ. *

      Thank you, this text was removed in revision.

      • Line 484: any role of BRCA1 in resection is highly controversial; but anyway no-one has claimed that BRCA1 is required for short-range resection by MRE11, which is all that would be required for TMEJ. *

      While not strictly required for either short or long-range resection, we are quite open to the model of BRCA1 stimulating resection via its interaction with CtIP and general antagonism of 53BP1 recruitment (Cruz-Garcia, et al. 2014 Cell Rep, Densham et al. 2016 Nat Struct Mol Biol). Regardless, this section has been removed to better focus on the cell cycle control of TMEJ.

      • Line 500: deficient is mis-spelled. *

      Thank you, this error has been corrected.

      *Lines 500-520: work by van de Kooij et al. and Tsukada et al. is not properly referenced here. Both already showed that BLM loss is synthetic lethal with BRCA1 deficiency, but interestingly, van de Kooij et al. showed that 53BP1 loss rescues the BRCA1-BLM synthetic lethality, whereas Tsukada et al. found that it does not, despite the fact that it rescues olaparib sensitivity. The authors' results seem to support the latter. Also, hypersensitivity to olaparib in BLM-depleted cells was already shown previously by Gottipati et al. Cancer Res. 2010. *

      We thank the reviewer for their comments; however, this data and discussion of BRCA1 and BLM has been removed.

      • Lines 519-520: it is not clear to me why the results in Figure 6 provide an alternative mechanism contributing to synthetic lethality between BLM and BRCA1 from that shown previously. *

      See Point 12.

      • Methods: siRNA sequences should be provided. *

      We apologize for the oversight. While some siRNA sequences are proprietary, we have included the source for each commercially available reagent in the Materials and Methods.

      ***Referees cross-commenting** *

      *I agree with most of the comments from the other reviews. As stated by Reviewer 2, it is nice to see significant overlap between the three reviews. *

      *It seems all three of us think the manuscript needs a significant re-write for clarity purposes, to make it read less like a "journey" and more like the synthesis of existing observations that it actually is. It is important for the field to know that EXO1, BLM and DNA2 do not contribute directly to TMEJ, and that any effects that can be observed indicating that they do, can likely be put down to cell-cycle progression defects. *

      *Where I differ from reviewer 1 and 2, is in the need to carry out experiments with the POLQ-10A mutant from the Ceccaldi paper. There are multiple reasons why that might not work, so I think that is unnecesary. But I otherwise agree with reviewer 2 that the authors need to address all the other points from all three of us. *

      We have endeavored to address the general critiques of the reviewers by rewriting the manuscript and focusing directly on the cell cycle regulation of TMEJ.

      *Reviewer #3 (Significance (Required)): *

      *The key conclusions the authors draw are in large part convincing, as most experiments in the manuscript are replicating previously published findings. There is no reason to believe that long-range DNA-end resection would be required for POLQ-mediated end-joining (TMEJ), because TMEJ does not need longer overhangs than those MRE11 and CtIP can already provide via initial end-processing (50-100 nt). The fact that BLM and EXO1 do not promote microhomology-mediated end-joining was already shown in 2013 by Truong et al., PNAS. *

      The manuscript reads a bit like a warning that cell cycle progression defects must be taken into account when measuring POLQ-mediated end-joining (TMEJ) repair events; but this is not really a novel insight, given the recent reports that POLQ is most active in mitosis (work from Löbrich, Sfeir and Ceccaldi labs).

      We thank the Reviewer for their comments and have endeavored to refocus, clarify and emphasize the novel aspects of our study, namely evidence for a period of late G2 during which TMEJ is licensed through Aurora A-PLK1 with additional complex, TMEJ-mediated repair occurring in mitosis.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary and significance:

      In this manuscript, the authors describe mutational outcomes at a single site-specific DNA double-strand break induced by Cas9 in RPE-1 and U2OS cells. They find that depletion of BLM or DNA2 using siRNA or treatment of cells with small molecule inhibitors causes a shift from DNA polymerase theta (POLQ)-dependent repair outcomes to classical non-homologous end-joining (NHEJ)-mediated ones. In contrast, EXO1 depletion has no effect. They go on to show that this is not due to any potential role for BLM and DNA2 in DNA-end resection, but rather because under their experimental conditions, loss of BLM or DNA2 leads to cell cycle disruption such that fewer cells make it into mitosis, where POLQ is most active.

      The key conclusions the authors draw are in large part convincing, as most experiments in the manuscript are replicating previously published findings. There is no reason to believe that long-range DNA-end resection would be required for POLQ-mediated end-joining (TMEJ), because TMEJ does not need longer overhangs than those MRE11 and CtIP can already provide via initial end-processing (50-100 nt). The fact that BLM and EXO1 do not promote microhomology-mediated end-joining was already shown in 2013 by Truong et al., PNAS.

      The manuscript reads a bit like a warning that cell cycle progression defects must be taken into account when measuring POLQ-mediated end-joining (TMEJ) repair events; but this is not really a novel insight, given the recent reports that POLQ is most active in mitosis (work from Löbrich, Sfeir and Ceccaldi labs).

      The manuscript would be suitable for publication if the authors could address the points below.

      Major comments:

      1. Major conclusions are drawn about effects of BLM loss on cell cycle progression based on the use of only a single siRNA, and the use of reported chemical inhibitors of BLM DNA-binding activity. Unfortunately, the ML216 inhibitor the authors use was found not to be a selective or potent inhibitor of BLM (Oliver lab, eLife 2021), and BLM-IN-1 has not been sufficiently well-characterised in cells yet as far as I am aware. The authors need to remove the ML216 data, as any effects they see are likely just due to the toxic effects of this compound rather than any effect on BLM specifically. Have the authors checked SCEs in cells treated with BLM-IN-1? If they do not see the characteristic 5-10x increase in SCEs that is observed in all BLM-deficient cells, then they can also not trust any results they get with that compound either.
      2. I would strongly recommend that the authors confirm their BLM siRNA results (experiments in Fig. 1C, 3C and 4A) by examining whether add-back of exogenous, siRNA-resistant BLM can rescue the phenotypes they see on cell cycle progression and TMEJ. Alternatively, they could also make use of at least one of the many BLM knockout cell models that have been generated previously by many labs (including in both RPE-1 (Tsukada et al., Mol. Cell 2024) and U2OS (Jiang et al., Mol. Cell 2024) backgrounds), where exogenous BLM and helicase-dead BLM have been added back. This would be especially important to do if the authors cannot confirm the potency and selectivity of BLM-IN-1, as suggested above.
      3. How do the authors know whether their EXO1 depletion is working in Figure S4C? EXO1 loss is synthetic lethal with BRCA1 deficiency (van de Kooij et al., Mol. Cell 2024 and Tsukada et al., Mol. Cell 2024), so I would have expected to see a drop in cell viability when EXO1 is depleted in BRCA1-deficient cells. Also, western blots for RPE-1 in Fig. 1C are missing; as are ones for Figures S4A-C. In Figure S4D, blots are not of sufficient quality, alternative ones should be provided. What does "stain-free" mean here?

      Minor comments:

      1. I find the title of the manuscript somewhat lacking clarity/precision. It is not clear what the word "its" is referring to.
      2. Lines 23-24: TMEJ is not just a back-up for HR, but also for NHEJ.
      3. Lines 27-28: who has proposed this? Consider re-phrasing this (see second paragraph of summary above).
      4. Line 32: DNA should be DNA2.
      5. Line 71: Sgs1 should be in italics.
      6. Line 147: NHEJ is not active throughout the cell cycle; it is inhibited during mitosis.
      7. Line 242: the word "on" is missing after the word "Based".
      8. Line 282: replace resolution with dissolution.
      9. Line 482: it is already known that BRCA-deficient tumours accumulate mutational signatures attributed to TMEJ.
      10. Line 484: any role of BRCA1 in resection is highly controversial; but anyway no-one has claimed that BRCA1 is required for short-range resection by MRE11, which is all that would be required for TMEJ.
      11. Line 500: deficient is mis-spelled.
      12. Lines 500-520: work by van de Kooij et al. and Tsukada et al. is not properly referenced here. Both already showed that BLM loss is synthetic lethal with BRCA1 deficiency, but interestingly, van de Kooij et al. showed that 53BP1 loss rescues the BRCA1-BLM synthetic lethality, whereas Tsukada et al. found that it does not, despite the fact that it rescues olaparib sensitivity. The authors' results seem to support the latter. Also, hypersensitivity to olaparib in BLM-depleted cells was already shown previously by Gottipati et al. Cancer Res. 2010.
      13. Lines 519-520: it is not clear to me why the results in Figure 6 provide an alternative mechanism contributing to synthetic lethality between BLM and BRCA1 from that shown previously.
      14. Methods: siRNA sequences should be provided.

      Referees cross-commenting

      I agree with most of the comments from the other reviews. As stated by Reviewer 2, it is nice to see significant overlap between the three reviews.

      It seems all three of us think the manuscript needs a significant re-write for clarity purposes, to make it read less like a "journey" and more like the synthesis of existing observations that it actually is. It is important for the field to know that EXO1, BLM and DNA2 do not contribute directly to TMEJ, and that any effects that can be observed indicating that they do, can likely be put down to cell-cycle progression defects.

      Where I differ from reviewer 1 and 2, is in the need to carry out experiments with the POLQ-10A mutant from the Ceccaldi paper. There are multiple reasons why that might not work, so I think that is unnecesary. But I otherwise agree with reviewer 2 that the authors need to address all the other points from all three of us.

      Significance

      The key conclusions the authors draw are in large part convincing, as most experiments in the manuscript are replicating previously published findings. There is no reason to believe that long-range DNA-end resection would be required for POLQ-mediated end-joining (TMEJ), because TMEJ does not need longer overhangs than those MRE11 and CtIP can already provide via initial end-processing (50-100 nt). The fact that BLM and EXO1 do not promote microhomology-mediated end-joining was already shown in 2013 by Truong et al., PNAS.

      The manuscript reads a bit like a warning that cell cycle progression defects must be taken into account when measuring POLQ-mediated end-joining (TMEJ) repair events; but this is not really a novel insight, given the recent reports that POLQ is most active in mitosis (work from Löbrich, Sfeir and Ceccaldi labs).

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, the authors use two cell lines, U2OS and RPE1, to investigate the effects of impeding long-range end resection on alternative end-joining (TMEJ). They monitor repair of a CRISPR-induced DSB, presenting their findings as the ratio of TMEJ to cNHEJ outcomes. Initially, the authors observe that inhibiting BLM and DNA2 produces a TMEJ-deficient phenotype similar to that resulting from pharmacological inhibition of PolQ, suggesting a potential role for long-range end resection. However, this inference is challenged by evidence showing that genetically or pharmacologically perturbed cells fail to progress through the cell cycle, not reaching the previously reported cell cycle phase in which TMEJ typically occurs. Although it remains an open question whether TMEJ acts exclusively in M phase (given MMEJ manifestations in G0/G1 cells), the authors attempt to pinpoint the cell cycle stage of TMEJ-mediated DSB repair by using cell cycle inhibitors. However, in my opinion, these assays and experiments fall short of providing conclusive evidence. I therefore feel that the data is best presented focusing on the cellular consequences of impeding BLM/DNA2, and not on TMEJ, which is basically used as a marker/proxy for mitosis/mitotic cells

      In the discussion, the authors themselves suggest several additional experimental approaches that would strengthen their conclusions or deepen their investigations. I believe that implementing such strategies would broaden the impact and interest; in their absence (I do not suggest the authors to start performing these time and resource consuming experimentation to be included in the current manuscript) the study has a more specific target audience.

      Major comments

      i) The primary issue with this manuscript is that it does not present the experimental conclusions in a straightforward manner. Instead of focusing on what was learned, it reads more like a "journey." This issue is apparent from the abstract: 13 out of 16 lines are devoted to describing the authors' initial intent to investigate the role of long-range end resection enzymes in TMEJ, only to reveal that the observed effect is actually an indirect consequence of cell cycle perturbations. The manuscript uses TMEJ as a proxy for cells not reaching mitosis, thus supporting the conclusions reached from the more direct cell cycle analyses. While the majority of the experiments address the effects of inhibiting BLM and DNA2, the abstract suggests that these findings reveal new insights about TMEJ, which is doubtful or at most limited. The only potential novelty regarding TMEJ, aside from confirming its regulation by the cell cycle, is the narrowing down of the specific substage in which it operates. However, this conclusion is inferred from indirect effects rather than direct measurement, and also somewhat debatable by being dependent on a very explicit interpretation of the cell cycle inhibitors that are being used (whose action in my experiences are not as strict/narrow as here considered).

      ii) I do not have an immediate solution to this concern but feel the data itself is valuable. I also appreciate the authors' thorough approach in conclusively establishing that the observed disruption of TMEJ upon BLM/DNA2 depletion is a downstream consequence of cellular phenotypes, rather than an anticipated resection issue at CRISPR-induced DNA breaks. I would thus recommend rewriting the manuscript to reflect this perspective.

      iii) Some conclusions are overinterpreted, especially regarding the timing of DNA repair based on mutant spectra analysis. For instance, in lines 257-258, the authors conclude, "by the same timepoints, we find over 65% of DSBs have been repaired with mutagenic indels, indicating high transfection efficiency and rapid DSB induction." This statement is inaccurate for several reasons: (1) Given the iterative cycles of DSB induction, error-free repair, and recutting, it's possible that, for example, only 15% of DSBs have actually been repaired with mutagenic indels; (2) The assay detects only intact molecules, so unrepaired DSBs/broken molecules are not accounted for, especially those that are not resected and thus escape detection in Figure 3; and (3) The assay captures only deletions that retain the primer binding site. A more accurate statement would thus be something like, "65% of intact alleles at this timepoint contain mutations indicative of error-prone repair, indicating high transfection efficiency and robust (rather than rapid) DSB induction." The use of "rapid" is questionable here, as I consider 24 hours a long time, given that NHEJ typically occurs within an hour.

      iv) While I'm hesitant to request additional experiments, I believe the HR measurements of siDNA2 through the mClover assay, alongside the siNT condition, require additional replicates. Currently, these results are marked as non-significant in Figure 3D but labeled with a "+" instead of "+++" in Figure 3E. Additionally, the combination siBLM+siEXO1 is marked with a "-" in Figure 3E, though a statistical comparison of single siDNA2 to the double combination would likely result in a non-significant outcome. This makes it difficult to draw firm conclusions, and further biological replicates would help clarify the data; two of the current data points are wildtype, while one aligns with siBLM levels.

      v) Another major concern is the potential off-target effects of siRNA, as the reported phenotypes might partly stem from this issue. None of the experiments have been validated using complementing constructs resistant to targeting. Although the inhibitor data is useful, not all experiments have been validated with this method. Adding these controls would be beneficial but is ultimately "optional". In their absence, I recommend the authors temper their conclusions regarding siRNA-only data.

      Minor comments

      1. The title is unclear: "its" refers to a repair pathway, which is here not demonstrated to be "sensitive" but instead not employed in cells where BLM-DNA2 is inhibited and/or downregulated. It is to me also somewhat off point: it turns out that TMEJ is a proxy for reading out a cell cycle stage, and I feel that the title should reflect the more direct consequences induced by loss of BLM-DNA. Otherwise one could substitute TMEJ for any other mitotic process creating a "true" statement.
      2. To me, the readability of the manuscript would be improved by shortening the text: Especially the intro is very lengthy and reads like a review instead of focusing on providing the information necessary to understand the experiments/logic of the result section. Additional information that is relevant for discussing the outcomes of the experiments can be introduced at that stage. I realize this is also a matter of style but I felt that I had to go through a lot of context before getting to the actual data, which did not necessarily brought new insight into all what was discussed before.
      3. Typo's: Line 242, missing "on" (Based on...); line 590, missing "to" (important to test)
      4. I suggest to have the y-axis of the histograms in the figures to be identical. For example Figure 1 C en D have three histograms with Y-axis going to 4, but one going to 3. For consistency and comparison it is more useful to have all of them going to 4. Idem for Figure 2 where D deviates from A-C (I understand that e.g. has no datapoint exceeding the arbitrary mark 3 but it allows for a better comparison of D to A-C if the Y-axes are identical.
      5. Please specify, upon first usage, e.g. in the legend of a Figure how the ratio TMEJ:NHEJ is defined (instead of only in the method section). Also mention in the text that this is an arbitrary and subjective unit used for clarity which is indicative but not an absolute measure for the underlying biology: all product that have a MH of 2 and higher are binned under TMEJ, but surely non-sequence guided NHEJ will also produce deletions that, just by pure chance, have junctions in which 2 bp overlap. Also, not all TMEJ outcomes have 2bp of MH. NHEJ outcomes are thus in the TMEJ subclass and vice versa. This is not problematic to me but it should be kept in mind, especially in interpreting mutant backgrounds.
      6. Related to the above I also want to propose that representative spectra of all relevant experiments are presented as supplementary information instead of flattening the entire spectrum in one number, in which all nuance but also details may be lost, which can be informative to the TMEJ and NHEJ afficionados.

      Referees cross-commenting

      I agree with all the points brought up by the other 2 reviewers (and glad to see so much overlap). And thus feel that the authors need to address all them.

      I initially was somewhat reserved on asking for additional experiments addressing potential off-targets of the siRNAs underlying some of the phenotypes (no experience myself using si's as opposed to sg's and clean knock-out models) wanting to see how the other reviewers potentially more experienced with this technology would weigh this concern. Having read their reports I thus als underwrite reviewer #3's remark: 2. I would strongly recommend that the authors confirm their BLM siRNA results (experiments in Fig. 1C, 3C and 4A) by examining whether add-back of exogenous, siRNA-resistant BLM can rescue the phenotypes they see on cell cycle progression and TMEJ. Alternatively, they could also make use of at least one of the many BLM knockout cell models that have been generated previously by many labs (including in both RPE-1 (Tsukada et al., Mol. Cell 2024) and U2OS (Jiang et al., Mol. Cell 2024) backgrounds), where exogenous BLM and helicase-dead BLM have been added back. This would be especially important to do if the authors cannot confirm the potency and selectivity of BLM-IN-1, as suggested above."

      Final comment from my end: I perhaps was somewhat unclear or went too fast: I've read concern #3 of reviewer 1 in the light of "the authors should consider..." thinking it was "optional". I agree with reviewer #3 that it is not essential.

      Significance

      This study is of interest to those studying the long range end resection enzymes including DNA2, BLM and EXO1, and to researchers with in depth knowledge and interest into the regulation of TMEJ. The study is limited in providing very concrete new information - it is somewhat descriptive (e.g. the study doesn't provide insight into why cells arrest at a given stage in the absence of BLM or DNA2 functionality). Hence, I feel that the outcomes are of interest to specialist as I feel that the individual observations are of relevance but there is not a impactful overarching gain of knowledge. The data can be used by others to further design experiments aimed to elucidate the roles of specific DNA repair enzymes.

      Of note and as requested, my field of interest is repair of DNA breaks: EJ, cNHEJ, altEJ.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      DNA double-strand breaks can be repaired by NHEJ, TMEJ, or HR. While there have been many studies of the factors that regulate double-strand break repair pathway choice, the exact mechanisms by which TMEJ is regulated remain under active study. In this study, McBrine and colleagues conducted experiments in two human cell lines to investigate the contribution of DNA resection to TMEJ. After induction of Cas9 breaks at a single locus, they used NGS amplicon sequencing and a pre-existing computational pipeline to quantify repair by inaccurate NHEJ or TMEJ. Their initial data suggested that loss/inhibition of the BLM DNA helicase or DNA2 nuclease inhibit resection and thereby decrease TMEJ repair. Probing further, they conducted carefully controlled cell cycle analysis and determined that loss of either BLM or DNA2 slows progression to the G2/M transition, when TMEJ is activated through PLK1 phosphorylation of polymerase theta. Based on these findings, they conclude that both BLM and DNA2 promote TMEJ repair, likely by promoting efficient replication and timely progression into G2/M.

      Major comments

      Overall, the authors' conclusions are well-supported by their data. They used multiple approaches to validate their findings, including the use of siRNAs and small molecule inhibitors that largely produce similar results. However, three major additions/experiments should be included.

      1. First, most of the data from the NGS sequencing results are displayed as TMEJ/NHEJ ratios. While this makes it easy to compare between different experimental conditions, it could mask differences in overall repair efficiency. I recommend that the authors include the initial percentages of TMEJ-like and NHEJ-like repair junctions in supplemental information, so that readers can determine the efficiencies of the different repair pathways in each condition.
      2. Second, the DSB repair spectrum (as displayed in Figure 1B) should be provided for each TMEJ:NHEJ plot in supplemental information, at the very least the for knockdown/inhibitor experiments where a significant difference in TMEJ:NHEJ ratio is observed.
      3. Finally, the conclusion that transition to the G2 stage (where polymerase theta becomes activated through phosphorylation by PLK1) is necessary for TMEJ is logical, based on the data and previously published studies. However, to provide additional support for this model, the authors should consider repeating the experiments from Figure 5B/5C using cells with the PLK1 phosphorylation sites on polymerase theta mutated (e.g., using the 10A construct from Gelot et al., doi.org/10.1038/s41586-023-06506-6). The expectation is that the TMEJ reduction observed with BLM or DNA2 inhibition should not decrease further, if indeed the decrease is largely due to an inability to progress to the point in the cell cycle where polymerase theta is activated by phosphorylation. This experiment should be able to be completed within 1-2 months, if the appropriate cell line can be obtained.

      Minor comments

      1. The cell cycle plots in Figures 4 and 5 are difficult to interpret given the current color scheme (especially the difference between S and G2). I suggest changing the color scheme and arranging the legend so that the key is in the same order as the plots (G1 on the bottom, S in the middle, G2 on the top).
      2. In Figure 3B, the resection time course is interesting, but I wondered why they are no time points between 12 and 24 hours? Also, if the time course was performed multiple times, it would be nice to see these data represented in the plot, along with standard error bars.

      Referees cross-commenting

      I agree with Reviewer #2 that "The primary issue with this manuscript is that it does not present the experimental conclusions in a straightforward manner. Instead of focusing on what was learned, it reads more like a journey." Based on the abstract, I had expected to learn more about the role of BLM and DNA2 in regulating TMEJ via resection, but the crux of the story is about cell cycle effects impacting TMEJ when the levels of these proteins are reduced. Rewriting would help address this.

      Reviewer 2 also asked that that representative spectra of all relevant experiments are presented as supplementary information instead of flattening the entire spectrum in one number. This seems critical in a revised manuscript.

      Significance

      I am a basic scientist studying DNA repair mechanisms and have a long-standing interest in the mechanism of TMEJ.

      In my opinion, the results from this study will be interesting to scientists studying double-strand break repair pathway choice and those thinking about how inhibition of different DSB repair proteins can be used in cancer treatments. The strongest aspect of the study is the way in which the authors investigated the effect of BLM, DNA2, and EXO1 knockdown/inhibition on cell cycle progression, and used the data to make conclusions about the most important factors influencing TMEJ repair. The experiments are carefully controlled, and the most important conclusions are supported by multiple lines of experimental reasoning. As stated above in the summary, the study can be improved by the inclusion of more of the primary data in the supplemental information, and by the addition of a key experiment that will further support the authors' model.

      The study supports the recent findings from the Sfier and Ceccaldi labs that the main driver of TMEJ, polymerase theta, operates primarily during mitosis, and its activation depends on PLK1. Further, it presents initial data on the intriguing finding that BRCA1 deficiency is synthetic lethal with BLM deficiency, and this can be enhanced with Olaparib treatment, which will be of interest to translational and clinical scientists.

      Overall, the study is notable because of its careful attention to cell cycle progression on DSB repair outcomes, and why caution needs to be taken when interpreting how genetic perturbation of DNA repair proteins impacts repair mechanisms.

  2. Sep 2026
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      Reply to the reviewers

      We thank the reviewers for the constructive criticism. All issues are addressed in the following point-by-point response.

      We addressed a major issue of the reviewers by adding a domain-deletion study. This study shows that Ig-like domain 3 of CADM1 is responsible for the interaction. Thus, we now propose a clear mechanism for CD82 regulation of CADM1 adhesion function.

      Point-by-point description of the revisions

      Reviewer #1:

      Major Comments:

      • Using a variety of elegant and sophisticated studies, the authors clearly present data that strongly support their conclusions of the interaction between CADM1 and CD82. Additionally, they speculate on the specific interaction sites/mechanism through which CD82 may affect the spacing of CADM1, which is supported by cryo EM. However, they do not go on to demonstrate a physiological role for this inhibitory interaction.

      In this MS we identify an interaction between CADM1 and CD82. Though we find an inhibitory activity, indeed we do not present in vivo data supporting a physiological role.

      Our observation of CADM1-CD82 interaction in HEK cells and Jurkat Tcells, co-expression of the two proteins in several tissues (based on human protein atlas) and shared relevance in metastasis support a physiological role.

      Minor Comments:

      • In Figure 4, there seems to be a significant size difference between WT and CD82KO Jurkat cells. If this is the case, this needs to be described a bit more. Additionally, how does this potential size difference impact the quantification of the percentage space covered by CADM1 clusters? Also, for these experiments, it is more appropriate to use the mean of each independent replicate rather than simply counting each cell as an n. Including these data as super plots so the reader can better understand the spread and reproducibility of the data would be ideal. Best practices suggest that statistical analysis should be performed on data from independent experiments (Lord et al 2020 PMID: 32346721).

      With regard to the size difference, this may indeed represent a phenotype associated with CD82 knockout. A modest difference in cell size was observed, although this did not reach statistical significance (Reviewer Fig. A). As all quantitative analyses in Figure 4 were performed at the cell surface, cell volume was not a parameter used in the primary analysis. To provide a more complete visualisation of the cellular context, we additionally assessed 3D cell dimensions and used these measurements to examine whether the observed clustering phenotype was influenced by cell size. Restricting the analysis to cells with a Z-projected area of 100-150 µm² yielded the same trends in percentage space covered (Reviewer Fig. B), cluster density (Reviewer Fig. C), and cluster size (Reviewer Fig. D). These findings support the conclusion that the differences reported in Figure 4 are maintained independently of the modest differences in cell size observed. We have not included these additional analyses in the revised manuscript, as they do not alter the overall conclusions and may distract from the main message, particularly as the size difference could represent an indirect consequence of CD82 loss. We would, however, be happy to include these data should the editor feel they would strengthen the manuscript. In addition, we have replaced the representative image of the CD82 KO cell with one that more accurately reflects the modest size difference observed.

      We have now updated the graphs (visualizing the independent experiments) to be in-line with Lord et al. 2020.

      Reviewer #2:

      __ __ 2.1 Line 101-102- In MS analysis, Table 1, several common proteins are present in all three bands analyzed by MS including CD82. Authors should explain why CD82 is observed in 250 KDa band in relatively high abundance?

      The intensity of CD82 in the 250-kDa band is 30-50x lower than observed in the other two bands (90-100 and 100-110 kDa).

      The detection of some CD82 in the 250-kDa band likely originates from partial aggregation due to freeze thawing or temporary storage at 4 degrees. Reducing sample buffer was added to all samples but samples were not boiled and partial aggregation is visible in SDS-PAGE gel.

      2.2 Line 118-119, authors should make a better explanation why the lower MW CADM1 band is not observed in co-expression, especially that lower MW band is also absent in CADM1-Strep purification FigS2b? Although the migration pattern of CD82 and CADM1 is similar to glycosylated proteins authors should confirm this band doubling is due to glycosylation. It is crucial because a great portion of the manuscript relies on protein separation on SEC that can be greatly impacted by glycosylation state of the proteins.

      A lower band was observed in the SDS-PAGE gel in fig. S2b (Now Fig. S3b). We now clearly indicate the double band with a bracket instead of the single arrow.

      Regarding the N-glycosylation of CD82, Wang et al. (https://doi.org/10.1016/j.jprot.2011.11.013) demonstrate band shifts of the upper band of CD82 by side-directed mutagenesis.

      In case of CADM1, we tried to validate the bands with additional experiments not included in the manuscript. If the reviewer finds it necessary this figure can be included. We demonstrated that at least the upper band at 90 kDa purified with CD82-gfp-strep shifts to 45 kDa, the expected MW of CADM1, when mutating all N-glycosylation sites to Glu. However, this mutant showed low expression and low stability. To further confirm that the double-band pattern is caused by glycosylation, we treated CADM1(ECD) and CADM1(F42S, ECD) with O-glycosidase, neuroaminidase and PNGaseF. The SDS-PAGE gels show a double band before and a single band after digestion.

      With respect to N-glycan variants in SEC:

      We often observed narrow double peaks for CD82-strep purifications (Fig. S6A, S8B,G), likely caused by the two N-glycosylation variants. However, when fused to eGFP, we can no longer distinguish the two states on SEC (although we still see a double band on SDS-PAGE gel). Consistently, we observed single peaks for different constructs of CADM1 in SEC, while observing double bands on SDS-PAGE gel. Likely, the two variants migrate very close to each other and can only be separated when the added MW of glycans is relatively high. We therefore don’t think that the large shifts of peaks for CADM1 or CADM1-CD82 complex can be caused by N-glycosylation.

      2.3 Line 120-considering the lower yield of CADM1 in co-expression studies and presence of CD82 protein in MS analysis of band 3, it raises the question whether the peak eluted at 10 mL in F-SEC chromatogram of co-expressed proteins is from a legit CD82-CADM1 complex or it is simply CD82 oligomerization/interaction with other proteins. I would suggest authors purify GFP-CD82 and CADM individually and run the mixture over F-SEC to confirm complex formation rather than running co-purified protein. The same experiment should be done with CD82-CADM1mixture without fusing GFP to rule out the possible influence of GFP on dimerization.

      The detection of CD82 in the 250 kDa is likely due to aggregation (see 2.1).

      Indeed, based on SEC results resulting from our small-scale expressions (Fig. S2E) we cannot conclude the composition of the 10 mL peak. In our large-scale experiment, we put single fractions of the SEC on gel and only see bands of CADM1 and CD82 for earlier peaks (Fig.1A-B). From this, we conclude the 12.5 mL peak in fig. 1A is composed of CADM1 and CD82.

      Mixing two detergent-solubilized membrane proteins to analyze complex formation is not straight forward due to the possible interference of micelles.

      2.4 Figure 3. The color scheme used makes it really difficult to follow curves for different concentrations. I would suggest using distinct colors for each concentration.

      We changed the colors of graphs showing different concentrations in figure 2, figure 3, S7 and S8 to a better distinguishable color palette.

      2.5 274-275. Authors argue that the CADM clusters occupy more surface area in CD82KO cells compared to normal cells. From figure 4C. it seems that CD82KO cells are smaller in size than normal cells? Is that true? If that's the case, authors should provide evidence that the expression/surface area is the same for both normal and CD82KO cells before making the conclusion. If the expression level remains the same between two cell types but the size of KO cell is smaller, it is expected that CADM will occupy more surface area on KO cells. Consequently, the clusters are expected to be bigger. Therefore, the smaller cluster size in normal cells is not directly caused by CD82 but by smaller cell size due to CD82KO.

      With regard to the size difference, this may indeed represent a phenotype associated with CD82 knockout. A modest difference in cell size was observed, although this did not reach statistical significance (Reviewer Fig. A). As all quantitative analyses in Figure 4 were performed at the cell surface, cell volume was not a parameter used in the primary analysis. To provide a more complete visualisation of the cellular context, we additionally assessed 3D cell dimensions and used these measurements to examine whether the observed clustering phenotype was influenced by cell size. Restricting the analysis to cells with a Z-projected area of 100-150 µm² yielded the same trends in percentage space covered (Reviewer Fig. B), cluster density (Reviewer Fig. C), and cluster size (Reviewer Fig. D). These findings support the conclusion that the differences reported in Figure 4 are maintained independently of the modest differences in cell size observed. We have not included these additional analyses in the revised manuscript, as they do not alter the overall conclusions and may distract from the main message, particularly as the size difference could represent an indirect consequence of CD82 loss. We would, however, be happy to include these data should the editor feel they would strengthen the manuscript. In addition, we have replaced the representative image of the CD82 KO cell with one that more accurately reflects the modest size difference observed.

      2.6 Line 205-207. Authors show that CADM1(ECD) is not interacting with CD82 and conclude that interaction between transmembrane helices is required. It raises the question that how in cryoEM, authors could capture interaction between Ig domain of CADM1 and CD82 between two different micelles? Providing such details about experimental setup helps readers to adopt the method for structural determination of elusive complexes.

      For the cryo EM sample a complex of full-length CADM1 and CD82 was purified. The 2D classes we observe showing two connected micelles are likely caused by interaction of the CADM1 ectodomain, forming a complex of two dimers of CADM1 and CD82 in one micelle (CD82-CADM1)—(CADM1-CD82).

      Reviewer #3:

      Major comments:

      3.1 In Figure 1, the second co-purification is much less efficient than the first (1C vs 1A). Perhaps removing Figure 1C and the associated SEC trace (or moving to the supplement) would make for an easier-to-understand figure panel. I was initially confused about the low efficiency, and it raised some doubts while I was first interpreting this figure. The large-scale purification in 1F and 1G looks much more convincing than what is shown in 1C.

      We moved all small-scale experiments into a supplemental figure (S2) and truncated the text where possible to leave the main focus on the large-scale purification.

      3.2 I found the experiments in Figure 2 very hard to follow, and the accompanying text needs improvement before publication. Why was CADM1 ECD rather than full-length used for SEC-MALS? It seems like the authors are trying to claim that CD82 reduces CADM1 oligomerization based on a smaller shoulder peak at ~10-12 mL with the fusion construct compared to CADM1 alone. However, it looks like 10 mL is the void volume of this column- wouldn't another explanation be the CADM1 sample simply has a higher percentage of misfolded or aggregated protein, and it not necessarily an "oligomer"?

      We modified the text describing the results in Fig. 2 to improve readability.

      Performing SEC-MALLS with membrane proteins is not straight forward, due to the presence of empty micelles causing an unstable baseline and the possibility that micelles fuse or separate. Therefore, we only performed SEC-MALLS with soluble CADM1(ECD) to have a clear readout.

      The void volume of the column is at ca. 8.5 mL EV. The void peak is indeed barely visible as we use the protein directly after the SEC for purification without concentrating. In the SECs for protein purification the void peak at 8.5 mL is more visible (Fig. S3B,D). The same column has been used for both experiments connected to different systems (AKTA and HPLC).

      Further, we think the 10 mL peaks are complexes instead of aggregation because we see a shift to higher EV after dilution during the SEC after reinjection as expected for concentration dependent complex formation.

      3.3 The liposome assay is a clever way to assess if CD82 alters CADM1 oligomerization. However, details of how this assay was performed need clarification, and I am not convinced of the authors' conclusions given the current presentation. In the S4A, the authors show that CADM1 ECD His does not co-purify with CD82, but then CADM1 ECD His was used in liposome experiments to assess how CD82 changes clustering. Why was full-length CADM1 not used? For quantification, the average liposome radius is reported, but to me, a more robust quantification would be the percentage of liposome area contacting another liposome (i.e., ~35 nm distance between two liposomes), if the authors are trying to assess how CD82 affects CADM1 clustering. How does the average liposome radius reflect CADM1 clustering?

      Reconstituting both membrane proteins in full-length would indeed be the more native approach. However, we chose to anchor CADM1(ECD)-his to the surface of the liposome for several practical reasons. We used CADM1(ECD)-his because it gave us the possibility to “titrate” different CADM1 concentration against one sample of reconstituted liposomes. We cannot estimate how much CD82 is successfully reconstituted into the liposome. When we reconstitute full length CADM1 mixed with CD82 it was difficult to validate if both proteins and in which ratio they are reconstituted.

      We were trying to use cryo-EM pictures to quantify cluster sizes using the grey gradient but this proved difficult even using a single grid with consistent imaging settings because of the ice gradients over the grid and ice contamination. Observed clusters are 3-dimensional and we can only observe single liposomes at the very edge of it.

      The clusters we observed when looking and reconstituted liposomes looked very different from non-reconstituted liposomes. We added examples to figure S5. The clusters are overall less dense and liposomes cluster but not with a consistent distance as we observe it for WT CADM1.

      The DLS reads out the particle sizes and shows two peaks at 100 and 300 nm after adding CADM1(ECD)-His. We interpret this as CADM1 connecting micelles as observed on cryoEM images and therefore causing an increase in average particle size.

      3.4 The AlphaFold model shown in main text Figure 5 is not high confidence, and I suggest moving this to the supplement. To validate that the LEL of CD82 interacts with Ig1 of CADM1 as suggested by the EM density, the authors should perform mutagenesis or a domain swap with a different tetraspanin, and also binding assays with Ig1, Ig2, and Ig3 of CADM1 to validate this claim in the absence of high-resolution structural data.

      We removed the AlphaFold predictions from figure 5 and added it to the other predictions in Fig. S13. We did not see interactions of soluble CADM1(ECD) (Fig. S12) or single Ig domains (not included in MS) with purified CD82. Instead, we added a domain-truncation study in Fig. 5. The study shows the same level of co-purifications of CD82-StrepII3 and CADM1-His, CADM1ΔIg1 and CADM1Δ1-2 but strongly reduced co-purification when deleting all 3 Ig like domains. No CADM1-His constructs were detected in absence of CD82-StrepII3 and expression controls are shown in Fig. S12. The additional data made the overall interaction mechanism more clear and we rephrased it in the discussion.

      3.5 The EM processing workflow figure (S10) is missing many important details. FSC curve needs to be shown for the final map, and details like what was used for motion correction, CTF estimation, and how 31k particles became 45k particles (were these classes used as templates for picking?), and how many classes were used in heterogeneous refinement (and what those volumes looked like) should be included.

      We created a new processing figure. The figure includes the FSC curve and particle distribution for the finale volume. We added the first ab initio and heterogeneous refinement steps for each round of particle picking and marked volumes of which particles were selected for further processing. We indicated the number of selected and total particles for the final 2D classes and then indicated which group of particles was used and the percentage of particles in the resulting 3D volumes instead of indicating the particle number for each volume for better overview. We added what jobs were used for motion correction and CTF estimation.

      3.6 A short model figure at the end of the manuscript would be helpful to summarize the authors' findings.

      We added a figure displaying how CD82 hinders the tight formation of CADM1 multimers between membranes as Fig. 6.

      Minor comments:

      __ __3.7 Line 67: "In cancer, CADM1 can function as an oncogene as well as a tumor suppressor by regulating different signaling cascades, such as the Hippo pathway that promotes cell proliferation [27]." I found this sentence confusing- I think it is worth including another sentence or two explaining how CADM1 can have both tumor suppressor or promoter effects for readers that aren't familiar with CADM1 biology.

      We expended this part of the introduction to better explain the different functions of CADM1 and how it acts as a tumor suppressor and promotor simultaneously using examples:

      “Cell adhesion molecule 1 (CADM1) is a widely expressed adhesion molecule that has a dual function in cell signaling and adhesion and is involved in several biological functions. CADM1 overexpression promotes recruitment of T cells or mast cells in autoinflammatory diseases, like type 1 diabetes, asthma or neuroinflammatory diseases [24, 25, 26]. In most cancers, low CADM1 expression is associated with more aggressive tumors as it promotes apoptosis and inhibits proliferation [27].However, in T-cell lymphoma high CADM1 expression is associated with poor outcomes as its overexpression can lead to increased organ infiltration of tumors. The role of CADM1 in signaling and adhesion might explain its dual association with poor or good cancer prognosis and in both cases the change in expression level makes it a suitable therapeutic target and a biomarker [28, 29]. First in vivo studies using antibody-drug conjugates or chimeric-antigen receptors show a specific response against cancer cells and are considered promising candidates for clinical trials [30, 31, 32]. Considering these recent successes, it is crucial to understand the different functions and regulation of CADM1.”

      3.8 The introduction spends time discussing glycosylation sites on CD82 and CADM1. When I first read the intro, I thought glycosylation would be a focus of some of the experiments, but it wasn't. Instead, I think the paper would be stronger if you spent a bit more time in the intro talking about what is known about CADM1- as some examples, are there other interaction partners known to regulate its oligomerization? Therapeutic targeting is briefly mentioned, but has there been any success or progress in that area?

      The glycosylation explains why we consistently observe double bands in our SDS-PAGE gels and therefore needs to be mentioned in the introduction.

      We added more information about CADM1 as a cancer therapy target and recent developments in the introduction (see 3.6).

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      Referee #3

      Evidence, reproducibility and clarity

      Tetraspanin CD82 reduces the formation of CADM1 oligomers Lamottke et al. present data identifying CADM1 as a partner of the tetraspanin CD82. They then characterize this interaction using binding experiments, liposome assays, microscopy, and single-particle cryo-EM. Overall, I found this work to be an interesting read, and in general, the experiments were well explained and well controlled. Overall, nice work and an interesting finding! Below are my comments for improvements:

      Major comments:

      • In Figure 1, the second co-purification is much less efficient than the first (1C vs 1A). Perhaps removing Figure 1C and the associated SEC trace (or moving to the supplement) would make for an easier-to-understand figure panel. I was initially confused about the low efficiency, and it raised some doubts while I was first interpreting this figure. The large-scale purification in 1F and 1G looks much more convincing than what is shown in 1C
      • I found the experiments in Figure 2 very hard to follow, and the accompanying text needs improvement before publication. Why was CADM1 ECD rather than full-length used for SEC-MALS? It seems like the authors are trying to claim that CD82 reduces CADM1 oligomerization based on a smaller shoulder peak at ~10-12 mL with the fusion construct compared to CADM1 alone. However, it looks like 10 mL is the void volume of this column- wouldn't another explanation be the CADM1 sample simply has a higher percentage of misfolded or aggregated protein, and it not necessarily an "oligomer"?
      • The liposome assay is a clever way to assess if CD82 alters CADM1 oligomerization. However, details of how this assay was performed need clarification, and I am not convinced of the authors' conclusions given the current presentation. In the S4A, the authors show that CADM1 ECD His does not co-purify with CD82, but then CADM1 ECD His was used in liposome experiments to assess how CD82 changes clustering. Why was full-length CADM1 not used? For quantification, the average liposome radius is reported, but to me, a more robust quantification would be the percentage of liposome area contacting another liposome (i.e., ~35 nm distance between two liposomes), if the authors are trying to assess how CD82 affects CADM1 clustering. How does the average liposome radius reflect CADM1 clustering?
      • The AlphaFold model shown in main text Figure 5 is not high confidence, and I suggest moving this to the supplement. To validate that the LEL of CD82 interacts with Ig1 of CADM1 as suggested by the EM density, the authors should perform mutagenesis or a domain swap with a different tetraspanin, and also binding assays with Ig1, Ig2, and Ig3 of CADM1 to validate this claim in the absence of high-resolution structural data.
      • The EM processing workflow figure (S10) is missing many important details. FSC curve needs to be shown for the final map, and details like what was used for motion correction, CTF estimation, and how 31k particles became 45k particles (were these classes used as templates for picking?), and how many classes were used in heterogeneous refinement (and what those volumes looked like) should be included.
      • A short model figure at the end of the manuscript would be helpful to summarize the authors' findings.

      Minor comments:

      • Line 67: "In cancer, CADM1 can function as an oncogene as well as a tumor suppressor by regulating different signaling cascades, such as the Hippo pathway that promotes cell proliferation [27]." I found this sentence confusing- I think it is worth including another sentence or two explaining how CADM1 can have both tumor suppressor or promoter effects for readers that aren't familiar with CADM1 biology
      • The introduction spends time discussing glycosylation sites on CD82 and CADM1. When I first read the intro, I thought glycosylation would be a focus of some of the experiments, but it wasn't. Instead, I think the paper would be stronger if you spent a bit more time in the intro talking about what is known about CADM1- as some examples, are there other interaction partners known to regulate its oligomerization? Therapeutic targeting is briefly mentioned, but has there been any success or progress in that area?

      Significance

      This work reports a new direction interaction between CADM1 and CD82, which is interesting and signficant because relatively few direct interactions between tetraspanins and their partners are well characterized. The manuscript can be improved by relating this new interaction discovery back to how this may help CADM1 biology or developing new agents targeting CADM1, which I mention above in my comments.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Lamottke et al. characterizes the interaction of CD82 with CADM1 protein. Although, the data provides important insight into how CD82 regulates CADM1 clustering, I have few questions that needs to be addressed to improve the manuscript for readers before acceptance of the paper.

      Line 101-102- In MS analysis, Table 1, several common proteins are present in all three bands analyzed by MS including CD82. Authors should explain why CD82 is observed in 250 KDa band in relatively high abundance?

      Line 118-119, authors should make a better explanation why the lower MW CADM1 band is not observed in co-expression, especially that lower MW band is also absent in CADM1-Strep purification FigS2b? Although the migration pattern of CD82 and CADM1 is similar to glycosylated proteins authors should confirm this band doubling is due to glycosylation. It is crucial because a great portion of the manuscript relies on protein separation on SEC that can be greatly impacted by glycosylation state of the proteins.

      Line 120-considering the lower yield of CADM1 in co-expression studies and presence of CD82 protein in MS analysis of band 3, it raises the question whether the peak eluted at 10 mL in F-SEC chromatogram of co-expressed proteins is from a legit CD82-CADM1 complex or it is simply CD82 oligomerization/interaction with other proteins. I would suggest authors purify GFP-CD82 and CADM individually and run the mixture over F-SEC to confirm complex formation rather than running co-purified protein. The same experiment should be done with CD820-CADM1mixture without fusing GFP to rule out the possible influence of GFP on dimerization.

      Figure 3. The color scheme used makes it really difficult to follow curves for different concentrations. I would suggest using distinct colors for each concentration.

      Line 274-275. Authors argue that the CADM clusters occupy more surface area in CD82KO cells compared to normal cells. From figure 4C. it seems that CD82KO cells are smaller in size than normal cells? Is that true? If that's the case, authors should provide evidence that the expression/surface area is the same for both normal and CD82KO cells before making the conclusion. If the expression level remains the same between two cell types but the size of KO cell is smaller, it is expected that CADM will occupy more surface area on KO cells. Consequently, the clusters are expected to be bigger. Therefore, the smaller cluster size in normal cells is not directly caused by CD82 but by smaller cell size due to CD82KO.

      Line 205-207. Authors show that CADM1(ECD) is not interacting with CD82 and conclude that interaction between transmembrane helices is required. It raises the question that how in cryoEM, authors could capture interaction between Ig domain of CADM1 and CD82 between two different micelles? Providing such details about experimental setup helps readers to adopt the method for structural determination of elusive complexes.

      Significance

      This solid study provides new insight into the interaction between CD82 and CADM1, how self-assembly and clustering is regulated using a complemenatry biochemical approaches. While the structural details remain wanting, the sound biochemical work lends confidence in the study's findings.

      The greater community interested in tetraspanin biochemistry will appreciate this work.

      We have experience understanding related plasma membrane remodeling proteins (from the Prom family) and their interactions with other binding partners.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: In this study, the authors identify cell adhesion molecule 1 (CADM1) as an interaction partner of tetraspanin CD82. They used a combination of biochemical, cellular and structural studies to support this conclusion and identify an inhibitory effect of CD82 on CADM1 oligomerization.

      Major Comments: Using a variety of elegant and sophisticated studies, the authors clearly present data that strongly support their conclusions of the interaction between CADM1 and CD82. Additionally, they speculate on the specific interaction sites/mechanism through which CD82 may affect the spacing of CADM1, which is supported by cryo EM. However, they do not go on to demonstrate a physiological role for this inhibitory interaction.

      Minor Comments: In Figure 4, there seems to be a significant size difference between WT and CD82KO Jurkat cells. If this is the case, this needs to be described a bit more. Additionally, how does this potential size difference impact the quantification of the percentage space covered by CADM1 clusters? Also, for these experiments, it is more appropriate to use the mean of each independent replicate rather than simply counting each cell as an n. Including these data as super plots so the reader can better understand the spread and reproducibility of the data would be ideal. Best practices suggest that statistical analysis should be performed on data from independent experiments (Lord et al 2020 PMID: 32346721).

      Significance

      General comments useful to editors and readers: This is a well written manuscript with compelling data that supports the overall conclusion of a protein-protein interaction between CADM1 and CD82. The abstract could be strengthened by the addition of a bit more biological rationale for the presented work and some context of impact.

      Advance: Through compelling structural work, the authors establish an interaction between CADM1 and CD82 and suggest based on the cryo-EM that the interaction occurs between the CD82(LEL) and an Ig-like domain of CADM1. The manuscript does not provide a physiological impact of the identified interaction, as this was suggested to be outside the scope of the current work. As such this limits the overall advance in knowledge some to a thorough description of a protein-protein interaction that impacts protein clustering.

      Audience: This is largely a structural study evaluating protein-protein interactions and the impact of a membrane scaffold protein on the clustering of a cell adhesion molecule. Thus, it is well suited for structural biologists and cell biologists studying cell adhesion molecules, membrane scaffold proteins and their regulation.

      Describe your expertise: My expertise is in cell biology, so admittedly, some of the structural data was difficult from me to critically evaluate.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis because it is usually diagnosed when already metastasized. Therefore it is important to identify early markers of disease. In this work, the authors developed a zebrafish model of PDAC which shows similarity to human tumor formation. Single cell RNA sequencing was used to identify transcriptional changes associated with early disease and acinar-to-ductal metaplasia, which precedes cancer formation. Cross-species comparison revealed transcriptional changes that were highly conserved between zebrafish and mouse cancer models, and human disease. These include reactivation of developmental genes and increased expression of genes related to cytoskeleton and cell migration.

      Major comments:

      The authors' conclusions are well supported through comparison of newly generated zebrafish datasets with available published data from a mouse model and human patient samples. Providing an accessible website for further data exploration will be a valuable service to the research community (this website is not yet available).

      Optional additional experiments:

      The claims of this work would be strengthened by experiments to detect the identified upregulated pathways in the zebrafish cancer model at the tissue level. This could be performed by RNA in situ hybridization or antibody staining, if reagents for zebrafish are available. This would substantiate the likely biological relevance of changes detected in the single cell datasets. Correlation of marker expression with regions of varying differentiation or tumor progression may implicate mechanistic importance. qPCR would be an alternative method to confirm gene expression changes, but this does not provide the possibility for correlation of gene expression with histology.

      There is extensive speculation in the results section relating to cellular acquisition of invasive potential, which is more suitable for the discussion. Focal adhesions, integrin-mediated signaling and actin-ECM coupling are implicated but not examined. Ultrastructure of cytoskeletal elements and focal adhesions could be examined in tissue samples by electron microscopy. These structures could also be studied with immunofluorescence labeling of individual components and phalloidin staining. Such experiments may show regional variations within tumor samples and reveal changes specific to regions of invasion.

      The SCENIC analysis was performed only using mouse and human datasets. Factors that are part of conserved regulons relevant for tumor progression could be examined in the zebrafish model - at a minimum in the scRNAseq dataset, or better by using RNA in situ hybridization or antibody approaches.

      Minor comments:

      Methods - Differential expressed genes: the justification for use of Seurat v4 versus v5 is confusing. Figure 3 F-H - Alcian Blue staining is hard to interpret. It would be helpful to include a negative control (no staining) and/or a positive control (normal tissue), and close-up views. (Possibly the images provided for review are of low quality and therefore difficult to appreciate.) p. 16 To clarify the statement "Early metaplastic cells were excluded" it would be helpful to refer to Figure 5A p. 21 "Visible" WISH is an odd term, better to instead say 'chromogenic' WISH p. 25 Refers to Figure 11. This should be Figure 8?

      Significance

      The zebrafish cancer model used in this work, which combines acinar cell expression of KrasG12D with a tp53-mutant background, is superior to previously reported models which showed lower frequency of tumor formation. Here, the authors report tumor development detectable at 3 months of age which affects 100% of animals by one year.

      Cross-species scRNAseq analysis comparing zebrafish to mouse and human tumor samples showed conserved activation of progenitor factors, and cytoskeletal and migration related genes. The involvement of such pathways in tumorigenesis is not novel. However, the demonstration of similarity of tumor-promoting pathways between zebrafish and rodent models, and human patient samples, is an important reinforcement of the value of the zebrafish model for preclinical investigations. Additional experiments as outlined above would substantiate functional relevance for identified pathways.

      One issue warranting clarification is the onset of tumor formation in the reported model. Since KrasG12D expression is detectable starting at 5dpf, neoplastic changes may initiate already at larval and juvenile stages. A further issue is that KrasG12D expression decreases as cells shift away from the acinar fate, and this may limit the progression to advanced cancer stages. An inducible model, for example using a Cre/loxP system with the ela3l promoter driving Cre, could provide adult-onset tumor initiation and would maintain oncogene expression independent of changes in the cells' differentiation state. Using fish that are transparent as adults could allow detection of tumor growth before a visible mass is detectable externally. Subsequent decrease of GFP expression could indicate progression to more advanced tumor stages.

      Authors should acknowledge that transcriptional changes may not directly reflect protein abundance and activity, and therefore functional biological relevance is only suggested.

      This paper will be of interest to scientists interested in mechanisms of cancer formation and in the development of cancer therapies. The datasets that will be made accessible to the research community will be a valuable resource for researchers studying development and diseases of the pancreas.

      My background includes medical training, research in developmental and cancer biology, and ongoing research using zebrafish for modeling disease processes.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript is an interesting comparative biology resource. It describes the development of a novel model of pancreatic metaplasia and tumorigenesis in Danio rerio. Authors characterize histopathological features of pancreatic tumors. In a more molecular-oriented approach, they perform single-cell RNA sequencing of KRASG12D-expressing pancreata at different timepoints. Comparing both mouse and human datasets, they describe a conserved cross-species signaling program in PDAC carcinogenesis, which includes the reactivation of developmental genes.

      The sequencing approach chosen suffers from several technical hindrances that limits, in the opinion of this reviewer, the quality of the results.

      Following is listed a brief summary of the points that we would suggest in the revision of this work:

      1. The sequencing of the zebrafish samples involves heavy steps of pancreatic tissue enzymatic dissociation, and, as the authors also acknowledge, this could lead to major contamination of ambient RNA. The authors state the application of functions coming from the CellBender and DoubletFinder packages to improve the quality of the results. We realize that, while it would be beneficial, the employment of less aggressive dissociation techniques (if any) is unfeasible in this stage of the work, but it would be useful for the authors to comment and describe how much the samples were initially contaminated by ambient RNA and to provide parameters to show the improvements obtained with their pre-processing pathway.
      2. The choice to perform sample integration just on the zebrafish samples appears to insert an additional difference in the processing of datasets coming from different species. In our opinion, since this is a complete reanalysis of published datasets, the analysis pipeline employed should be the same for all the datasets.
      3. Figures 1K-L does not appear of sufficient quality to assess tissue morphology.
      4. Authors state that the reduction in GFP signal in staining of tumoral samples is probably due to a downregulation of ela3l promoter in tumoral cells; however, from the provided images, it is not clear if the assessed area is indeed cellularized, or if it may match to a fibrotic acellular region, consequently hampering the validity of the other immunostaining findings. Clearer pictures (probably including a nuclear/cellular staining should be provided to ensure the cellularity of the tissue portion.
      5. In multiple points of the manuscript, the authors refer to S and G2/M phase signatures, which are not described in the methods section. Additionally, if these signatures derive from the CellCycleScoring framework from the Seurat package, the authors should show the pattern of expression of at least some relevant genes, since it is debated if the small signatures used by Seurat might be sufficient to infer cell cycle phases.
      6. Authors emphasize a possible role of OLFM4 and ribosomal/translation proteins upregulation as putative mechanisms of metaplastic progression to overt tumors; however, these reports are purely speculative and based on reports performed in other cancer context, or on hypothesis on the possible functions of these factors in metaplastic cells. The authors should provide experimental validation of their claims on the role of these factors.
      7. The authors state that reactivation of a pancreatic developmental program is a central feature of metaplastic transition. However, this is based on developmental genes being an (albeit significant) minor fraction (~10%) of the total regulated genes. Moreover, many of the cited pathways are already widely known regulator of pancreatic metaplasia and early tumorigenesis.
      8. Authors state that transcriptional program activated at the terminal stages of metaplasia in zebrafish closely mirrors that observed in mammals. This however is based on sequencing of pre-neoplastic samples in zebrafish, and in an overlap of just the 25% of the DEGs in mouse and human samples.

      Significance

      The newly developed zebrafish model appears sound and well-characterized, and could definitely provide a useful platform to perform preliminary in vivo experiments for scientists in the field. It is really difficult to grasp the carcinogenic stage and authors might want to do a better job in characterizing the multi-step evolution of PDAC in their model. Terminology and histo-pathological accuracy should be improved. While the use of trajectory analysis and GRNs assessment with SCENIC is a very appreciated and elegant approach, the major novelty of this paper is related to the description of a correspondence between zebrafish models and other organisms in the molecular mechanisms subtending the early phases of pancreatic carcinogenesis, but the pathways described are almost all already known and linked to tumor development in this context. Yet, very informative from a comparative biology perspective.

      The opinion of this reviewer is that this paper constitutes a very interesting study harnessing a novel zebrafish model which could be quite useful to researchers in the field, given its similarity to more advanced model organisms, but lacks major molecular findings describing the early phases of tumorigenesis in the pancreas. It should benefit from a stronger/more robust histological characterization - where possible.

      My expertise: pancreatic carcinogenesis

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      Referee #1

      Evidence, reproducibility and clarity

      Major points

      The manuscript is very descriptive, and very long on the molecular analyses. The work would gain impact from a clearer focus on what is learned about pancreatic tumour biology and metaplastic processes, rather than on exhaustive dataset description.

      Tumour latency is very long (up to one year in a 2-5 year lifespan ), with relatively low penetrance and few tumours progressing to PDAC. These features raise concerns about the robustness and translational relevance of the model as a PDAC system.

      All experiments involve male fish only, despite known sex differences in PDAC biology. Either inclusion of sex stratified data or a clear rationale for restricting the study to males will be important for assessing generalisability.

      The behaviour of non progressing tumours, and its relationship to oncogene status (such as possible Kras silencing), is mentioned but not mechanistically explored. A more explicit analysis or discussion of why some lesions fail to progress would add valuable biological insight.

      Molecular profiling is based on a small number of tumours sampled at different time points, capturing inter tumour heterogeneity rather than true biological replication. As a result, statements about conserved pathways and transcriptional programmes are supported by limited replication and would benefit from more cautious wording.

      Functional validation of key regulators or pathways is lacking. Even limited functional work-such as manipulation of one candidate gene, or derivation of a transplantable line to enable testing of tumour responses to standard of care treatment-would considerably enhance the biological and translational impact of the study; in the absence of such data, the limitations of purely descriptive findings warrant clearer acknowledgement.

      Pathology assessment is incompletely documented. It is not clear whether ductal adenocarcinoma was actually recognised in the zebrafish lesions (differentiation grade was scored).

      Minor comments

      SCENIC is mentioned in the abstract without explanation. A brief indication of its role (for example, inference of transcription factor regulons associated with specific tumour states) would aid readers unfamiliar with the method.

      Tumour assessment is described as occurring "as soon as they were visible," but the practical detection method, and surveillance intervals are not specified. A clearer description of how tumours were detected and monitored would make the methodology more transparent.

      The Methods section, particularly the bioinformatics part, is disproportionately long and includes interpretative elements that read like Results. Relocating detailed computational pipelines and secondary explanations to the Supplementary Methods could improve flow and readability. The Discussion is lengthy (around seven pages) with extensive molecular detail. A more concise Discussion that emphasises main conclusions, limitations, and translational implications would likely improve readability without loss of content.

      Wording about invasion and metastasis sometimes appears stronger than the data support, given that only a minority of tumours show metastatic behaviour. Statements about invasive potential would benefit from closer alignment with observed frequencies.

      The number of figures, particularly supplemental ones, is high, and some the main figures appear to be included twice. Streamlining the figure set and removing redundancies would make the presentation more focused.

      Significance

      The manuscript is very descriptive, and very long on the molecular analyses. The work would gain impact from a clearer focus on what is learned about pancreatic tumour biology and metaplastic processes, rather than on exhaustive dataset description.

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      Referee #3

      Evidence, reproducibility and clarity

      This study investigated c-di-GMP (cdG)-dependent gene expression in Vibrio cholerae, an area that has been thoroughly researched in earlier studies. The transcriptomics results of the first sections largely confirm what has been previously described, but identify an unexpected, sudden c-di-GMP drop at OD600=1, which may be either the result of a feedback loop, as the authors suggest, or an experimental artifact (as explained in Major concerns). The most intriguing result of the study, in my opinion, is the identification of a potential antisense RNA, designated SnrC, that may counteract expression of the main cdG phosphodiesterase gene, cdgC. Unfortunately, the data are insufficient to conclude that SnrC is real. The follow-up competitive analysis of the cdgC mutants and growth analysis of the mutants with super-high cdG levels are not insightful because of low biological relevance. Overall, while some intriguing leads are described in this work, major experimental concerns exist and some conclusions may have to be revised.

      Major Concerns:

      1. The experimental setup may have had a major flaw. A sudden drastic drop in c-di-GMP levels at OD600=1 may be due to sampling/inducer addition. Until this is solved, it impossible to evaluate the adequacy of conclusions based on transcriptomics analysis.

      Fig. 1B shows an approx. 3-fold drop in c-di-GMP levels within 15 min upon induction of the QrgB-mutant protein expression. This drop cannot be explained by "transitioning to the high-cell density quorum state", as suggested by the authors, because such a transition likely takes more than 15 min, and it does not start so abruptly at precisely OD600=1 (when IPTG was added). A different, more benign explanation is more likely. Below are a couple of ideas to investigate. - Can the cdG decrease be due to the sudden drop in dissolved oxygen levels during the sample withdrawal for c-di-GMP measurements and IPTG addition? If so, the cultures were experiencing first hypoxia, then (upon restoration of shaking) oxidative stress, which affect the c-di-GMP transcriptome. I suspect that an approx. 3-fold drop in cdG levels does not take place in the undisturbed cultures that are allowed to grow for 15 min after reaching OD600=1. - Does the mutant QrgB protein contain a cdG-binding site? If so, QrgB may soak up intracellular cdG, whereas the intact QrgB may counteract the soaking effect by synthesizing cdG. 2. Evidence of the potential existence of the antisense RNA, SnrC, is weak.

      It is not convincing that snrC is real despite the notion that bioinformatics data lack statistical significance (l. 197-203). A functional test, where an overexpressed antisense RNA lowers expression of the sense RNA (Fig. 3D) is obviously insufficient because this would work for any gene, whether antisense RNA is real or not. To verify that SnrC is real, one needs to show, at least, a snrC RNA band on a Northern blot and/or results of the snrC transcriptional fusion.

      Additional concerns:

      l. 95: More information about QrgB is needed, eg, is QrgB a cytoplasmic or membrane protein; does its DGC activity depend on interactions with small molecules or other proteins; does V. cholerae contain a QrgB homolog; does it have a cdG-binding site? l. 106: It is erroneous to suggest that in the wild type, c-di-GMP levels increased at 15 & 30 min when they were largely unchanged (Fig. 1A). l.143-52: That cdgC may be part of the negative VspR-mediated feedback on cdG levels is evident from refs. 11,13,14 and from the confirmation data in Fig. S1. I'm afraid it is not at all clear from Fig. 2A that cdgC is "the sole clear candidate for negative feedback on cdG levels". Please provide more substantive reasoning.

      Methods section: For major methods, provide a reference that served as the basis for experiments (as done for c-di-GMP measurements, l. 422), or method validation if the method is newly developed.

      Referees cross-commenting

      Serious concerns were raised by me and other reviewers about the quality of the data and the validity of conclusions. The major concerns raised by all reviewers need to be resolved (which, in my estimate, will likely take several months).

      Significance

      Significance is dificult to assess because of the major concerns about data validity.

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      Referee #2

      Evidence, reproducibility and clarity

      The work from Rangarajan, Schroeder et al. examines dynamics of c-di-GMP (cdG) gene regulation and homeostasis in Vibrio cholerae following cdG overexpression. Relative increase in cdG levels are stimulated by the heterologous QrgB DGC enzyme, and the resulting changes in gene expression (biofilm up, motility down) are largely due to the cdG-dependent transcriptional activator. VpsR induction leads to activation of GGDEF/EAL protein CdgC; only its EAL (cdG-degrading) domain is active, and this activation leads to a feedback loop that results in a reduction in cdG levels. ChIP-Seq with RNAP led the authors to an unannotated sRNA that is transcribed within the cdgC ORF in the antisense direction that they name sncR, and whose expression is positively regulated by cdG. Expression of sncR negatively regulates cdG levels through CdgG.

      The authors could thus break the negative feedback loop they discovered and generate sustained high levels of cdG in the cell using ΔvpsR or ΔcdgC single or double mutants, and in those cases they observe growth defects.

      Overall, the experiments presented are high quality and the results offer significant novelty. The RNA-seq studies in Fig. 1 provide higher resolution than previous studies, including following the early dynamics upon cdG induction and resolving the VpsR/VpsT-dependence of individual genes and classes of genes. The figures are exceptionally clear, and the text is accurate and well cited/referenced. Multiple instances of the text do require additional clarification as noted below.

      Major comments:

      1. High levels of sncR are identified only in a ΔvpsR strain (Fig. 3B), yet the authors' model is that VpsR is not directly regulating sncR expression (Fig. 5). Instead, they suggest that the VpsR-dependence of the phenotype is due to only observing the highest levels of cdG in the absence of VpsR. If there is a way to clarify the connection of VpsR and sncR that would strengthen the manuscript.
      2. In Fig. 4BC, are these strains similarly vpsL- as most of the paper? If so, is it expected that they can form biofilms? I would recommend explaining this assay further, or if they are vpsL+ then clarifying in the text.
      3. L106-116: In Fig. 1A, why does QrgB overexpression not lead to elevated cdG at 15 m except upon normalization to QrgB* (Fig. 1C)? Is this connected to the feedback loop characterized in the manuscript (does it act that quickly?) or due to another effect?

      Related, the authors attribute the decrease in the denominator (QrgB* levels; Fig. 1B) to the cells transitioning to the high cell density state. Is it clear that this would occur in the time frame of 60 min?

      Minor comments:

      4a. In Fig. 1D, what does the null symbol represent?

      4b. L200-203, more explanation would benefit the assertion of the Type II error proposed. Are the authors claiming this is noted as not significant due to the high number of multiple comparisons?

      Referees cross-commenting

      The skepticism from the other reviewers on the bona fide structure and role for sncR are warranted, and they offer experiments that could test the ideas proposed in the manuscript.

      The other reviewers also highlight the issue I raised with the QrgB induction data. I was more generous to the authors given the Waters Lab's long history in the field, in using this particular construct to induce cdG, and in sharing the construct with other labs to enable its use for this purpose. Nonetheless I think these consensus concerns require additional scrutiny for the time frame and under the media conditions examined.

      Significance

      The work represents a substantial conceptual advance in understanding c-di-GMP signaling. The feedback loop described is likely to be a complicating factor in many studies that analyze c-di-GMP in V. cholerae specifically, and comparable regulation may be at play across bacteria.

      The authors do not address the antisense mechanism in this work. They note this limitation.

      In the Discussion, the authors identify a potential conflict with Ref. 35 on the role of VpsT. I found the authors' data convincing with this regard and it remains possible that the different results are due to distinct laboratory conditions.

      I expect that following audiences will be interested in the work: gene expression, gene regulatory networks, transcriptional regulation, bacterial small RNAs/antisense RNAs, small molecule signaling, c-di-GMP signaling, Vibrio cholerae biofilm formation/dispersal, and Vibrio cholerae colonization/transmission.

      My field: bacterial genetics, signal transduction, Vibrio bacteria.

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      Referee #1

      Evidence, reproducibility and clarity

      The study by Rangarajan, Schroeder et al. uncovers a negative feedback loop that controls cdG levels in Vibrio cholerae. They demonstrate that this feedback loop requires VpsR, one of the master regulators of cdG-dependent responses in this organism. Furthermore, they show that mutating the phosphodiesterase CdgC eliminates this negative feedback loop. This research is timely and addresses an important question about the regulation of this important secondary messenger molecule in bacterial species. The manuscript is well written and clearly presented. However, some of the results are fairly preliminary and there are alternative models that could explain the results obtained. Also, I disagree with the authors interpretations of some of their data. Specific points for the authors to consider are discussed below.

      Line 113-115: This reduction in cdG levels observed with expression of QrgB is quite striking. In fact, the ratiometric "increase" in cdG levels observed for QrgB/QrgB is largely driven by the decrease in cdG levels in the QrgB condition as opposed to an increase in cdG in QrgB. Are the authors suggesting that the decrease observed with QrgB is due to the natural reduction of cdG levels due to quorum sensing and the transition of cells to the high cell density state? What is the change in OD600 during this time course? Alternatively, isn't it possible that the expression of QrgB is causing a response that results in a decrease in cdG? If so, this would change a lot of the conclusions in the present study. Perhaps one way to address this is if the authors have data on the cdG levels in wildtype cells during a similar time course without any induction of QrgB or QrgB.

      Fig 2B: The authors indicate that the results with the PcdgC reporter confirm their RNA-seq results. I don't agree with this based on my interpretation of the presented data. While statistical comparisons are not provided, the relative change in PcdgC reporter activity when comparing 0 min to 60 min is very modest for all samples. This is especially relevant for the WT+QrgB condition, which should see a marked increase in reporter activity based on the RNA-seq results presented in 2A. The potential post-transcriptional regulation of CdgC discussed in the subsequent figures further complicates this. The data in 2C suggest that cdgC is associated with a negative feedback circuit that keeps cdG levels low in the wildtype as shown in 1A. But the mechanism underlying how this actually occurs remains unclear. Defined reporters or direct analyses of cdgC transcription and translation would be valuable to elucidate this.

      The data in 2C show that mutating cdgC now allows for similarly high levels of cdG in parent vs ∆vpsR. But how specific is this response to cdgC? An alternative possibility is that elevating the baseline cdG level is sufficient to bypass this feedback loop. For example, CdgJ is a phosphodiesterase that has previously been characterized in Vibrio cholerae and cdgJ mutants exhibit phenotypes consistent with elevated cdG. Performing similar experiments in the cdgJ mutant to show that the results seen in 2C do not occur when another phosphodiesterase is mutated would strengthen the conclusion that CdgC is specifically required for the negative feedback circuit that controls cdG levels.

      The only data that link CdgC to the negative feedback circuit are the assays performed in 2C that directly measure intracellular cdG levels. But the authors make a compelling case for the negative feedback circuit in generating a pulsatile response to QgrB induction in the wildtype in 1F. if CdgC is required for this negative feedback response, then a CdgC mutant should lose this pulsatile response. This should be tested to further support the model presented.

      Fig. 3B: It would be valuable to also plot the sense transcripts in this same region so that the expression of cdgC can be directly compared to the expression of SnrC. Also, can the authors speculate on why the RNAP enrichment at PsnrC in 3A does not reflect the expression levels of SnrC observed in 3B?

      Line 173-174: I am not sure how the authors are concluding that VpsR induces SnrC. I believe their data in 3B indicates the exact opposite, which is that SnrC expression is negatively regulated by VpsR. This finding is also not reflected in Fig.5.

      The data presented in Fig. 3 are compelling, but they only suggest that SnrC may be an anti-sense small RNA. More rigorous analyses are needed to make any firm conclusions about whether this is a bona fide sRNA and to define its potential mechanism of action. Northern blots should be performed to directly assess SnrC levels. Also, can the PsnrC promoter be mutated to prevent production of this small RNA while leaving cdgC function intact? This would allow for more rigorous genetic analysis to assess the impact of SnrC on CdgC transcription and translation. At the very least, claims of SnrC as a small RNA need to be toned down.

      Fig. 4C: this model system and data are very interesting. But they do not necessarily indicate that it is the negative feedback circuit that CdgC participates in that is important for these transitions. They may simply reflect that elevated cdG results in a defect in these assays. As mentioned above, cdgJ mutants have previously been shown to exhibit phenotypes consistent with elevated cdG. So, what is the phenotype of a cdgJ mutant in these assays? Since the cdgJ mutant should still have CdgC-dependent negative feedback control of cdG levels, this mutant may not have any defect. This result would be particularly compelling and support the authors conclusions that CdgC-dependent feedback control of cdG is critical for balancing biofilm formation and dispersion.

      Line 299-302: This is an interesting hypothesis, and could be tested with a cdgC catalytic site mutant. Or by otherwise inactivating CdgC phosphodiesterase activity while leaving the cdgC gene largely intact so that PsnrC expression can still be monitored. Again, these types of experiments are needed to make any firm conclusions about SnrC.

      Referees cross-commenting

      It appears as though all of the major points I raised in my review were also reflected in one or both of the other reviewers comments. So, I do not have anything to add.

      Significance

      The data presented in the study are strong. But some of the results are fairly preliminary and there are alternative explanations for the results that would make many of the conclusions incorrect or less compelling.

      The study helps fill a gap in knowledge about cdG regulation in bacteria. While the study focuses on Vibrio cholerae, the principles underlying this work are likely relevant to many bacterial species. So this work would be broadly interesting to the field of microbiology.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This manuscript investigates the evolutionary relationships among domains I and II of three families of insecticidal proteins: Cry proteins from Bacillus thuringiensis, Prb proteins from bacterial symbionts of nematodes, and IPD113 proteins from ferns. These protein families were previously thought to have evolved independently because they share very low sequence identity (approximately 10%), despite exhibiting notable structural similarities. Through phylogenetic analyses and structural comparisons, the authors propose that domains I and II of these protein families share a common evolutionary origin and have likely undergone extensive horizontal gene transfer (HGT) across diverse taxa, including bacteria, eukaryotes, and archaea. In contrast, domain III does not show a shared evolutionary history between Cry proteins and the Pra component of Prb proteins. The authors also report evidence of domain II swapping and identify multiple conserved motifs across clades, suggesting a more complex evolutionary history than previously appreciated.

      Major Comments 1. The study proposes a common evolutionary origin for Cry, Prb, and IPD113 proteins; however, it cannot definitively exclude the possibility of convergent evolution, given the very low amino acid identity (~10%) among these families. For example, the authors note that eukaryotic genes encoding these proteins often contain introns (Table S2), which may also be interpreted as supporting convergent evolution rather than horizontal acquisition. This uncertainty warrants a more cautious interpretation and highlights the need for additional analyses or discussion to better discriminate between common ancestry and convergence.

      Response: We thank the reviewer for this important comment. We agree that convergent evolution cannot be formally excluded on the evidence available, and we have revised the manuscript in two ways. First, we added an analysis designed specifically to separate the two explanations, described under (1) below. Second, we rewrote the opening of the Discussion so that the basis for our preference, and its limits, are stated explicitly (lines 406–419): "Our findings support common ancestry as the more plausible and parsimonious explanation for the similarities among the three insecticidal protein families, Cry, Prb, and IPD113. Although convergent evolution cannot be formally excluded, we consider it an unlikely account of the similarities reported here, because it would require their independent emergence at multiple hierarchical levels: motifs occurring at corresponding positions and favoring the same residues, the core structures of both domains, and a mosaic of domain-specific relationships in which the domain II placements of some sequences cross family boundaries whereas their domain I placements do not. A shared structural context alone does not specify which residue is favored at a given position (Fig. S7B), and selection for insecticidal activity alone does not predict the mosaic pattern observed in the domain-level trees (Fig. 3). Under common ancestry, both observations can instead be explained by a single evolutionary origin: the motifs were retained by descent under residue-level selection, and the mosaic pattern is consistent with domain swapping between lineages. We therefore regard descent from a shared ancestor, followed by extensive sequence divergence, as the more parsimonious explanation for these observations."

      We set out the evidence behind each of these three levels below.

      __ _(1) Amino acid motifs._ __The motif conservation that we emphasize does not consist of a few scattered matching residues. As noted in lines 200–202, domain I contains motifs of approximately 20 amino acids corresponding to α-helices, and each sequence retains, on average, more than four such motifs. These similarities are not randomly distributed, but are repeatedly observed across distinct clades in corresponding domain regions and structural contexts.

      We provide the additional data showing that this conservation cannot be explained simply by constraints imposed by secondary structure. Even when both secondary structure and residue burial were held fixed, individual positions strongly favored the same residues across clades (median frequencies of 66% in domain I helices and 59% in domain II strands; Fig. S7B), whereas the most frequent hydrophobic residue accounts for only ~25% in experimental structures (Škrbić et al. 2024). Thus, shared structural context alone does not explain the residue-level conservation, which is more consistent with common descent than with independent convergence on the same fold. We added this explanation in lines 188–198.

      • Phylogenetic relationships. The phylogenetic pattern suggestive of domain II swapping is also difficult to explain by simple convergence alone. While convergent evolution can in principle generate superficial similarity, it does not readily explain the discordant phylogenetic relationships observed between domains. In our case, the fact that domains I and II show different phylogenetic affinities is more naturally interpreted as reflecting a history of domain exchange or recombination than repeated independent convergence. We added this explanation in lines 174–176.

      • _Structural conservation _The structural conservation we observed is not limited to overall resemblance in fold, but extends to the core elements of domains I and II. In particular, the α-helices and β-strands that constitute these domains show substantial correspondence in their relative orientations and spatial arrangement. Given the structural diversity of bacterial insecticidal proteins (Crickmore et al. 2021) and pore-forming toxin strategies (Dal Peraro and van der Goot 2016), insecticidal activity alone would not necessarily be expected to drive both domains independently toward the specific core architectures observed here. Thus, the cross-clade conservation of these domain cores is more consistent with divergence while maintaining functional architecture than with repeated independent convergence. We added this explanation in lines 252–267.

      _Regarding the presence of introns in eukaryotic homologs, we do not consider this to be decisive evidence for convergent evolution, nor does it directly exclude horizontal gene transfer (HGT). An ancient HGT event followed by intron gain in eukaryotic genomes remains a plausible scenario. We added this explanation in lines 61–62. _

      ____Nevertheless, we have revised the title of the manuscript to "Remote homology between bacterial and fern insecticidal proteins reveals a broadly distributed superfamily shaped by recurrent horizontal transfer", which no longer asserts divergent evolution. The title also reflects the results of a more exhaustive homolog search suggested by Reviewer #3.__ __

      1. Although the study proposes multiple HGT events, it does not conclusively demonstrate the mechanisms or pathways underlying these transfers. The inferred HGT events would benefit from further validation, potentially through experimental approaches or population-level analyses. The authors are encouraged to expand their discussion on possible mechanisms facilitating HGT, such as the involvement of transposable elements, and to consider how ecological interactions (e.g., symbiosis or shared niches) may have promoted the horizontal transfer and diversification of these insecticidal protein families across different taxa.

      Response: We thank the reviewer for this helpful comment. The primary aim of this study was to infer the evolutionary history of these insecticidal protein families, and the possible HGT events that emerged from our analyses will require further investigation of their genetic basis. In response to the reviewer’s suggestion, we have added following discussion (lines 522–530):

      “cry genes are generally associated with plasmids and mobile genetic elements (Schnepf et al. 1998; Mahillon and Chandler 1998), which provide biologically plausible genomic contexts for HGT, at least in bacterial lineages. However, it remains unclear whether similar signatures are also present in the genomic regions surrounding genes encoding Cry-like domain-containing proteins in eukaryotes and archaea. Addressing this question will require future comparative analyses of flanking regions and broader genomic context. In addition, ecological interactions such as symbiosis or shared niches can be regarded as general factors that could increase opportunities for HGT by bringing phylogenetically distant organisms into repeated contact”.

      1. The conclusions of the study rely entirely on computational analyses, including phylogenetic inference and structural prediction. While these approaches are appropriate, experimental validation would considerably strengthen the manuscript. For instance, biochemical or functional assays assessing the insecticidal or nematocidal activity of selected proteins from different taxa would provide direct support for the proposed evolutionary relationships. This could include heterologous expression of candidate proteins in model organisms or cell lines, followed by toxicity assays, or genetic manipulation (knockout or overexpression) in native or model systems to assess physiological function. Although such experiments may be beyond the scope of the present study, they should at least be acknowledged as important directions for future research.

      __Response: __We thank the reviewer for this important comment. We agree that experimental work would strengthen the biological interpretation of our findings, and functional analyses of representative proteins from different taxa will be an important direction for future research. Interestingly, recent experimental work by Li et al. (2026) showed that two-domain proteins from the bacterium Cohnella faecalis, the sea anemone Actinia tenebrosa, and the arbuscular mycorrhizal fungus Rhizophagus clarus are insecticidal. All three of these proteins are present in our dataset and fall within the IPD113 clade (Table S2). We now cite this study and have added a discussion of it (lines 501–513). At the same time, we note that functional similarity alone would not necessarily distinguish common ancestry from convergent evolution because similar activities may evolve independently, whereas homologous proteins may also diverge substantially in function after descent from a common ancestor.

      Minor Comments • Line 19: The authors refer to five clades, but only four clades are initially described (Cry, IPD113, Prb, and the mixed 2D/3D20 clades). The fifth clade (Photobacterium) is only mentioned later (line 103). This should be clarified earlier for consistency.

      __ ____Response:__ We have revised the abstract (lines 21–22) to list all five clades explicitly: “Phylogenetic analyses separated the structurally similar proteins into five clades: Cry, IPD113, Prb, 2D/3D-mixed, and Photobacterium.”

      Line 39: The phrase "although its precise role remains unclear" should be removed. Domain III has been clearly demonstrated to be involved in host recognition, and genetic engineering studies using domain III swaps have strongly supported its role in specificity.

      Response: We have removed the phrase “although its precise role remains unclear” from the revised manuscript (lines 42–43).

      Line 94: The authors report the identification of six putative novel Cry proteins using BLASTp with Cry1Aa1 as a query. It would be helpful to clearly specify which proteins these are, identify the closest known Cry proteins to each candidate, and indicate whether they originate from B. thuringiensis or from other organisms.

      __Response: __The six sequences identified by BLASTp using Cry1Aa1 (GenBank: AAA22353) as a query are: AYF84850.1 (Bacillus thuringiensis), MED1304786.1 (Bacillus pacificus), WP_284654161.1 (Paenibacillus thiaminolyticus), WP_377941243.1 (Bacillus mycoides), WP_390212565.1 (B. thuringiensis), and WP_002169796.1 (B. mycoides). Two therefore derive from B. thuringiensis and four from other species. AYF84850.1 is 96.8% identical to Cry8Ca5 and belongs to the Cry8Ca rank, although it is distinct from all currently named Cry8Ca proteins. Among the remaining five sequences, four show 45–75% identity to their closest named Cry proteins and therefore fall within existing primary ranks but outside existing secondary ranks, whereas WP_284654161.1 is below the 45% threshold and cannot be assigned to any named primary rank. Their closest named Cry proteins are Cry41Ab1 (MED1304786.1), Cry1Na1 (WP_284654161.1), Cry42Aa1 (WP_377941243.1), Cry69Ab1 (WP_390212565.1), and Cry41Ba1 (WP_002169796.1). We have added the closest named Cry protein and the corresponding full-length sequence identity for each sequence to Table S2.

      Line 110: Many proteins contain additional domains beyond the canonical two. The authors should describe which types of domains are most frequently found in addition to domains I and II.

      __Response: __We performed InterProScan analysis on the BLASTp-derived set. We have added a description of these domain types to the main text (lines 142–150) and a new supplementary figure (Fig. S5) showing the Pfam domain architecture grouped by clade.

      Beyond the canonical D1 and D2, the most common additional Pfam domains are Jacalin-like lectin (primarily in IPD113 and 2D/3D-mixed), Ricin B lectin (IPD113), and PirA-like (Prb). In the IPD113 clade, Jacalin-like lectin domains overlap with D2 as defined in this study. N-terminal domains found upstream of D1 include Ricin B lectin, Sushi/SCR, SET domain, and PirA-like (Prb only), while C-terminal domains found downstream of D2 include PLL-like, EF-hand, and Ricin B lectin. All Prb sequences with additional domains carry an N-terminal PirA-like domain. Manual structural inspection identified additional domains in 61 proteins; Pfam detected additional domains in 21 of these, leaving 40 without an additional Pfam annotation. This suggests that some accessory regions may represent novel or highly divergent domain types not yet represented in Pfam (lines 142–150).

      Line 120: Although the authors state that the phylogenetic tree of domain I resembles that of the full-length toxin, the domain I phylogeny shows that Prb and IPD113 families are not as closely related as they appear in the full-protein analysis. Furthermore, the phylogenetic trees are information-dense, making it difficult for the reader to identify which proteins cluster in each branch. The authors are encouraged to label branches numerically and provide a supplementary table listing the proteins included in each branch for all three phylogenetic trees.

      __Response: __We note that branch lengths in the GS tree do not represent evolutionary distances. Nevertheless, the five clades defined in the two-domain tree (Fig. 2A) are consistently recovered in the domain I tree (Fig. 3A), including the separation of Prb and IPD113 into distinct clades. We have added a fully labeled version of all three phylogenetic trees as Fig. S3.

      Line 132: The MEME analysis identified 60 motifs, but some detailed information on these motifs is missing. Alignments of the identified MEME blocks should be included as supplementary material, as this information is critical to support the claim that domain I is unlikely to have arisen through convergent evolution. In addition, in Figure S4, the authors could indicate the positions of individual α-helices in domain I and β-strands in domain II to allow readers to clearly associate specific MEME blocks with defined structural elements. Currently, only domain boundaries are shown, and additional structural detail (localization of each alpha helix and beta strand) would improve clarity.

      __Response: __Alignments of the identified MEME blocks will be available in HTML format in the data repository upon acceptance. In addition, we revised Fig. S4 (now Fig. S7A) to show the secondary-structure frequency (helix/strand/loop) across all sequences, together with the helix and strand positions of three representative proteins (Cry1Aa1, P. akhurstii Prb, and L. wrightii IPD113), above the MEME motif locations mapped onto the same FoldMason structural alignment and grouped by clade.

      Lines 174-181: The structural analyses of the predicted models are highly interesting; however, Figure 5 lacks clarity. Adding labels for the individual α-helices of domain I, particularly those other than α-helix 5 that are reported to show high conservation, would be helpful. The authors should also specify which sequences exhibit the highest degree of structural conservation.

      __Response: __We thank the reviewer for these suggestions. Figure 5 has been substantially revised, and we have added the information requested.

      First, we changed what the figure measures. The RMSD-based panels have been removed. Fig. 5A and B now show, for every cross-clade pair, the fraction of the shorter domain that could be superimposed after MatchMaker pruning (coverage), alongside the same comparisons against control folds. TM-scores for these comparisons are reported separately in Fig. S8. This also addresses the concern raised by Reviewer #3 regarding the use of RMSD. Each lane in the revised panels corresponds to a single clade pair or control set, which we hope makes the comparison easier to follow than the previous presentation.

      Second, the secondary-structure elements are now shown explicitly. Fig. 5C–F present the best-matching pair of each cross-clade comparison as a structural superposition in two orientations, with the superimposable residues opaque and the remainder transparent, next to the corresponding residue-level sequence alignment in which α-helices are drawn as boxes and β-strands as arrows. Individual helices and strands can therefore be located along the sequence and mapped onto the superposition, rather than only the domain boundaries being indicated. In domain I, the strongest conservation is centered on α5 and extends to several adjacent helices (Fig. 5C and D); in domain II, one or more of the antiparallel β-sheets forming the β-prism overlap almost completely (Fig. 5E and F).

      Third, we now identify the most conserved sequences explicitly. Across all cross-clade comparisons, excluding the five Photobacterium sequences as in Fig. 5, the best-matching pair superimposed more than 55% of the shorter domain and the strongest reached 90%, whereas the best pair against a non-homologous domain, that is the α-helical pore-forming domains in Fig. 5A and the dirigent domains in Fig. 5B, reached only 22%. The four best-matching pairs are shown in Fig. 5C–F. Their accessions, matched cores and amino acid identities are given on the panels themselves; the Results summarize the coverage, amino acid identities and conserved structural elements (lines 252–259). The corresponding values for every other pair are contained in the raw data to be released with the paper, so that any pair can be ranked directly.

      Line 274: The manuscript raises an important but insufficiently developed question regarding the physiological roles of these proteins across different organisms and whether they all function as pore-forming toxins. This represents one of the weaker aspects of the study. The roles of Cry-like two-domain proteins in bacteria, eukaryotes, and archaea remain unclear, and the manuscript would benefit from a more in-depth discussion or speculation on their possible biological functions and ecological significance in non-bacterial taxa.

      __Response: __Direct experimental evidence bearing on this question became available during the revision of this manuscript. Li et al. (2026) used the fern IPD113_Cow protein as a structural query against the AlphaFold Protein Structure Database, selected ten two-domain candidates, and expressed them in a Bacillus thuringiensis chassis; eight yielded detectable protein and were assayed against five insect pests. Three proved insecticidal: a protein from the bacterium Cohnella faecalis against Spodoptera frugiperda, one from the sea anemone Actinia tenebrosa against Mythimna separata, and one from the arbuscular mycorrhizal fungus Rhizophagus clarus against the beetle Colaphellus bowringi. All three of these sequences are present in our dataset and fall within the IPD113 clade (Table S2). Some two-domain proteins carried by bacteria, fungi and animals can therefore retain insecticidal activity. We note, however, that the remaining five candidates were inactive against all five insects tested, so insecticidal activity cannot be assumed across the family as a whole. We have added these points to the Discussion (lines 501–513).

      Reviewer #1 (Significance (Required)):

      Overall, the study broadens our understanding of the distribution and evolution of insecticidal proteins. To our knowledge, this is the only study that has systematically analyzed genomes from diverse organisms to identify additional proteins showing similarities to domains I and II of the three-domain Cry toxins produced by Bacillus thuringiensis. The expected audience for this manuscript consists of specialists working in the field of insecticidal Cry toxins or, more broadly, in the study of pore-forming toxins. I am an expert with more than 30 years of experience studying the mechanisms of action of insecticidal proteins produced by Bacillus thuringiensis, as well as the mechanisms of resistance developed by insects. My work includes multiple studies involving genomic analyses, transcriptomics, and proteomics.

      __Response: __We thank the reviewer for this positive assessment of our work and for recognizing the broader significance and novelty of the study.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The authors investigated the evolutionary relationships among Cry, Prb, and IPD113 proteins using AlphaFold3-based structure prediction, structure-guided phylogenetic analyses, motif conservation, and structural distance comparisons, and proposes divergent evolution with multiple horizontal gene transfer events. The manuscript is well organized, and some comments need to be concerned.

      Major comments: 1. The conclusion of divergent evolution is based on structural similarity, motif conservation, and GS phylogenies. However, the authors should explain that these methods cannot be used to conclude long-term structural constraint or deep convergent evolution.

      __Response: __We thank the reviewer for this important comment, and we agree. Structural similarity, motif conservation, and GS phylogenies cannot by themselves establish that a fold has been maintained by long-term structural constraint, and they cannot establish deep convergent evolution either. We make neither claim.

      The revised manuscript now states this limitation explicitly at the outset and adopts a more cautious interpretation throughout (lines 406–419). We have also added an opening sentence to the Abstract (lines 10–11), so that this point is stated at the outset rather than only in the Discussion: “When proteins are too divergent in sequence to establish homology, structural similarity alone cannot distinguish common ancestry from convergence on the same fold.”

      At the same time, our preference for common ancestry does not rest on an assumption about constraint. The revised Discussion states the comparison and its basis explicitly (lines 407–415): “Although convergent evolution cannot be formally excluded, we consider it an unlikely account of the similarities reported here, because it would require their independent emergence at multiple hierarchical levels: motifs occurring at corresponding positions and favoring the same residues, the core structures of both domains, and a mosaic of domain-specific relationships in which the domain II placements of some sequences cross family boundaries whereas their domain I placements do not. A shared structural context alone does not specify which residue is favored at a given position (Fig. S7B), and selection for insecticidal activity alone does not predict the mosaic pattern observed in the domain-level trees (Fig. 3).”

      The second of these two points is an analysis added during revision rather than an assertion. When secondary structure and the degree of residue burial were both held fixed, individual positions still favored the same residues, which a shared structural context alone does not specify. The evidence behind each of the three levels is set out in our response to Major Comment 1 by Reviewer 1.

      1. While GS is an appropriate choice for low-similarity sequences, the manuscript does not discuss its resolution limits or error characteristics in this dataset.

      Response: We agree that the manuscript should have discussed its resolution limits and error characteristics rather than leaving them implicit. We have added a paragraph to the Discussion that does so (lines 421–433):

      “The GS and ML analyses were informative at different phylogenetic scales. Across the full dataset, extensive sequence divergence limited the resolution of conventional MSA-based maximum-likelihood inference: even in the initial 557-sequence dataset, relationships involving Prb and IPD113 were weakly supported (Fig. S2A). In contrast, GS recovered well-supported deep branches, with the clade-defining branches in Fig. 2A reaching EP ≥ 0.9, although EP support is not directly comparable to bootstrap support. Well-supported deep branches were also recovered after the Foldseek-derived sequences were added, and Prb and IPD113 remained distinct, strongly supported groups (Fig. S13). The separation between Cry and the 2D/3D-mixed group, however, was not retained in the expanded dataset, indicating that this boundary is sensitive to sequence sampling. Within individual groups, ML provided finer resolution in some cases. The 3D-Cry proteins in the 2D/3D-mixed group formed a single well-supported group in the ML tree (Fig. S12A), whereas GS did not recover them as a group (Fig. 2A). We therefore used GS to characterize broad relationships among these highly divergent proteins and ML to examine relationships within more closely related subsets.”

      We have also stated the GS branch support in the Results, where the two-domain tree and the domain II tree are presented (lines 123–125 and 169–170).

      1. Multiple downstream analyses depend on Alphafold predicted structures, including domain definition, motif localization, and 3Di distance calculations. The authors didn't discuss how prediction confidence (e.g., pLDDT or PAE) affects these analyses or whether low-confidence regions were excluded.

      Response: We agree that prediction confidence matters here, and we addressed it in two ways: by testing the accuracy of the models against experiment, and by restricting the structural analyses to high-confidence models. Confidence scores are indirect, so we checked the models directly. For the proteins in our dataset that have an experimentally determined structure of the identical sequence, the models reproduce the experiment closely, including PDB 8D2J (L. wrightii IPD113) and 7FDP (P. akhurstii Prb) at TM-score 0.970 and 0.991. Both structures were released after AlphaFold 3's template cutoff and so were not available to the predictions that recovered them (Fig. S16). Where the residual uncertainty sits also matters. Residues with pLDDT

      1. An opening question is whether this evolutionary analysis could help us understand the specificity of the insecticidal proteins based on defined structures or not. It has been proved that 3D Cry toxins have different target spectrums with very similar structure.

      Response: We consider that our analyses do not directly address insecticidal specificity. However, from the sequence perspective, proteins with similar sequences are generally expected to show similar insecticidal spectra, as is often the case for Cry protein subfamilies defined by amino acid sequence identity (e.g., Cry1 and Cry2). In particular, given the importance of domain II in target specificity, the phylogeny in Fig. 3B may be useful for generating hypotheses about specificity. For example, Cry58A, whose insecticidal spectrum is not well characterized, falls into the same domain II clade as Cry21 proteins, raising the possibility that it may also have nematocidal activity. By contrast, we consider it difficult to discuss specificity based on our structural analyses alone, because loop regions, which may be important for specificity, are also more likely to include lower-confidence predictions.

      Minor comments 1. Lines 23-24. Please specify whether this conclusion is based primarily on GS topology, AA/3Di distance analysis, or motif similarity.

      __Response: __We revised the abstract as follows: “Amino acid motifs conserved in corresponding positions across clades, along with shared secondary structure organization within domain cores, further support a shared evolutionary origin” (lines 24–25).

      1. Lines 49-54. The term "midnight zone" is introduced with a classical reference. A brief explanation of its relevance in the context of modern structure prediction would improve clarity.

      __Response: __The midnight zone describes a limit on what sequence identity can establish, and this is not altered by the availability of accurate structural models. Even when reliable models exist for both proteins, a shared fold in this regime remains equally consistent with descent from a common ancestor and with independent convergence, so whether a structural resemblance reflects homology or analogy has to be evaluated case by case, on evidence other than the resemblance itself.

      What accurate structure prediction has changed is the scope of what can be compared, and we have added a sentence on this to the final paragraph of the Introduction, where we set out our approach: "Accurate structure prediction now yields models for proteins that have no experimental structure, so these families could be examined structurally in their entirety rather than through the few members whose structures have been solved." (lines 66–68).

      1. Lines 81-83. Did the authors examine genomic context or flanking gene beside introns.

      __Response: __We have not yet performed a systematic comparison of the flanking nucleotide regions to investigate domain swapping. Such comparative analyses will be an important subject of future work for clarifying the detailed evolutionary histories of individual domains and the mechanism of domain swapping.

      1. Please indicate the number of sequences per clade in the main text, not only figures.

      __Response: __We have added the number of sequences per clade to the main text where the five clades are first introduced: Cry (n = 247), 2D/3D-mixed (n = 109), IPD113 (n = 115), Prb (n = 81), and Photobacterium (n = 5) (lines 126–137).

      1. Lines 119-123. Please clarify whether this inference applies to all four clades equally or only to specific branches.

      __Response: __We have clarified that this inference does not apply uniformly across all clades. In the domain II tree (Fig. 3B), the Prb-clade sequences form a single, distinct group, and the majority of the Cry-clade sequences (~92%) form a cohesive block. By contrast, sequences of the IPD113, 2D/3D-mixed, and Photobacterium clades, together with several nematocidal Cry proteins (Cry5A, Cry5D, Cry5E, and Cry12), as well as Cry31 and Cry70, that clustered far from the rest of the Cry proteins in the Cry and 2D/3D-mixed clade, are extensively intermixed. We have revised the text accordingly (lines 164–172).

      1. Lines 132-133. Please provide the minimum motif length and information content thresholds here.

      __Response: __The minimum motif length in the MEME search was set to 8 amino acids (maximum: 50). The shortest motif identified was 11 amino acids in length, and the longest was 50. These motif-width settings and the observed length range are now stated in the Methods (lines 617–618). Information content thresholds for retaining motifs are described in the Methods (lines 613–616).

      1. Lines 241-243. Indicating which types of additional data can help to resolve this question would strengthen the discussion.

      __Response: __We thank the reviewer for this suggestion. We have added analyses of the distribution of domain III and a new Discussion paragraph that sets out the evidence and the remaining uncertainty (lines 447–462). The restricted distribution of domain III is consistent with its addition to a pre-existing two-domain scaffold, but the available unrooted phylogenies do not resolve the number or order of domain gains and losses.

      The new analyses are as follows. We searched our full sequence dataset using Cry domain III sequences and AF3-predicted structures as queries, with BLASTp, HMMER and Foldseek. The sequence-based searches detected domain III only in Cry proteins, and Pfam likewise did not detect the Cry domain III family (PF03944) outside them, consistent with the InterProScan analysis we performed in response to Reviewer #1 (Minor comment #4). A jelly-roll domain III was found only within the Cry and 2D/3D-mixed clades, including the three-domain Cry proteins that we newly identify in Pseudomonadota and amoebozoans, and in no two-domain member of the IPD113, Prb or Photobacterium clades (Table S2). The structure-based search additionally recovered the Pra domain of the insect-derived three-domain Prb proteins; Pra shares the jelly-roll fold with domain III, but the resemblance does not exceed that of unrelated proteins of the same fold, so it provides no evidence of specific common ancestry (Fig. S9). Meanwhile, several two-domain lineages carry additional domains of their own that are unrelated to domain III, such as Ricin B lectin domains in IPD113 and PirA-like domains in Prb.

      Taken together, these observations are consistent with the addition of domain III to a pre-existing two-domain protein. However, as the revised Discussion states, the unrooted phylogenies and the interspersion of two-domain and three-domain proteins do not resolve the number or order of domain gains and losses. An ancestral three-domain architecture followed by secondary losses therefore remains an alternative (lines 447–462).

      We have also added a sentence at the end of the same paragraph indicating what would test this interpretation further: “Reconstruction of ancestral domain architecture under alternative root placements, together with comparative genomic analyses of closely related 2D and 3D domain proteins, would help distinguish these scenarios.” (lines 460–462). Reconstruction of ancestral domain architecture, which Reviewer #3 also raised, would address the direction of change explicitly, and we set out what it would require in our reply to that comment. Comparative genomics of genera such as Paenibacillus and Lysinibacillus, which carry both architectures, could in addition show the flanking regions and recombination signatures associated with gain or loss of the C-terminal module.

      Reviewer #2 (Significance (Required)):

      The study provides a conceptual advance by proposing a unified evolutionary framework for Cry, Prb, and IPD113 proteins, integrating structural phylogenetics with cross-kingdom comparative analysis.

      The work directly addresses and extends previous hypotheses of convergent evolution (e.g., Wei et al., PNAS 2023) by incorporating structure-based phylogenetic methods and distance metrics.

      Researchers in protein evolution, microbial toxins, structural bioinformatics, horizontal gene transfer, and insecticidal protein engineering will be interested in this study.

      Response: We thank the reviewer for this positive assessment of the conceptual significance and potential impact of our study.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      General comments: the main point is an interesting descriptive one: Cry, Prb IPD113 proteins share an origin and have been spread through multiple HGT events due to proximity/symbiosis. Evidence is presented for this conclusion but there are some things to reconsider in the analysis to make the evolutionary portrait of this protein family more complete and little more reliable.

      Proteins containing Cry-like two domains are broadly distributed across taxa

      Ideally, this step of gathering homologs should cast as wide of a net as possible in order to get the most complete set of homologs for downstream analysis. Since you're leaning on folds in parts of the manuscript ( e.g. alignment and phylogenetics ) I don't understand why you use blastp here for homology detection. If folds are too far in sequence space to align then homology search will be too near sighted as well. Use foldseek or justify this choice somehow. As it stands I don't understand why you chose this strategy if the goal was to be exhaustive in your search for homologs

      Response: The main goal of the present study was not to comprehensively catalog all proteins with similar folds, but rather to test whether Cry, Prb, and IPD113 proteins have emerged from common ancestry. From that perspective, we considered it important to recover proteins that could bridge these three families not only at the structural level but also in amino acid sequence space. Encouragingly, our BLAST-based search did identify such intermediate links. In particular, among the bacterial IPD113 hits, four Bacillus sequences including Bacillus thuringiensis were recovered at an E-value threshold of Meanwhile, we fully agree that a more exhaustive survey of proteins sharing this fold is highly valuable. We recovered 170 proteins containing the domain I + II architecture that were not retrieved by BLASTp and that shared less than 95% sequence identity with any BLASTp-derived sequence. When these Foldseek-derived sequences were added to the GS phylogeny (Fig. S13), they were accommodated within the clade structure defined by the BLASTp-derived set rather than forming a separate lineage. The Prb, IPD113 and Photobacterium clades were each recovered essentially intact, but the Cry and 2D/3D-mixed clades were no longer separated, and many of the added sequences also showed relatively low AF3 structural-confidence metrics. Such outcomes are to be expected because these sequences are distant from the original set in amino-acid sequence space.

      For these reasons, we kept the analyses in the original manuscript that address whether these families share a common origin restricted to the BLASTp-derived set, and we report the Foldseek-derived set separately as an additional analysis that illustrates the whole picture of this protein family. This expanded survey was valuable in its own right. The 170 Foldseek-derived proteins comprise 101 bacterial, 27 fungal, 9 dinoflagellate, 4 insect, 27 amoebozoan and 2 ciliate sequences, and among those accommodated within the Cry and 2D/3D-mixed clades we found amoebozoan and Pseudomonadota sequences carrying a Cry-type jelly-roll domain III directly after domain II, that is, the same three-domain (3D-Cry) architecture as the Cry proteins of Bacillus thuringiensis. Prompted by this observation we collected further related sequences and, in the revised manuscript, show by domain-wise structural comparison that these amoebozoan and Pseudomonadota proteins are indeed three-domain Cry proteins nested within the Cry radiation (Fig. 7). We thank the reviewer for this suggestion.

      Because this expanded survey substantially widened the taxonomic scope of the study, we have also changed the title of the manuscript. The original title, "Divergent evolution of insecticidal protein families across bacteria and ferns", no longer describes the content accurately: the family as we now report it is not confined to bacteria and ferns, and it includes amoebozoan and Pseudomonadota three-domain Cry proteins that fall within the Cry radiation itself. The revised title is "Remote homology between bacterial and fern insecticidal proteins reveals a broadly distributed superfamily shaped by recurrent horizontal transfer".

      Phylogenetic analysis identifies the 2D/3D-mixed clade

      Using foldmason and graph splitting approaches to build the tree is novel and interesting. However, this method hasn't been benchmarked. Do other structural phylogenetic approaches give you similar results? Are there differences between the trees produced by this method and current standard practice for structural phylogenetics? Please also report a standard MSA and tree built with only sequence data (e.g. a typical mafft /iqtree type approach ) to show a clear comparison to a sequence-based baseline.

      __Response: __We thank the reviewer for this important comment. We would first like to clarify that the phylogenies underlying Figs. 2A, 3A and 3B are not structural-phylogenetic inferences but amino-acid-based ones. FoldMason was used solely to delimit the boundaries of the structurally corresponding, homologous Cry-like core domains, so that these regions could be trimmed consistently across highly divergent proteins. Phylogenetic inference was then performed by Graph Splitting on the amino-acid sequences of these trimmed regions. Structural information therefore enters only at the domain-delimitation (trimming) step, whereas these trees are amino-acid (sequence-based) phylogenies of structurally corresponding regions, reconstructed with a graph-based method. We have made this explicit in the revised Methods (lines 586–591) to avoid the impression that the domain trees are structural phylogenies.

      In connection with this clarification, we should also report a change that we made after identifying a problem in our original analysis. In the original submission, Fig. S5 and panel B of Fig. S6 showed Graph Splitting trees inferred from 3Di structural-alphabet sequences. While preparing this revision, we realized that this application of the method was not appropriate: 3Di states are derived from local structural geometry rather than representing independently evolving sequence characters, so a Graph Splitting tree inferred from them is not fully independent of the structural similarity that originally motivated our homology hypothesis. We have therefore removed both trees, together with the two sentences in the Results that referred to them, and now restrict Graph Splitting analyses to amino-acid sequences. Every Graph Splitting tree in the revised manuscript is inferred from the amino-acid sequence of the structurally delimited core region, with structural information used only to define the domain boundaries.

      According to the suggestion, we analyzed the same amino-acid data used for the GS tree in Fig. 2A with two conventional approaches: a standard MSA + ML tree (MAFFT + IQ-TREE, WAG+F+R7) and a NeighborNet split network, which is useful for visualizing conflicting phylogenetic signals, such as those arising from domain swapping (Figure S2). In both, the main clades in Fig. 2A were recovered as broadly cohesive groups with minor intermixing, but the deeper relationships among them were weakly supported. In the ML tree, the branch uniting the Cry sequences has SH-aLRT 25 / UFBoot 45 and the two basal splits have UFBoot 86 and 71; in the NeighborNet, the central splits are poorly supported and the core of the network is highly reticulate. Consistently, branches defining relationships within individual clades are generally supported, whereas the backbone branches relating three or more clades are unsupported in both conventional analyses.

      In the initial 557-sequence dataset, the conventional analyses leave the deeper relationships among the major groups poorly resolved. In contrast, GS separates the five clades, with every clade-defining deep branch supported at EP ≥ 0.9. EP support is not directly comparable to bootstrap support, and the Cry/2D/3D-mixed boundary was not retained after the dataset was expanded. We therefore use GS to characterize broad relationships among these highly divergent proteins and ML to examine relationships within more closely related subsets, as now explained in the Discussion (lines 421–433).

      Also, did you trim the columns with the domains that aren't shared across all members of the family when using them as input for the global tree? I see that domain 1 and 2 are likely shared across all members from figure 1a but have divergent histories ( figure 3b ).Please use a method to find the consensus structural region. If the topology of the tree changes in trees inferred with and without these domains, this will change many of the conclusions of the paper since the order of domain architecture changes and transfers will impact the order in which the events are inferred to have happened with respect to the species tree.

      __Response: __We agree that, for the global tree, it is essential to restrict the analysis to regions that are structurally corresponding and homologous across all proteins being compared, and this is precisely what we did. As also clarified in our response above, we did not use full-length sequences. Instead, we used FoldMason to identify and delimit the consensus, structurally corresponding homologous region — the shared domain I–II core — and only this trimmed region was used as input for the global Graph Splitting tree in Fig. 2A. This is the “method to find the consensus structural region” that the reviewer requests: FoldMason provides a structure-based, multiple-structure delimitation of the shared core, and columns corresponding to non-shared domains (for example domain III of the three-domain Cry proteins, and other accessory domains present in only some members) were therefore not included in the global phylogeny. We have described this delimitation and trimming procedure explicitly in the revised Methods (lines 562–583).

      We also agree that domain I and domain II, although shared across all the proteins analyzed here, do not necessarily share identical evolutionary histories. This is why the two domains were also analyzed separately (Fig. 3A, B), revealing the discordant domain II placements. We use the combined domain I–II tree to characterize broad relationships, not to date events or place them relative to a species tree. The domain I tree has a topology similar to the combined tree (Fig. 3A; Fig. S3B). When 170 Foldseek-derived sequences were added, Prb, IPD113 and Photobacterium were recovered essentially intact, but Cry and the 2D/3D-mixed group were no longer separated (Fig. S13; lines 328–331). The absence of Cry domain III from IPD113, Prb and Photobacterium was established by sequence and structure searches and Pfam annotation (Table S2), independently of the combined topology. Its restricted distribution is consistent with addition to a pre-existing two-domain scaffold, but neither these unrooted trees nor the observed architectures resolve the number or order of gains and losses. An ancestral three-domain architecture followed by secondary losses remains an alternative (lines 447–462).

      Domain II swapping across clades except for Prb

      Line 125-127 have you verified the flanking regions? Do some of them still have homology at the nucleotide level?

      Response: We have not yet performed a systematic comparison of the flanking nucleotide regions to investigate domain swapping. Such comparative analyses will be an important subject of future work for clarifying the detailed evolutionary histories of individual domains and the mechanism of domain swapping.

      Could you use ancestral trait reconstruction to show the architecture changes on the consensus region tree and indicate critical transitions. It would be interesting to see how often this is inferred to happen. Also, using different tree building strategies, any major divergence in the number of changes required to explain the extant domain architecture could be an argument for or against a particular evolutionary model. This would require running several different trees of the consensus region... also, If there is some data on function related to the extant proteins it would be interesting to see if the shifts in architecture explain any of them.

      Response: Thank you for very interesting suggestion. We agree that ancestral trait reconstruction of domain architecture, and comparison of the inferred number of transitions across alternative trees, would be highly informative. However, we consider this to be beyond the scope of the present study, as it would require extensive additional analyses across multiple tree-building strategies and careful treatment of uncertainty in both topology and domain-specific histories. Our goal here was more limited, namely to test for common ancestry and to resolve the broad relationships among the major clades.

      In the paragraph discussing the gain and loss of domain III, we noted the potential future utility of ancestral state reconstruction as follows: “Reconstruction of ancestral domain architecture under alternative root placements, together with comparative genomic analyses of closely related 2D and 3D domain proteins, would help distinguish these scenarios.” (lines 460–462).

      Figure 4a What exactly do you mean by raster plot? Presence absence matrix might be a more appropriate title? There is no way of knowing what each pixel corresponds to though.

      Response: We agree that “raster plot” was not the clearest term for Fig. 4A. We have therefore revised the legend to describe panel A as a “motif presence–absence matrix” and to clarify that each gray bar indicates the presence of the corresponding motif (lines 809–811).

      Many horizontal gene transfers shaped broad taxonomic distribution

      Domain core structures aare highly conserved beyond clades

      171-172 RMSD is less than ideal for considering homology. Use metrics like lddt which account for flex or at least TM score which has literature related to cutoffs when things should be considered homologous. Im sure they are in your case but its best practice to use those scores for arguing this type of thing.

      Response: We thank the reviewer for this helpful suggestion. We agree that RMSD alone is not the most appropriate metric for arguments related to structural conservation, as it depends on the number of aligned residues and lacks well-established cutoffs for inferring fold similarity. To clarify, lines 171–172 of the original manuscript were not intended to argue for homology through RMSD, but rather to describe the degree of structural overlap at the domain level. The primary evidence for divergent evolution in this section relies on the observation that specific secondary-structure elements—α-helices in domain I and β-sheets in domain II—are spatially conserved across clades.

      We now report two complementary measures in place of RMSD. First, TM-scores computed with US-align for all pairwise comparisons of domain I and of domain II are shown in Fig. S8. Essentially every cross-clade pair exceeds the conventional 0.5 threshold for fold identity, whereas the α-helical pore-forming domains of different CATH topologies used as controls for domain I (ClyA/HlyE, colicin and the diphtheria toxin translocation domain) fall below it, as do the dirigent domains used as a control for domain II. Second, because a TM-score establishes that a fold is shared but does not by itself discriminate within a fold, Fig. 5A and B now report the fraction of the shorter domain that could be superimposed (“coverage”), which is sensitive to how much of the ancestral core each lineage has retained.

      We also want to be explicit about a limitation of these data that became clear during the revision. For domain II, jacalin-like lectins reach median coverage values of 36% and 37% against IPD113 and Prb, matching the highest cross-clade values. High similarity in domain II therefore distinguishes the β-prism fold from unrelated folds but does not resolve relationships within it, and we now state this in the Results rather than treating domain II structural similarity as independent evidence of common ancestry. The structural argument in this section accordingly rests on domain I, where the non-homologous controls are cleanly separated, and on the residue-level motif evidence presented in the preceding section.

      The RMSD-based panels have been removed and the corresponding text rewritten (lines 227–267).

      Methods, phylgoenetic analysis.

      What was used to root these trees? In general I use mad or midpoint rooting when there is no outgroup. In your case there is no easy outgroup since you have eukaryoters and bacteria on the tree so rooting the trees should be done with one of these outgroup free methods.

      Response: We should have been more precise, and we thank the reviewer for catching this. The distinction is between the trees on which our inferences rest and the way those trees are displayed. The Graph Splitting trees (Figs. 2, 3, S3, S9A, S10, and S13) and the maximum-likelihood trees in Fig. 6A and Fig. S12 are unrooted, and no root was assumed in their interpretation. Because the dataset spans bacteria, eukaryotes, and archaea, there is no suitable outgroup that lies outside the group being analyzed, and we therefore preferred not to impose a root using a criterion that the data themselves cannot test. Where a root is shown in a figure, it is a midpoint root used for display purposes only; this applies to the maximum-likelihood trees in Figs. 7C, S2A, S14, and S15C.

      We also accept the consequence highlighted by the reviewer: the direction of transfer cannot be inferred from an unrooted tree. Where we argue for a bacterial origin, the direction is inferred from the taxonomic composition of each clade, from the interleaving of bacterial and non-bacterial sequences within it, and from the comparison of amino-acid and 3Di distances, rather than from the position of a root. The rooting of every tree is now stated explicitly in the Methods (lines 603–607), and the evidence on which the directional argument rests is presented in the Results (lines 285–319; Figs. 6, S10, S11 and S12).

      Line 203. The single bacterial member inside this clade appears to be a MAG from its name. Are you certain of its provenance? Could this be contamination? If this is really a point you want to put forward you should probably dig into where this thing comes from, whats on the contig and if its really what you think it is before arguing for HGT.

      __Response: __We are particularly grateful to the reviewer for drawing our attention to this issue. As the reviewer noted, the BioSample record (SAMN04544940) lists the sample type as “Metagenomic assembly” and the host as the terrestrial isopod Trachelipus rathkii. We therefore examined the contig directly.

      OIZ96594.1 is encoded at positions 1,632–2,930 of the 3,719-bp contig LUKY01000019.1. The gene model is complete, with 789 bp of contig sequence downstream of the stop codon, and is therefore not truncated by an assembly boundary. However, the available annotations of the flanking genes do not establish the taxonomic origin of the contig. We therefore agree with the reviewer that the contig alone is insufficient to establish its provenance. We next tested the most immediate alternative suggested by the record, namely that the contig might be derived from the host. A tblastn search against the Trachelipus rathkii genome assembly (GCA_015478945.1), produced by the same laboratory, returned no hit even at the permissive threshold of E Taken together, these searches do not allow us to exclude either host contamination or contamination from another organism present in the sample. We have therefore followed the reviewer's advice and no longer use this sequence as evidence for HGT. We retain OIZ96594.1 in the homology dataset as a Prb-family sequence, because its family assignment is not in question. In the figures it also keeps its deposited taxonomic designation as a bacterial sequence, since we likewise have no evidence that would justify overturning that annotation; what we withdraw is the inference we drew from it. This is now stated in the legend of Fig. S12 (lines 994–996), where the sequence is identified as being of unresolved provenance and the pairs involving it are excluded from interpretation as candidate transfers. In addition, we no longer use it as evidence for bacterial provenance, for the direction of transfer, or for the identity of a donor lineage. Accordingly, we have removed the statement that the Prb clade contains a single bacterial member nested within an animal clade, and we no longer identify this lineage as a candidate donor; the corresponding annotation has also been removed from Fig. S12.

      Finally, the reviewer’s comment also drew our attention to an error in the sequence label. We have corrected the label in Fig. S10 to “Candidatus_Rickettsiella_OIZ96594.1”.

      Reviewer #3 (Significance (Required)):

      Studies of deeply divergent protein families are now becoming possible due to the advent of structural phylogenetics and homology detection methods. The set of best practices are evolving day to day as people experiment with new methods and approaches.

      This particular family, the Cry proteins, are a good case study. The authors present the case for their version of evolutionary history. While the paper is very descriptive of the evolutionary history of this family, it offers little discussion about the relationship between the phylogeny and the function of the proteins within the extant organisms its found in and the possible relationship between the different organisms that possess this protein, their symbiotes and the insects they are producing these proteins to ward off. After all, the objective of phylogenetic analysis is, at least in part, to explain the emergence of extant biology. So it would be good to see some more of that in this manuscript.

      Some interesting new approaches to deep phylogenetics are presented that I haven't seen elsewhere and could be a valuable addition to the discussion around which phylogenetic methods are best suited for working with very distant homology. Unfortunately these aren't validated and this isn't really a methods paper. There are a few things that are unclear to me as far as methodological choices as well.

      I would like to see a bit more of an exhaustive search for the starting set of homologs to be sure that everything is included. Downstream of that, multiple evolutionary scenarios should be presented for the core shared domains between all homologs using various phylogenetic methods employing structural phylogenetics and traditional maximum likelihood analysis. For now the authors rely on a graph splitting method after aligning using structural information that I haven't seen benchmarked anywhere. Also, it's unclear what the consensus/core region of the protein is across all homologs. On first glance at the architecture it appears to be domains 1 and 2 but they have different evolutionary histories. Please chop the structures down to the consensus domain shared between all of the homologs and perform phylogenetic analysis on this core. Then look at the N and C terminals separately.

      __Response: __We thank the reviewer for this summary, which we used to organize the revision. We address the four points in turn.

      • __Phylogeny and function in extant organisms. __ We have expanded the Discussion on this point (lines 501–513). Li et al. (2026) recently expressed ten two-domain candidates selected from the AlphaFold database in a Bacillus thuringiensis chassis; eight yielded detectable protein and were assayed, of which three were insecticidal; all three fall within the IPD113 clade of our dataset (Table S2).

      • __Exhaustiveness of the homolog search. __ We carried out the structure-based search the reviewer asked for. A Foldseek search of structure space recovered 170 further proteins with the domain I + II architecture that BLASTp had missed, including the first members from amoebozoans and ciliates, and a follow-up search around those hits retained 305 additional three-domain Cry proteins after structural assessment and filtering (Table S3; lines 719–742). This work now forms a separate Results section (“Foldseek searches uncover many putative novel 3D Cry proteins”), with Fig. 7 and Figs. S13–S15. It also produced a result we would not otherwise have found: amoebozoans and Pseudomonadota carry the same three-domain architecture as Bacillus thuringiensis, and they are placed within the radiation of the Bacillota Cry proteins rather than outside it.

      • __Consensus core region and the terminal domains. __ FoldMason was used to delimit the structurally corresponding domain I–II core across all proteins, and only that trimmed region was used for the global tree; domains I and II were then analyzed separately (Fig. 3), which reveals the domain II swapping. The regions outside the core were analyzed in their own right: Cry domain III, Pra and the insect Pra-like domains in Fig. S9, and the other N- and C-terminal accessory domains by Pfam annotation in Fig. S5, according to the suggestion by the reviewer #1.

      • __Benchmarking of the method. __ As described in our response to the reviewer’s comment above, we compared the Graph Splitting tree with a conventional MSA + maximum-likelihood tree and with a NeighborNet split network built from the same amino-acid data. We also agree that this is not a methods paper. However, Graph Splitting itself was extensively benchmarked in the original study (Matsui and Iwasaki, 2020). What we can do here is make clear what the method resolves on this particular dataset and what it does not, and we now do so in the revised Discussion (lines 421–433). GS and ML proved informative at different phylogenetic scales. In the initial dataset, Graph Splitting recovered well-supported branches separating the five major clades, whereas conventional alignment-based inference left deeper relationships poorly resolved. The Cry/2D/3D-mixed boundary was sensitive to sequence sampling and was not retained in the expanded dataset. Maximum likelihood provided finer resolution within some groups. We therefore now use the two methods according to the scale at which each is most informative.

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      Referee #3

      Evidence, reproducibility and clarity

      General comments: the main point is an interesting descriptive one: Cry, Prb IPD113 proteins share an origin and have been spread through multiple HGT events due to proximity/symbiosis. Evidence is presented for this conclusion but there are some things to reconsider in the analysis to make the evolutionary portrait of this protein family more complete and little more reliable.

      Proteins containing Cry-like two domains are broadly distributed across taxa

      Ideally, this step of gathering homologs should cast as wide of a net as possible in order to get the most complete set of homologs for downstream analysis. Since you're leaning on folds in parts of the manuscript ( e.g. alignment and phylogenetics ) I don't understand why you use blastp here for homology detection. If folds are too far in sequence space to align then homology search will be too near sighted as well. Use foldseek or justify this choice somehow. As it stands I don't understand why you chose this strategy if the goal was to be exhaustive in your search for homologs

      Phylogenetic analysis identifies the 2D/3D-mixed clade

      Using foldmason and graph splitting approaches to build the tree is novel and interesting. However, this method hasn't been benchmarked. Do other structural phylogenetic approaches give you similar results? Are there differences between the trees produced by this method and current standard practice for structural phylogenetics? Please also report a standard MSA and tree built with only sequence data (e.g. a typical mafft /iqtree type approach ) to show a clear comparison to a sequence-based baseline.

      Also, did you trim the columns with the domains that aren't shared across all members of the family when using them as input for the global tree? I see that domain 1 and 2 are likely shared across all members from figure 1a but have divergent histories ( figure 3b ).Please use a method to find the consensus structural region. If the topology of the tree changes in trees inferred with and without these domains, this will change many of the conclusions of the paper since the order of domain architecture changes and transfers will impact the order in which the events are inferred to have happened with respect to the species tree.

      Domain II swapping across clades except for Prb

      Line 125-127 have you verified the flanking regions? Do some of them still have homology at the nucleotide level?

      Could you use ancestral trait reconstruction to show the architecture changes on the consensus region tree and indicate critical transitions. It would be interesting to see how often this is inferred to happen. Also, using different tree building strategies, any major divergence in the number of changes required to explain the extant domain architecture could be an argument for or against a particular evolutionary model. This would require running several different trees of the consensus region... also, If there is some data on function related to the extant proteins it would be interesting to see if the shifts in architecture explain any of them.

      Figure 4a What exactly do you mean by raster plot? Presence absence matrix might be a more appropriate title? There is no way of knowing what each pixel corresponds to though.

      Many horizontal gene transfers shaped broad taxonomic distribution

      Domain core structures aare highly conserved beyond clades

      171-172 RMSD is less than ideal for considering homology. Use metrics like lddt which account for flex or at least TM score which has literature related to cutoffs when things should be considered homologous. Im sure they are in your case but its best practice to use those scores for arguing this type of thing.

      Methods, phylgoenetic analysis.

      What was used to root these trees? In general I use mad or midpoint rooting when there is no outgroup. In your case there is no easy outgroup since you have eukaryoters and bacteria on the tree so rooting the trees should be done with one of these outgroup free methods.

      Line 203. The single bacterial member inside this clade appears to be a MAG from its name. Are you certain of its provenance? Could this be contamination? If this is really a point you want to put forward you should probably dig into where this thing comes from, whats on the contig and if its really what you think it is before arguing for HGT.

      Significance

      Studies of deeply divergent protein families are now becoming possible due to the advent of structural phylogenetics and homology detection methods. The set of best practices are evolving day to day as people experiment with new methods and approaches.

      This particular family, the Cry proteins, are a good case study. The authors present the case for their version of evolutionary history. While the paper is very descriptive of the evolutionary history of this family, it offers little discussion about the relationship between the phylogeny and the function of the proteins within the extant organisms its found in and the possible relationship between the different organisms that possess this protein, their symbiotes and the insects they are producing these proteins to ward off. After all, the objective of phylogenetic analysis is, at least in part, to explain the emergence of extant biology. So it would be good to see some more of that in this manuscript.

      Some interesting new approaches to deep phylogenetics are presented that I haven't seen elsewhere and could be a valuable addition to the discussion around which phylogenetic methods are best suited for working with very distant homology. Unfortunately these aren't validated and this isn't really a methods paper. There are a few things that are unclear to me as far as methodological choices as well.

      I would like to see a bit more of an exhaustive search for the starting set of homologs to be sure that everything is included. Downstream of that, multiple evolutionary scenarios should be presented for the core shared domains between all homologs using various phylogenetic methods employing structural phylogenetics and traditional maximum likelihood analysis. For now the authors rely on a graph splitting method after aligning using structural information that I haven't seen benchmarked anywhere. Also, it's unclear what the consensus/core region of the protein is across all homologs. On first glance at the architecture it appears to be domains 1 and 2 but they have different evolutionary histories. Please chop the structures down to the consensus domain shared between all of the homologs and perform phylogenetic analysis on this core. Then look at the N and C terminals separately.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors investigated the evolutionary relationships among Cry, Prb, and IPD113 proteins using AlphaFold3-based structure prediction, structure-guided phylogenetic analyses, motif conservation, and structural distance comparisons, and proposes divergent evolution with multiple horizontal gene transfer events. The manuscript is well organized, and some comments need to be concerned.

      Major comments:

      1. The conclusion of divergent evolution is based on structural similarity, motif conservation, and GS phylogenies. However, the authors should explain that these methods cannot be used to conclude long-term structural constraint or deep convergent evolution.
      2. While GS is an appropriate choice for low-similarity sequences, the manuscript does not discuss its resolution limits or error characteristics in this dataset.
      3. Multiple downstream analyses depend on Alphafold predicted structures, including domain definition, motif localization, and 3Di distance calculations. The authors didn't discuss how prediction confidence (e.g., pLDDT or PAE) affects these analyses or whether low-confidence regions were excluded.
      4. An opening question is whether this evolutionary analysis could help us understand the specificity of the insecticidal proteins based on defined structures or not. It has been proved that 3D Cry toxins have different target spectrums with very similar structure.

      Minor comments

      1. Lines 23-24. Please specify whether this conclusion is based primarily on GS topology, AA/3Di distance analysis, or motif similarity.
      2. Lines 49-54. The term "midnight zone" is introduced with a classical reference. A brief explanation of its relevance in the context of modern structure prediction would improve clarity.
      3. Lines 81-83. Did the authors examine genomic context or flanking gene beside introns.
      4. Please indicate the number of sequences per clade in the main text, not only figures.
      5. Lines 119-123. Please clarify whether this inference applies to all four clades equally or only to specific branches.
      6. Lines 132-133. Please provide the minimum motif length and information content thresholds here.
      7. Lines 241-243. Indicating which types of additional data can help to resolve this question would strengthen the discussion.

      Significance

      The study provides a conceptual advance by proposing a unified evolutionary framework for Cry, Prb, and IPD113 proteins, integrating structural phylogenetics with cross-kingdom comparative analysis.

      The work directly addresses and extends previous hypotheses of convergent evolution (e.g., Wei et al., PNAS 2023) by incorporating structure-based phylogenetic methods and distance metrics.

      Researchers in protein evolution, microbial toxins, structural bioinformatics, horizontal gene transfer, and insecticidal protein engineering will be interested in this study.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript investigates the evolutionary relationships among domains I and II of three families of insecticidal proteins: Cry proteins from Bacillus thuringiensis, Prb proteins from bacterial symbionts of nematodes, and IPD113 proteins from ferns. These protein families were previously thought to have evolved independently because they share very low sequence identity (approximately 10%), despite exhibiting notable structural similarities. Through phylogenetic analyses and structural comparisons, the authors propose that domains I and II of these protein families share a common evolutionary origin and have likely undergone extensive horizontal gene transfer (HGT) across diverse taxa, including bacteria, eukaryotes, and archaea. In contrast, domain III does not show a shared evolutionary history between Cry proteins and the Pra component of Prb proteins. The authors also report evidence of domain II swapping and identify multiple conserved motifs across clades, suggesting a more complex evolutionary history than previously appreciated.

      Major Comments

      1. The study proposes a common evolutionary origin for Cry, Prb, and IPD113 proteins; however, it cannot definitively exclude the possibility of convergent evolution, given the very low amino acid identity (~10%) among these families. For example, the authors note that eukaryotic genes encoding these proteins often contain introns (Table S2), which may also be interpreted as supporting convergent evolution rather than horizontal acquisition. This uncertainty warrants a more cautious interpretation and highlights the need for additional analyses or discussion to better discriminate between common ancestry and convergence.
      2. Although the study proposes multiple HGT events, it does not conclusively demonstrate the mechanisms or pathways underlying these transfers. The inferred HGT events would benefit from further validation, potentially through experimental approaches or population-level analyses. The authors are encouraged to expand their discussion on possible mechanisms facilitating HGT, such as the involvement of transposable elements, and to consider how ecological interactions (e.g., symbiosis or shared niches) may have promoted the horizontal transfer and diversification of these insecticidal protein families across different taxa.
      3. The conclusions of the study rely entirely on computational analyses, including phylogenetic inference and structural prediction. While these approaches are appropriate, experimental validation would considerably strengthen the manuscript. For instance, biochemical or functional assays assessing the insecticidal or nematocidal activity of selected proteins from different taxa would provide direct support for the proposed evolutionary relationships. This could include heterologous expression of candidate proteins in model organisms or cell lines, followed by toxicity assays, or genetic manipulation (knockout or overexpression) in native or model systems to assess physiological function. Although such experiments may be beyond the scope of the present study, they should at least be acknowledged as important directions for future research.

      Minor Comments

      • Line 19: The authors refer to five clades, but only four clades are initially described (Cry, IPD113, Prb, and the mixed 2D/3D20 clades). The fifth clade (Photobacterium) is only mentioned later (line 103). This should be clarified earlier for consistency.
      • Line 39: The phrase "although its precise role remains unclear" should be removed. Domain III has been clearly demonstrated to be involved in host recognition, and genetic engineering studies using domain III swaps have strongly supported its role in specificity.
      • Line 94: The authors report the identification of six putative novel Cry proteins using BLASTp with Cry1Aa1 as a query. It would be helpful to clearly specify which proteins these are, identify the closest known Cry proteins to each candidate, and indicate whether they originate from B. thuringiensis or from other organisms.
      • Line 110: Many proteins contain additional domains beyond the canonical two. The authors should describe which types of domains are most frequently found in addition to domains I and II.
      • Line 120: Although the authors state that the phylogenetic tree of domain I resembles that of the full-length toxin, the domain I phylogeny shows that Prb and IPD113 families are not as closely related as they appear in the full-protein analysis. Furthermore, the phylogenetic trees are information-dense, making it difficult for the reader to identify which proteins cluster in each branch. The authors are encouraged to label branches numerically and provide a supplementary table listing the proteins included in each branch for all three phylogenetic trees.
      • Line 132: The MEME analysis identified 60 motifs, but some detailed information on these motifs is missing. Alignments of the identified MEME blocks should be included as supplementary material, as this information is critical to support the claim that domain I is unlikely to have arisen through convergent evolution. In addition, in Figure S4, the authors could indicate the positions of individual α-helices in domain I and β-strands in domain II to allow readers to clearly associate specific MEME blocks with defined structural elements. Currently, only domain boundaries are shown, and additional structural detail (localization of each alpha helix and beta strand) would improve clarity.
      • Lines 174-181: The structural analyses of the predicted models are highly interesting; however, Figure 5 lacks clarity. Adding labels for the individual α-helices of domain I, particularly those other than α-helix 5 that are reported to show high conservation, would be helpful. The authors should also specify which sequences exhibit the highest degree of structural conservation.
      • Line 274: The manuscript raises an important but insufficiently developed question regarding the physiological roles of these proteins across different organisms and whether they all function as pore-forming toxins. This represents one of the weaker aspects of the study. The roles of Cry-like two-domain proteins in bacteria, eukaryotes, and archaea remain unclear, and the manuscript would benefit from a more in-depth discussion or speculation on their possible biological functions and ecological significance in non-bacterial taxa.

      Significance

      Overall, the study broadens our understanding of the distribution and evolution of insecticidal proteins. To our knowledge, this is the only study that has systematically analyzed genomes from diverse organisms to identify additional proteins showing similarities to domains I and II of the three-domain Cry toxins produced by Bacillus thuringiensis. The expected audience for this manuscript consists of specialists working in the field of insecticidal Cry toxins or, more broadly, in the study of pore-forming toxins. I am an expert with more than 30 years of experience studying the mechanisms of action of insecticidal proteins produced by Bacillus thuringiensis, as well as the mechanisms of resistance developed by insects. My work includes multiple studies involving genomic analyses, transcriptomics, and proteomics.

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      Reply to the reviewers

      We thank all reviewers for their supportive comments and constructive feedback.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      In their manuscript "An Aurora Kinase A/TPX2 complex phosphorylates CKAP2 to control mitotic spindle growth", Kucharski and colleagues combined immunoprecipitation and mass-spectrometry to identify mitotic interactors of the microtubule-associated protein CKAP2. By this approach, the authors revealed the Aurora A-TPX2 complex as a prominent interactor of CKAP2. Based on a series of further in-cellulo and in-vitro experiments, they propose that phosphorylation of CKAP2 by Aurora A decreases the binding affinity of CKAP2 to microtubules, thereby controlling mitotic spindle growth and stability. The authors propose an intriguing model containing a negative feedback loop of Aurora A activity at the spindle. Initially, Aurora A activity promotes mitotic spindle microtubule growth, but when the spindle matures, CKAP2 phosphorylation reduces microtubule polymerization, preventing excessive spindle growth/elongation and stabilizing its size and architecture. How microtubule polymerization activity is restrained to prevent excessive spindle elongation during mitosis is an important question and the proposed model is interesting. The study is overall well-designed and performed. However, several controls and experiments could be performed to strengthen the conclusions and further elucidate the underlying molecular mechanism.

      Major points: 1) Page 4, Fig. 1D - the authors state: "Given the central role of AurKA and TPX2 in spindle regulation, we focused on their interaction with CKAP2." - Aurora A and TPX2 are indeed interesting candidates to study in the context of interaction with CKAP2. However, KIF2A is a much stronger candidate according to the mass-spectrometry data, and it also has a central role in spindle regulation. Moreover, it has also been reported as a Aurora A-TPX2 interactor. Although not a primary topic of this study, the finding of KIF2A and potential meaning of this interaction could be better discussed in the text.

      We agree with the reviewer and will include a discussion around KIF2A as a potential CKAP2 interactor as well as a potential subject in future studies.

      2) Fig. S1A - After performing immunoprecipitation with CKAP2, reciprocal immunoprecipitation of TPX2 confirmed its association with both Aurora A and CKAP2. To provide another control and strengthen the results, reciprocal immunoprecipitation of Aurora A could be performed.

      We thank the reviewer for pointing this out and are suggesting performing the reciprocal immunoprecipitation in our revision plan.

      3) The main part that has been left unexplored, and which could significantly strengthen the conclusions and further elucidate the underlying molecular mechanism is generation and analysis of phospho-null and phospho-mimetic mutants of identified residues at CKAP2 N-terminus (Ser39, Ser76, Thr209, and Ser347). For instance, mutating all four residues in parallel could be a good starting point to test their effect on cellular phenotypes, microtubule binding and polymerization.

      In our revision, we plan to identify all Aurora A sites in CKAP2. We will introduce combinatorial and single-site mutations (both phosphomimetic and non-phosphorylatable) and assess their impact on mitotic spindles.

      4) The protein-protein interactions are proven using cell lysates, which contain all cellular proteins and resulting interactions may be indirect. Since the authors have already shown the ability to purify CKAP2, Aurora A and TPX2, it would be relevant to use these purified components and show direct interactions between individual proteins, including the used truncations.

      We agree with the reviewer that this is an important point to test and suggest performing Microscale Thermophoresis using purified proteins to test interactions directly, as well as measure the precise affinity between CKAP2, Aurora A and TPX2.

      5) If TPX2 promoted CKAP2 phosphorylation by Aurora A, reducing the levels of CKAP2 at microtubules, would TPX2 depletion (e.g. by siRNAs) lead to an increase of microtubule-bound CKAP2?

      We agree with the reviewer that this is a prediction from our model and our revision plan suggests performing the TPX2 knockdown and measuring changes of CKAP2 levels on spindles.

      6) Fig. 4A - It is difficult to understand how CKAP2 KO clone C12 exhibited reduced tubulin intensity but maintained normal TPX2 levels. Immunoblots showing the level of CKAP2 knock-out in all clones are required to understand the depletion efficiency.

      We agree that providing immunoblots for CKAP2 KO clones will be useful and suggest performing these in our revision plan.

      7) How microtubule polymerization activity is restrained to prevent excessive spindle elongation during mitosis is an important question and the proposed model based on AuroraA-TPX2 and CKAP2 is intriguing. The proposed negative feedback model could be further discussed in order to offer potential explanation how CKAP2 becomes more phosphorylated in later mitosis compared to the early stages. Is this simply time-dependent? Does it depend on specific localization of individual elements over time, or on regulation of Aurora A activity?

      These are indeed very interesting questions that we will attempt to address in the discussion. In short: While we do believe that Aurora A phosphorylation of CKAP2 is crucial in Metaphase, we do think that other kinases add crucial phosphorylation, particularly at the conserved C-terminus of CKAP2 in later stages, for example during Anaphase when CKAP2 switches localization from microtubules to chromatin.

      Minor points: 1) Page 6 - the authors state: "To test this directly, we expressed and purified CKAP2-GFP from E. coli and incubated the protein in vitro with recombinant GFP-AurKA (Fig. S1E) in the presence or absence of ATP." - However, the Fig. S1E does not exist and thus this needs to be corrected.

      This will be corrected in the revised manuscript.

      2) Page 8 - the authors state: "Our proteomic and immunoprecipitation analyses indicated that CKAP2 interacts directly with TPX2...". As mentioned above, direct interactions could be indicated only by using purified components.

      We agree and will perform direct measurements on purified components.

      3) Page 11 - the authors state: "Consistent with this, AurKA-dependent phosphorylation of CKAP2 reduces its microtubule binding affinity in vitro and in cells (Figs. 3 and 6G)." - Fig. 6G does not exist and thus this needs to be corrected. The authors probably referred to Fig. 3A,G.

      This will be corrected in the revised manuscript.

      4) It may be useful for the readers to provide a flow-chart-like model of the proposed feedback loop.

      We would be happy to provide a better graphical representation of the model, if space and editorial constraints allow.

      Reviewer #1 (Significance (Required)):

      Strengths/Advance: The model is interesting and novel, with potential to expand our understanding of spindle microtubule growth and stability regulation during mitosis. The study is overall well-designed and performed. It has a potential to fill an important gap: how microtubule polymerization activity is restrained to prevent excessive spindle elongation during mitosis.

      Limitations: Listed above within the comments, along with suggested experiments that may serve to overcome the limitations. In brief, the proposed negative feedback model should be further discussed in order to offer potential explanation how CKAP2 becomes more phosphorylated in later mitosis compared to the early stages. Phospho-mutants of CKAP2 and pull-down with purified components could be used to strengthen the main conclusions.

      Audience: Broader cell biology-related audience, including more specialized in cell division.

      Reviewer's expertise: cell biology, mitosis, microtubules, mitotic spindle.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This study reports that CKAP. a microtubule associated protein involved in the regulation of the mitotic spindle by promoting microtubule polymerization, is regulated by the Aurora-A kinase in association with its co-factor TPX2. The authors propose that this regulation fine-tunes the ability of CKAP to promote microtubule depolymerization and regulate the size of the mitotic spindle.

      The study is based on both in vitro assays with recombinant proteins and in cellulo assays in human tissue culture cells. the study makes the following key claims:

      • that CKAP interacts directly with Aurora-A/TPX2 based on IP-MS data and co-localization data.
      • that Aurora-A/TPX2 regulate CKAP via protein phosphorylation and that this phosphorylation event prevents CKAP from binding to microtubules. The authors also propose that CKAP presence favors the recruitment of TPX2 to the mitotic spindle
      • that this phosphorylation event fine-tunes the ability of CkAP to regulate spindle size

      Major comments: - with regard to the first claim, the presented data demonstrate that Aurora-A and TPX2 co-immunoprecipitate and co-localize, but they never show evidence that those proteins are direct interactors. To demonstrate this claim, the authors should perform co-IP experiments with recombinant proteins (which the authors) to exclude an indirect interaction.

      We agree with the reviewer that this is an important point to test. Instead of co-IP with purified proteins, we suggest performing microscale thermophoresis to test interactions directly, as well as measure the precise affinity between CKAP2, Aurora A, and TPX2, which we wouldn’t get out of co-IPs. We believe that Kd measurements would be helpful in sharpening our model.

      • a central claim of the study is that Aurora-A/TPX2 regulate CKAP by protein phosphorylation. While the presented data is interesting, it is far from conclusive.

      1) the authors first show that CKAP is phosphorylated in mitotic cells. They then report that this phosphorylation depends on Aurora-A by using an Aurora-A inhibitor on cells arrested in mitosis with the Eg5-inhibitor STLC. The first caveat here, is that Aurora-A inhibition will dramatically delay mitotic entry. The authors should show that the cells treated with STLC or STLC + the Aurora-A inhibitor are indeed both in mitosis, e.g. by blotting for cyclin B1.

      We agree with the reviewer and plan to provide assurance that cells are indeed in mitosis in our revised manuscript

      .

      2) the authors report via a series of protein kinase assays that Aurora-A phosphorylates CKAP (Figure 2F). The problem of such an assay is that recombinant kinases can be very promiscuous, especially when the kinase to substrate ratio is around 1 or higher than 1, as shown in Figure 2F. Moreover, in the present figure, the phosphorylation mark on CKAP is very low compared to Aurora-A itself. These experiments at this stage do not indicate that CKAP is a good Aurora-A substrate. A better way to carry out these epxeriments would be to a) have a kinase to substrate ratio of 1:100, and to compare the phospho-incoporation in CKAP with another known Aurora-A substrate.

      We agree with the reviewers that a 1:100 protein kinase ratio would give us a stronger confirmation of CKAP2 phosphorylation by Aurora A, and we suggest performing these in our revision plan.

      3) These concerns are not further alleviated by the kinase assays shown in Figure 4D, where the authors add TPX2 to the kinase reaction, and claim that TPX2 acts as a bridge between CKAP and Aurora-A. First the authors report that CKAP phosphorylation is markedly increased in the presence of TPX2: the presented data is not quantified, and the increase is not particularly strong. Moreover, TPX2 is an Aurora-A activator. Is it therefore not logic to see more CKAP phosphorylation without having to invoke a bridging function for TPX2? Again a comparison of Aurora-A activity on a known substrate as comparison would help.

      We agree that the bridging vs activating question is important. On the other hand, it is hard to address. We will follow the reviewer’s suggestion and attempt to tease these apart with a known Aurora A substrate.

      3) the most convincing data showing that Aurora-A can phosphorylate CKAP is presented in Figure 5, where the CKAP phosphorylation is more consequent, but again those experiments should be repeated with a different kinase to substrate ratio, to exclude a promiscuity artefact.

      As mentioned above, we agree with the reviewers that a 1:100 protein kinase ratio would give us a stronger confirmation of CKAP2 phosphorylation by Aurora A, and we suggest performing these in our revision plan.

      • another central claim of the study is that CKAP phosphorylation reduces its affinity for microtubules. While the in vitro data with recombinant proteins is convincing, the data presented in cells is less so, for the following reasons:

      1) in Figure 3A-C the authors measure beta-tubulin and CKAP levels over the whole spindle in a single Z-plane. the problem is that in the presented images the spindle is vastly different between DMSO and MLN8237-treated cells (in fact the DMSO-treated cells looks like an anaphase cells). To compensate for this the authors should first make sure that the cells are all the same stage of mitosis (MG132 treatment), and if there is still a strong difference in spindle length and height, consider quantifying these two proteins cumulatively over the whole spindle over several z-planes. Indeed, it cannot be excluded that the decrease in beta-tubulin observed by the authors, which is the parameter that changes, arise due to differences in spindle height.

      We agree with the reviewer that experiments in cells will never be as clear-cut as our in vitro experiments. Cell manipulation inherently introduces a slew of uncontrollables, which is why combining both approaches is the most powerful way to address both the direct biological impact of a manipulation (e.g., phosphorylation) on our system and its physiological relevance in cells. We will address both the experimental and conceptual concerns in our revised manuscript.

      2) the figure legend of figure 3C indicates that the authors performed 3 independent experiments with 50 cells each. However, they don't report the individual means of the experiments. Moreover, are the SEM error bars and the statistical tests generated based on N=3 independent experiments or 150 cells (which are not independent)? this information is essential to evaluated the reproducibility.

      We will provide improved plots and analysis for this data in our revised manuscript.

      • to support their model the authors also report that TPX2-levels decrease in 2 out 3 CKAP KO cells. These data are, at the moment, not particularly strong. Indeed, the decrease is only moderate and only seen in 2 out of 3 clones. The fact that in a third clone the authors could not see a decrease could indicate that the subtle changes in TXP2 levels, could just represent inter-clone variability. If the authors wanted to strengthen this aspect, they should in addition deplete acutely CKAP by siRNA and test for a decrease in TPX2-levels.

      In our revision plan, we suggest to perform this experiment exactly as outlined by the reviewer.

      • finally the last and most important claim of the study, is that Aurora-A/TPX-2 fine-tune the microtubule polymerization ability of CKAP in order to regulate spindle size. Unfortunately, there is no data supporting this particular model. The authors do not, e.g., provide data showing how a non-phosphorylatable CKAP would affect spindle size, which would be a direct test of their model. Instead at the current stage the presented model is only a speculation. Either the authors should provide direct evidence for a such a model, or strongly reduce their claim.

      We agree with the reviewer that this type of data would significantly strengthen our model. In our revision, we plan to identify all Aurora A sites in CKAP2. We will introduce combinatorial and single-site mutations (both phosphomimetic and non-phosphorylatable) and assess their impact on mitotic spindles.

      OPTIONAL: - the authors strongly imply that CKAP interacts with Aurora-A/TPX2 and not with Aurora B, which had been previously suggested. To strengthen this conclusion, the authors could also measure CKAP levels on the spindle in the presence of an Aurora B inhibitor.

      This experiment is already in the manuscript: Fig. S1D shows that treatment with the AurKB inhibitor ZM447439, at doses sufficient to cause severe chromosome segregation defects, does not alter CKAP2 localization on the spindle or chromatin. We will ensure this experiment is outlined clearer in a revised version of our manuscript.

      Reviewer #2 (Significance (Required)):

      This study is potentially interesting and novel, linking the microtubule polymerisation driver CKAP to the Aurora-A/TPX2 regulation module. This interaction has not been reported so, far, and if the authors can substantiate their claims, this would be a very nice contribution to the mitosis/microtubule community. Unfortunately, at present stage many of the claims are (not yet) well supported by the experimental data

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      This manuscript investigates the regulation of CKAP2, a microtubule-associated protein whose polymerizing activity is essential for proper spindle formation during mitosis, and whose dysregulation (through either loss or overexpression) is known to drive chromosomal instability and aneuploidy. Despite this clear link to genome maintenance, the upstream mechanisms controlling CKAP2 activity have remained largely undefined. To address this gap, the authors performed immunoprecipitation of endogenous CKAP2 from mitotic RPE1 cells followed by mass spectrometry, in order to map its interaction partners. This approach identified the Aurora A kinase and its coactivator TPX2 as specific CKAP2 binding partners, notably excluding Aurora B, in contrast to earlier reports implicating that kinase. Building on this finding, the authors demonstrate that CKAP2 colocalizes with Aurora A-TPX2 complexes across mitotic stages and is directly phosphorylated by Aurora A, both in cellulo and in vitro. Functionally, this phosphorylation event reduces CKAP2's affinity for microtubules. Together, these results position CKAP2 as a new direct substrate of the Aurora A-TPX2 signaling axis and propose a phosphorylation-dependent mechanism that fine-tunes spindle microtubule growth and stability.

      Major Comments: 1. On page 7, the authors state that "These findings suggest that AurKA-mediated phosphorylation negatively regulates CKAP2's microtubule binding in cells." I feel that this is an overstatement, at least as it relates to the in-cell experiments. In fact in Fig. 3D, the authors show that CKAP2 levels are decreased after Aurora A inhibition (albeit not as much as microtubule density which is why relative CKAP2 levels appear higher). The authors should consider additional quantifications/normalizations/explanations or co-staining experiments to convincingly show the effect of AurA inhibition on CKAP2 in cells. Their in vitro TIRF data is much more convincing, however these are not mitotic microtubules (with all their modifications and MAPs).

      We agree with the reviewer that experiments on cells are, by their very nature, less controllable than our in vitro assays, since cellular manipulations inevitably introduce additional variables. It is precisely for this reason that we view the two approaches as complementary: The in vitro experiments allow us to define the direct mechanistic consequences of specific disruptions, such as phosphorylation, while the cellular experiments demonstrate their physiological relevance in a more complex biological context. Together, these approaches provide a more robust assessment than either system alone.

      We will address this with the same MG132-synchronized, whole-z-stack, properly normalized quantification described under Reviewer 2's parallel comment on Fig. 3A-C.

      Figure 4. A-C: It's very difficult to interpret the quantitative data from 4A because the authors only show non-normalized TPX2 levels and separately b-tubulin levels. To properly appreciate the function of CKAP2 on TPX2 localization, the authors need to show relative data per cell, either in a scatter plot or as the TPX2/b-tubulin ratio in a dot blot. Additionally, the authors need to clarify for quantifications of signal intensity and whether the same Z-section was used for all signals reported for an individual cell. This should be added to the methods.

      We will replot the data by cell and address all quantifications in our revised manuscript and methods.

      1. For the AlphaFold model, the authors need (at minimum) to show the pLDDT scores/local confidence scores and PAE plots in order to evaluate the validity of this model. This is a critical point because there's a significant amount of results and discussion that are built on this model.

      This is a great point. We will add pLDDT and PAE plots for the CKAP2-TPX2 AlphaFold model as a supplementary figure in our revised manuscript.

      Minor Comments: 1. Some of the figure legends are lacking sufficient clarity/detail to sufficiently describe the figure. The following legends need to be corrected/clarified. a. 1a: Presumably, there's a release step between the palbociclib treatment and the STLC. It would be helpful to place this consequently on the figure. Moreover, either in the methods or the figures legends, the authors should clarify what was used as the control for the IP. This is important for the correct interpretation of the results. b. 1b: This is not a phosphoproteomics screen (as far as this reviewer understood) and the wording of the legends needs to be corrected. c. 1c: The colours of the nodes in the network in figure 1c presumably reflect the enrich subnetworks that were identified. However, what these actually are and the statistical analysis remains unclear and thus it is difficult to appreciate the significance of this network. d. Fig. 2 and others: Overall, avoid using yellow for any labelling in the figures as this is quite difficult to read on a white background. e. Fig 2d, e (pro-diamond Q staining) are not particularly convincing and the total IP appears to be lower as well. The authors are encouraged to quantify signa intensity in a minimum of triplicate experiments. f. P6 : Reference to a FigS1E but there is no E in figure S1.

      We will address all these point in our revised manuscript.

      OPTIONAL experiment g. A major weakness of this manuscript is that the functional consequences of the AuroraA-Tpx2-CKAP2 complex on microtubule/spindle polymerization in the cell has not been addressed. For example, is the loss of spindle polymerization in TPX2/CKAP2 lacking cells exacerbated or rescued when the other protein is lost? There are certain predictions as to this functional interplay that can be tested with the tools and cell lines already in place, and this lab has certainly the expertise to see these through. These experiments would significantly increase the impact of this manuscript.

      We agree with the reviewer that this is an interesting question, but we think the scope of the implied experiments exceeds that of a regular revision.

      Identification of Aurora A phosphosites on CKAP2 either in vitro or in cells and testing the functional consequences would also significantly increase impact of this story. The authors may already have some clues to this in their mass spec data that they can exploit.

      In our revision, we plan to identify all Aurora A sites in CKAP2. We will introduce combinatorial and single-site mutations (both phosphomimetic and non-phosphorylatable) and assess their impact on mitotic spindles.

      Reviewer #3 (Significance (Required)):

      • This study reports a new interaction between spindle binding/regulatory proteins that adds to our current understanding of microtubule dynamics and allows for a better understanding of the mitotic spindle dynamic and mitosis. It provides new insights on the function of CKAP2, a protein that is frequently overexpressed in cancer. A better understanding of the mechanisms of mitosis is essential in a lot of different processes but especially in cancer where it is almost always deregulated. • This is a well-written and easy to follow manuscript (for a cell biology expert at least) describing a new relationship between AurkA, TPX2 and CKAP2. Overall, with some minor exceptions, the conclusions provided by the authors are well-supported by the evidence. Methods are well presented and could be reproduced easily. A major limitation is that there's no evidence provided by the authors of the exact sites (other than from proteomics databases) that are regulated by AurKA, and even these sites were no tested. Nevertheless, this manuscript will be quite suitable for a specialized audience with an interest in the fundamental workings of mitosis and spindle biology.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript investigates the regulation of CKAP2, a microtubule-associated protein whose polymerizing activity is essential for proper spindle formation during mitosis, and whose dysregulation (through either loss or overexpression) is known to drive chromosomal instability and aneuploidy. Despite this clear link to genome maintenance, the upstream mechanisms controlling CKAP2 activity have remained largely undefined. To address this gap, the authors performed immunoprecipitation of endogenous CKAP2 from mitotic RPE1 cells followed by mass spectrometry, in order to map its interaction partners. This approach identified the Aurora A kinase and its coactivator TPX2 as specific CKAP2 binding partners, notably excluding Aurora B, in contrast to earlier reports implicating that kinase. Building on this finding, the authors demonstrate that CKAP2 colocalizes with Aurora A-TPX2 complexes across mitotic stages and is directly phosphorylated by Aurora A, both in cellulo and in vitro. Functionally, this phosphorylation event reduces CKAP2's affinity for microtubules. Together, these results position CKAP2 as a new direct substrate of the Aurora A-TPX2 signaling axis and propose a phosphorylation-dependent mechanism that fine-tunes spindle microtubule growth and stability.

      Major Comments:

      1. On page 7, the authors state that "These findings suggest that AurKA-mediated phosphorylation negatively regulates CKAP2's microtubule binding in cells." I feel that this is an overstatement, at least as it relates to the in-cell experiments. In fact in Fig. 3D, the authors show that CKAP2 levels are decreased after Aurora A inhibition (albeit not as much as microtubule density which is why relative CKAP2 levels appear higher). The authors should consider additional quantifications/normalizations/explanations or co-staining experiments to convincingly show the effect of AurA inhibition on CKAP2 in cells. Their in vitro TIRF data is much more convincing, however these are not mitotic microtubules (with all their modifications and MAPs).
      2. Figure 4. A-C: It's very difficult to interpret the quantitative data from 4A because the authors only show non-normalized TPX2 levels and separately b-tubulin levels. To properly appreciate the function of CKAP2 on TPX2 localization, the authors need to show relative data per cell, either in a scatter plot or as the TPX2/b-tubulin ratio in a dot blot. Additionally, the authors need to clarify for quantifications of signal intensity and whether the same Z-section was used for all signals reported for an individual cell. This should be added to the methods.
      3. For the AlphaFold model, the authors need (at minimum) to show the pLDDT scores/local confidence scores and PAE plots in order to evaluate the validity of this model. This is a critical point because there's a significant amount of results and discussion that are built on this model.

      Minor Comments:

      1. Some of the figure legends are lacking sufficient clarity/detail to sufficiently describe the figure. The following legends need to be corrected/clarified.

      a. 1a: Presumably, there's a release step between the palbociclib treatment and the STLC. It would be helpful to place this consequently on the figure. Moreover, either in the methods or the figures legends, the authors should clarify what was used as the control for the IP. This is important for the correct interpretation of the results.

      b. 1b: This is not a phosphoproteomics screen (as far as this reviewer understood) and the wording of the legends needs to be corrected.

      c. 1c: The colours of the nodes in the network in figure 1c presumably reflect the enrich subnetworks that were identified. However, what these actually are and the statistical analysis remains unclear and thus it is difficult to appreciate the significance of this network.

      d. Fig. 2 and others: Overall, avoid using yellow for any labelling in the figures as this is quite difficult to read on a white background.

      e. Fig 2d, e (pro-diamond Q staining) are not particularly convincing and the total IP appears to be lower as well. The authors are encouraged to quantify signa intensity in a minimum of triplicate experiments.

      f. P6 : Reference to a FigS1E but there is no E in figure S1.

      OPTIONAL experiment g. A major weakness of this manuscript is that the functional consequences of the AuroraA-Tpx2-CKAP2 complex on microtubule/spindle polymerization in the cell has not been addressed. For example, is the loss of spindle polymerization in TPX2/CKAP2 lacking cells exacerbated or rescued when the other protein is lost? There are certain predictions as to this functional interplay that can be tested with the tools and cell lines already in place, and this lab has certainly the expertise to see these through. These experiments would significantly increase the impact of this manuscript.

      h. Identification of Aurora A phosphosites on CKAP2 either in vitro or in cells and testing the functional consequences would also significantly increase impact of this story. The authors may already have some clues to this in their mass spec data that they can exploit.

      Significance

      • This study reports a new interaction between spindle binding/regulatory proteins that adds to our current understanding of microtubule dynamics and allows for a better understanding of the mitotic spindle dynamic and mitosis. It provides new insights on the function of CKAP2, a protein that is frequently overexpressed in cancer. A better understanding of the mechanisms of mitosis is essential in a lot of different processes but especially in cancer where it is almost always deregulated.
      • This is a well-written and easy to follow manuscript (for a cell biology expert at least) describing a new relationship between AurkA, TPX2 and CKAP2. Overall, with some minor exceptions, the conclusions provided by the authors are well-supported by the evidence. Methods are well presented and could be reproduced easily. A major limitation is that there's no evidence provided by the authors of the exact sites (other than from proteomics databases) that are regulated by AurKA, and even these sites were no tested. Nevertheless, this manuscript will be quite suitable for a specialized audience with an interest in the fundamental workings of mitosis and spindle biology.
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      Referee #2

      Evidence, reproducibility and clarity

      This study reports that CKAP. a microtubule associated protein involved in the regulation of the mitotic spindle by promoting microtubule polymerization, is regulated by the Aurora-A kinase in association with its co-factor TPX2. The authors propose that this regulation fine-tunes the ability of CKAP to promote microtubule depolymerization and regulate the size of the mitotic spindle.

      The study is based on both in vitro assays with recombinant proteins and in cellulo assays in human tissue culture cells. the study makes the following key claims:

      • that CKAP interacts directly with Aurora-A/TPX2 based on IP-MS data and co-localization data.
      • that Aurora-A/TPX2 regulate CKAP via protein phosphorylation and that this phosphorylation event prevents CKAP from binding to microtubules. The authors also propose that CKAP presence favors the recruitment of TPX2 to the mitotic spindle
      • that this phosphorylation event fine-tunes the ability of CkAP to regulate spindle size

      Major comments:

      • with regard to the first claim, the presented data demonstrate that Aurora-A and TPX2 co-immunoprecipitate and co-localize, but they never show evidence that those proteins are direct interactors. To demonstrate this claim, the authors should perform co-IP experiments with recombinant proteins (which the authors) to exclude an indirect interaction.
      • a central claim of the study is that Aurora-A/TPX2 regulate CKAP by protein phosphorylation. While the presented data is interesting, it is far from conclusive.

      1) the authors first show that CKAP is phosphorylated in mitotic cells. They then report that this phosphorylation depends on Aurora-A by using an Aurora-A inhibitor on cells arrested in mitosis with the Eg5-inhibitor STLC. The first caveat here, is that Aurora-A inhibition will dramatically delay mitotic entry. The authors should show that the cells treated with STLC or STLC + the Aurora-A inhibitor are indeed both in mitosis, e.g. by blotting for cyclin B1.

      2) the authors report via a series of protein kinase assays that Aurora-A phosphorylates CKAP (Figure 2F). The problem of such an assay is that recombinant kinases can be very promiscuous, especially when the kinase to substrate ratio is around 1 or higher than 1, as shown in Figure 2F. Moreover, in the present figure, the phosphorylation mark on CKAP is very low compared to Aurora-A itself. These experiments at this stage do not indicate that CKAP is a good Aurora-A substrate. A better way to carry out these epxeriments would be to a) have a kinase to substrate ratio of 1:100, and to compare the phospho-incoporation in CKAP with another known Aurora-A substrate.

      3) These concerns are not further alleviated by the kinase assays shown in Figure 4D, where the authors add TPX2 to the kinase reaction, and claim that TPX2 acts as a bridge between CKAP and Aurora-A. First the authors report that CKAP phosphorylation is markedly increased in the presence of TPX2: the presented data is not quantified, and the increase is not particularly strong. Moreover, TPX2 is an Aurora-A activator. Is it therefore not logic to see more CKAP phosphorylation without having to invoke a bridging function for TPX2? Again a comparison of Aurora-A activity on a known substrate as comparison would help.

      4) the most convincing data showing that Aurora-A can phosphorylate CKAP is presented in Figure 5, where the CKAP phosphorylation is more consequent, but again those experiments should be repeated with a different kinase to substrate ratio, to exclude a promiscuity artefact. - another central claim of the study is that CKAP phosphorylation reduces its affinity for microtubules. While the in vitro data with recombinant proteins is convincing, the data presented in cells is less so, for the following reasons:

      1) in Figure 3A-C the authors measure beta-tubulin and CKAP levels over the whole spindle in a single Z-plane. the problem is that in the presented images the spindle is vastly different between DMSO and MLN8237-treated cells (in fact the DMSO-treated cells looks like an anaphase cells). To compensate for this the authors should first make sure that the cells are all the same stage of mitosis (MG132 treatment), and if there is still a strong difference in spindle length and height, consider quantifying these two proteins cumulatively over the whole spindle over several z-planes. Indeed, it cannot be excluded that the decrease in beta-tubulin observed by the authors, which is the parameter that changes, arise due to differences in spindle height.

      2) the figure legend of figure 3C indicates that the authors performed 3 independent experiments with 50 cells each. However, they don't report the individual means of the experiments. Moreover, are the SEM error bars and the statistical tests generated based on N=3 independent experiments or 150 cells (which are not independent)? this information is essential to evaluated the reproducibility. - to support their model the authors also report that TPX2-levels decrease in 2 out 3 CKAP KO cells. These data are, at the moment, not particularly strong. Indeed, the decrease is only moderate and only seen in 2 out of 3 clones. The fact that in a third clone the authors could not see a decrease could indicate that the subtle changes in TXP2 levels, could just represent inter-clone variability. If the authors wanted to strengthen this aspect, they should in addition deplete acutely CKAP by siRNA and test for a decrease in TPX2-levels. - finally the last and most important claim of the study, is that Aurora-A/TPX-2 fine-tune the microtubule polymerization ability of CKAP in order to regulate spindle size. Unfortunately, there is no data supporting this particular model. The authors do not, e.g., provide data showing how a non-phosphorylatable CKAP would affect spindle size, which would be a direct test of their model. Instead at the current stage the presented model is only a speculation. Either the authors should provide direct evidence for a such a model, or strongly reduce their claim.

      Optional:

      • the authors strongly imply that CKAP interacts with Aurora-A/TPX2 and not with Aurora B, which had been previously suggested. To strengthen this conclusion, the authors could also measure CKAP levels on the spindle in the presence of an Aurora B inhibitor.

      Significance

      This study is potentially interesting and novel, linking the microtubule polymerisation driver CKAP to the Aurora-A/TPX2 regulation module. This interaction has not been reported so, far, and if the authors can substantiate their claims, this would be a very nice contribution to the mitosis/microtubule community. Unfortunately, at present stage many of the claims are (not yet) well supported by the experimental data

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      Referee #1

      Evidence, reproducibility and clarity

      In their manuscript "An Aurora Kinase A/TPX2 complex phosphorylates CKAP2 to control mitotic spindle growth", Kucharski and colleagues combined immunoprecipitation and mass-spectrometry to identify mitotic interactors of the microtubule-associated protein CKAP2. By this approach, the authors revealed the Aurora A-TPX2 complex as a prominent interactor of CKAP2. Based on a series of further in-cellulo and in-vitro experiments, they propose that phosphorylation of CKAP2 by Aurora A decreases the binding affinity of CKAP2 to microtubules, thereby controlling mitotic spindle growth and stability. The authors propose an intriguing model containing a negative feedback loop of Aurora A activity at the spindle. Initially, Aurora A activity promotes mitotic spindle microtubule growth, but when the spindle matures, CKAP2 phosphorylation reduces microtubule polymerization, preventing excessive spindle growth/elongation and stabilizing its size and architecture. How microtubule polymerization activity is restrained to prevent excessive spindle elongation during mitosis is an important question and the proposed model is interesting. The study is overall well-designed and performed. However, several controls and experiments could be performed to strengthen the conclusions and further elucidate the underlying molecular mechanism.

      Major points:

      1) Page 4, Fig. 1D - the authors state: "Given the central role of AurKA and TPX2 in spindle regulation, we focused on their interaction with CKAP2." - Aurora A and TPX2 are indeed interesting candidates to study in the context of interaction with CKAP2. However, KIF2A is a much stronger candidate according to the mass-spectrometry data, and it also has a central role in spindle regulation. Moreover, it has also been reported as a Aurora A-TPX2 interactor. Although not a primary topic of this study, the finding of KIF2A and potential meaning of this interaction could be better discussed in the text.

      2) Fig. S1A - After performing immunoprecipitation with CKAP2, reciprocal immunoprecipitation of TPX2 confirmed its association with both Aurora A and CKAP2. To provide another control and strengthen the results, reciprocal immunoprecipitation of Aurora A could be performed.

      3) The main part that has been left unexplored, and which could significantly strengthen the conclusions and further elucidate the underlying molecular mechanism is generation and analysis of phospho-null and phospho-mimetic mutants of identified residues at CKAP2 N-terminus (Ser39, Ser76, Thr209, and Ser347). For instance, mutating all four residues in parallel could be a good starting point to test their effect on cellular phenotypes, microtubule binding and polymerization.

      4) The protein-protein interactions are proven using cell lysates, which contain all cellular proteins and resulting interactions may be indirect. Since the authors have already shown the ability to purify CKAP2, Aurora A and TPX2, it would be relevant to use these purified components and show direct interactions between individual proteins, including the used truncations.

      5) If TPX2 promoted CKAP2 phosphorylation by Aurora A, reducing the levels of CKAP2 at microtubules, would TPX2 depletion (e.g. by siRNAs) lead to an increase of microtubule-bound CKAP2?

      6) Fig. 4A - It is difficult to understand how CKAP2 KO clone C12 exhibited reduced tubulin intensity but maintained normal TPX2 levels. Immunoblots showing the level of CKAP2 knock-out in all clones are required to understand the depletion efficiency.

      7) How microtubule polymerization activity is restrained to prevent excessive spindle elongation during mitosis is an important question and the proposed model based on AuroraA-TPX2 and CKAP2 is intriguing. The proposed negative feedback model could be further discussed in order to offer potential explanation how CKAP2 becomes more phosphorylated in later mitosis compared to the early stages. Is this simply time-dependent? Does it depend on specific localization of individual elements over time, or on regulation of Aurora A activity?

      Minor points:

      1) Page 6 - the authors state: "To test this directly, we expressed and purified CKAP2-GFP from E. coli and incubated the protein in vitro with recombinant GFP-AurKA (Fig. S1E) in the presence or absence of ATP." - However, the Fig. S1E does not exist and thus this needs to be corrected.

      2) Page 8 - the authors state: "Our proteomic and immunoprecipitation analyses indicated that CKAP2 interacts directly with TPX2...". As mentioned above, direct interactions could be indicated only by using purified components.

      3) Page 11 - the authors state: "Consistent with this, AurKA-dependent phosphorylation of CKAP2 reduces its microtubule binding affinity in vitro and in cells (Figs. 3 and 6G)." - Fig. 6G does not exist and thus this needs to be corrected. The authors probably referred to Fig. 3A,G.

      4) It may be useful for the readers to provide a flow-chart-like model of the proposed feedback loop.

      Significance

      Strengths/Advance: The model is interesting and novel, with potential to expand our understanding of spindle microtubule growth and stability regulation during mitosis. The study is overall well-designed and performed. It has a potential to fill an important gap: how microtubule polymerization activity is restrained to prevent excessive spindle elongation during mitosis.

      Limitations: Listed above within the comments, along with suggested experiments that may serve to overcome the limitations. In brief, the proposed negative feedback model should be further discussed in order to offer potential explanation how CKAP2 becomes more phosphorylated in later mitosis compared to the early stages. Phospho-mutants of CKAP2 and pull-down with purified components could be used to strengthen the main conclusions.

      Audience: Broader cell biology-related audience, including more specialized in cell division.

      Reviewer's expertise: cell biology, mitosis, microtubules, mitotic spindle.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Under hypoxic conditions, plants produce H2 by a hitherto unknown molecular mechanism; here the authors demonstrate - using multiple experimental strategies and kinetic modelling - a mitochondrial origin for this enzyme activity, implicating an electron-bifurcating activity associated with a plant mitochondrial complex I assembly intermediate.

      Major comments:

      I find the data in the manuscript convincing and well-explained, focusing particularly on the area of mitochondrial complex I biology where I have some experience and expertise. I think it is fair to say that the authors themselves are at pains to stress the potential limitations of the study, and whilst the evidence points towards a mechanism linking H+ production with the stable, plant-specific complex I assembly intermediate (termed CI* in the manuscript) - something that the mature holoenzyme cannot do - several limitations to the study are discussed in depth, providing an honest consideration of all the data presented and the conclusions drawn. In this regard, it is helpful to provide a platform to publish these results for others to test, explore and demonstrate a plausible mechanism for.

      Minor comments:

      The manuscript is well-written, the data are clearly presented, and experimental methods are provided in full.

      Referees cross-commenting

      Thank you for asking us to take part in this consultation and cross-commenting exercise.

      I have read the comments shared by the other reviewers, and think that this is a helpful exercise to engage in. With expertise in human mitochondrial biology/complex I biology - particularly relate to a disease context - I was invited to read this manuscript knowing that this was somewhat away from my own area of expertise (and comfort zone!). I therefore had to review the aspects of the manuscript which were unfamiliar to me assessing the narrative, access to difficult concepts for the non-specialist, quality and reproducibility of the data etc... I found the manuscript to be well-written, the data carefully presented and the authors careful not to overinterpret their findings.

      I have now read the comments from the other reviewers and appreciate their own perspective and expertise in contributing to this review. In particular, the comments regarding the NADH oxidation activity of the CI* assembly intermediate and the the existence of reverse electron transfer through the respiratory chain via CII in this plant species - well-documented in mammals - are well made, and do require further discussion; I agree.

      I am willing to accept that further experimentation is required to clarify some of these points, and that further validation of the author's model is required.

      Significance

      My expertise lies in mammalian complex I biology and complex I function, from the perspective of studying its dysfunction in human genetic disease. I am not fully conversant with this field but have been asked to review certain aspects of the manuscript through the lens of mitochondrial biology.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study the authors put forward a model for plant mitochondrial hydrogen production under hypoxic conditions, a phenomenon observed decades ago but still unresolved. This model is based on theoretical analyses and physiological as well as biochemical measurements using the plant model organism Vigna radiata (mung bean). The here proposed model describes hydrogen production via electron bifurcation by the plant mitochondria specific Complex I (CI) assembly intermediate Complex I (CI). CI* harbours the flavin mononucleotide (FMN) containing domain which catalyses NADH oxidation. Hydrogen production is here proposed to be catalysed by the formation of a flavin hydride (FMNH•) by electron bifurcation to the N3 4Fe-4S cluster (canonical forward electron transfer within CI) and the N1a 2Fe-2S cluster (not involved in canonical forward electron transfer). By exposure to matrix H+, FMNH• is proposed to release molecular hydrogen. The authors propose this pathway to be kinetically favoured by hypoxic growth conditions, low mitochondrial matrix pH and Complex II (CII)-mediated reverse electron transfer.

      Major comments:

      The described model of hydrogen production involving CI has been supported by the authors thermodynamic calculations of FMN to hydrogen production, dependent on pH, hypoxic conditions, and necessitating electron bifurcation mechanisms. Additionally, the calculated electron transfer rates from FMN to the neighbouring Fe-S clusters N3 and N1a within mature CI and CI support a possible kinetic shift towards reverse electron transfer within CI but not CI. However, the here presented study is based on two main considerations which are not fully supported by the literature: (i) that CI exhibits NADH oxidation activities in vivo and (ii) that reverse electron transfer exists as a physiologically relevant pathway in planta.

      Regarding (i), the assembly intermediate CI has been shown repeatedly to exhibit NADH oxidation after BN-PAGE and subsequent in-gel NADH oxidation activity staining (Schertl et al. 2012, Kuhn et al. 2015, Maldonado et al. 2020). However, in-gel activity staining does not reflect in vivo enzyme activity. Possible in vivo activity has been discussed in literature (Kuhn et al. 2015, Maldonado et al. 2020) but to date no experimental evidence is available. CI lacks the distal part of the CI membrane arm and according to some proton pumping mechanism models should therefore be unable to pump protons. As proton pumping and electron transfer are coupled, CI would be inactive in vivo. As the model presented here is largely based on the assumption that CI has NADH oxidation and electron transfer activity in vivo, the claims presented here are highly speculative. Thus, it may be important to include mitochondrial mutant lines with altered CI:CI ratio to attribute the observed effects to a CI specific enzymatic activity. Regarding (ii), reverse electron transfer via CII/succinate dehydrogenase is an established concept in the mammalian field (Murphy 2009). In plant systems, biochemical indicative evidence is available (Storey 1971, Rustin and Lance 1991) but physiologically this concept is not established (Huang et al. 2019). Similar to (i), the claims being made in this manuscript are therefore highly speculative and require more supporting evidence. In order to attribute the here observed effects, of substrate/inhibitor treatments on hydrogen production, to reverse electron transfer, NADH and ubiquinone measurements would be appropriate.<br /> Also, the proposed model appears only valid if reverse electron transfer is not possible at the level of complex I*, otherwise electrons would have to oscillate between FMN and ubiquinone at the level of this complex. This point is not discussed at all.

      In addition to being based on unsubstantiated considerations, the experimental data presented often do not support the authors claims. Many experimental designs are unclear and unreproducible. The initial representation of the authors model (Fig. 1) is difficult to navigate and would profit from a schematic representation of the respective complexes including CI, CI, Fe-S cluster labelling and electron flow. The experimental set up for hydrogen measurements appears adequate. Hydrogen production was measured via GC-MS in etiolated mung bean seedlings grown under hypoxia, which was induced by flushing growth vials with nitrogen gas (Fig.2a). However, values for control measurements without seedlings are generally missing from the plots presented (Fig.2-4), so that evaluation of passive hydrogen accumulation due to nitrogen gas flushing or other non-biological processes is not possible. Hence, evaluations of physiological relevance cannot be made. Along the same lines, control measurements of seedlings grown under normoxic conditions are not included in most of the analyses, leading to a similar deficit. In contrast to the authors claims, hydrogen production was also measured in seedlings grown under normoxic conditions, not only under hypoxic conditions (Fig. 2c). Only after 72 h growth were hydrogen levels elevated in hypoxia-grown seedlings when compared to normoxic growth. This observation is the only one supporting the physiological relevance of hypoxia induced hydrogen production and rather accentuates the need for a normoxia control, which is mostly missing, as mentioned above. In addition, the experimental procedure for tissue specific analysis of hydrogen production is not obvious (Figure. 2e), as values for control samples are not readable. Furthermore, based on their observation that upon the addition of the metal ion chelator EDTA, hydrogen elevations were not detectable in isolated mitochondria (Fig. 2f), the authors reason that Fe-S cluster containing enzymes must be the source of accumulating hydrogen. However, EDTA does not only block Fe-S clusters but also binds any other (at least) bivalent metal ions. Hence, this conclusion is not supported by the data. For most subsequent experiments investigating hydrogen production, the authors use crude isolated mitochondria (Fig.2-4). It is unfortunately unclear whether control samples represent actively respiring mitochondria (with access to substrates). The way the data is presented here, hydrogen production upon any given treatment is mostly compared to isolated mitochondria without any substrate. These control samples represent non-respiring mitochondria, as endogenous substrates are lost during mitochondria isolation (Bonner 1961). Therefore, this control is not suitable to evaluate basal hydrogen production of respiring mitochondria. This experimental setup (absence of substrate) strongly suggest that hydrogen production is non-metabolic which contrast with the conclusions of this study. In addition, while the authors report suitable mitochondrial integrity, respiratory activity measurements are missing, but would be necessary to validate mitochondrial extracts for enzymatic assays before any measurements. As the authors do not provide any information on the basal respiratory activity of their mitochondrial extracts and their contribution to basal hydrogen production, it is not possible to draw conclusions about increased or decreased levels of hydrogen production upon any treatment.<br /> As explained above, inhibitor treatments presented in figure 3b are not meaningful in the absence of substrates as the respiratory chain is inactive in such experimental setup. Aside from that, the inhibition of CIII2 and CIV, by antimycin A and NaN3 respectively, should lead to similar effects but they do not. In addition, these inhibitions should drive reverse electron transport (Storey 1971) by partially blocking downstream oxidation of ubiquinone and fully blocking cytochrome c oxidation, and thereby stimulate hydrogen production. The opposite was observed by the authors, but these conflicting results are not discussed. In addition, the inhibition of CV by oligomycin did not lead to a significant alteration of hydrogen production compared to non-respiring mitochondria (Fig.3b). The authors interpret the non-significant increase as functional correlation between the proton gradient being maintained by active CV and hydrogen production. However, it has been shown previously, that ATP levels rapidly decline under hypoxic conditions (De Col et al. 2017), suggesting an inactivation of ATP-production via CV. Therefore, oligomycin treatment under hypoxic conditions should not alter the proton gradient across the inner mitochondrial membrane, as the ATP-synthase is already inactive. Hence, the authors conclusion appears inadequate. <br /> Further, the authors show that the addition of rotenone (CI inhibitor) or malonate (CII inhibitor) decrease the levels of hydrogen production, compared to mitochondria provided with substrates for the respective complex (Fig.3d,e,f). According to the proposed model, the combined addition of rotenone and malonate should fully inhibit hydrogen accumulation. Unfortunately, this important control experiment is missing. The model predicts that hydrogen production is favourable at low matrix pH. However, the proposed optimal pH of 6 is way below the published pH value under this condition (around 7), Therefore, the authors should measure the matrix pH under their experimental setup to validate their model. Data presented in figure 4 is interpreted as reflecting mitochondrial metabolic processes during hypoxia. However, as mentioned above, the authors do not mention whether or not substrates were provided to the isolated mitochondria for maintaining mitochondrial activity. If the experiment was performed in the absence of substrates, the interpretation of the obtained results is problematic as starved mitochondria cannot metabolically produce hydrogen. Finally, the experimental system used in this study might not be optimal. The main form of complex I isolated from etiolated mung beans (Maldonado et al. 2020) is not the complex I assembly intermediate but a degradation fragment of complex I, it lacks the assembly factor GLDH. Therefore, as mung bean was shown by the authors to produce the most hydrogen, this production is likely not enzymatic as it results from a degraded complex I. The authors should clarify this point as it suggests that the hydrogen production is only due to mitochondria damage and would not be a metabolic response to hypoxia and can therefore not be engineered for improving plant to flooding

      In general, data representation in this manuscript is not clear due to missing explanation of experimental procedures (e.g., no detailed description of tissue specific measurements of hydrogen accumulation) and unspecific labelling (e.g. Fig. 2b, which y-axis represents which data?; Fig. 3b no y-axis labelling; Fig. 4c data points start in the y-axis-gap and are not interpretable). This leads to the circumstance that most experiments described cannot be reproduced and/or accurately interpreted. Statistical analyses are provided for most data presented here, however as described above, statistical comparisons were not meaningful where suitable controls were missing.

      Significance

      The authors present a model for plant mitochondrial hydrogen production under hypoxic conditions. This study combines modelling approaches and physiological/biochemical validations. Due to the low quality of the validations, this work is mostly conceptual and would require more thorough validating experiments.

      In the context of hypoxia, plant mitochondria are mostly studied in regards to reactive oxygen species production and no mechanism of hydrogen production by plant mitochondria has been described so far. Further, the here presented model includes CII-driven reverse electron transfer, which is an open field in plant mitochondria research and not clearly evidenced to date (Huang et al. 2019).

      Lastly, hydrogen production under hypoxic conditions is presented here via the enzymatic activity of the plant mitochondria specific CI assembly intermediate CI. CI is well established as the last assembly intermediate in CI assembly (Schimmeyer, Bock and Meyer 2016, Ligas et al. 2019) and its structure has also been resolved (Maldonado et al. 2020, Soufari et al. 2020). However, its in vivo activity is debated (Kuhn et al. 2015, Schimmeyer et al. 2016, Maldonado et al. 2020).

      In theory, this work addressed new aspects of the role of mitochondrial respiration during hypoxia but it appears too preliminary to provide solid conclusions. Overall, the findings of this study may interest researchers studying plant mitochondria metabolism. This reviewer is not an expert on modeling approaches and therefore could not assess if they were performed adequately.

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      Referee #1

      Evidence, reproducibility and clarity

      This is an excellent biochemical study into the role of an electron bifurcating hydrogenase activity associated with complex I and hypoxia in vascular plant. The scientific problem is clearly stated the experiments well planned and executed, the conclusions appropriate and well documented. The manuscript is very well written and the Figures are exemplary in their details.

      The data appear to be highly reproducible in that the error bars are exclusively small. The manuscript is very well written but I would suggest that the authors do add some further explanations for the non-specialist. I am reviewing this as a specialist in the field so assumed knowledge for me is OK but for the casual reader better background information should be provided.

      Significance

      This is an excellent study with an important conclusion. My only concern is that not all readers will grasp this. I suggest that the authors take a step back and try to provide a more basic explanation of the problem and why their results are so significant. Currently this is available to the expert reviewer but may be hard for an interested but non-specialist reader to spot.

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      Reply to the reviewers

      Point by point response to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required):

      In the manuscript by Dembska et al titled, "High-throughput identification of protein turnover modulators in human neurons," the authors engineered human embryonic stem cell that expresses the MCFT reporter (FT-NGN-hESC). The FT-NGN-hESC line can be induced to differentiate into a near pure population of neurons (FT-iNGN). These neurons were subsequently used to screen almost 6000 compounds to identify candidates that could alter protein turnover. They selected three and characterized the resulting changes to the transcriptome and proteome of iNGN neurons. They identified ribosomal proteins, initiation factors and snoRNA as some of the top up-regulated genes-which have not been demonstrated before in neurons. Additionally, the authors proceeded to test the compounds on their ability to resolve alpha-synuclein aggregation in mouse hippocampal neurons. The manuscript is an elegant demonstration of how a fluorescent reporter can be applied to human ES-derived cells to assess changes in protein turnover and disease pathology.

      Major comments:

      -Development of the FT-NGN-hESC line that can be differentiated into FT-iNGN neurons serves as an excellent tool for screening small molecule drugs that can alter protein turnover in human ES-derived lines.

      However, if the goal of the study is to demonstrate the efficacy of a compound to mitigate disease pathology, it would benefit from directly addressing a specific neurodegenerative disease (i.e. Parkinson's or other related synucleinopathies) by using patient-derived iPSCs.

      As outlined in Fig1A, MCFT can be inserted into the CLYBL site and differentiated into neural cells to assess the impact of the disease on protein turnover in patient cells. Although the population of neural cells can vary, that diversity or heterogeneity of differentiating cells could provide information as to when in development that protein turnover begins to change in the context of disease progression. [OPTIONAL]

      We thank the reviewer for the interesting suggestion to use patient-derived iPSCs. First, we would like to highlight the fact that we did show that our lead compound CI-994 was efficient in clearing alpha-synuclein fibrils from dopaminergic neurons derived from human iPSCs. Nevertheless, we indeed did so far not perform experiments on iPSCs derived from patients suffering from Parkinson’s disease. Here we believe that the MCFT readout would not be directly useful in testing the efficiency of compounds on the pathological hallmarks of dopaminergic neurons differentiated from such iPSCs. Nevertheless, we agree that testing the efficiency of our lead compound in clearing alpha-synuclein fibrils in these patient-derived cells would enrich the manuscript. Therefore, we propose to test the efficacy of CI-994 in clearing alpha-synuclein fibers from dopaminergic neurons derived from a sporadic patient-derived iPSC cell line.

      -The rationale for why the aSyn aggregation assay was performed on primary mouse neurons was not clear. The authors invested much effort into the development and characterization of FT-iNGNs and the effect of the three drugs on transcription and translation of iNGNs (Figs1-4). Yet, to assess pathology, they employ mouse neurons.

      We thank the reviewer for raising this point and agree that the rationale for using primary mouse neurons as the initial screening model was not sufficiently explained.

      Primary neurons were selected because, at the time the study was initiated, they represented the most robust and fully characterized neuronal model of Lewy body–like pathology. This model reproducibly captures the progressive formation and maturation of alpha-synuclein aggregates, including the accumulation of key molecular and structural features of Lewy body pathology. Importantly, it is also highly compatible with compound screening in 96-well plates, provides a robust pathological signal within approximately 10 days, and allows multiple compounds, concentrations and treatment windows to be compared in parallel. It has therefore become a widely used model for alpha-synuclein drug-screening studies in both academic and pharmaceutical settings. Furthermore, as we now show in Fig.S5J, iNGNs do not express endogenous aSyn, thus preventing them from using these cells for this assay.

      Until recently, no human iPSC-derived neuronal model had been shown to reproduce the formation and maturation of Lewy body–like pathology with such extensive molecular and ultrastructural characterization. We have now established this in human iPSC-derived dopaminergic neurons and recently published the model (Mahul-Mellier et al., Science Advances, 2026). This human model represents an important advance, as it reproduces several key features of Lewy body pathology in the disease-relevant neuronal population. However, pathology develops more slowly and at a lower level than in primary neurons. At 21 days after seeding, only approximately 3% of neurons display somatic pathology, and cultures must be maintained for up to 56 days to reach higher pathological levels. At these extended time points, neuronal attachment in 96-well plates also becomes a limiting factor, making the model less suitable for the initial screening of multiple compounds across several concentrations.

      For these reasons, we used a two-step strategy: primary neurons provided a robust and scalable system for the initial screening of the compounds across multiple concentrations, while the most promising compounds were subsequently validated in human iPSC-derived dopaminergic neurons. This approach combines the screening capacity and reproducibility of the primary neuronal model with the increased human and disease relevance of the iPSC-derived neuronal model. We have clarified this rationale in the revised manuscript.

      We have now edited the manuscript to better justify the use of mouse primary neurons for the aSyn assay.

      If the use of mouse neurons was essential, then I would expect the authors to show that introduction of preformed fibrils (PFF) increased MCFT half-life (more mOrange fluorescence) and demonstrate that reduction of phospho-aSyn following drug treatment correlated with increased RPs, eIFs or snoRNAs expression-as was shown for iNGNs.

      We agree with the reviewer that these experiments would significantly improve the manuscript, and we will thus perform them to following way:

      1. We will transduce primary neurons with lentiviral vectors expressing the MCFT, and after 48 hours, we will add PFFs, and then image the cells every other day for 10 days.
      2. We will treat primary neurons with PFFs and with or without drugs, and then perform RNA extraction after 10 days followed by QPCR of the following genes: snora63, snord97, snora12, rpl39, rps3a, rps27a,rpl34, eif3e, eif3l.

        Conversely, could the aggregation assay be performed on mature iNGNs to make the experimental model more consistent?

      We understand the point of the reviewer. We initially planned to perform this experiment, which requires target neurons to express endogenous synuclein (Mahul-Mellier et al., PNAS 2020). As can be seen from the Western Blot below (also now shown as Fig.S5J), iNGNs do not express endogenous alpha-synuclein.

      -The identification of RPs, eIFs and snoRNAs as up-regulated genes upon drug treatment is a novel finding in post-mitotic neurons. It should be noted that elevated expression of components of the translational machinery are hallmarks of dividing cells. The authors should denote whether neurogenesis-related or proneural genes are up- or down-regulated in their volcano plots (Figs3&4). Additional evidence should show that these drugs do not interfere with differentiation and that the cells are not regressing to a mitotic-state.

      We thank the reviewer for this very good point. We have now analyzed the distribution of neurogenesis-related genes, and we found no evidence of a significant downregulation or upregulation of these genes (revised Fig. S3J-L), excluding a drastic change in cell identity upon treatment. Concerning the mitotic state the reviewer mentions, the lack of changes in cell number (Fig. S2F), nuclear size (Fig. S2G), or cellular morphology (Fig. S2H, newly included) suggests that drug treatment did not induce overt changes in the cellular state relative to the pre-treatment post-mitotic condition (Fig. S1B).

      -From identified genes that are up- or down-regulated in omics results, typically one or more genes are validated using immunofluorescence or smFISH. I would urge the authors to include one or both methods to confirm their transcriptomic or proteomic results.

      We agree with the reviewer, and we have now selected a panel of ribosomal proteins (RPL29, RPL31, RPL23, RPL8, RPL24, RPL27, RPL21, RPL35, RPL32, RPL36, RPS15 and RPL36AL), that are increased at the proteome level upon treatment with all three compounds. Antibodies are available for these proteins, and thus we will first test which ones can be reliably detected by immunofluorescence, and then we will perform their quantification with or without drug treatment in iNGNs using immunofluorescence.

      __ Minor comments:__

      -As some of the results are fluorescence readouts, it is recommended that higher magnification images (greater than 20x) or enlarged insets be included (Figs1B, 5B,G,K).

      This is a good point, we now show higher magnification images as enlarged insets.

      In particular, neurons that have long polarized extensions should be clearly and discretely visible as structure can be a proxy for neuronal identity and maturity. -Inclusion of images of iNGNs over time (i.e. during dox induction and after treatment of drugs at day 0,1,3,5,14) at greater than 20x magnification would enhance the conclusions of the manuscript.

      We agree with the reviewer that morphology of neurons is a useful indication of neuronal identity and maturity, and we have now included greater magnifications of iNGNs in the supplement. However, we do not think that including images of iNGNs over time, especially over such an extended time window will be very informative in the context of our work, since we are focusing on a single time point for the drug screening.

      -For Fig3F-N, please demarcate cut-offs for significant up- or down-regulated genes (red lines) and include number of genes as was done in Fig3A-C.

      We thank the reviewer for this point, and we have now modified the figure accordingly.

      -For Fig4A-I, including cut-offs for significant up- or down-regulated genes and counts would facilitate comparison of the results.

      In this case, there are very few (in fact only 1 protein) that is significantly downregulated, and we have included the corresponding plots in the supplementary material as FigS4A-C.

      -Test pairwise combination of the three drugs.

      We thank the reviewer for this interesting point, however to make this relevant it would imply testing a large number of combinations at different doses in different systems, which is another project per se. It is also not entirely clear to us whether such as large amount of work would be worthwhile, given the fact that only CI-994 showed efficiency in clearing PFFs from dopaminergic neurons derived from human iPSCs.

      **Referees cross commenting**

      I believe Reviewer #2's comments were quite thoughtful and excellent. It should be noted that whether proteostasis deficits observed in neurodegenerative diseases are linked to proteasomal, lysosomal or both pathways should be addressed, if not experimentally, in the text of the manuscript as it is central to how the reporter works and what cellular mechanism is probed under pathological conditions. I believe the comments from the two reviewers are complementary.

      Reviewer #1 (Significance Required):

      General assessment: The work presented in the manuscript, as is, was well done and performed with rigor, but the results do appear preliminary and without additional work can seem descriptive. The strengths are the development of the human ES cell line that expresses the MCFT reporter that can be differentiated into human neurons. These cells can then be used to screen a large number of compounds, therapeutic agents or orphan drugs to rapidly assess the resulting physiological changes to the cells. Having said this, a high-throughput screen is just the beginning and validating the observed changes need additional experiments, well-defined controls and thorough analyses.

      Another caveat of this work is that the authors use a different cell-type to study alpha-synuclein aggregation. While it can be argued that authors were trying to demonstrate the effect of the drugs on diverse cell-types or pathological condition, then the burden of proof lies with the authors to show that protein turnover, transcriptome and proteome within mouse neurons are also similarly affected by the drugs as was shown for human cells.

      Conceptual advances:

      The approach used in the manuscript can be implemented to engineer human neurons expressing any fluorescence reporter that assesses changes in cellular physiology. Moreover, the finding that translation-related genes are up-regulated by drugs that increase protein turnover could reveal new potential targets for disease intervention. Ultimately, if this approach can be readily adapted to patient-derived iPSCs, then it would be possible to personalize a drug (or cocktail) treatment or regimen that is specific to a patient's prognosis.

      Audience:

      I believe this work would be of interest to a broad audience of fluorescence tool-builders, translational researchers and neuroscientists. The authors make a great effort to bridge basic science with drug discovery and translational research.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): ____

      __Summary: The manuscript titled 'high-throughput identification of protein turnover modulators in human neurons" reported using a previously developed MCFT reporter system for screening compounds that increase protein turnover in stem cell-derived human neurons, and showcased three compounds that suppress pS129 aSyn pathology in a seeded aggregation model. The platform has throughput advantage and the pS129 clearance data across systems is the strong. My main concerns are about how directly some of the mechanistic claims are actually supported by the data, outlined below. __ Major comments: __

      1. It is known that GFP is quenched in acidic environments including the lysosome, while RFP is much less sensitive to this. This is exactly why tandem GFP-RFP constructs are so widely used as lysosomal degradation sensors in the autophagy field. I understand the MCFT here is a genuine Fluorescent Timer based on differential maturation kinetics rather than pH quenching, and the authors validated it against orthogonal methods under basal conditions in ref 42. My concern is specifically about what happens once cells are treated with a compound that perturbs proteostasis more broadly. Under basal conditions the readout may cleanly reflect proteasomal turnover, but once you add a compound, both proteasomal and lysosomal/autophagic degradation are likely engaged. If a compound shifts the sensor pool toward lysosomal handling, GFP will get quenched , it will still show up as a lower G/R ratio and get scored as a hit for the same reason.

      We understand that indeed, if a compound shifts the sensor pool towards lysosomal handling, GFP will get quenched. However, the hits in our screen are those with a higher, not a lower G/R ratio. Therefore the compounds that shift the sensor pool towards lysosomal detection will not by assigned as hits, in fact on the contrary they will be de facto be considered as non-hits.

      1. Since this is an imaging-based screen, I think the authors already have what they need to at least partially address this: looking at total signal intensity changes and colocalization between GFP and RFP signal, or with a lysosomal marker like LAMP1 could help tease apart whether the ratio changes are coming from proteasomal degradation, lysosomal degradation, or both. Right now "increased protein turnover" is a reasonable interpretation but it's not really demonstrated for the three lead compounds specifically.

      Because we show above that comment 1. is in fact misguided, we believe these experiments are not required.

      1. PFF are known to be cleared primarily by lysosomes, not proteasomes, once it is aggregated. But the MCFT sensor is specifically built around a PEST degron targeting proteasomal degradation. So there's a disconnect: the screen identifies compounds based on a proteasome-linked readout, but the clearance phenotype in Fig. 5 is happening in a system where lysosomal degradation is expected to dominate. Nothing in the paper actually shows these are the same mechanism. I think this needs a direct test (bafilomycin/chloroquine co-treatment to ask if clearance is autophagy-dependent, and a proteasome inhibitor (marizomib/bortezomib) co-treatment to ask if it's UPS-dependent).

      We thank the reviewer for raising this important point. We agree that our current data do not demonstrate that the reduction in phospho-alpha-synuclein pathology is directly mediated by increased proteasomal degradation. We have now clarified in the manuscript that we did not establish a firm mechanistic link between the MCFT readout and the reduction in alpha-synuclein pathology.

      However, we would like to clarify an important distinction between the exogenous PFF seeds and the alpha-synuclein pathology quantified in Fig. 5. Exogenously added PFFs are internalized through the endolysosomal pathway. A fraction of these seeds subsequently escapes from damaged or perforated endolysosomal compartments and reaches the cytosol, where it induces the recruitment and conversion of soluble endogenous alpha-synuclein into newly formed fibrils. These newly formed fibrils progressively accumulate in neurites and later in the neuronal soma, where they mature into Lewy body–like inclusions.

      Thus, the phospho-alpha-synuclein signal quantified in Fig. 5 does not primarily measure the lysosomal clearance of the internalized PFF inoculum. It reflects the accumulation of newly formed pathological fibrils composed predominantly of endogenous alpha-synuclein. We agree that the mechanisms regulating the formation, turnover or clearance of this newly generated pathology may involve the ubiquitin–proteasome system, the autophagy–lysosomal pathway, or interactions between both pathways, but the present experiments were not designed to distinguish between these possibilities.

      Importantly, approximately 10 days after PFF exposure are required to generate a robust and reproducible level of phospho-alpha-synuclein pathology that allows differences between treatment conditions to be reliably quantified. Sustained inhibition of either proteasomal or autophagy–lysosomal degradation over this period is not experimentally feasible in primary neurons because it is highly toxic. Based on our previous experience with this model, proteasome or autophagy inhibitors can only be applied for short periods, typically no longer than 24 hours, without inducing substantial neuronal toxicity (unpublished observations).

      A short co-treatment with bafilomycin or chloroquine would also be difficult to interpret in this context. In addition to inhibiting lysosomal degradation, these compounds can alter PFF intracellular trafficking, lysosomal integrity, seed escape and consequently the initial seeding process itself. Similarly, proteasome inhibitors have broad effects on neuronal proteostasis and can independently promote alpha-synuclein accumulation and cellular stress. A 24-hour inhibition at a selected stage of the 10-day process would therefore not reveal which pathway is responsible for the overall reduction in pathology and could instead reflect acute effects on several distinct steps of pathology formation.

      The purpose of Fig. 5 was to determine whether compounds identified through the MCFT screen could also reduce the formation or accumulation of endogenous alpha-synuclein pathology in a complementary PFF-seeding model. We have now clarified this distinction in the manuscript and explicitly state that the mechanistic link between the proteasome-related MCFT readout and the reduction in alpha-synuclein pathology remains to be established.

      1. Around lines 233 and 367 the authors propose that increased degradation without corresponding increases in proteasomal protein levels is likely due to changes in assembly, capping, chaperone engagement, or PTMs on the proteasome. This is plausible but it's supported entirely by citations to other people's work, not anything shown here. Comparing assembly state by native PAGE, a proteasome activity assay, or ubiquitin-conjugate levels between DMSO and treated cells would support the hypothesis. If this isn't feasible for this manuscript, I'd at least ask that the text be softened so this reads as a hypothesis for future work rather than a likely mechanism.

      We think this is an interesting point. We thus propose to determine the changes in proteasome activity in iNGNs after 24h treatment with the 3 drugs.

      1. The authors mention in the Limitations section that they can't rule out that some pathways are still dynamically changing at 24h, but this is left as an acknowledged gap rather than something they test. Given this is a live-cell imaging platform, it seems like it would be straightforward to collect a time course (6/12/24/48h) at least for the three lead compounds to see whether the G/R shift at 24h is a stable new state or not. Especially, mTOR inhibition is known to shut down global transcription while upregulating ribosomal proteins to compensate for slowed protein synthesis. I'd want to know whether this is a specific turnover-promoting mechanism or a broader stress/adaptation response that just happens to also show up as a G/R change. Cell count alone isn't a very sensitive readout for distinguishing these. Also, ribosomal protein levels go up without a corresponding increase in mRNA, and the authors attribute this to post-transcriptional regulation of translation. But this could just as easily be explained by decreased degradation/stabilization of existing ribosomal proteins, which is a different mechanism entirely, and arguably doesn't actually support the "enhanced turnover" claim.

      We thank the reviewer for this interesting point. We agree that a time course of the three lead compounds to see how the G/R shift is changing would be of interest, and we will perform this experiment. We also do agree that understanding how the mTOR pathway reacts to compound treatment is of interest, and we will thus perform ELISA tests based on reactivity to p70S6K phosphorylation to measure mTOR activity over time after treatment with the three compounds, as we have done previously in Sun et al., Cell Systems 2026.

      Concerning the mechanism that causes ribosomal proteins to increase: we agree with the reviewer that we cannot claim that this is caused by an increased synthesis rather than decreased degradation of the ribosomes, and we have now specified this in the manuscript discussion. However, we do not think that the latter is against the claim we make for enhanced turnover. First, we show that MCFT turnover is de facto enhanced by treatment with the compound. Second, an increase in ribosomal proteins is likely to drive the increased protein synthesis rate since as we demonstrated in our previous work (Sun et al., Cell Systems 2026), changes in global protein synthesis rates directly feed into global protein degradation rate through a passive adaptation mechanism in various cell types, including human neurons. We thus believe that increase in ribosomal protein content, irrespective of the mechanism leading to this increase, will lead to an increase in protein degradation rate.

      __**Referees cross commenting** __

      I found Reviewer #1's assessment to be thorough and largely complementary to my own. Together, our comments raise the same underlying question: whether the turnover phenotype demonstrated in iNGNs is mechanistically connected to the pathology phenotype demonstrated in a different model system. The one area I would consider optional rather than essential is the suggestion to test pairwise drug combinations, since the mechanism is not yet clear, and combining two compounds would only make it harder to disentangle.

      Reviewer #2 (Significance (Required)):

      This work provides a technical advance of clear value to the proteostasis and neurodegeneration fields.

      The audience for this work includes researchers in proteostasis, autophagy-lysosome and ubiquitin-proteasome biology, drug discovery groups working on neurodegenerative disease, and stem cell/iPSC modeling labs interested in scalable phenotypic screening platforms. The main caveat, as detailed above, is that the mechanistic interpretation of the screen is not yet fully disentangled from lysosomal/autophagic contributions, which matters both for interpreting the primary screening data and for understanding how the lead compounds are actually acting on aSyn pathology.

      My field of expertise: proteostasis, autophagy-lysosome and ubiquitin-proteasome systems, selective autophagy receptors, proteomics, chemoproteomics, neurodegeneration-relevant cellular models.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: The manuscript titled 'high-throughput identification of protein turnover modulators in human neurons" reported using a previously developed MCFT reporter system for screening compounds that increase protein turnover in stem cell-derived human neurons, and showcased three compounds that suppress pS129 aSyn pathology in a seeded aggregation model. The platform has throughput advantage and the pS129 clearance data across systems is the strong. My main concerns are about how directly some of the mechanistic claims are actually supported by the data, outlined below.

      Major comments:

      1. It is known that GFP is quenched in acidic environments including the lysosome, while RFP is much less sensitive to this. This is exactly why tandem GFP-RFP constructs are so widely used as lysosomal degradation sensors in the autophagy field. I understand the MCFT here is a genuine Fluorescent Timer based on differential maturation kinetics rather than pH quenching, and the authors validated it against orthogonal methods under basal conditions in ref 42. My concern is specifically about what happens once cells are treated with a compound that perturbs proteostasis more broadly. Under basal conditions the readout may cleanly reflect proteasomal turnover, but once you add a compound, both proteasomal and lysosomal/autophagic degradation are likely engaged. If a compound shifts the sensor pool toward lysosomal handling, GFP will get quenched , it will still show up as a lower G/R ratio and get scored as a hit for the same reason. Since this is an imaging-based screen, I think the authors already have what they need to at least partially address this: looking at total signal intensity changes and colocalization between GFP and RFP signal, or with a lysosomal marker like LAMP1 could help tease apart whether the ratio changes are coming from proteasomal degradation, lysosomal degradation, or both. Right now "increased protein turnover" is a reasonable interpretation but it's not really demonstrated for the three lead compounds specifically.
      2. PFF are known to be cleared primarily by lysosomes, not proteasomes, once it is aggregated. But the MCFT sensor is specifically built around a PEST degron targeting proteasomal degradation. So there's a disconnect: the screen identifies compounds based on a proteasome-linked readout, but the clearance phenotype in Fig. 5 is happening in a system where lysosomal degradation is expected to dominate. Nothing in the paper actually shows these are the same mechanism. I think this needs a direct test (bafilomycin/chloroquine co-treatment to ask if clearance is autophagy-dependent, and a proteasome inhibitor (marizomib/bortezomib) co-treatment to ask if it's UPS-dependent).
      3. Around lines 233 and 367 the authors propose that increased degradation without corresponding increases in proteasomal protein levels is likely due to changes in assembly, capping, chaperone engagement, or PTMs on the proteasome. This is plausible but it's supported entirely by citations to other people's work, not anything shown here. Comparing assembly state by native PAGE, a proteasome activity assay, or ubiquitin-conjugate levels between DMSO and treated cells would support the hypothesis. If this isn't feasible for this manuscript, I'd at least ask that the text be softened so this reads as a hypothesis for future work rather than a likely mechanism.
      4. The authors mention in the Limitations section that they can't rule out that some pathways are still dynamically changing at 24h, but this is left as an acknowledged gap rather than something they test. Given this is a live-cell imaging platform, it seems like it would be straightforward to collect a time course (6/12/24/48h) at least for the three lead compounds to see whether the G/R shift at 24h is a stable new state or not. Especially, mTOR inhibition is known to shut down global transcription while upregulating ribosomal proteins to compensate for slowed protein synthesis. I'd want to know whether this is a specific turnover-promoting mechanism or a broader stress/adaptation response that just happens to also show up as a G/R change. Cell count alone isn't a very sensitive readout for distinguishing these. Also, ribosomal protein levels go up without a corresponding increase in mRNA, and the authors attribute this to post-transcriptional regulation of translation. But this could just as easily be explained by decreased degradation/stabilization of existing ribosomal proteins, which is a different mechanism entirely, and arguably doesn't actually support the "enhanced turnover" claim.

      Referees cross commenting

      I found Reviewer #1's assessment to be thorough and largely complementary to my own. Together, our comments raise the same underlying question: whether the turnover phenotype demonstrated in iNGNs is mechanistically connected to the pathology phenotype demonstrated in a different model system. The one area I would consider optional rather than essential is the suggestion to test pairwise drug combinations, since the mechanism is not yet clear, and combining two compounds would only make it harder to disentangle.

      Significance

      This work provides a technical advance of clear value to the proteostasis and neurodegeneration fields.

      The audience for this work includes researchers in proteostasis, autophagy-lysosome and ubiquitin-proteasome biology, drug discovery groups working on neurodegenerative disease, and stem cell/iPSC modeling labs interested in scalable phenotypic screening platforms. The main caveat, as detailed above, is that the mechanistic interpretation of the screen is not yet fully disentangled from lysosomal/autophagic contributions, which matters both for interpreting the primary screening data and for understanding how the lead compounds are actually acting on aSyn pathology.

      My field of expertise: proteostasis, autophagy-lysosome and ubiquitin-proteasome systems, selective autophagy receptors, proteomics, chemoproteomics, neurodegeneration-relevant cellular models.

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript by Dembska et al titled, "High-throughput identification of protein turnover modulators in human neurons," the authors engineered human embryonic stem cell that expresses the MCFT reporter (FT-NGN-hESC). The FT-NGN-hESC line can be induced to differentiate into a near pure population of neurons (FT-iNGN). These neurons were subsequently used to screen almost 6000 compounds to identify candidates that could alter protein turnover. They selected three and characterized the resulting changes to the transcriptome and proteome of iNGN neurons. They identified ribosomal proteins, initiation factors and snoRNA as some of the top up-regulated genes-which have not been demonstrated before in neurons. Additionally, the authors proceeded to test the compounds on their ability to resolve alpha-synuclein aggregation in mouse hippocampal neurons. The manuscript is an elegant demonstration of how a fluorescent reporter can be applied to human ES-derived cells to assess changes in protein turnover and disease pathology.

      Major comments:

      • Development of the FT-NGN-hESC line that can be differentiated into FT-iNGN neurons serves as an excellent tool for screening small molecule drugs that can alter protein turnover in human ES-derived lines. However, if the goal of the study is to demonstrate the efficacy of a compound to mitigate disease pathology, it would benefit from directly addressing a specific neurodegenerative disease (i.e. Parkinson's or other related synucleinopathies) by using patient-derived iPSCs. As outlined in Fig1A, MCFT can be inserted into the CLYBL site and differentiated into neural cells to assess the impact of the disease on protein turnover in patient cells. Although the population of neural cells can vary, that diversity or heterogeneity of differentiating cells could provide information as to when in development that protein turnover begins to change in the context of disease progression. [OPTIONAL]
      • The rationale for why the aSyn aggregation assay was performed on primary mouse neurons was not clear. The authors invested much effort into the development and characterization of FT-iNGNs and the effect of the three drugs on transcription and translation of iNGNs (Figs1-4). Yet, to assess pathology, they employ mouse neurons. If the use of mouse neurons was essential, then I would expect the authors to show that introduction of preformed fibrils (PFF) increased MCFT half-life (more mOrange fluorescence) and demonstrate that reduction of phospho-aSyn following drug treatment correlated with increased RPs, eIFs or snoRNAs expression-as was shown for iNGNs. Conversely, could the aggregation assay be performed on mature iNGNs to make the experimental model more consistent?
      • The identification of RPs, eIFs and snoRNAs as up-regulated genes upon drug treatment is a novel finding in post-mitotic neurons. It should be noted that elevated expression of components of the translational machinery are hallmarks of dividing cells. The authors should denote whether neurogenesis-related or proneural genes are up- or down-regulated in their volcano plots (Figs3&4). Additional evidence should show that these drugs do not interfere with differentiation and that the cells are not regressing to a mitotic-state.
      • From identified genes that are up- or down-regulated in omics results, typically one or more genes are validated using immunofluorescence or smFISH. I would urge the authors to include one or both methods to confirm their transcriptomic or proteomic results.

      Minor comments:

      • As some of the results are fluorescence readouts, it is recommended that higher magnification images (greater than 20x) or enlarged insets be included (Figs1B, 5B,G,K). In particular, neurons that have long polarized extensions should be clearly and discretely visible as structure can be a proxy for neuronal identity and maturity.
      • Inclusion of images of iNGNs over time (i.e. during dox induction and after treatment of drugs at day 0,1,3,5,14) at greater than 20x magnification would enhance the conclusions of the manuscript.
      • For Fig3F-N, please demarcate cut-offs for significant up- or down-regulated genes (red lines) and include number of genes as was done in Fig3A-C.
      • For Fig4A-I, including cut-offs for significant up- or down-regulated genes and counts would facilitate comparison of the results.
      • Test pairwise combination of the three drugs.

      Referees cross commenting

      I believe Reviewer #2's comments were quite thoughtful and excellent. It should be noted that whether proteostasis deficits observed in neurodegenerative diseases are linked to proteasomal, lysosomal or both pathways should be addressed, if not experimentally, in the text of the manuscript as it is central to how the reporter works and what cellular mechanism is probed under pathological conditions. I believe the comments from the two reviewers are complementary.

      Significance

      General assessment: The work presented in the manuscript, as is, was well done and performed with rigor, but the results do appear preliminary and without additional work can seem descriptive. The strengths are the development of the human ES cell line that expresses the MCFT reporter that can be differentiated into human neurons. These cells can then be used to screen a large number of compounds, therapeutic agents or orphan drugs to rapidly assess the resulting physiological changes to the cells. Having said this, a high-throughput screen is just the beginning and validating the observed changes need additional experiments, well-defined controls and thorough analyses. Another caveat of this work is that the authors use a different cell-type to study alpha-synuclein aggregation. While it can be argued that authors were trying to demonstrate the effect of the drugs on diverse cell-types or pathological condition, then the burden of proof lies with the authors to show that protein turnover, transcriptome and proteome within mouse neurons are also similarly affected by the drugs as was shown for human cells.

      Conceptual advances: The approach used in the manuscript can be implemented to engineer human neurons expressing any fluorescence reporter that assesses changes in cellular physiology. Moreover, the finding that translation-related genes are up-regulated by drugs that increase protein turnover could reveal new potential targets for disease intervention. Ultimately, if this approach can be readily adapted to patient-derived iPSCs, then it would be possible to personalize a drug (or cocktail) treatment or regimen that is specific to a patient's prognosis.

      Audience: I believe this work would be of interest to a broad audience of fluorescence tool-builders, translational researchers and neuroscientists. The authors make a great effort to bridge basic science with drug discovery and translational research.

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      Reply to the reviewers

      We thank the three reviewers for their critical reading of our manuscript. We also appreciate that the three reviewers highlighted the novelty and the broad interest of our study. They have identified key limitations of our analyses, and we have either provided explanations for our choice, or performed new analyses to circumvent biases. We believe that the manuscript is greatly improved and that our main conclusion, that PhDEF has a major binding and regulatory action in the petal epidermis, is strongly supported by our data.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary

      The authors previously generated two cell-layer-specific mutants of petunia for the petal identity gene PhDEF. In this study, they profiled differential gene expression in those mutants through single-cell RNA sequencing (scRNA-seq). They found that more genes are highly and specifically expressed in the epidermal cell layer than in mesophyll cells. In addition, they identified cell-layer-specific and -aspecific PhDEF target genes. Using the extensive single-cell transcriptome and layer-specific target identification, the authors concluded that different cell identities affect homeotic regulator PhDEF, thereby influencing transcriptional regulation.

      Major comments

      This presented work provides comprehensive evidence, that pre-existing cell layer identity (epidermis and mesophyll) modulate transcriptional output of homeotic transcription factor, PhDEF.

      However, a disconnection between PhDEF bindings to genome and transcriptional output undermines the robustness of their conclusion although some binding loci were shown to be correlated with DEG. This may indicate the chromatin state, the existence of interacting partners, and the non-productive binding of PhDEF, suggesting that PhDEF binding alone is not sufficient to predict transcriptional outcomes and additional regulatory mechanisms that shape gene expression in addition to the layer-specific regulatory mechanisms. This disconnection may also be due to the developmental timing. Indeed, it appears authors used different flower stages for ChIP-seq and scRNA-sequencing. In fully differentiated organs, PhDEF binding itself may be no longer transcriptionally productive, and differential gene expression results primarily from the pre-established cell identity rather than directly from the homeotic regulation of PhDEF. Therefore, the main question the authors asked-how homeotic identity works with cell-layer identity and how the homeotic gene, PhDEF, acts in mature organs-was not clearly explained by this study.

      We thank the reviewer for raising this important issue. We agree that the difference in developmental timing between the scRNA-Seq and ChIP-Seq experiments might contribute to the disconnection that this reviewer pointed out.

      First, we want to explain that the reason for performing scRNA-Seq on fully differentiated petals was purely technical, as we were initially aiming to obtain protoplasts at stage 8 (stage used for the ChIP-Seq) but could never retrieve enough of them for proper encapsulation in the 10X Chromium chips. We have now clearly explained this in the manuscript (lines 104-107).

      A hypergeometric test shows that our ChIP-Seq and scRNA-Seq datasets overlap more than by chance (p = 0.000137); however, we agree that differences in developmental stages possibly bring a confounding effect to our conclusions. Therefore, we have decided to add to our manuscript the intersection between ChIP-Seq and bulk RNA-Seq data performed on WT, star and wico flowers at stage 8, that we published previously (Chopy et al., 2024). In that case, both datasets have been obtained with the same exact genetic material and at the same exact developmental stage.

      This intersection confirms that very similar binding profiles are observed for PhDEF target genes, whether they are differentially expressed in the epidermis (star only), in the mesophyll (wico only) or in both layers (star and wico) (Figure 4A). However, star-specific DEGs were more often bound by PhDEF by epidermis+shared binding sites, and wico-specific DEGs more often with mesophyll-specific binding sites, suggesting a weak but significant association between binding and regulatory profiles. Performing similar tests for individual binding categories for DEGs identified by scRNA-Seq also revealed that epidermis-specific DEGs displayed more epidermis+shared binding sites than expected. Since the association between epidermal DEGs and shared+epidermal binding sites is found both in the intersection with RNA-Seq and scRNA-Seq data, we have now stated that « layer-specific binding and transcriptional regulation are partially linked, at least in the epidermis » (line 354).

      In Figure 2, the use of the term "target" is potentially misleading. It sounds like direct target genes (direct binding and differential expression) for PhDEF, but it refers only to DEGs.

      Indeed, the term "target" was referring to both indirect and direct targets of PhDEF. To avoid any possible confusion, we have replaced it by differentially expressed genes (DEGs) throughout the manuscript, when appropriate.

      Lines 496-497: When the authors state, "~ demonstrates for the first time that the regulatory function of homeotic factor is influenced by cell layer identity," it sounds overstated, as prior studies have shown that pre-existing tissue or cell identity can shape transcriptional activity and developmental output.

      We agree that previous studies have shown that cell identity influences transcriptional activity in general. While this might not have been specifically assessed in the context of different cell layers, we have rewritten this sentence accordingly.

      Minor comments

      In the UMAP presentation, as depicted in Figures 2C, S3, and S5, the cells with zero expression can be colored in light gray (or an inverted color scheme). The purple hue masks the gene expressions of other cells, making it difficult to see the yellow or light green colored cells.

      We have modified all UMAPs depicting gene expression as suggested, in Figures 2C and S5, and replaced UMAPs with DotPlots in Figure S3.

      Reviewer #1 (Significance (Required)):

      General assessment

      This study is well-designed and technically sound. They utilize single-cell transcriptomics and ChIP-seq by using genetically well-defined genetic materials and layer-specific PhDEF deletion mutants. The analysis showed where PhDEF binds to genomic loci and which genes are differentially expressed in petal epidermis and mesophyll, providing evidence of cell-layer-specific function of homeotic gene in mature organs. Although certain mechanistic aspects were not elucidated, the data from the extensive genome-wide study contributed to drawing their conclusions.

      Advances

      This research goes beyond classical models of floral organ identity by showing that homeotic gene function is not uniform in the same floral organ. It represents a conceptual advance in our understanding by integrating cell layer identity into the framework of homeotic gene regulation.

      Audience

      This study will be of broad interest to scientists who study transcription networks, cell and organ identity in the context of plant development.

      My field of expertise:

      Transcriptional regulation by transcription factor, epigenetic regulation of gene expression, plant development

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary:

      This study from Cavallini-Speisser et al. cleverly leverages a tissue layer-specific mutant, single cell and bulk RNA-sequencing, and ChIP-sequencing to decipher tissue layer-specific regulation of petal development in petunia by the PhDEF transcription factor. The authors find common and unique targets of PhDEF between the epidermis and mesophyll and conclude that the activity of transcription factors like PhDEF are influenced by the pre-existing environment in the cell they are expressed in. Understanding when and how a given transcription factor drives expression of unique target genes in various contexts is an important aspect of developmental biology that can be elusive outside of highly tractable model systems. As such, I think this study is of high value and has strong potential to expand our understanding of how developmental specificity is mediated by commonly employed transcriptional regulators. However, I think there are some issues with possible over-interpretation and some places where documentation of experimental design and data quality control are lacking. I elaborate on these concerns below.

      Major Comments:

      Line 155: Assigning mesophyll clusters by default without any positive marker genes strikes me as problematic, especially as much of the analysis rests on comparing the transcriptomes of epidermis and mesophyll cells. Can the authors perhaps leverage published scRNA-seq datasets to find potential mesophyll markers, even homologs from other species, to improve confidence in the cluster assignment?

      We thank the reviewer for raising this important point. Our statement of defining mesophyll identity by default was not entirely true (and we have now removed it), since it is supported by GO-enriched terms for cluster markers. For cluster "mesophyll 3", there is a strong enrichment for photosynthesis-related genes; and for cluster "mesophyll 2", there is a strong enrichment for water transport-related genes, both functions being fulfilled by the petal mesophyll. For cluster "mesophyll 1", the most enriched GO term is "glutathione metabolic process" that rather points to stress response. These three clusters also do not express any of the epidermal-identity genes, in contrast to the clusters that we assigned as epidermal. Cluster markers from the mesophyll display the lowest enrichment of all clusters (the best cluster markers have a log2FC between 2.8 and 4.9, in contrast to a log2FC between 8.1 and 9.9 for all other clusters), which is in line with our finding that the mesophyll expresses less specific genes than the epidermis, and suggests a basal identity with a transcriptomic signature that is less clear than in the epidermis. Therefore, it is not entirely trivial to find positive marker genes for the mesophyll with a strong specificity.

      In order to be more transparent about the expression patterns of the genes we selected to assign cluster identity, we modified Figure S3 to include DotPlots of selected photosynthesis-related genes, histone genes, S-phase genes and vasculature genes in the same figure, to compare with DotPlots of epidermal genes and pigmentation genes from Figure 1D, to allow for an informed comparison. Our conclusions remain the same as previously: epidermal clusters are defined based on the specific expression of epidermal genes and/or pigmentation genes. We notice, however, that the cluster "upper limb epidermis" strongly expresses photosynthesis genes, which is likely why it is close in the UMAP space to the "mesophyll 3" cluster. We have no explanation for that, but the extremely high expression of pigmentation genes in this cluster, however, identifies it as epidermal without a doubt. We have also added in Figure S3 the DotPlots of expression levels of homologs of 15 epidermis-enriched and 9 mesophyll-enriched genes from tobacco petal scRNA-Seq published by Kang et al. (doi: 10.1111/nph.17992). This shows that our definition of epidermal and mesophyll clusters and the one from Kang et al. generally overlap.

      Line 228: Through the description and interpretation of the ChIP-seq dataset, the authors use the fact that peaks are more abundant and bigger in the epidermis to conclude that binding of PhDEF is "stronger" in the epidermis. This implies a difference in physical interaction between the TF and the DNA that I don't think can be concluded from the data presented. This could be confounded by biology; if expression of PhDEF is more heterogeneous in the mesophyll than in the epidermis, the peaks from that tissue will be averaged out and appear smaller when in fact the binding is the same strength. This could also be a technical artifact if the ChIP was less efficient in one sample versus another. This conclusion requires reinterpretation. The authors have the power to address this at least partially with the scRNA-seq by measuring PhDEF heterogeneity. I believe assessing ChIP efficiency would have required a spike in control, but perhaps there is a computational way to address this. It is important to discuss these confounding factors in the text.

      This is another important point. We agree that the word "stronger", to describe PhDEF binding in the epidermis, was not appropriate and we have replaced it with "more frequent" which is a more factual interpretation of our results. We also agree that even this interpretation depends on potential ChIP artifacts that we have now evaluated.

      We have used our WT scRNA-Seq data to explore the heterogeneity of PhDEF expression in the mesophyll and the epidermis, as suggested. The barplot in Figure S10B represents the number of cells (y-axis) with given PhDEF RNA counts (x-axis) in the clusters that we assigned as epidermis (left) and mesophyll (right). We have performed this analysis after removing cells that do not express PhDEF at all, which represents 47.7% and 47.4% of epidermal and mesophyll cells, respectively, hence very similar proportions. The distributions of expression of PhDEF in the epidermis and in the mesophyll are within the same ranges, with a slightly higher expression of PhDEF in the mesophyll than in the epidermis. The coefficients of variation (cv) of the two distributions are similar and slightly higher in the epidermis (cv = 0.34 in the mesophyll and cv = 0.36 in the epidermis, p = 0.00106 with Feltz and Miller’s asymptotic test). Therefore, it appears that the expression of PhDEF is actually higher and slightly less variable in the mesophyll than in the epidermis, meaning that it should not result in averaging out the peaks detected. This relies on the assumption that PhDEF protein levels are directly correlated with PhDEF RNA levels, which we have not explored in this study and remains a limitation. We have included this analysis lines 275-278 and Figure S10B.

      Regarding ChIP efficiency, we had run different tests prior to sequencing: first, we have tested different amounts of chromatin, keeping the quantity of antibody unchanged, and tested ChIP enrichment by qPCR on a set of two positive (PhDEF and Pos2) and one negative (Neg1) control binding sites. Second, after selecting the best chromatin quantity, we have performed 4 independent ChIP replicates for each genotype (split between two assays named ChIP-1 and ChIP-2) and measured ChIP efficiency by qPCR. This is depicted in Figure S10A, with the replicates chosen for sequencing highlighted with an orange star.

      We have now explained in greater detail in the Methods our preliminary tests. There is variation of enrichment between replicates, and particularly between assays here (ChIP-1 vs. ChIP-2), which is inherent to the ChIP experiment. It might be particularly prominent in our case due to our custom antibody directed against PhDEF, in contrast to commercial antibodies that are commonly used in ChIP experiments with tagged transgenic lines. However, we see consistently lower enrichment for star as compared to wico and WT, in line with the more frequent epidermal binding of PhDEF. We have followed the ENCODE guidelines for our analysis pipeline, in particular applying the IDR. We have now added other mapping statistics in Table S5 including the FRiP (fraction of reads in peaks) metric, that is in the range of expected values but is lower for star (around 0.5%) than for WT and wico (0.7-1.2 %), again consistent with the more frequent epidermal binding of PhDEF.

      Line 376: The authors risk overinterpreting a lack of differential gene expression detection in their analysis of PhDEF binding profiles. This can be affected by how deeply a library was sequenced or how many cells were analyzed per cell type. A gene might not be found to be DE if low depth or few cells resulted in noise or dropout. Lack of detection does not mean lack of differential regulation so the biological relevance of this portion of the analysis should be interpreted with caution.

      We have added to this new version of the manuscript an intersection between ChIP-Seq and bulk RNA-Seq in WT, star and wico, as bulk RNA-Seq is much more sensitive than scRNA-Seq in detecting lowly expressed genes. We have also added the sentence that bulk RNA-Seq "better captures lowly expressed genes" than scRNA-Seq, line 333. This intersection revealed an association between epidermal DEGs (star-specific DEGs) and the presence of epidermal+shared binding sites for PhDEF. We have modified our conclusions accordingly.

      Line 476: The authors state there is a mismatch in developmental timing between the RNAseq and ChIP datasets. Why is this? This is mentioned briefly in the Discussion, but has the potential to be majorly confounding to the joint interpretation of the ChIP and RNAseq datasets. This experimental design choice should be justified more thoroughly and a consideration of the limitations it brings to data interpretation should be more prominent in the text.

      This concern has also been raised by the first reviewer, and we have now added to our study an intersection between ChIP-Seq and bulk RNA-Seq performed at the same stage. We have also explained the technical reasons for performing scRNA-Seq at a mature stage only (lines 104-107). Indeed, the intersection between bulk RNA-Seq and ChIP-Seq performed at the same stage revealed a significant association between epidermal DEGs (star-specific DEGs) and the presence of epidermal+shared binding sites for PhDEF. Testing for individiual binding categories, we could also detect an enrichment of epidermal+shared binding sites for epidermal-specific DEGs identified by scRNA-Seq. Therefore, we have now stated that « layer-specific binding and transcriptional regulation are partially linked, at least in the epidermis » (line 354).

      Minor Comments:

      Line 229: The authors compare correlations between pseudo-bulked transcriptomes and argue that in the star mutant the epidermis adopts a mesophyll-like identity. The correlation between epidermis and mesophyll in star is 0.97 and the correlations were 0.94 and 0.93 in the other genotypes tested. What is the meaningful cutoff for saying the transcriptomes are similar or not? Is 0.97 so much higher than 0.94 that this conclusion is supported?

      The comparison of pseudo-bulk transcriptomes is a very global and exploratory approach. Given the high number of genes underlying these pseudo-bulk datasets, any difference in the correlation between them is statistically significant, which is not very informative. We agree with this reviewer that the interpretation of these correlation coefficients is somewhat arbitrary. We have simplified this part of the manuscript and have mostly focused on comparing star and wico pseudo-bulk transcriptomes to the WT ones, but not to each other's, which aligns well with our main message of a specific epidermal identity, easily shifting to a mesophyll-identity when PhDEF is missing or not entirely functional. We have also removed Figure 2E to a supplementary figure to give less emphasis to this analysis.

      Line 264: What are the "manually chosen thresholds for differential expression"? Can the authors explain and justify this? There is very little detail on this in the materials and methods and this raises some concerns regarding how a threshold was chosen.

      Seurat gives a default threshold of 0.25 for log2FC, which we found to be very permissive. In order to capture the most informative targets of PhDEF, but still to capture a meaningful number of targets, we empirically decided to increase this threshold to 0.75. On the WT scRNA-Seq dataset, we observed that the layer-specificity factor was also capturing meaningful differences in layer-specific expression (see Figure 1E). We chose a cut-off at 10% since it was the lowest to give a significant difference in the numbers of epidermis- vs. mesophyll-enriched genes in the WT petal. We have added these explanations in the methods.

      Figure 3G: Could this plot be annotated with the classification of peak layer specificity? It is a little difficult for me as the reader to keep up with all the categories in the text, and showing them in the figure might make that easier to follow.

      We have now annotated the Venn diagram in Figure 3G with the classifications of binding profiles.

      Figure 4A: A comparison of only two cell categories should not use scaled expression, as this can over-emphasize small differences in gene expression. Can this be replaced with a dot plot that uses unscaled expression values?

      We thank the reviewer for noticing this issue, we have built a DotPlot with unscaled values and replaced it in Figure 4A, which does not change our conclusions. We have also used unscaled values in Figure S14.

      Figure 4C: Could this be represented more legibly with stacked, space-filled bar charts? As is, this is difficult to read, and might be impossible for someone who is color blind. In addition, the authors claim this analysis shows similar proportions across all categories, but I wonder if that would hold true if they performed an over-representation analysis normalized to the categories shown in "all genes expressed". This could allow them to statistically test whether there are real differences in representation among the categories.

      We have now used a different and color-blind-friendly palette for pie charts of PhDEF binding profiles. Following reviewers' comments, we have analyzed the intersection of bulk RNA-Seq with ChIP-Seq (both performed at the same stage), and performed Chi2 goodness-of-fit tests that indeed support some association between binding profile and regulation profile, although this remains limited.

      Supplemental Figure 2: It's great that the authors include these metrics, but it would be ideal to also include the plots of standard QC metrics for scRNA-seq such as those found here to give a better sense of per cell quality: https://satijalab.org/seurat/articles/pbmc3k_tutorial

      In addition, it is important to include QC metrics for ChIPseq, which I did not find in the supplement. Metrics such as FRiP are important for interpreting ChIP library quality.

      We have now added the standard QC plots (Feature number per cell and RNA counts per cell) to Figure S2, as suggested. Mitochondrial and ribosomal genes are not annotated in the Petunia axillaris nuclear genome that we used, therefore we could not compute mitochondrial or ribosomal gene counts. We have removed cells expressing less than 200 genes, but did not apply any upper thresholds as there were no obvious outliers.

      FastQC reports for scRNA-Seq and ChIP-Seq have been deposited at https://entrepot.recherche.data.gouv.fr/dataverse/PhDEF_Flower_layer, as indicated in the Methods.

      We have also added ChIP metrics in Table S5, including the number of reads, duplicated reads, mapped reads and computed the FriP score. This score ranges between 0.5 % and 1.2 %, which is satisfactory and above the minimum recommended score by ENCODE of 0.3 %.

      Line 654: What model was used for DESeq2?

      We have used default parameters for DESeq2, ie a negative binomial GLM fitting and Wald significance tests. We have added this information in the Methods.

      Line 752: Can the authors justify why peaks were called separately on input and ChIP samples rather than allowing MACS2 to call peaks in the ChIP sample over input background? That differs from the standard MACS2 pipeline and no explanation for this is provided in the text.

      Our analysis pipeline has indeed been customized, in particular to detect peaks in our positive control PhDEF, for which a binding site of PhDEF in the promoter has been demonstrated experimentally by others in many different species. This binding has a strong experimental support, and we expected PhDEF to bind to its own promoter in the two cell layers. Our ChIP-Seq results show a posteriori that this particular peak is far from being the strongest one over the genome; therefore, we believe it represents a good control to detect binding enrichment for average targets. We have first tried the standard MACS2 pipeline that calculates the enrichment of IP over Input, but we could only detect PhDEF binding for one WT and one wico replicate, although the peaks were visually clear in the two replicates. Our input sample being generally noisy, we explored how separate peak calling between IP and Input would behave (as already done in e.g. Durand et al., 2023, doi: 10.1093/plcell/koad025). We also explored how thresholds for FDR in MACS2, and IDR thresholds for reproducibility between IP samples, would influence peak detection in IP and Input.

      We found that calling peaks on the IP with a relaxed FDR (0.1), then applying the IDR at 0.1, allowed the capture of PhDEF binding to its own promoter in the two wico replicates (but still not in WT, because the peaks were lost after applying the IDR threshold). No peak was detected in the input with these settings, however for other genes we observed spurious peak detection in the input, therefore we decided to lower the FDR thresholds for input peaks to 0.05. Our choices have been made in an attempt to increase specificity at the risk of losing sensibility, and we probably lose true binding events. Considering that this ChIP has been performed on the endogenous PhDEF protein directly, and in chimeric flowers that only express PhDEF in half of the tissue, adapting the ChIP analysis pipeline was a necessary step.

      We have now added a few lines in the Methods (lines 696-706) to explain our rationale.

      Reviewer #2 (Significance (Required)):

      General Assessment:

      Strengths: The authors employ a unique and powerful mutant system to explore a fundamental developmental biology question. In addition, the datasets generated will likely be useful to other researchers working in petunia or flower development.

      Limitations: While the mutant system employed here is a creative way to get at tissue-layer specific transcription factor activity, the ChIP samples still include heterogeneous cell types, which may impact the findings presented here.

      Advance: This study uses a unique system to test the function of a transcription factor in distinct cell types. As stated above, understanding when and how a given transcription factor drives expression of unique target genes in various contexts is an important aspect of developmental biology.

      Audience: I believe this work will be of primary interest to the plant development, single cell, and chromatin biology communities. These are specialized, basic research communities.

      Reviewer Expertise: I am a plant developmental biologist who works with multiple modes of cell-type-specific NGS datasets including bulk and single cell RNAseq and ChIPseq among others.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary: This study addresses a fundamental but underexplored aspect of homeotic gene function: how regulators of cell identity act during late stages of organ development. The authors take advantage of layer-specific mutants of the MADS-box gene PhDEF in Petunia hybrida to dissect the roles of this floral identity regulator in the epidermis and mesophyll. By combining single-cell RNA sequencing with ChIP-seq analyses in wild-type and mutant chimeric petals, the work demonstrates that, although PhDEF is expressed at comparable levels in both petal layers, it binds to and regulates a substantially larger and more layer-enriched set of genes in the epidermis than in the mesophyll. The identification of both layer-specific and shared PhDEF binding sites supports a model in which pre-existing layer identity modulates the regulatory output of homeotic transcription factors.

      Major comments:

      Dissecting PhDEF binding preferences in the epidermis versus the mesophyll using the star and wico mutants is a clever and powerful approach. However, conclusions involving cell identity should be drawn with caution for two reasons. First, cell identities appear to be altered in the mutants: scRNA-seq data suggest that even cells assigned to the same cluster can be molecularly distinct across genotypes. For example, the transcriptomes of wild-type epidermis and mesophyll are highly similar (Pearson correlation R = 0.94), yet both show lower correlation with epidermal cells from star or wico petals. These results raise questions such as are the cells identified as epidermal cells really strictly epidermal in the mutants? Do you need to take into consideration cell composition of the mutants when you do differential expression analysis?Second, PhDEF is expressed in both epidermal and mesophyll clusters in all genotypes, albeit at different levels and in fewer cells in the mutants. As a result, the ChIP-seq profiles should be interpreted as cell type-enriched rather than cell type-specific.

      We fully agree with this reviewer's comments, and alterations in cell identity in the star and wico mutants is indeed a main pitfall for our analysis. The phdef mutation alters the transcriptomic signatures of cells physically located in the epidermis or in the mesophyll, which can result in their artificial clustering with cells located elsewhere in the petal. It might be particularly true for the epidermis, since we see strong depletion in epidermal clusters in the star flowers (and even in the wico flowers), whereas the mesophyll is not much affected in wico flowers. As a result, it is likely that we lose many epidermal cells in star flowers that end up labeled as mesophyll cells, resulting in the under-estimation of DEGs in this layer. We had explored other possible ways to define epidermal or mesophyll cells in our dataset, based for instance on PhDEF or PhGLO1 expression, but since for the majority of cells PhDEF expression is simply not captured, we would have wrongly assigned phdef mutant identity to WT cells. In spite of this limitation, we find more DEGs in the epidermis than in the mesophyll, showing that even an underestimation of DEGs in the epidermis does not affect our main conclusion, which is that PhDEF has a major regulatory action in the epidermis. We have explicitly written this limitation in our manuscript, lines 234-236.

      We agree that ChIP-Seq profiles are rather cell type-enriched than cell type-specific, which we have stated explicitly in the sentence line 308, saying that "differential binding between layers is quantitative". However, for simplicity we prefer to retain the term "specific" since our conclusions are based on the definition of peaks that can either be present or absent in a given genotype, and hence in a given cell layer.

      And there are a few things that need clarification:

      a. Protoplasting and tissue dissection analyses suggest that mesophyll cells constitute more than 80% of the cells in wild-type petals, whereas the scRNA-seq data indicate a substantially lower proportion. Could this discrepancy reflect technical biases in cell recovery or capture efficiency, or issues related to cell identity assignment during clustering and annotation? Notably, the scRNA-seq data from star and wico petals show mesophyll proportions close to 80%. Is this difference due to an increased abundance of mesophyll cells in the mutants, or could it instead reflect differences in transcriptomic separability? In wild-type petals, the epidermal and mesophyll transcriptomes are highly correlated and express similar numbers of genes, with epidermal cells distinguished mainly by higher expression of a subset of genes. This raises the possibility that mesophyll cells in the wild type occupy a more plastic or less differentiated transcriptional state and may therefore be misclassified as epidermal cells, whereas disruption of regulatory mechanisms in the mutants enhances transcriptional divergence and alters cell clustering outcomes.

      Our protoplasting and tissue sections show that the mesophyll should represent 80% of cells in WT tissue, while we estimate it at 70% in our WT scRNA-Seq data based on our cluster assignment (mesophyll = 60% + vasculature = 10%, that we separated from the mesophyll cells but is actually embedded within this tissue). This is not a very strong difference, and considering the multiple steps that protoplasting and cell capture entail, we considered that this was reasonably close to the expected proportions.

      In the star and wico flowers, we assign mesophyll identity to a greater proportion of cells, but as explained above, we believe that cells with altered epidermal identity are easily clustered as mesophyll cells, since they lose their specific transcriptomic signature. This is actually in line with one of the main messages of our article, that petal epidermis transcriptional identity is highly specific.

      b. Cluster 0 appears to show internal heterogeneity, as the expression patterns of KCS3 and LLE2 are largely mutually exclusive. Do these patterns reflect the presence of distinct epidermal cell types within the limb that are currently grouped into a single cluster?

      Indeed, there appears to be some internal heterogeneity within cluster 0. Since our main focus was to compare epidermal and mesophyll clusters, we did not explore further the heterogeneity within epidermal clusters and kept a coarse resolution.

      c. Clusters 0 and 7 both exhibit high expression of pigmentation genes, while cluster 7 additionally shows strong enrichment for cell division genes. Are cell cycle genes the primary features distinguishing these two clusters? If cell cycle effects are regressed out, would cluster 7 merge with cluster 0, potentially yielding a more continuous cell state trajectory and helping to resolve the pattern noted in point (b)?

      To answer one of Reviewer 1's comments, we have added additional DotPlots to better describe our clusters, in Figure S3. Clusters 0 (limb epidermis), 6 (upper limb epidermis) and 7 (replicating cells) exhibit high expression of pigmentation genes (in particular cluster 6), as depicted in Figure 1D. Cluster 7, consisting of only 30 cells, is the only cluster expressing histone genes and S-phase genes. It is possible that these few cells are cluster-6 cells that are replicating, but considering the very low number of cells involved, we have not explored any further their identity and decided to remove them, as it would only marginally affect the conclusions of our analyses. It is indeed surprising that cluster 6 (upper limb epidermis) is quite distinct in the UMAP space to the other epidermal clusters 0 (limb epidermis) and 4 (upper tube epidermis), and we have not observed similar situations in other scRNA-Seq studies. We speculate that this is due to the joint expression of anthocyanin-related and photosynthesis-related genes, which convey a very strong transcriptomic signature to these cells that distinguish them from other epidermal cells.

      d.Pearson correlation is largely driven by highly expressed genes and may therefore be insensitive to changes in cell identity markers. The conclusions that the less clear separation of mesophyll and epidermal cells in star is due to altered cell identity would be more convincing if supported by independent validation, such as in situ hybridization or reporter analyses, to directly visualize molecular alterations in the relevant cell types when comparing wild-type and mutant tissues.

      Following another reviewer's comments, we have now given less emphasis on the Pearson correlation analysis, that was to some extent subjective. Therefore, our conclusion that mesophyll and epidermal cells in star are less separated than in WT has been removed.

      Minor comments:

      1.For color-coded figure legends (e.g., Fig. 1C), please also include the cluster numbers. This would facilitate interpretation, particularly for readers with reduced color sensitivity.

      We have now used colour-blind-friendly palettes and we have added cluster numbers in Figure 1C.

      2.For figures containing abbreviations (e.g., Fig. 1D, st. / ca.), please explicitly define all abbreviations in the figure legend.

      We have defined all abbreviations in the figure legends.

      3.For all UMAP figures, and for figures involving comparisons across clusters, please use consistent color schemes for the same clusters throughout the manuscript.

      We have modified color schemes across the manuscript for colour-blind-friendly palettes, consistently used throughout the manuscript.

      4.In Fig. S6, the plot showing all genes does not exactly match Fig. 1C, although it appears to represent the same data. Please use the same version of packages, seed values and parameters for all UMAP plots to avoid such discrepancies.

      Figure S6 is the result of integrating with Harmony the WT dataset (as shown in Figure 1C) with WT datasets after removing genes differentially expressed by the protoplasting process. Therefore these UMAPs are a result of integrating different datasets than in Figure 1C, which changes the shape of the UMAPs but cannot be controlled by the seed values, to our knowledge.

      Reviewer #3 (Significance (Required)):

      Overall, this work significantly advances our understanding of late homeotic gene function. It establishes compelling evidence for how developmental context constrains transcription factor activity and offers broadly relevant insights for studies of organ patterning. The combination of genetic mosaics with single-cell and chromatin-level analyses represents a powerful and generalizable strategy that will be of interest to both plant developmental biologists and researchers studying transcriptional regulation.

      My expertise: single cell genomics, chromatin biology and plant development

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: This study addresses a fundamental but underexplored aspect of homeotic gene function: how regulators of cell identity act during late stages of organ development. The authors take advantage of layer-specific mutants of the MADS-box gene PhDEF in Petunia hybrida to dissect the roles of this floral identity regulator in the epidermis and mesophyll. By combining single-cell RNA sequencing with ChIP-seq analyses in wild-type and mutant chimeric petals, the work demonstrates that, although PhDEF is expressed at comparable levels in both petal layers, it binds to and regulates a substantially larger and more layer-enriched set of genes in the epidermis than in the mesophyll. The identification of both layer-specific and shared PhDEF binding sites supports a model in which pre-existing layer identity modulates the regulatory output of homeotic transcription factors.

      Major comments:

      Dissecting PhDEF binding preferences in the epidermis versus the mesophyll using the star and wico mutants is a clever and powerful approach. However, conclusions involving cell identity should be drawn with caution for two reasons. First, cell identities appear to be altered in the mutants: scRNA-seq data suggest that even cells assigned to the same cluster can be molecularly distinct across genotypes. For example, the transcriptomes of wild-type epidermis and mesophyll are highly similar (Pearson correlation R = 0.94), yet both show lower correlation with epidermal cells from star or wico petals. These results raise questions such as are the cells identified as epidermal cells really strictly epidermal in the mutants? Do you need to take into consideration cell composition of the mutants when you do differential expression analysis?Second, PhDEF is expressed in both epidermal and mesophyll clusters in all genotypes, albeit at different levels and in fewer cells in the mutants. As a result, the ChIP-seq profiles should be interpreted as cell type-enriched rather than cell type-specific.And there are a few things that need clarification:

      a. Protoplasting and tissue dissection analyses suggest that mesophyll cells constitute more than 80% of the cells in wild-type petals, whereas the scRNA-seq data indicate a substantially lower proportion. Could this discrepancy reflect technical biases in cell recovery or capture efficiency, or issues related to cell identity assignment during clustering and annotation? Notably, the scRNA-seq data from star and wico petals show mesophyll proportions close to 80%. Is this difference due to an increased abundance of mesophyll cells in the mutants, or could it instead reflect differences in transcriptomic separability? In wild-type petals, the epidermal and mesophyll transcriptomes are highly correlated and express similar numbers of genes, with epidermal cells distinguished mainly by higher expression of a subset of genes. This raises the possibility that mesophyll cells in the wild type occupy a more plastic or less differentiated transcriptional state and may therefore be misclassified as epidermal cells, whereas disruption of regulatory mechanisms in the mutants enhances transcriptional divergence and alters cell clustering outcomes.

      b. Cluster 0 appears to show internal heterogeneity, as the expression patterns of KCS3 and LLE2 are largely mutually exclusive. Do these patterns reflect the presence of distinct epidermal cell types within the limb that are currently grouped into a single cluster?

      c. Clusters 0 and 7 both exhibit high expression of pigmentation genes, while cluster 7 additionally shows strong enrichment for cell division genes. Are cell cycle genes the primary features distinguishing these two clusters? If cell cycle effects are regressed out, would cluster 7 merge with cluster 0, potentially yielding a more continuous cell state trajectory and helping to resolve the pattern noted in point (b)?

      d.Pearson correlation is largely driven by highly expressed genes and may therefore be insensitive to changes in cell identity markers. The conclusions that the less clear separation of mesophyll and epidermal cells in star is due to altered cell identity would be more convincing if supported by independent validation, such as in situ hybridization or reporter analyses, to directly visualize molecular alterations in the relevant cell types when comparing wild-type and mutant tissues.

      Minor comments:

      1.For color-coded figure legends (e.g., Fig. 1C), please also include the cluster numbers. This would facilitate interpretation, particularly for readers with reduced color sensitivity. 2.For figures containing abbreviations (e.g., Fig. 1D, st. / ca.), please explicitly define all abbreviations in the figure legend. 3.For all UMAP figures, and for figures involving comparisons across clusters, please use consistent color schemes for the same clusters throughout the manuscript. 4.In Fig. S6, the plot showing all genes does not exactly match Fig. 1C, although it appears to represent the same data. Please use the same version of packages, seed values and parameters for all UMAP plots to avoid such discrepancies.

      Significance

      Overall, this work significantly advances our understanding of late homeotic gene function. It establishes compelling evidence for how developmental context constrains transcription factor activity and offers broadly relevant insights for studies of organ patterning. The combination of genetic mosaics with single-cell and chromatin-level analyses represents a powerful and generalizable strategy that will be of interest to both plant developmental biologists and researchers studying transcriptional regulation.

      My expertise: single cell genomics, chromatin biology and plant development

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      This study from Cavallini-Speisser et al. cleverly leverages a tissue layer-specific mutant, single cell and bulk RNA-sequencing, and ChIP-sequencing to decipher tissue layer-specific regulation of petal development in petunia by the PhDEF transcription factor. The authors find common and unique targets of PhDEF between the epidermis and mesophyll and conclude that the activity of transcription factors like PhDEF are influenced by the pre-existing environment in the cell they are expressed in. Understanding when and how a given transcription factor drives expression of unique target genes in various contexts is an important aspect of developmental biology that can be elusive outside of highly tractable model systems. As such, I think this study is of high value and has strong potential to expand our understanding of how developmental specificity is mediated by commonly employed transcriptional regulators. However, I think there are some issues with possible over-interpretation and some places where documentation of experimental design and data quality control are lacking. I elaborate on these concerns below.

      Major Comments:

      Line 155: Assigning mesophyll clusters by default without any positive marker genes strikes me as problematic, especially as much of the analysis rests on comparing the transcriptomes of epidermis and mesophyll cells. Can the authors perhaps leverage published scRNA-seq datasets to find potential mesophyll markers, even homologs from other species, to improve confidence in the cluster assignment?

      Line 228: Through the description and interpretation of the ChIP-seq dataset, the authors use the fact that peaks are more abundant and bigger in the epidermis to conclude that binding of PhDEF is "stronger" in the epidermis. This implies a difference in physical interaction between the TF and the DNA that I don't think can be concluded from the data presented. This could be confounded by biology; if expression of PhDEF is more heterogeneous in the mesophyll than in the epidermis, the peaks from that tissue will be averaged out and appear smaller when in fact the binding is the same strength. This could also be a technical artifact if the ChIP was less efficient in one sample versus another. This conclusion requires reinterpretation. The authors have the power to address this at least partially with the scRNA-seq by measuring PhDEF heterogeneity. I believe assessing ChIP efficiency would have required a spike in control, but perhaps there is a computational way to address this. It is important to discuss these confounding factors in the text.

      Line 376: The authors risk overinterpreting a lack of differential gene expression detection in their analysis of PhDEF binding profiles. This can be affected by how deeply a library was sequenced or how many cells were analyzed per cell type. A gene might not be found to be DE if low depth or few cells resulted in noise or dropout. Lack of detection does not mean lack of differential regulation so the biological relevance of this portion of the analysis should be interpreted with caution.

      Line 476: The authors state there is a mismatch in developmental timing between the RNAseq and ChIP datasets. Why is this? This is mentioned briefly in the Discussion, but has the potential to be majorly confounding to the joint interpretation of the ChIP and RNAseq datasets. This experimental design choice should be justified more thoroughly and a consideration of the limitations it brings to data interpretation should be more prominent in the text.

      Minor Comments:

      Line 229: The authors compare correlations between pseudo-bulked transcriptomes and argue that in the star mutant the epidermis adopts a mesophyll-like identity. The correlation between epidermis and mesophyll in star is 0.97 and the correlations were 0.94 and 0.93 in the other genotypes tested. What is the meaningful cutoff for saying the transcriptomes are similar or not? Is 0.97 so much higher than 0.94 that this conclusion is supported?

      Line 264: What are the "manually chosen thresholds for differential expression"? Can the authors explain and justify this? There is very little detail on this in the materials and methods and this raises some concerns regarding how a threshold was chosen.

      Figure 3G: Could this plot be annotated with the classification of peak layer specificity? It is a little difficult for me as the reader to keep up with all the categories in the text, and showing them in the figure might make that easier to follow.

      Figure 4A: A comparison of only two cell categories should not use scaled expression, as this can over-emphasize small differences in gene expression. Can this be replaced with a dot plot that uses unscaled expression values?

      Figure 4C: Could this be represented more legibly with stacked, space-filled bar charts? As is, this is difficult to read, and might be impossible for someone who is color blind. In addition, the authors claim this analysis shows similar proportions across all categories, but I wonder if that would hold true if they performed an over-representation analysis normalized to the categories shown in "all genes expressed". This could allow them to statistically test whether there are real differences in representation among the categories.

      Supplemental Figure 2: It's great that the authors include these metrics, but it would be ideal to also include the plots of standard QC metrics for scRNA-seq such as those found here to give a better sense of per cell quality: https://satijalab.org/seurat/articles/pbmc3k_tutorial In addition, it is important to include QC metrics for ChIPseq, which I did not find in the supplement. Metrics such as FRiP are important for interpreting ChIP library quality.

      Line 654: What model was used for DESeq2?

      Line 752: Can the authors justify why peaks were called separately on input and ChIP samples rather than allowing MACS2 to call peaks in the ChIP sample over input background? That differs from the standard MACS2 pipeline and no explanation for this is provided in the text.

      Significance

      General Assessment:

      Strengths: The authors employ a unique and powerful mutant system to explore a fundamental developmental biology question. In addition, the datasets generated will likely be useful to other researchers working in petunia or flower development.

      Limitations: While the mutant system employed here is a creative way to get at tissue-layer specific transcription factor activity, the ChIP samples still include heterogeneous cell types, which may impact the findings presented here.

      Advance: This study uses a unique system to test the function of a transcription factor in distinct cell types. As stated above, understanding when and how a given transcription factor drives expression of unique target genes in various contexts is an important aspect of developmental biology.

      Audience: I believe this work will be of primary interest to the plant development, single cell, and chromatin biology communities. These are specialized, basic research communities.

      Reviewer Expertise: I am a plant developmental biologist who works with multiple modes of cell-type-specific NGS datasets including bulk and single cell RNAseq and ChIPseq among others.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The authors previously generated two cell-layer-specific mutants of petunia for the petal identity gene PhDEF. In this study, they profiled differential gene expression in those mutants through single-cell RNA sequencing (scRNA-seq). They found that more genes are highly and specifically expressed in the epidermal cell layer than in mesophyll cells. In addition, they identified cell-layer-specific and -aspecific PhDEF target genes. Using the extensive single-cell transcriptome and layer-specific target identification, the authors concluded that different cell identities affect homeotic regulator PhDEF, thereby influencing transcriptional regulation.

      Major comments

      This presented work provides comprehensive evidence, that pre-existing cell layer identity (epidermis and mesophyll) modulate transcriptional output of homeotic transcription factor, PhDEF. However, a disconnection between PhDEF bindings to genome and transcriptional output undermines the robustness of their conclusion although some binding loci were shown to be correlated with DEG. This may indicate the chromatin state, the existence of interacting partners, and the non-productive binding of PhDEF, suggesting that PhDEF binding alone is not sufficient to predict transcriptional outcomes and additional regulatory mechanisms that shape gene expression in addition to the layer-specific regulatory mechanisms. This disconnection may also be due to the developmental timing. Indeed, it appears authors used different flower stages for ChIP-seq and scRNA-sequencing. In fully differentiated organs, PhDEF binding itself may be no longer transcriptionally productive, and differential gene expression results primarily from the pre-established cell identity rather than directly from the homeotic regulation of PhDEF. Therefore, the main question the authors asked-how homeotic identity works with cell-layer identity and how the homeotic gene, PhDEF, acts in mature organs-was not clearly explained by this study. In Figure 2, the use of the term "target" is potentially misleading. It sounds like direct target genes (direct binding and differential expression) for PhDEF, but it refers only to DEGs. Lines 496-497: When the authors state, "~ demonstrates for the first time that the regulatory function of homeotic factor is influenced by cell layer identity," it sounds overstated, as prior studies have shown that pre-existing tissue or cell identity can shape transcriptional activity and developmental output.

      Minor comments

      In the UMAP presentation, as depicted in Figures 2C, S3, and S5, the cells with zero expression can be colored in light gray (or an inverted color scheme). The purple hue masks the gene expressions of other cells, making it difficult to see the yellow or light green colored cells.

      Significance

      General assessment

      This study is well-designed and technically sound. They utilize single-cell transcriptomics and ChIP-seq by using genetically well-defined genetic materials and layer-specific PhDEF deletion mutants. The analysis showed where PhDEF binds to genomic loci and which genes are differentially expressed in petal epidermis and mesophyll, providing evidence of cell-layer-specific function of homeotic gene in mature organs. Although certain mechanistic aspects were not elucidated, the data from the extensive genome-wide study contributed to drawing their conclusions.

      Advances

      This research goes beyond classical models of floral organ identity by showing that homeotic gene function is not uniform in the same floral organ. It represents a conceptual advance in our understanding by integrating cell layer identity into the framework of homeotic gene regulation.

      Audience

      This study will be of broad interest to scientists who study transcription networks, cell and organ identity in the context of plant development.

      My field of expertise:

      Transcriptional regulation by transcription factor, epigenetic regulation of gene expression, plant development

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Animals ability to escape from threat is a crucial survival behaviour exhibited across the animal kingdom. In vertebrates, hard-wired circuits allow animals to escape from predators without the need for learning. One of the main vertebrate brain regions that controls escape from threat is the brainstem dorsal periaqueductal gray. Research in the last decades has shown that dorsal PAG glutamatergic neurons control the initiation of escape (from imminent threat) and escape vigour, whereas GABAergic neurons are spontaneously and tonically active and have been shown to provide an inhibitory threshold for eliciting escape and further signal escape termination (for reviews on this topic see for example: Gross and Canteras, 2012; Silva, Gross and Graeff, 2016; Motta, Carobrez and Canteras, 2017; Franklin, 2019; Silva and McNaughton, 2019; Lefler and Branco, 2020; Stempel, 2024). In addition to their role in the escape action, the dPAG has been suggested to have a broader function in threat processing. Specifically, a subset of neurons in the dorsal PAG has been shown have activity correlated to the approach/distance to a threat zone or predator. These neurons have been called 'risk assessment' neurons (Deng et al. 2016; Masferrer et al. 2020; Reis et al. 2021). Whether risk assessment neurons integrate threats across different modalities and contexts (here: social vs different predatory threats) is currently not known.

      The present study addresses two important and long-standing questions in the field: what is the cell-type identity of dorsal PAG neurons encoding threat assessment versus escape?, and are different classes of threat processed by shared or dedicated neuronal populations? The study design is careful and methodologically performed well. While some of the experiments have been published in the past, the comparison of social and predator threat responses across contexts and a description of assessment+ cells across both main excitatory and inhibitory PAG neuron types is interesting.

      Briefly, using miniaturized fluorescence microscopy (miniscope calcium imaging) in Vglut2::Cre and Vgat::Cre mice during a live predator (rat) exposure paradigm, the authors characterize neuronal activity in identified glutamatergic and GABAergic dPAG neurons across approach-escape cycles. The central finding is that both excitatory and inhibitory populations contain Assessment+ neurons (active during approach, silent at escape onset) and Escape+ neurons (suppressed during approach, activated at escape onset) and the authors propose that the activity profiles of Assessment+ and Escape+ cells may reflect local circuit wiring rules with putative GABAergic inhibition between excitatory neuronal subsets. Consistent with this, optogenetic activation of GABAergic dPAG neurons suppressed risk assessment behavior and promoted exploratory rearing but did not affect 'baseline' locomotion, building on Tsang et al. 2023 and Stempel et al. 2024. Population-level decoding using CEBRA (Schneider et al., 2023) confirmed that both Vglut2+ and Vgat+ ensembles independently encode behavioral state with high accuracy.

      A second finding of this paper concerns threat generalization. Sequential exposure to a predator, an aggressive conspecific, and a prey insect (cockroach; paradigm from Rossier et al., 2021) revealed that more than half of responsive excitatory neurons and nearly half of responsive inhibitory neurons were activated by two or three threat types. This substantial overlap argues for at least partially convergent, rather than parallel, encoding of threat in dPAG, consistent with its role as a general trigger for defensive avoidance (Silva et al., 2013), and contrasts with the anatomically segregated upstream processing of predator and social threats in the medial hypothalamic defensive network. Taken together, this study suggests the dPAG as a site of coordinated excitatory-inhibitory computation in the control of innate threat assessment and avoidance across biologically diverse threat contexts.

      Below are comments related to the figures/ data analysis and generally to the discussion part which we recommend should be expanded/changed to put the findings of the authors more in context of published literature and to discuss in more detail the proposed circuit mechanisms that align with the author's findings. Generally, this is a very nice and large dataset that could benefit from a more fine-grained and in-depth analysis of escape+ and risk assessment+ cells and their precise temporal activity profiles. We do not suggest to perform further experiments and think the work in this manuscript is publishable as is with some additional analyses and changes to the text.

      Related Figure 1 and calcium imaging methods.

        • The classification of neurons as 'Assessment+' and 'Escape+' positive is unclear and should be formally described in the methods, presumably these are the neurons with significantly increased "positive" or absolute slope? __Author’s response: __We apologize that the classification was not sufficiently clear. We will add a more detailed description of the criteria used to classify both neuron classes in the methods section. Escape+ neurons were those with a significant increase of activity, whereas Assessment+ neurons had a significant decrease in activity (negative slope). Absolute maximum slope values were used for comparison to previously categorized Assessment+ and Escape*+ neurons in Figure 2M.
      1. Can the authors clarify what they mean by escape? In the methods under "manual behavioral annotation", "Escape behavior" is defined as including retractions, retreats and flight. However, under "unsupervised behavioral annotation", escape is defined as "high-velocity locomotion aiming at increasing distance between threat source and subject". Are Escape+ neurons ones that are significantly modulated (presumably positively) at flight (latter definition), or throughout retractions, retreats and flight (former definition)? This also relates to the plots for 'escape+' neurons, where it would be useful to separately plot 'successful flights to shelter' to make the results comparable to previous studies and where trajectories are at least relatively stereotyped.*

      __Author’s response: __We apologize for any confusion raised by the duplicated definition. We have time locked neural activity to ‘escape’ behaviors that we defined to include: retraction, retreat or flight. However, we agree with the reviewer that this includes a wide range of escape quality and that a finer analysis may be helpful. To assess any potential differences in neural encoding related to this variation we will include an analysis in the revision that separates high-intensity from low-intensity escapes.

      • More example traces of different FOVs aligned to escape-to-shelter onset and to risk assessment onset would be useful, as well as a plot with the % of escape-active neurons and a reliability index (in how many trials is each neuron active?). Similarly, are the proportions of escape+ and assessment+ neurons similar across animals and FOVs recorded?*

      __Author’s response: __We thank the reviewer for these excellent suggestions and will add additional example traces and nimiscope FOVs, together with a table with the proportions of each neuron class per mouse and reliability indices for all neurons.

      • Some further basic analyses of the neurons' responses would be helpful to gage their activity profiles. Do these correspond to previously published descriptions of the two classes of escape+ neurons? Are they active during baseline locomotion? Do you observe the same clusters of GABAergic neurons that have been previously described where some dip at escape onset and some ramp up towards escape termination? Please add these plots as a supplement.*

      __Author’s response: __We appreciate the reviewer's suggestions and request to link our findings better to published work. We will provide plots showing the correlations between neural activity and baseline locomotion, speed, distance to the threat, and escape termination. We expect that aligning neural activity to escape termination will allow us to observe the two subclasses of GABAergic neurons described by Stempel et al. (2024): neurons that gradually increase their activity, peaking at escape termination, and neurons that gradually decrease their activity, reaching a trough at escape onset.

      • It is generally assumed that assessment+ cells 'map'/correlate to the distance to a threat zone. Plots with quantification of this correlation would be useful, and whether they are also speed modulated or not? If the animal stops on the way to the threat zone, does the risk assessment signal plateau for example?*

      Author’s response: We will provide supplementary plots for correlations of neural activity with baseline locomotion, speed, and distance to threat. We will also examine the data for cases in which the approach was interrupted, as suggested by the reviewer.

      • Both in Figures 1 and 2, both the single trial examples of individual neurons and averages across neurons in the Vgat+ and Vglut2+ recordings show very fast changes that seem to be shorter than Gcamp6s kinetics would allow, and that happen exactly at escape onset when there is presumable a fast head turn movement. What motion correction controls do the authors have in place to make sure that some of the fast changes they see are not motion artefacts? (e.g., see Figure 2, panel C bottom.) (Importantly, see also comment 8 below).*

      __Author’s response: __We acknowledge the reviewer’s concern about motion artefacts. Several precautions were taken to minimize such artifacts. During experiments and prior to recording we carefully tapped the miniscope attached to the baseplate and looked at the image to make sure no obvious image movement occurred due to gross mechanical instability. Within the miniscope image analysis we applied five rounds of NoRMCorre motion correction implemented within CaImAn and confirmed the stability of the ROIs by manual scanning of all videos. While subtle motion artifacts could persist in our data despite these precautions, we believe that artifactual variations of GCaMP signal at escape onset (head turn followed by escape) are unlikely because similar changes in GCaMP signal are seen at the onset of risk assessment when sudden head movement occured. Nevertheless, to better address this potential confound we will: 1) manually annotate instances of head turning events outside of escapes and examine their neural correlations, and 2) provide frame-by-frame FOV images from examples of Assessment+ and Escape+ cells across head turns.

      • Related to this, the overall escape velocity is extremely low (around 10cm/s). When the authors only analyze high speed escapes (>50cm/s), do they see different cell activity profiles emerge that they might miss with these very low speed escapes that presumably activate less neurons that high-speed escapes? While slow-speed escapes still elicit activity in both Vgat+ and Vglut2+ neurons, their calcium activity changes will be much lower, potentially hindering a more detailed analysis, as clear signals might be sparser for escape+ neurons.*

      Author’s response: As discussed above, we used a relatively broad definition of escape so as to increase the number of trials and strengthen the power of our statistical analysis. However, to test this possibility more explicitly we will split our trials by high and low-intensity escapes and check for such correlations.

      • With the min/max normalization that has been applied across the entire session it is hard to see 'local changes' in the heatmaps. Given that glutamatergic neurons are thought to only sparsely fire outside of escape episodes, the heatmaps are hard to read with the 'min/max' Z-scoring, and we would strongly encourage the authors to plot 'locally' Z-scored traces without a min/max normalization for each cell (at least for some examples). Importantly we would suggest to change the color scheme for the heatmaps to allow visual identification of the baseline / 'F0'.*

      __Author’s response: __We agree that this could be a useful alternative way to visualize the data and will plot locally Z-scored traces and shift to standard colormaps that allow for easier visual identification of the baseline as suggested by the reviewer.

      • The calculation of the slope estimated on the calcium signal is somewhat unclear in the methods section. The way it is described right now, it is not clear whether the window is centered at the behavioral event of interest or not. In the figures, it appears that this is an absolute slope, which is also not clear from the methods. Further, considering the half-decay time of GCamP6f, which is ~ 0.5s (Chen et al., 2013), it seems like the result of this calculation could be cancelation or a slope near-zero if the signal both rises and decays in this broader window. Why is not the peak (e.g. max) or the AUC used for this analysis? The authors should clarify the calculation and provide rationale for their choice? Is the sign (positive/negative) of the slope not of interest?*

      __Author’s response: __We apologize that the description of the slope analysis was not clear. We will revise the Methods to specify the exact time window used – which is centered on the behavioral event – and whether signed or absolute slope values were used. We will also report whether the sign of the slope is used for classification and clarify the rationale for retaining the slope metric which derives from the argument that quantifying the slope around the behavioral event of interest is less sensitive to signal-to-noise ratio variability between cells within the same field of view. We also favored a slope-based, rather than AUC-based assessment because we were looking to identify cells with previously-identified properties (Masferrer et al. 2020). This criterion allowed for increasing the sensitivity of classification even in cases where peak or AUC criteria analyses were not significant.

      • The GRIN lens placement in the example in Figure 1 is in the lateral PAG, whereas most others are located in the dorsolateral PAG. It would be useful to have a sentence in the introduction to state that the authors include the lateral, dorsolateral and dorsomedial PAG as 'dorsal PAG' and a rational for this placement.*

      Author’s response: __We agree that anatomical precision is important given potential functional differences across PAG columns. Based on our histological reconstruction and comparison with the anatomical atlas, we interpret the example shown in __Figure 1 as being located within the dorsolateral PAG rather than the lateral PAG. To make this clearer we will revise the figure labeling and add PAG column boundary overlays. We will also add a statement in the Methods/Results clarifying which PAG subdivisions were included under the term “dorsal PAG” and provide a rationale for this grouping.

      • Could the authors comment on how the proportions of Assessment+ and Escape+ neurons relate to previously published literature (e.g., Deng et al. 2016)?*

      __Author’s response: __We will add a paragraph and table comparing our findings with those from published manuscripts (Deng et al. 2016; Masferrer et al. 2020).

      Related to Figure 2

      • In panel K of Figure 2, both Vgat+ and Vglut2+ assessment+ neurons seem to have a rise at escape onset in addition to the slow rise during their movement towards the threat zone. Also here, the offset kinetics of the signal seem to now correlate well to the slow decay kinetics you would expect for GCamp6f and a quantification of controls and motion correction quality metrics would be very helpful to add. Depending on the baselining the peaks during escape would probably be significant as well. The authors could try and cluster the neurons further to see if they can disentangle further 'sub classes/clusters'.*

      Author’s response: In these time-warped analyses only cells with a significant correlation to escape onset were included. No statistical testing was performed to explore significance at the events highlighted by the reviewer. Nevertheless, we will add data from our clustering analyses as well as motion correction quality metrics and example FOV and ROIs.

      • The calculation of the slope estimated on the calcium signal is somewhat unclear in the methods section. The way it is described right now, it is not clear to me whether the window is centered at the behavioral event of interest or not. In the figures, it appears that this is an absolute slope, which is also not clear from the methods. Further, considering the half-decay of GCamP6f which is ~ 0.5s (Chen et al., 2013) it seems like the result of this calculation could be cancelation or a slope near-zero if the signal both rises and decays in this broader window. Why is not the peak (e.g. max) or the AUC used for this analysis? The authors should clarify the calculation and provide rationale for their choice? Is the sign (positive/negative) of the slope not of interest?*

      __Author’s response: __As discussed above, we chose to use a slope-based analysis because this feature best distinguishes the Assessment+ and Escape+ cell classes. This choice made it possible for us to maximize the identification of such cells even in cases in which AUC or peak analysis were not significant. We felt it was appropriate given that the aim was primarily to maximize the identification of previously well-described cell classes rather than de novo search. We will include a more detailed rationale for this approach in the manuscript.

      • The authors find a significant increase in the accuracy of the classification of the two defensive behaviors (risk assessment versus escape) from models trained on the Vgat+ population calcium activity. This is an interesting finding which should be to the very least discussed in the discussion. Is there more information content in this population? Does this have anything to do with the slower dynamics of the signal? As mentioned above, would clustering these populations further reveal a more fine-grained detail of their population activity, e.g. see previously published work by Stempel et al. who suggest that there are at least two broad clusters of escape-active GABAergic neurons.*

      __Author’s response: __We agree that the increase in classification accuracy is intriguing. While we do not have a precise explanation for this effect, we note that the higher decoding accuracy may reflect the fact that the Vgat+ population collectively contains richer information about defensive behaviors than the Vglut2+ population. However, because our recordings measure calcium activity rather than spiking, we cannot exclude the possibility that differences in signal-to-noise ratio, or other population-level recording characteristics like the number of recorded neurons contribute to this effect. Therefore, we have been careful not to overinterpret the improved decoding performance as necessarily reflecting greater information content. We also agree that further subdivision of the Vgat+ population could reveal additional functional organization. We are happy to incorporate further analysis for clustering (see above) into the manuscript as suggested.

      Related to Figure 3

      • In Figure 3A, the ChR2 seems to be significantly spread throughout the entire PAG and also the superior colliculus. As it is hard to see cell bodies with ChR2 at this magnification, we would recommend to add a supplemental figure with the outlines of the infection sites.*

      __Author’s response: __We acknowledge that the viral spread of these infections is larger than the dPAG, but we are confident that we are recovering spatial specificity with the placement of the optic fibers in the dPAG. We are happy to provide outlines of the infection sites for all animals.

      • The authors state that: "Interestingly, although stimulation of Vgat dPAG neurons significantly reduced the peak speed of escape, it did not change the overall time spent escaping". Could they comment on how this may relate to a previous study where the probability of escape is decreased upon activation of Vgat+ neurons and initiated escapes can be induced (Stempel et al. 2024)? Do they think this could be differences in location of stimulation fiber or one vs two injection sites (bigger vs smaller spread along the AP axis of the PAG?) - or do they think there could be a fundamental difference between fast escape from imminent looming stimuli to slower escapes to rat/prey/social predators?*

      __Author’s response: __The publication mentioned by the reviewer found that Vgat+ activation decreased the probability of escape to a looming stimulus. We believe the discrepancy with our results could arise from differences in the temporal pattern of the stimulation. While Stempel and colleagues stimulated at escape onset, we stimulated for longer and regularly spaced, one minute long light pulses.

      Related to Figure 4.

      • Individual examples of cells responding to one threat vs multiple threats would be very useful to add to see their dynamics across threats.*

      __Author’s response: __Thanks for the suggestion. We will provide full trace examples for the three tests for neurons responding to three, two or one threat.

      • The Venn diagrams in panel I are very hard to read, and color-blind people might struggle with the red/green (also in panel C). We would suggest to change colors, and replace Venn diagrams to make results more interpretable/readable.*

      __Author’s response: __We thank the reviewer for this useful feedback. We will remove the Venn diagrams and stick to the pie charts and make sure that all figure panels are color blind friendly.

      • Have any statistical tests been performed to assess whether there is a difference between the proportions in figure D and H as well as Figure 4J?*

      __Author’s response: __No. We will perform and include the results of these statistical tests.

      General and other comments

      • We strongly encourage the authors to add example videos of the behaviors tested and of all major findings including example calcium activity and optogenetic manipulations.*

      __Author’s response: __We will include videos for approach and escape behaviors for representative mice performing the three tests as well as representative calcium traces. We will also include exemplary behavior videos of experimental and control animals in the optogenetic activation experiments.

      • Relating to a more fine-grained analysis of escape-active cells (as recommended above), the authors state in the discussion that: 'A notable paradox of our findings is that while GABAergic and glutamatergic dPAG neurons showed nearly indistinguishable neural firing correlates of approach and avoidance (both harbored Assessment+ and Escape+ cells) [...]". Since previous works have already looked at escape-active cells in more detail, with differences between Vgat+ and Vglut2+ neurons having been described, we would encourage the authors to look at unsupervised clustering of the escape-active neuron populations, as the firing rates of these neurons should be distinguishable along the escape sequence and especially when taking into account speed correlations and activity profiles aligned to escape onset or offset. If they can't find differences these should be discussed, as major differences may relate to the paradigms used (rats vs visual 'looming' threats).*

      Author’s response: As described above, we will correlate neural activity with speed and cluster the response types with unsupervised methods (e.g. K-Means clustering, hierarchical clustering) in order to uncover further differences between Vglut2+ and Vgat+ neurons. We will discuss the finding in the discussion section in more detail.

      • We would ask the authors to make sure that the cited literature supports their statements. Some statements are in the manuscript are not clearly, only partially correct or the literature itself is inconsistent or citations are confusing/misleading.*

      e.g.: "A functional cellular and circuit architecture of dorsal PAG is emerging in which stimulation of glutamatergic neurons in dorsal PAG promotes freezing at low intensity and flight at high intensity (Tovote et al. 2016, Deng et al. 2016, Evans et al. 2018, Tsang et al. 2023)."

      Not all of the cited papers support that graded stimulation of glutamatergic neurons in the dorsal PAG results in escape/flight or freezing but rather that there is some (dis-)inhibitory interplay between dorsal and ventrolateral PAG which results in the selection of one or the other behaviors, and that the ventrolateral PAG more specifically drives freezing (e.g., Tovote et al. 2016). Some studies have shown that activation of the dorsal PAG and in particular of glutamatergic neurons elicits 'all-or-none' flight behavior. Additionally, studies where freezing has been observed with dorsal PAG stimulation have often used CamKIIa as a promoter (e.g. Deng et al. 2016*), which is expressed at significant levels in both excitatory and inhibitory neurons. Thus, we would argue it is not entirely clear whether freezing through 'low intensity' dPAG stimulation is a biological feature of the dPAG network, especially also as inhibition of the dPAG promotes freezing - probably through disinhibition of the vlPAG.

      * In the results section on page 6, it is stated that Deng et al. 2016 use VGlut2-Cre to target ChR2 for eliciting escape, but they have used CamKIIa ("Previous studies have shown that optogenetic activation of Vglut2+ dPAG neurons elicited flight behavior[...]").

      Similarly, on page 3, the following statement on GABAergic neurons isn't clear: "Stimulation of GABAergic neurons, on the other hand, does not elicit defensive behavior (Tsang et al., 2023) and recent evidence shows that they can inhibit looming stimulus-evoked flight behavior (Stempel et al., 2024) suggesting they might act as part of a tonic inhibitory circuit that receives primarily local inputs (Franklin et al., 2017; but see Wu et al. 2024)."

      Multiple papers across all columns have shown that GABAergic neurons are tonically and spontaneously active (e.g., Chen et al., 2023; Wang et al., 2023; Stempel et al., 2024) and Stempel et al. estimated that GABAergic PAG neurons make up >50% of spontaneous inputs to glutamatergic PAG neurons, thus positioning them well to control their activity. Franklin et al. 2017 (and others) have shown that GABAergic neurons receive input from thalamic and hypothalamic regions (probably also from midbrain regions that were not analyzed in that study), thus arguing that they receive significant input from outside of the PAG to integrate - currently unknown - inputs from other brain regions to presumably guide their activity in a context-specific manner. The statement that they should thus be part of a circuit that receives primarily local inputs is not well supported by previous studies. Also, how this enables a 'push-pull' circuit and what is meant here exactly, could be clarified. It is not clear why the presence of risk assessment neurons would support such a circuit model rather than an integrative/threshold one. Further, Stempel et al. 2024 have also shown that GABAergic neurons ramp up activity during escape and are thus technically 'flight+' cells. We agree that there are interesting and presumably complicated intra-PAG dynamics to be studied where their connectivity will define different circuit model possibilities.

      __Author’s response: __We thank the reviewer for this careful presentation and analysis of the literature and apologize where we may have inaccurately referenced previous work. We will carefully revise citations and make sure all statements are supported adequately by the literature and more explicitly declare the speculative tone where relevant for our conclusions.

      **Referee cross-commenting**

      We generally agree with most comments made by reviewer 2, and their concerns. While the novelty of this manuscript is limited, highlighting the advances better, also through more accurate analyses, should allow the authors to better build up their novel findings ('major concern 1'). We agree with the reviewers 'major concerns' 2-5 and that they should be addressed by the authors, for scientific reasons beyond novelty, as some of the analysis feels incomplete. For example, while we do not think that new experiments are necessary, the authors can discuss the issue of spatiotemporal overlap of variables that do not allow a definitive description of 'risk assessment' cells. This issue is in fact present in most published work on risk assessment cells (usually described with a rat predator that is continuously present). Concerns 7-9 are valid, and touch on similar points raised by our review. We do not think that modelling would add anything substantial to this manuscript that couldn't be hypothesised in the discussion beyond their proposed model. In summary, we agree with the points raised by reviewer too, but find that the data is useful and should be published, after some revising and additional more fine-grained analyses to strengthen the manuscript's claims.

      Reviewer #1 (Significance (Required)):

      Overall, even though the overall novelty is somewhat limited considering the paper shows significant overlap with recent findings on the involvement of glutamatergic and GABAergic dorsal PAG neurons in risk assessment and escape, there are some important and novel findings that advance the knowledge in the field of neuronal circuits underlying defensive behaviors. For example, the direct comparison of how different threat contexts modulate the activity of individual neurons and the finding that both GABAergic and glutamatergic population activity can predict defensive behavioral output are interesting and important findings. While this study may of limited interest to a very broad audience, it is of importance to the fields of neuroethology and the study of the neural circuits underlying innate defensive behaviors.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This manuscript examines how excitatory and inhibitory neurons in the dorsal periaqueductal gray (dPAG) encode defensive behavior during exposure to natural threats. The question is important, and the experiments are technically ambitious. In particular, the attempt to compare Vglut2+ and Vgat+ populations across predator, social, and prey-related contexts is potentially valuable. However, in its current form, I do not think the manuscript yet establishes a sufficiently strong conceptual advance over the existing literature, and several of the main interpretations appear stronger than the data support.

      *

      Major Concerns:*

      1)One main concern is novelty. The broader claim that dPAG contains neuronal populations related to threat assessment, approach/avoidance, and escape is not new. Prior work has already shown that PAG neurons differentiate distinct components of defensive behavior during predator exposure, including assessment-like and flight-related activity patterns, and that dPAG ensembles encode approach versus avoidance states across. In parallel, earlier circuit studies had already established a central role for excitatory dPAG neurons in driving escape and freezing, and for local GABAergic dPAG neurons in modulating instinctive escape. The study would benefit for better clarification for each of the main findi.ngs on where the novel contribution to the literature sits.

      __Author’s response: __We agree with the reviewers that the novelty provided by our work is incremental and that our findings in some aspects overlap with those of previous studies that described Assessment+ and Escape+ cells in dPAG during approach and avoidance of a rat or that performed GCaMP miniscope recordings in glutamatergic and GABAergic neurons in dPAG under conditions of looming stimulus escape. However, we are pleased to see that the reviewers also acknowledge the novelty of our data and its usefulness for researchers in the field of innate defensive behavior. We will restructure the introduction and discussion sections to highlight the unique contributions of our work and better frame our findings with respect to the existing literature (see also our rebuttal introduction).

      2)A second major issue concerns anatomical specificity. From the histology, it is not fully clear that all lens, fibers and injections are confined to the same functional PAG columns. This matters because the manuscript interprets the results at the level of "dorsal PAG," yet functional and input differences across PAG columns are well established in the literature (including dm/dl vs l), with a more prominent involvement of dm/dl in escape in respect to l. I therefore suggest the authors to repeat the key analyses using only neurons, injections and fiber placements clearly restricted dm/dl dPAG, and see if this generalises to l.

      Author’s response: __We agree that anatomical precision is important given potential functional differences across PAG columns. Based on our histological reconstruction and comparison with the anatomical atlas, we interpret the example shown in __Figure 1 as being located within the dorsolateral PAG (dlPAG) rather than the lateral PAG (lPAG). To make this clearer we will revise the figure labeling and add PAG column boundary overlays as suggested by the reviewers. We will also repeat the core analyses using only cells from the dlPAG and incorporate those findings into the manuscript as relevant. We will also add a statement in the Methods/Results section clarifying which PAG subdivisions were included under the term “dorsal PAG” and provide a rationale for this grouping.

      3)A third concern is the interpretation of "risk assessment" neurons recorded in this work. While this neurons have been previously reported in similar conditions, here the predator is continuously present and the behavioral space is highly constrained. Under those conditions, neuronal activity classified as related to risk assessment could in principle reflect a combination of position in the corridor, heading direction, distance from the safe chamber, distance to the threat compartment, body elongation, or locomotor state, etc rather than threat assessment per se. I do not think the current analyses are sufficient to separate these possibilities. To support the central interpretive claim, the manuscript would benefit from additional control analyses accounting for positional and kinematic variables, and additional experiments including control conditions that dissociates threat presence from spatial configuration.

      __Author’s response: __We agree with the reviewer that at present it is not clear which particular aspect of the approach behavior, if any, is best correlated with Assessment+ cell activity. In order to better understand the activity patterns of these cells, we will correlate their activity also to speed, distance to threat, and baseline locomotion.

      4)Relatedly, I found the treatment of behavioural annotation too qualitative for the strength of the neural claims. Behaviours such as risk assessment, retraction, retreat, and flight are described verbally, but the manuscript would be much more reproducible if the authors provided explicit operational criteria, quantitative thresholds, and inter-rater reliability. At present, the reader is asked to accept a fairly subjective labelling scheme, yet many of the neural conclusions depend directly on those labels. Critically no major differences have been observed between cell type responses, hinting that perhaps more rigorous behavioural quantification may be required.

      Author’s response: __For consistency across datasets our miniscope data were manually annotated by a single expert scorer and we did not use precise quantitative thresholds for each behavior nor quantify inter-rater reliability. However, in our optogenetic activation dataset we complemented our manually annotated data with automatically extracted measures as well as unsupervised behavior categorization with Keypoint MoSeq, with a satisfactory overlap for behaviors (see __Figure 3F). We agree that there are differences in the pattern of correlations of Vglut2+ and Vgat+ neurons across behaviors. Unfortunately, despite major efforts on our part to test these differences – including looking at variations in behavioral vigor and quality across the datasets – we were unable to do so in a statistically reliable fashion. Thus, we do not think that this failure depended on a lack of consideration of the quality of behaviors involved. Instead, we conclude that we lacked the statistical power to see what may be subtle differences between these cell types.

      5)The statistical framework used to define neuronal response classes also needs clarification. Responsive cells are identified relative to shuffled null distributions, but it is not clear that the analysis adequately controls for multiple comparison: multiple testing across neurons, behavioural epochs, and response classes.

      __Author’s response: __We thank the reviewer for raising this important point and agree that multiple-comparison control was insufficiently described. We will implement a permutation-based maximum-statistic correction across behavioral epochs within each neuron, thereby controlling the family-wise error rate across the behavioral comparisons used to define responsivity. The resulting corrected permutation p-values will subsequently be controlled across neurons using the Benjamini–Hochberg FDR procedure (q = 0.05), separately for each cell type and experimental condition. We will also clarify that Assessment+ and Escape+ categories are assigned after significance testing based on the pattern and direction of behavioral modulation and therefore do not constitute additional independent statistical tests. We will update the Methods and corresponding analyses and figures accordingly and report whether these corrections affect the main conclusions.

      6)I also think the interpretation of the CEBRA analyses is too strong. These analyses show that the recorded populations contain information sufficient to decode the annotated behaviors, but they do not demonstrate that the population encodes an abstract "behavioral state" rather than a mixture of posture, speed, position, and other correlated sensorimotor variables. Because the labelled behaviours are themselves associated with distinct kinematic structure, decoding alone is not enough to support the stronger conceptual claim. That interpretation would require nuisance-controlled analyses or matched comparisons showing that decoding persists beyond simple sensorimotor differences.

      __Author’s response: __We agree that the CEBRA analysis is difficult to interpret and have been cautious in extracting actionable conclusions from this data analysis tool. Nevertheless, give the widespread interest in such advanced dimensionality reduction tools, we think it is a useful addition to the manuscript and will reframe our interpretation of the CEBRA results to adhere more closely to a strict statement of the observed correlations.

      7)The optogenetic results in Vgat+ mice should also be interpreted more cautiously. I was not convinced by the conclusion that stimulation reduces risk-assessment behavior. Increased rearing does not straightforwardly imply reduced assessment, as in some contexts rearing itself can be part of assessment-related behavior. More generally, if stimulation alters locomotor structure, this could secondarily change the frequency of other scored behaviors and syllables without demonstrating that the manipulated neurons specifically control risk assessment. For example, if locomotion speed is reduced by the manipulation, then the mouse may engage in other behaviour that do not require locomotion such as rearing or grooming. I therefore think the claims in this section should be toned down and are not easily interpretable with the data provided.

      __Author’s response: __We agree with the reviewer’s statement on the caveats of the optogenetic experiments and wholeheartedly appreciate the difficulty in distinguishing direct and indirect consequences of such manipulations. We will revise the Results and Discussion to be more cautious in our interpretations that prolonged activation of Vgat+ dPAG neurons altered the behavioral structure during predator exposure, reducing risk assessment behavior while increasing rearing/exploration and pointing out the difficulties inherent in interpreting the overall impact of such an artificial manipulation.

      8)The functional interpretation of the inhibitory population is not sufficiently novel relative to prior work. Previous studies have already shown that GABAergic neurons in dPAG modulate instinctive escape behavior, so the present result that inhibitory neurons affect escape-related responding is, on its own, not a major conceptual advance. What would make the current study more compelling is clear evidence that these neurons specifically encode or regulate risk assessment. At present, however, that conclusion remains uncertain. Because the predator is continuously present in the assay, the timing of threat delivery is not well defined by construction, making it difficult to separate neural activity linked to threat assessment from activity linked to the suppression, gating, or delayed initiation of escape during approach. In other words, the observed slowing during "risk assessment" could reflect inhibition of escape-related motor output rather than a distinct effect of assessment itself.

      __Author’s response: __We agree with the reviewer that our findings on manipulating Vgat+ neurons overlap in part with prior work. We started this work before those publications appeared and hope that the overlapping findings can, nevertheless, be a useful confirmation for researchers in the field. We completely agree with the reviewer on the difficulty inherent in interpreting neural activity correlations with the types of self-paced behaviors we are interested in here and the alternative explanations provided are eminently possible and even probable. Our data do not offer the possibility to distinguish these cases, and we apologize if our discussion appeared to draw definitive conclusions on a role of dPAG neurons in controlling risk assessment behaviors per se. We will take a more cautious approach in our discussion to argue for a role in controlling behavior during approach to threat and that this may be mediated by changes in risk assessment or other pre-escape behaviors.

      9) The author states: "Unfortunately, we were not able to identify testing conditions under which the two cell types differed statistically, leaving open the question of whether GABAergic and glutamatergic neurons in dPAG reliably encode different aspects of defensive behavior. Our assessment of ensemble encoding of behavior also failed to shed light on cell-type specific differences in encoding, with both cell-types showing significant predictive correlations of approach and avoidance behaviors." This statement is surprising and substantially limits the conceptual advance, because the main distinction the study sets out to test is ultimately not resolved. In my view, one likely reason is the experimental design itself. Because the predator is continuously present, threat presentation, exploration, risk assessment, sensory sampling, escape decision, and escape onset are all temporally entangled. Under these conditions, it becomes very difficult to isolate which component of the defensive sequence is actually being encoded, and this may reduce the ability to detect meaningful differences between excitatory and inhibitory populations. This is especially important given that prior work has already shown that dPAG populations vglut and vgat population encode and modulate differently defensive behaviours such as escape. In that context, the present study would need a cleaner behavioral design to demonstrate a distinct contribution of cell type, rather than a mixed representation of overlapping sensory, motor, and defensive variables.

      __Author’s response: __We thank the reviewer for raising the issue of the surprising lack of differences in encoding between excitatory and inhibitory neurons as the findings were equally unexpected for us. We will conduct the proposed analyses related to trial splitting according to speed and other kinematics, together with unsupervised clustering. In the eventuality of being unable to find them, we will add a paragraph in the discussion about the limitations of our experimental design.

      • 10)Finally, the proposed model in which local GABAergic inhibition organizes Assessment+ and Escape+ excitatory populations is interesting, but at present it remains speculative. The data may be consistent with this idea, but they do not directly test it. Since computational modelling of this framework would not be too complex, I would suggest additional analyses and network modelling to substantiate this claim, for example by showing that a network model under these constraints would recapitulate the neural responses observed in vivo.*

      Author’s response: We agree that the proposed local inhibitory circuit model is speculative. To make this more clear to the reader we will revise the manuscript to present the model as purely hypothetical. Because our current experiments do not measure synaptic connectivity or selectively manipulate Assessment+ versus Escape+ subpopulations we do not think that a network model alone would provide definitive mechanistic evidence. We will therefore tone down the circuit interpretation and clearly state which aspects are supported by the present data and which require future experiments, such as cell-type- and projection-specific recordings, connectivity mapping, or targeted manipulation of functionally defined neuronal subpopulations.

      Overall, I think the manuscript contains useful data, but in its present form its novel contribution is unclear. Additional experiments and analyses may be needed to strengthen its central claims, as described above.

      *

      Reviewer #2 (Significance Required):*

      This manuscript examines how excitatory and inhibitory neurons in the dorsal periaqueductal gray (dPAG) encode defensive behavior during exposure to natural threats. The question is important, and the experiments are technically ambitious. In particular, the attempt to compare Vglut2+ and Vgat+ populations across predator, social, and prey-related contexts is potentially valuable. However, in its current form, I do not think the manuscript yet establishes a sufficiently strong conceptual advance over the existing literature, and several of the main interpretations appear stronger than the data support.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript examines how excitatory and inhibitory neurons in the dorsal periaqueductal gray (dPAG) encode defensive behavior during exposure to natural threats. The question is important, and the experiments are technically ambitious. In particular, the attempt to compare Vglut2+ and Vgat+ populations across predator, social, and prey-related contexts is potentially valuable. However, in its current form, I do not think the manuscript yet establishes a sufficiently strong conceptual advance over the existing literature, and several of the main interpretations appear stronger than the data support.

      Major Concerns:

      1)One main concern is novelty. The broader claim that dPAG contains neuronal populations related to threat assessment, approach/avoidance, and escape is not new. Prior work has already shown that PAG neurons differentiate distinct components of defensive behavior during predator exposure, including assessment-like and flight-related activity patterns, and that dPAG ensembles encode approach versus avoidance states across. In parallel, earlier circuit studies had already established a central role for excitatory dPAG neurons in driving escape and freezing, and for local GABAergic dPAG neurons in modulating instinctive escape. The study would benefit for better clarification for each of the main findings on where the novel contribution to the literature sits.

      2)A second major issue concerns anatomical specificity. From the histology, it is not fully clear that all lens, fibers and injections are confined to the same functional PAG columns. This matters because the manuscript interprets the results at the level of "dorsal PAG," yet functional and input differences across PAG columns are well established in the literature (including dm/dl vs l), with a more prominent involvement of dm/dl in escape in respect to l. I therefore suggest the authors to repeat the key analyses using only neurons, injections and fiber placements clearly restricted dm/dl dPAG, and see if this generalises to l.

      3)A third concern is the interpretation of "risk assessment" neurons recorded in this work. While this neurons have been previously reported in similar conditions, here the predator is continuously present and the behavioral space is highly constrained. Under those conditions, neuronal activity classified as related to risk assessment could in principle reflect a combination of position in the corridor, heading direction, distance from the safe chamber, distance to the threat compartment, body elongation, or locomotor state, etc rather than threat assessment per se. I do not think the current analyses are sufficient to separate these possibilities. To support the central interpretive claim, the manuscript would benefit from additional control analyses accounting for positional and kinematic variables, and additional experiments including control conditions that dissociates threat presence from spatial configuration.

      4)Relatedly, I found the treatment of behavioural annotation too qualitative for the strength of the neural claims. Behaviours such as risk assessment, retraction, retreat, and flight are described verbally, but the manuscript would be much more reproducible if the authors provided explicit operational criteria, quantitative thresholds, and inter-rater reliability. At present, the reader is asked to accept a fairly subjective labelling scheme, yet many of the neural conclusions depend directly on those labels. Critically no major differences have been observed between cell type responses, hinting that perhaps more rigorous behavioural quantification may be required.

      5)The statistical framework used to define neuronal response classes also needs clarification. Responsive cells are identified relative to shuffled null distributions, but it is not clear that the analysis adequately controls for multiple comparison: multiple testing across neurons, behavioural epochs, and response classes.

      6)I also think the interpretation of the CEBRA analyses is too strong. These analyses show that the recorded populations contain information sufficient to decode the annotated behaviors, but they do not demonstrate that the population encodes an abstract "behavioral state" rather than a mixture of posture, speed, position, and other correlated sensorimotor variables. Because the labelled behaviours are themselves associated with distinct kinematic structure, decoding alone is not enough to support the stronger conceptual claim. That interpretation would require nuisance-controlled analyses or matched comparisons showing that decoding persists beyond simple sensorimotor differences.

      7)The optogenetic results in Vgat+ mice should also be interpreted more cautiously. I was not convinced by the conclusion that stimulation reduces risk-assessment behavior. Increased rearing does not straightforwardly imply reduced assessment, as in some contexts rearing itself can be part of assessment-related behavior. More generally, if stimulation alters locomotor structure, this could secondarily change the frequency of other scored behaviors and syllables without demonstrating that the manipulated neurons specifically control risk assessment. For example, if locomotion speed is reduced by the manipulation, then the mouse may engage in other behaviour that do not require locomotion such as rearing or grooming. I therefore think the claims in this section should be toned down and are not easily interpretable with the data provided.

      8)The functional interpretation of the inhibitory population is not sufficiently novel relative to prior work. Previous studies have already shown that GABAergic neurons in dPAG modulate instinctive escape behavior, so the present result that inhibitory neurons affect escape-related responding is, on its own, not a major conceptual advance. What would make the current study more compelling is clear evidence that these neurons specifically encode or regulate risk assessment. At present, however, that conclusion remains uncertain. Because the predator is continuously present in the assay, the timing of threat delivery is not well defined by construction, making it difficult to separate neural activity linked to threat assessment from activity linked to the suppression, gating, or delayed initiation of escape during approach. In other words, the observed slowing during "risk assessment" could reflect inhibition of escape-related motor output rather than a distinct effect of assessment itself.

      9) The author states: "Unfortunately, we were not able to identify testing conditions under which the two cell types differed statistically, leaving open the question of whether GABAergic and glutamatergic neurons in dPAG reliably encode different aspects of defensive behavior. Our assessment of ensemble encoding of behavior also failed to shed light on cell-type specific differences in encoding, with both cell-types showing significant predictive correlations of approach and avoidance behaviors." This statement is surprising and substantially limits the conceptual advance, because the main distinction the study sets out to test is ultimately not resolved. In my view, one likely reason is the experimental design itself. Because the predator is continuously present, threat presentation, exploration, risk assessment, sensory sampling, escape decision, and escape onset are all temporally entangled. Under these conditions, it becomes very difficult to isolate which component of the defensive sequence is actually being encoded, and this may reduce the ability to detect meaningful differences between excitatory and inhibitory populations. This is especially important given that prior work has already shown that dPAG populations vglut and vgat population encode and modulate differently defensive behaviours such as escape. In that context, the present study would need a cleaner behavioral design to demonstrate a distinct contribution of cell type, rather than a mixed representation of overlapping sensory, motor, and defensive variables

      10)Finally, the proposed model in which local GABAergic inhibition organizes Assessment+ and Escape+ excitatory populations is interesting, but at present it remains speculative. The data may be consistent with this idea, but they do not directly test it. Since computational modelling of this framework would not be too complex, I would suggest additional analyses and network modelling to substantiate this claim, for example by showing that a network model under these constraints would recapitulate the neural responses observed in vivo.

      Overall, I think the manuscript contains useful data, but in its present form its novel contribution is unclear. Additional experiments and analyses may be needed to strengthen its central claims, as described above.

      Significance

      See previous section

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      Referee #1

      Evidence, reproducibility and clarity

      Peer review of Ayuso-Jimeno et al. "Excitatory and inhibitory neurons in the dorsal periaqueductal gray encode decisions to assess and escape natural threats"

      Animals ability to escape from threat is a crucial survival behaviour exhibited across the animal kingdom. In vertebrates, hard-wired circuits allow animals to escape from predators without the need for learning. One of the main vertebrate brain regions that controls escape from threat is the brainstem dorsal periaqueductal gray. Research in the last decades has shown that dorsal PAG glutamatergic neurons control the initiation of escape (from imminent threat) and escape vigour, whereas GABAergic neurons are spontaneously and tonically active and have been shown to provide an inhibitory threshold for eliciting escape and further signal escape termination (for reviews on this topic see for example: Gross and Canteras, 2012; Silva, Gross and Graeff, 2016; Motta, Carobrez and Canteras, 2017; Franklin, 2019; Silva and McNaughton, 2019; Lefler and Branco, 2020; Stempel, 2024). In addition to their role in the escape action, the dPAG has been suggested to have a broader function in threat processing. Specifically, a subset of neurons in the dorsal PAG has been shown have activity correlated to the approach/distance to a threat zone or predator. These neurons have been called 'risk assessment' neurons (Deng et al. 2016; Masferrer et al. 2020; Reis et al. 2021). Whether risk assessment neurons integrate threats across different modalities and contexts (here: social vs different predatory threats) is currently not known.

      The present study addresses two important and long-standing questions in the field: what is the cell-type identity of dorsal PAG neurons encoding threat assessment versus escape?, and are different classes of threat processed by shared or dedicated neuronal populations? The study design is careful and methodologically performed well. While some of the experiments have been published in the past, the comparison of social and predator threat responses across contexts and a description of assessment+ cells across both main excitatory and inhibitory PAG neuron types is interesting.

      Briefly, using miniaturized fluorescence microscopy (miniscope calcium imaging) in Vglut2::Cre and Vgat::Cre mice during a live predator (rat) exposure paradigm, the authors characterize neuronal activity in identified glutamatergic and GABAergic dPAG neurons across approach-escape cycles. The central finding is that both excitatory and inhibitory populations contain Assessment+ neurons (active during approach, silent at escape onset) and Escape+ neurons (suppressed during approach, activated at escape onset) and the authors propose that the activity profiles of Assessment+ and Escape+ cells may reflect local circuit wiring rules with putative GABAergic inhibition between excitatory neuronal subsets. Consistent with this, optogenetic activation of GABAergic dPAG neurons suppressed risk assessment behavior and promoted exploratory rearing but did not affect 'baseline' locomotion, building on Tsang et al. 2023 and Stempel et al. 2024. Population-level decoding using CEBRA (Schneider et al., 2023) confirmed that both Vglut2+ and Vgat+ ensembles independently encode behavioral state with high accuracy.

      A second finding of this paper concerns threat generalization. Sequential exposure to a predator, an aggressive conspecific, and a prey insect (cockroach; paradigm from Rossier et al., 2021) revealed that more than half of responsive excitatory neurons and nearly half of responsive inhibitory neurons were activated by two or three threat types. This substantial overlap argues for at least partially convergent, rather than parallel, encoding of threat in dPAG, consistent with its role as a general trigger for defensive avoidance (Silva et al., 2013), and contrasts with the anatomically segregated upstream processing of predator and social threats in the medial hypothalamic defensive network. Taken together, this study suggests the dPAG as a site of coordinated excitatory-inhibitory computation in the control of innate threat assessment and avoidance across biologically diverse threat contexts.

      Below are comments related to the figures/ data analysis and generally to the discussion part which we recommend should be expanded/changed to put the findings of the authors more in context of published literature and to discuss in more detail the proposed circuit mechanisms that align with the author's findings. Generally, this is a very nice and large dataset that could benefit from a more fine-grained and in-depth analysis of escape+ and risk assessment+ cells and their precise temporal activity profiles. We do not suggest to perform further experiments and think the work in this manuscript is publishable as is with some additional analyses and changes to the text.

      Related Figure 1 and calcium imaging methods.

      1. The classification of neurons as 'Assessment+' and 'Escape+' positive is unclear and should be formally described in the methods, presumably these are the neurons with significantly increased "positive" or absolute slope?
      2. Can the authors clarify what they mean by escape? In the methods under "manual behavioral annotation", "Escape behavior" is defined as including retractions, retreats and flight. However, under "unsupervised behavioral annotation", escape is defined as "high-velocity locomotion aiming at increasing distance between threat source and subject". Are Escape+ neurons ones that are significantly modulated (presumably positively) at flight (latter definition), or throughout retractions, retreats and flight (former definition)? This also relates to the plots for 'escape+' neurons, where it would be useful to separately plot 'successful flights to shelter' to make the results comparable to previous studies and where trajectories are at least relatively stereotyped.
      3. More example traces of different FOVs aligned to escape-to-shelter onset and to risk assessment onset would be useful, as well as a plot with the % of escape-active neurons and a reliability index (in how many trials is each neuron active?). Similarly, are the proportions of escape+ and assessment+ neurons similar across animals and FOVs recorded?
      4. Some further basic analyses of the neurons' responses would be helpful to gage their activity profiles. Do these correspond to previously published descriptions of the two classes of escape+ neurons? Are they active during baseline locomotion? Do you observe the same clusters of GABAergic neurons that have been previously described where some dip at escape onset and some ramp up towards escape termination? Please add these plots as a supplement.
      5. It is generally assumed that assessment+ cells 'map'/correlate to the distance to a threat zone. Plots with quantification of this correlation would be useful, and whether they are also speed modulated or not? If the animal stops on the way to the threat zone, does the risk assessment signal plateau for example?
      6. Both in Figures 1 and 2, both the single trial examples of individual neurons and averages across neurons in the Vgat+ and VGlut2+ recordings show very fast changes that seem to be shorter than Gcamp6s kinetics would allow, and that happen exactly at escape onset when there is presumable a fast head turn movement. What motion correction controls do the authors have in place to make sure that some of the fast changes they see are not motion artefacts? (e.g., see Figure 2, panel C bottom.) (Importantly, see also comment 8 below).
      7. Related to this, the overall escape velocity is extremely low (around 10cm/s). When the authors only analyze high speed escapes (>50cm/s), do they see different cell activity profiles emerge that they might miss with these very low speed escapes that presumably activate less neurons that high-speed escapes? While slow-speed escapes still elicit activity in both Vgat+ and Vglut2+ neurons, their calcium activity changes will be much lower, potentially hindering a more detailed analysis, as clear signals might be sparser for escape+ neurons.
      8. With the min/max normalization that has been applied across the entire session it is hard to see 'local changes' in the heatmaps. Given that glutamatergic neurons are thought to only sparsely fire outside of escape episodes, the heatmaps are hard to read with the 'min/max' Z-scoring, and we would strongly encourage the authors to plot 'locally' Z-scored traces without a min/max normalization for each cell (at least for some examples). Importantly we would suggest to change the color scheme for the heatmaps to allow visual identification of the baseline / 'F0'.
      9. The calculation of the slope estimated on the calcium signal is somewhat unclear in the methods section. The way it is described right now, it is not clear whether the window is centered at the behavioral event of interest or not. In the figures, it appears that this is an absolute slope, which is also not clear from the methods. Further, considering the half-decay time of GCamP6f, which is ~ 0.5s (Chen et al., 2013), it seems like the result of this calculation could be cancelation or a slope near-zero if the signal both rises and decays in this broader window. Why is not the peak (e.g. max) or the AUC used for this analysis? The authors should clarify the calculation and provide rationale for their choice? Is the sign (positive/negative) of the slope not of interest?
      10. The GRIN lens placement in the example in Figure 1 is in the lateral PAG, whereas most others are located in the dorsolateral PAG. It would be useful to have a sentence in the introduction to state that the authors include the lateral, dorsolateral and dorsomedial PAG as 'dorsal PAG' and a rational for this placement.
      11. Could the authors comment on how the proportions of Assessment+ and Escape+ neurons relate to previously published literature (e.g., Deng et al. 2016)?

      Related to Figure 2

      1. In panel K of Figure 2, both Vgat+ and VGlut2+ assessment+ neurons seem to have a rise at escape onset in addition to the slow rise during their movement towards the threat zone. Also here, the offset kinetics of the signal seem to now correlate well to the slow decay kinetics you would expect for GCamp6f and a quantification of controls and motion correction quality metrics would be very helpful to add. Depending on the baselining the peaks during escape would probably be significant as well. The authors could try and cluster the neurons further to see if they can disentangle further 'sub classes/clusters'.
      2. The calculation of the slope estimated on the calcium signal is somewhat unclear in the methods section. The way it is described right now, it is not clear to me whether the window is centered at the behavioral event of interest or not. In the figures, it appears that this is an absolute slope, which is also not clear from the methods. Further, considering the half-decay of GCamP6f which is ~ 0.5s (Chen et al., 2013) it seems like the result of this calculation could be cancelation or a slope near-zero if the signal both rises and decays in this broader window. Why is not the peak (e.g. max) or the AUC used for this analysis? The authors should clarify the calculation and provide rationale for their choice? Is the sign (positive/negative) of the slope not of interest?
      3. The authors find a significant increase in the accuracy of the classification of the two defensive behaviors (risk assessment versus escape) from models trained on the Vgat+ population calcium activity. This is an interesting finding which should be to the very least discussed in the discussion. Is there more information content in this population? Does this have anything to do with the slower dynamics of the signal? As mentioned above, would clustering these populations further reveal a more fine-grained detail of their population activity, e.g. see previously published work by Stempel et al. who suggest that there are at least two broad clusters of escape-active GABAergic neurons.

      Related to Figure 3.

      1. In Figure 3A, the ChR2 seems to be significantly spread throughout the entire PAG and also the superior colliculus. As it is hard to see cell bodies with ChR2 at this magnification, we would recommend to add a supplemental figure with the outlines of the infection sites.
      2. The authors state that: "Interestingly, although stimulation of Vgat⁺ dPAG neurons significantly reduced the peak speed of escape, it did not change the overall time spent escaping". Could they comment on how this may relate to a previous study where the probability of escape is decreased upon activation of Vgat+ neurons and initiated escapes can be induced (Stempel et al. 2024)? Do they think this could be differences in location of stimulation fiber or one vs two injection sites (bigger vs smaller spread along the AP axis of the PAG?) - or do they think there could be a fundamental difference between fast escape from imminent looming stimuli to slower escapes to rat/prey/social predators?

      Related to Figure 4.

      1. Individual examples of cells responding to one threat vs multiple threats would be very useful to add to see their dynamics across threats.
      2. The Venn diagrams in panel I are very hard to read, and color-blind people might struggle with the red/green (also in panel C). We would suggest to change colors, and replace Venn diagrams to make results more interpretable/readable.
      3. Have any statistical tests been performed to assess whether there is a difference between the proportions in figure D and H as well as Figure 4J?

      General and other comments

      1. We strongly encourage the authors to add example videos of the behaviors tested and of all major findings including example calcium activity and optogenetic manipulations.
      2. Relating to a more fine-grained analysis of escape-active cells (as recommended above), the authors state in the discussion that: 'A notable paradox of our findings is that while GABAergic and glutamatergic dPAG neurons showed nearly indistinguishable neural firing correlates of approach and avoidance (both harbored Assessment+ and Escape+ cells) [...]". Since previous works have already looked at escape-active cells in more detail, with differences between Vgat+ and Vglut2+ neurons having been described, we would encourage the authors to look at unsupervised clustering of the escape-active neuron populations, as the firing rates of these neurons should be distinguishable along the escape sequence and especially when taking into account speed correlations and activity profiles aligned to escape onset or offset. If they can't find differences these should be discussed, as major differences may relate to the paradigms used (rats vs visual 'looming' threats).
      3. We would ask the authors to make sure that the cited literature supports their statements. Some statements are in the manuscript are not clearly, only partially correct or the literature itself is inconsistent or citations are confusing/misleading.

      e.g.: "A functional cellular and circuit architecture of dorsal PAG is emerging in which stimulation of glutamatergic neurons in dorsal PAG promotes freezing at low intensity and flight at high intensity (Tovote et al. 2016, Deng et al. 2016, Evans et al. 2018, Tsang et al. 2023)."

      Not all of the cited papers support that graded stimulation of glutamatergic neurons in the dorsal PAG results in escape/flight or freezing but rather that there is some (dis-)inhibitory interplay between dorsal and ventrolateral PAG which results in the selection of one or the other behaviors, and that the ventrolateral PAG more specifically drives freezing (e.g., Tovote et al. 2016). Some studies have shown that activation of the dorsal PAG and in particular of glutamatergic neurons elicits 'all-or-none' flight behavior. Additionally, studies where freezing has been observed with dorsal PAG stimulation have often used CamKIIa as a promoter (e.g. Deng et al. 2016*), which is expressed at significant levels in both excitatory and inhibitory neurons. Thus, we would argue it is not entirely clear whether freezing through 'low intensity' dPAG stimulation is a biological feature of the dPAG network, especially also as inhibition of the dPAG promotes freezing - probably through disinhibition of the vlPAG.

      In the results section on page 6, it is stated that Deng et al. 2016 use VGlut2-Cre to target ChR2 for eliciting escape, but they have used CamKIIa ("Previous studies have shown that optogenetic activation of Vglut2+ dPAG neurons elicited flight behavior[...]"). Similarly, on page 3, the following statement on GABAergic neurons isn't clear: "Stimulation of GABAergic neurons, on the other hand, does not elicit defensive behavior (Tsang et al., 2023) and recent evidence shows that they can inhibit looming stimulus-evoked flight behavior (Stempel et al., 2024) suggesting they might act as part of a tonic inhibitory circuit that receives primarily local inputs (Franklin et al., 2017; but see Wu et al. 2024)." Multiple papers across all columns have shown that GABAergic neurons are tonically and spontaneously active (e.g., Chen et al., 2023; Wang et al., 2023; Stempel et al., 2024) and Stempel et al. estimated that GABAergic PAG neurons make up >50% of spontaneous inputs to glutamatergic PAG neurons, thus positioning them well to control their activity. Franklin et al. 2017 (and others) have shown that GABAergic neurons receive input from thalamic and hypothalamic regions (probably also from midbrain regions that were not analyzed in that study), thus arguing that they receive significant input from outside of the PAG to integrate - currently unknown - inputs from other brain regions to presumably guide their activity in a context-specific manner. The statement that they should thus be part of a circuit that receives primarily local inputs is not well supported by previous studies. Also, how this enables a 'push-pull' circuit and what is meant here exactly, could be clarified. It is not clear why the presence of risk assessment neurons would support such a circuit model rather than an integrative/threshold one. Further, Stempel et al. 2024 have also shown that GABAergic neurons ramp up activity during escape and are thus technically 'flight+' cells. We agree that there are interesting and presumably complicated intra-PAG dynamics to be studied where their connectivity will define different circuit model possibilities.

      Referee cross-commenting

      We generally agree with most comments made by reviewer 2, and their concerns. While the novelty of this manuscript is limited, highlighting the advances better, also through more accurate analyses, should allow the authors to better build up their novel findings ('major concern 1'). We agree with the reviewers 'major concerns' 2-5 and that they should be addressed by the authors, for scientific reasons beyond novelty, as some of the analysis feels incomplete. For example, while we do not think that new experiments are necessary, the authors can discuss the issue of spatiotemporal overlap of variables that do not allow a definitive description of 'risk assessment' cells. This issue is in fact present in most published work on risk assessment cells (usually described with a rat predator that is continuously present). Concerns 7-9 are valid, and touch on similar points raised by our review. We do not think that modelling would add anything substantial to this manuscript that couldn't be hypothesised in the discussion beyond their proposed model. In summary, we agree with the points raised by reviewer too, but find that the data is useful and should be published, after some revising and additional more fine-grained analyses to strengthen the manuscript's claims.

      Significance

      Overall, even though the overall novelty is somewhat limited considering the paper shows significant overlap with recent findings on the involvement of glutamatergic and GABAergic dorsal PAG neurons in risk assessment and escape, there are some important and novel findings that advance the knowledge in the field of neuronal circuits underlying defensive behaviors. For example, the direct comparison of how different threat contexts modulate the activity of individual neurons and the finding that both GABAergic and glutamatergic population activity can predict defensive behavioral output are interesting and important findings. While this study may of limited interest to a very broad audience, it is of importance to the fields of neuroethology and the study of the neural circuits underlying innate defensive behaviors.

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      Reply to the reviewers

      REPLY TO REVIEWERS

      We were happy to read that all 3 reviewers of our manuscript concluded that our work has the potential to make an important contribution to the cellular senescence field by challenging the assumption that a strong inflammatory SASP is a universal feature of fibroblasts induced into senescence by DNA damage. This assumption was based on the strong SASP response of a limited number of fetal lung and neonatal foreskin fibroblasts that have been used as models. In our study, we show that many adult fibroblasts induced into senescence by ionizing radiation (IR) do not express RELA-target genes at high levels. We provide data suggesting that low expression of IL1A and IL1B in low-responding adult fibroblasts distinguishes them from fetal/neonatal fibroblasts. We further provide molecular mechanisms that may contribute to higher inflammatory expression in WI38 fetal lung fibroblasts versus adult fibroblasts in terms of differential activity of putative IL1 intergenic enhancers and differential expression of the FOXF1 transcription factor.

      We respond below to all the points raised by the reviewers. We feel that our revised manuscript is greatly improved and we thank the reviewers for their remarks.

      1. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      __Reviewer #1 (Evidence, reproducibility and clarity): __

      In this paper, "Differential Enhancer Activity and FOXF1 Levels Contribute to Higher Inflammatory Gene Expression of Fetal/Neonatal Versus Adult Fibroblasts in IR-induced Senescence" the authors compared the inflammatory response of fetal vs adult fibroblasts in response to irradiation induced-senescence. They found that fetal fibroblasts express higher levels of pro-inflammatory genes compared to the adults' ones, notably on IL1A and IL1B genes expression. They suggest that this difference of response could be linked to chromatin accessibility and epigenetic regulation in enhancer region around the IL1 genes. And that also could be explained by different levels of FOXF1 transcription factor. Interestingly, even if adult fibroblast shows lower induction of pro-inflammatory gene expression, that level can be rescue by IL1a treatment. Showing that the cells didn't lack the capacity of inducing pro-inflammatory genes expression but is lower in DNA damage induce senescence compared to fetal fibroblasts.

      Some major comments in this manuscript could be:

      • Authors compare fetal and adult fibroblast from 2 different tissues of interest (lung vs mammary) for their first observation and the basis of this research. However, it is known and author also shows in figure 2 that different fibroblasts can be and behave differently from their tissues of origin. It is possible that the first statement could have a second bias on the tissue specificity.

      This point was also raised by Reviewer 2. It was not our intention to suggest that tissue origin makes no contribution to the differences observed between fetal lung/neonatal foreskin and adult fibroblasts. Indeed, it was for this reason that we included adult lung fibroblasts in our study to compare with the fetal lung fibroblasts. In response to this concern by Reviewers 1 and 2, we performed an additional analysis and we modified our text to make it clearer that the tissue origin of adult fibroblasts may contribute to differences in their inflammatory gene response. We included a new Fig 6 in which we display the RELA-target and Interferon-target gene expression scores after IR as a function of adult tissue type compared to WI38 fetal lung and HCA2 neonatal fibroblasts. We concluded (p. 6): Adult lung appeared to express RELA-target genes at higher levels than other adult tissues, but there was great donor to donor variability in the other tissues and a greater number of samples will be necessary to definitively test for adult tissue-specific differences in inflammatory gene expression. We also state in the Discussion (p. 15): Increased levels of FOXF1 in adult lung may be correlated with higher levels of inflammatory gene expression in adult lung fibroblasts compared to breast and abdominal fibroblasts. However, the levels of FOXF1 expression are not perfectly correlated with inflammatory gene expression in that neonatal foreskin fibroblasts express higher levels of inflammatory genes in IR-SEN compared to adult gingiva, palate, and vocal fold fibroblasts for similar levels of FOXF1 expression in these cells. We thus feel that other factors must contribute to differences in inflammatory potential between adult fibroblasts from different tissues and between adult and fetal/neonatal fibroblasts. In the Limitations of this study section at the end of the Discussion, we acknowledge that it would be worthwhile extending this survey of adult fibroblasts.

      • Moreover, two different fetal fibroblast cell lines can also have different levels of expression of pro-inflammatory factors (MRC5 vs IMR90 for example). The author mainly focuses on comparing 1 fetal line to some adult ones. The question here is: is it truly a foetal phenotype or cell specificity? Author used a second fetal cell line in their RNAseq comparison, but it was from dataset already available that could also bring some technical difference. Other fetal fibroblast could be used such as MRC5 or IMR90.

      We found 12 published studies derived from several different labs showing that fetal lung (WI38, IMR90, MRC5) and neonatal foreskin (BJ, HCA2) fibroblasts express high levels of inflammatory gene expression during IR-induced senescence. This observation is thus well established and we arbitrarily chose fetal lung WI38 as the positive control fibroblast in our work. We assume that this reviewer wanted us to verify that another fetal/neonatal fibroblast would express inflammatory genes at high levels with our protocol for inducing IR-SEN. In response, we added a figure (Fig S1) in our revised manuscript showing that BJ neonatal foreskin fibroblasts induce high levels of CXCL8 after irradiation following our standard protocol.

      Other comment: -Rationale for the HOX and non HOX TF graph?

      The rationale for the HOX and non-HOX TF heat maps (Figs S8, S9, S10 of our revised manuscript) is to support the proposal that differences in transcription factor gene expression contribute to the tissue identity of fibroblasts, presumably derived from developmentally-driven epigenetic modifications of regulatory elements controlling transcription factor expression. Furthermore, the expression of some of these transcription factor genes is modified by IR, so they represent candidate regulators contributing to potential differences of fibroblasts in their response to irradiation. We thus feel that these figures will interest researchers who would like to further explore the role of these transcription factors in tissue-specific fibroblast functions. We will however comply if the reviewers and editors prefer that we remove these figures.

      • Figure 5C hard to understand

      We have replaced the overlay of 2 scatter plots (WI38_IR versus M168_IR -/+ IL1-alpha) with 2 side by side scatter plots of WI38_IR versus M168_IR and WI38_IR versus M168_IR + IL1-alpha. This new version is Fig 7C of our revised manuscript. We also added additional explanations of how to interpret the graphs. We hope that these modifications clarify the figure and its interpretation.

      • Figure 6: Will be easier to follow if all the cells are sorted the same way from ATACseq and H3K27ac.

      We followed the suggestion of the reviewer. The new version of Fig 6 corresponds to Fig 8 of our revised manuscript.

      • Could add correlation curve between inflammatory gene response and nuclear translocation of RELA

      We quantified the CXCL8 IF signal and added a correlation curve as suggested to Fig S7 of our revised manuscript. We note in the legend of this figure that the CXCL8 signal could only be crudely approximated with our imaging platform by quantifying the signal in a ring around the nucleus.

      • Quantification of gH2AX foci in S3 is it different between cell lines?

      We quantified the gH2AX foci as suggested and added this quantification to the new version of Fig S3. We did not find a significant difference in gH2AX foci between WI38 and M168.

      Figure S22 doesn't seem to add more information. Rationale?

      This figure (new Fig S26) identifies transcription factors including FOXF1 that differ in expression between fetal lung/neonatal foreskin and adult fibroblasts. We later showed that FOXF1 contributes to inflammatory gene expression in WI38 fetal lung fibroblasts, but we note that probably other factors also influence inflammatory gene expression in adult fibroblasts. The transcription factors shown in this figure represent candidate factors for future work.

      • It is interesting that the fibroblast has been isolated from young and older adults. Would be interesting to see if aging impact in the same way the induction of senescence than fetal vs adult.

      We agree that this RNA-seq data set of human dermal fibroblasts from 133 donors aged 1 to 94 years (doi: 10.1186/s13059-018-1599-6) is a fabulous resource. Unfortunately, this data set only contains cells in the condition of proliferation.

      __Reviewer #1 (Significance): __I found that this study leads to nice and interesting observations and highlights the potential differences between studies in fetal fibroblasts and the response that could be found adult fibroblast that we could find in adult tissues. However, I find the story sometime difficult to follow, as some information and graphs don't seem to be useful for the purpose of the story. It also feels that sometime there is a lack of consistency in the use of cells lines or models in the different paragraphs. The authors wanted to explore lot of topics in this story, but the realization seems confused. The manuscript could benefit from more clarity in the explanation of the different experiments used and analysis and could also be more focused. This study could be used in the senescence field for people who study mechanistic study or pathways related to NFKb or ISG pathways. It is interesting to know that adult fibroblast might have lower response compared to the fetal fibroblast usually used in the mechanistic study. My expertise as reviewer was in the senescence field and mechanistic study in the SASP production in fibroblast.

      We have tried to improve the figures and clarify the description of experiments and interpretations. The initial part of the manuscript was focused on fetal lung WI38 versus adult mammary M168, but the second half of the manuscript greatly widened the number of adult fibroblasts to test the generality of the initial findings. We agree that some information and figures are not required for the principal conclusions of our study, but we feel that they provide valuable information for labs interested in pursuing the work that we have initiated. If the reviewers and the editors decide that these supplementary figures are too distracting, then we will remove them from the final version of the manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity): Summary This manuscript asks whether the strong inflammatory SASP typically seen after IR-induced senescence is a general feature of fibroblasts or something specific to the fibroblast lines used to define it. The authors profile a panel of primary adult fibroblast strains from several tissues by RNA-seq, alongside fetal/neonatal comparators, and find that many adult strains show only weak induction of RELA-dependent inflammatory genes after IR-induced senescence. They trace this to poor activation of IL1A/IL1B, show that exogenous IL-1α or IL-1β can rescue inflammatory gene induction in the low-responding adult lines, and implicate reduced activity at two candidate enhancers in the IL1A/IL1B locus. In WI38hTERT cells, deleting either enhancer blunts the inflammatory response to IR, and FOXF1 knockout produces a similar effect on both inflammatory and interferon-stimulated genes. Overall, this addresses a genuinely important question for the field: how well do inflammatory conclusions from the standard fetal/neonatal fibroblast models generalize to adult tissue?

      __Major comments __This is an important question, and the manuscript contains several genuinely interesting observations. The suggestion that many adult primary fibroblasts respond far more weakly than the fetal/neonatal lines that dominate the literature could be a significant finding for the field. The IL-1 rescue data are convincing and support the idea that failure to engage the IL-1 amplification loop underlies the low-response phenotype. That said, the manuscript currently overreaches in a few of its conclusions, and one part of the experimental foundation needs firmer support before the central claims can stand. One concern, especially at the beginning of the manuscript, is whether the key low-responding adult strains are convincingly senescent under the conditions used for the main comparisons. This matters because Figure 1 functions as the entry point for the paper's central argument, but the M168 IR-treated cells shown there don't display obvious senescent morphology in the DAPI images; I don't see clear SAHF-like chromatin reorganization. The supplementary evidence for reduced EdU incorporation is relevant, but given how much weight M168 carries in the paper's logic, burying this characterization in the supplement isn't sufficient. Additionally, the picture for SA-β-gal positivity is not convincing. Either additional senescence characterization for M168 moved into the main figures, or a tighter, more integrated presentation of what's already there would bring more confidence. As it stands, the paper doesn't clearly rule out that M168 reflects incomplete senescence induction rather than a "low-inflammatory" senescent state, and that distinction is fairly central to the whole argument.

      The reviewer is correct in observing that there is less chromatin compaction in irradiated M168 cells compared to irradiated WI38 cells, but this does not constitute an exclusionary criterion for senescence. We and others have shown previously that there is extensive variation in the amount of chromatin compaction in senescence as a function of the cell line and the senescence inducer. BJ fibroblasts for example exhibit much less compaction in senescence compared to fetal lung fibroblasts (Contrepois et al., Epigenetics Chromatin 2012. PMC3487866, Kosar et al. Cell Cycle 2011. PMID: 21248468). The reviewer is equally correct in noting that M168 cells develop lower levels of SA-ß-galactosidase staining in IR-induced senescence compared to WI38 cells. However, we find that there is a clear increase in SA-ß-gal activity for senescent M168 cells compared to proliferating M168 cells. To show this more clearly, we redid staining experiments in which we incubated the cells for longer times in reaction buffer to increase the staining and we used grey-scale images rather than color images to better visualize the stain. This new data has been moved to Fig 1 as requested. To further support the senescence induction of the M168 cells, we performed Ki67 staining showing a near total loss of staining for this proliferative marker in M168 and WI38 cells at 10 days post-IR (new Fig S2). This result is significant because Sabrina Spencer’s lab has shown that sustained loss of Ki67 is associated with a sustained proliferative arrest in senescence (Ashraf et al., Nature Comm. 2023. PMC10374620). Finally, we devised a transcriptomic senescence score as a composite metric based on 4 transcriptomic criteria: CDKN1A RNA increase, and suppression of LMNB1 RNA, E2F-target gene expression, and G2/M proliferation genes in senescence (detailed in the Materials and Methods section of our revised manuscript). This transcriptomic senescence score has the merit of scoring senescence engagement based on the same RNA-seq data that were used to evaluate inflammatory gene expression in senescence. Using this metric, we could demonstrate that inflammatory gene expression in senescence was not correlated with the transcriptomic senescence score: a low inflammatory gene expression was not correlated with a low transcriptomic senescence score, and a high inflammatory gene expression was not correlated with a high transcriptomic senescence score. This analysis is shown as Fig S11 and S12 of our revised manuscript.

      Second, the manuscript still frames its central finding largely as fetal/neonatal versus adult, but the data themselves point to tissue origin as a major axis of variation. There's clear heterogeneity across fibroblast types, but developmental stage, tissue identity, and donor-to-donor variation aren't cleanly separated in this cohort. The claim best supported by the current data is that many adult fibroblasts in this panel are weaker inflammatory responders relative to the fetal/neonatal lines commonly used in the field, and not that developmental stage per se is the driver.

      We agree with this interpretation of the reviewer, and we thought that we had expressed this idea in our manuscript, but apparently not in a clear enough fashion. It was not our intention to suggest that tissue origin makes no contribution to the differences observed between fetal lung/neonatal foreskin and adult fibroblasts. Indeed, it was for this reason that we included adult lung fibroblasts in our study to compare with the fetal lung fibroblasts. In response to this concern by Reviewers 1 and 2, we performed additional analyses and we modified our text to make it clearer that the tissue origin of adult fibroblasts may contribute to differences in their inflammatory gene response. We included a new Fig 6 in which we display the RELA-target and Interferon-target gene expression scores after IR as a function of adult tissue type compared to WI38 fetal lung and HCA2 neonatal fibroblasts. We concluded (p. 6): Adult lung appeared to express RELA-target genes at higher levels than other adult tissues, but there was great donor to donor variability in the other tissues and a greater number of samples will be necessary to definitively test for adult tissue-specific differences in inflammatory gene expression. We also state in the Discussion (p. 15): Increased levels of FOXF1 in adult lung may be correlated with higher levels of inflammatory gene expression in adult lung fibroblasts compared to breast and abdominal fibroblasts. However, the levels of FOXF1 expression are not perfectly correlated with inflammatory gene expression in that neonatal foreskin fibroblasts express higher levels of inflammatory genes in IR-SEN compared to adult gingiva, palate, and vocal fold fibroblasts for similar levels of FOXF1 expression in these cells. We thus feel that other factors must contribute to differences in inflammatory potential between adult fibroblasts from different tissues and between adult and fetal/neonatal fibroblasts. In the Limitations of this study section at the end of the Discussion, we acknowledge that it would be worthwhile extending this survey of adult fibroblasts.

      Related to this, the enhancer and FOXF1 data are interesting and plausible, but the manuscript should be more careful about the line between "identified a contributing mechanism" and "explained the phenomenon." The enhancer deletions and FOXF1 knockout give clean, strong phenotypes in WI38hTERT cells. The low-response phenotype in primary adult fibroblasts is never directly perturbed at the mechanistic level. Therefore, a more explicitly that these experiments identify plausible contributors to differential inflammatory output, rather than presenting them as a settled explanation for why adult fibroblasts behave this way.

      These are fair remarks and we have modified our text along the lines suggested by this reviewer. We changed our title to: Differential Enhancer Activity and FOXF1 Levels Contribute to Higher Inflammatory Gene Expression of Fetal Versus Adult Fibroblasts in IR-induced Senescence. We experimentally tested the role of the enhancers and FOXF1 only in fetal lung WI38 fibroblasts, so we removed the mention of neonatal fibroblasts from the title. In the Discussion, we now state: In conclusion, we find that many adult fibroblasts express low levels of RELA-targeted inflammatory genes in senescence induced by DNA damage compared to fetal/neonatal fibroblasts, and we trace this defect to a weak activation of IL1A/IL1B transcription. We removed the reference to differential enhancer activity and differential expression of FOXF1 because we did not experimentally demonstrate that activation of these enhancers or increased expression of FOXF1 in the adult fibroblasts would suffice to increase inflammatory gene expression. In the Limitations of this study at the end of the Discussion, we have added: Future work should test whether ectopic expression of FOXF1 in adult fibroblasts that do not normally express it suffices to increase RELA-target gene expression in IR-SEN.

      Minor comments 1. IL-1α activity in senescence is known to depend on post-transcriptional and post-translational regulation, not simply on IL1A transcript levels, so using "pro-IL1" in the figures is potentially meaningful, but this isn't addressed in the text. Please clarify what's actually being measured and discuss how it relates to prior work on IL-1α processing, localization, and activation state.

      We showed that IL1 RNAs were expressed at especially low levels in adult fibroblasts. Since this RNA expression defect is sufficient to explain low levels of active IL1 protein secretion, we did not describe post-transcriptional and post-translational regulation of IL1 in our initial manuscript. We still feel that the mention of this regulation would be a distraction in the Introduction and Results, but in response to this reviewer’s request, we have now added the following text to the Discussion: We measured IL1 RNA levels, but post-transcriptional [55] and post-translational regulation, including proteolytic processing and secretion, also control the levels of mature IL1 proteins in senescence [56]. The very low levels of IL1A and IL1B mRNAs in many adult fibroblasts are sufficient to explain weak production of active IL1-alpha and IL1-beta in these fibroblasts. We have not tested whether or not post-transcriptional control of IL1 proteins differs between adult and fetal/neonatal fibroblasts.

      The HOX/positional identity section is interesting on its own terms but doesn't currently tie back to the inflammatory phenotype that's the paper's central focus. Either strengthen that connection or move most of it to the supplement.

      There is one paragraph in the Results describing the HOX and non-HOX transcription factor differential expression analysis and its potential contribution to the differential phenotypes of adult fibroblasts, including inflammatory gene expression in IR-SEN. The 3 heat maps (HOX, non-HOX homeobox, and all other transcription factor genes) were already presented as 3 supplementary figures. We feel that these figures will interest researchers who would like to further explore the role of these transcription factors in tissue-specific fibroblast functions. We used a separate supplementary figure for each heat map so that all transcription factor genes in the heat maps would be legible. If the reviewers and editors disagree about the interest of this analysis, we will remove these figures in the final version of the manuscript.

      Cluster definitions and gene-grouping logic are scattered across several supplementary figures, which makes the argument harder to follow than it needs to be. At least the key cluster summaries should be brought into the main figures or text.

      In response to this request, we now define in the Results section 3 clusters of inflammatory genes: 1) IL1A and IL1B, 2) 14 variable-response RELA-target genes excluding IL1A and IL1B, and 3) 14 variable-response Interferon-Stimulated genes, and we present a new Fig 5 dot plot showing the mean-centered gene expression values for these 3 classes of inflammatory genes for all fibroblasts in proliferation and IR-SEN. We feel that this figure gives a nice overview of the diversity of fibroblast gene expression profiles for these 3 inflammatory gene clusters. We thank the reviewer for this suggestion.

      1. State the criteria for classifying fibroblasts as high-, mid-, and low-responding explicitly, either in the main text or in figure legends.

      In the Results, we now describe the fibroblast response with regards to the expression profiles that are evident in the new Fig 5 dot plot described above, as a function of the 3 classes of inflammatory genes: In order to summarize the diversity of inflammatory gene expression for all fibroblasts, we graphically compared the mean-centered expression data for each fibroblast cell line in proliferation and IR-induced senescence (Fig 5). The inflammatory genes were separated into three classes: IL1A and IL1B, the 14 principal RELA target genes excluding IL1A and IL1B, and the 14 highly-expressed ISGs. All the irradiated adult fibroblasts expressed IL1A and IL1B at appreciably lower levels than the irradiated WI38 and HCA2 fibroblasts. This trend was maintained for the remaining 14 RELA-target genes, but the adult S31_lung and S09_gingiva cells expressed the 14 RELA-target genes at levels that were similar to irradiated HCA2 fibroblasts, although lower than irradiated WI38 fibroblasts. Adult S15_lung cells were exceptional in that they expressed the 14 RELA-target genes, exclusive of IL1A and IL1B, at high levels during proliferation, but at lower levels after irradiation. Notably, 8/16 irradiated adult fibroblasts expressed ISGs at levels similar to irradiated HCA2 or WI38 fibroblasts. Six of these 8 fibroblasts expressed low levels of RELA-target gene expression after IR, indicating that ISG gene expression can be induced after IR independently of RELA-target gene expression. Finally, the adult S26_gingiva fibroblasts were exceptional in expressing ISGs at high basal levels in proliferation, and at lower levels after irradiation

      1. The RELA-target versus ISG distinction is one of the more compelling aspects of the paper and would benefit from being introduced earlier in the Results.

      We thank the reviewer for this suggestion and we now describe early in the Results the following 3 classes of inflammatory genes: 1) IL1A and IL1B, 2) 14 principal RELA-target genes excluding IL1A and IL1B, and 3) 14 highly-expressed Interferon-Stimulated genes.

      The rationale for using 40 Gy is covered in the Methods, but a brief comment in the main text on whether the conclusions would be expected to hold at lower, more physiologically relevant IR doses would help readers judge generalizability. In response to this reviewer’s request, we have added the following text to the Results section to justify our choice of X-ray dose: We tested 10, 20, and 40 Gy at 2 Gy/minute and we monitored senescence at 10 days post-irradiation by EdU and SA-ß-galactosidase assays. 10 Gy induced less than 50% of cells into senescence, 20 Gy yielded 50-80% of senescent cells, and 40 Gy provided > 80% senescent cells for all the fibroblasts (see Materials and Methods). We then performed RNA-seq analysis for all fibroblasts 10 days post-irradiation with 40 Gy, but we also compared transcriptomes for a select few irradiated at both 20 and 40 Gy.

      Regarding the brief comment on physiologically relevant IR doses, we feel that this discussion requires more than a brief comment which would be distracting in the Results section. We thus placed this Discussion in the Materials and Methods: The higher apparent doses in our work compared to some previous studies [10,14,17] may be attributed to attenuation of lower-energy X-rays (120 kV) by passage through the cell culture plastic and cell culture medium, or it may be that the dosimetry of our irradiator was over-estimated. The biological criterion of > 80% senescence induction is similar to that of previous papers studying SASP expression during IR-induced senescence [10,14,17]. Doses of 10 Gy or more are only used in cancer radiotherapies. Despite the elevated number of DNA double-strand breaks generated by these doses, the highly-efficient DNA repair leads to healing of most DSBs [15,62]. Immunostaining with antibodies to gH2AX showed that 10 days after 40 Gy IR, most cells retained only a few persistent DNA DSBs (Fig S3) similar to that observed for replicatively senescent fibroblasts (Fig S5). These persistent DSBs are thought to maintain the cells in senescence [15]. Although these experimental conditions are not “physiological”, they may be appropriate models for some senescent cells in vivo that have been attributed to the presence of persistent DNA damage [25,63].

      Concerning IR doses, we also had RNA-seq data comparing 0, 20 Gy, and 40 Gy irradiation for fetal lung WI38, and adult mammary M168 and M170 fibroblast cell lines. We now present a new Fig S12A showing that increasing irradiation from 20 Gy to 40 Gy increased slightly the transcriptomic senescence score of WI38 and M168 cells but not M170 cells. However, increasing from 20 to 40 Gy increased significantly the expression of RELA-target genes in WI38 cells, but not M168 and M170 cells. These observations also contributed to our choice of 40 Gy versus 20 Gy X-irradiation.

      __Reviewer #2 (Significance): __This study has the potential to make an important conceptual contribution by challenging the assumption that a strong inflammatory SASP is a universal feature of fibroblast senescence. Instead, it suggests that many adult primary fibroblasts mount a substantially weaker RELA-dependent inflammatory response to IR-induced senescence than the fetal/neonatal lines most commonly used in the field. If this holds up and is framed appropriately, it has real implications for how broadly current fibroblast-SASP models can be extrapolated. The manuscript also proposes a plausible mechanistic framework linking this heterogeneity to weak IL1A/IL1B induction, differential IL1-locus enhancer activity, and FOXF1-dependent regulation. Some of these mechanistic claims need to be presented with more caution than they currently are, but the underlying conceptual advance is still solid and should interest a fairly broad readership. This work should interest researchers working on cellular senescence, SASP regulation and inflammation, chromatin/enhancer control of inflammatory gene programs, DNA damage /therapy and a tissue microenvironments

      __Reviewer #3 (Evidence, reproducibility and clarity): __The study identifies low-responding fibroblasts, which present with limited expression of inflammatory cytokines (i.e. IL1A, IL1B, CXCL8) during IR-induced senescence, compared to others featuring high expression of inflammatory genes upon IR. Addition of recombinant IL1a and IL1b restores the inflammatory program in low-responding fibroblasts. The low responsiveness is correlated with reduced levels of RELA in the nucleus, reduced chromatin accessibility and H3K27acetylation at putative enhancers in the intergenic region between IL1A and IL1B. Depletion of these enhancers inhibited inflammatory gene expression after IR. Moreover, they identified FOXF1 transcription factor as a regulator of gene expression after IR. Overall, the study shows differences in capacity of activation of inflammatory response during IR-induced senescence in fibroblasts, driven by FOXF1 transcription factor and expression of IL1A and IL1B.

      Major comments: This is a thorough study of differences in inflammatory profiles in different types of fibroblasts, distinguishing between neonatal/fetal and adult fibroblasts undergoing senescence. The approach is straightforward and the conclusions are appropriate based on the data presented. Also, some mechanistic details are provided about factors that regulate inflammation through expression of IL1A and IL1B.

      We thank Reviewer 3 for the encouraging remarks.

      Minor comments: Figure 2 is a bit confusing. Is there a way to present it in a clearer manner?

      We provide a new clearer version of this Fig 2 PCA analysis in which we provide a color-coded legend identifying the tissue origin and we removed the labels identifying each sample point except for a few key samples.

      __Reviewer #3 (Significance): __The conclusions of this study are highly significant because in the senescence field, most transcriptomes analyzed focused on a limited number of fetal or neonatal fibroblasts. Some single-cell sequencing studies of fibroblasts have already shown great diversity depending on tissue origin and the pathology. This manuscript shows the transcriptional diversity of a great variety of primary adult and neonatal/fetal fibroblasts, which are induced to undergo IR-induced or oncogene-induced senescence. This knowledge definitely advance our understanding of inflammatory pathways that contribute to senescence phenotypes.

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      Referee #3

      Evidence, reproducibility and clarity

      The study identifies low-responding fibroblasts, which present with limited expression of inflammatory cytokines (i.e. IL1A, IL1B, CXCL8) during IR-induced senescence, compared to others featuring high expression of inflammatory genes upon IR. Addition of recombinant IL1a and IL1b restores the inflammatory program in low-responding fibroblasts. The low responsiveness is correlated with reduced levels of RELA in the nucleus, reduced chromatin accessibility and H3K27acetylation at putative enhancers in the intergenic region between IL1A and IL1B. Depletion of these enhancers inhibited inflammatory gene expression after IR. Moreover, they identified FOXF1 transcription factor as a regulator of gene expression after IR. Overall, the study shows differences in capacity of activation of inflammatory response during IR-induced senescence in fibroblasts, driven by FOXF1 transcription factor and expression of IL1A and IL1B.

      Major comments: This is a thorough study of differences in inflammatory profiles in different types of fibroblasts, distinguishing between neonatal/fetal and adult fibroblasts undergoing senescence. The approach is straightforward and the conclusions are appropriate based on the data presented. Also, some mechanistic details are provided about factors that regulate inflammation through expression of IL1A and IL1B.

      Minor comments: Figure 2 is a bit confusing. Is there a way to present it in a clearer manner?

      Significance

      The conclusions of this study are highly significant because in the senescence field, most transcriptomes analyzed focused on a limited number of fetal or neonatal fibroblasts. Some single-cell sequencing studies of fibroblasts have already shown great diversity depending on tissue origin and the pathology. This manuscript shows the transcriptional diversity of a great variety of primary adult and neonatal/fetal fibroblasts, which are induced to undergo IR-induced or oncogene-induced senescence. This knowledge definitely advance our understanding of inflammatory pathways that contribute to senescence phenotypes.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This manuscript asks whether the strong inflammatory SASP typically seen after IR-induced senescence is a general feature of fibroblasts or something specific to the fibroblast lines used to define it. The authors profile a panel of primary adult fibroblast strains from several tissues by RNA-seq, alongside fetal/neonatal comparators, and find that many adult strains show only weak induction of RELA-dependent inflammatory genes after IR-induced senescence. They trace this to poor activation of IL1A/IL1B, show that exogenous IL-1α or IL-1β can rescue inflammatory gene induction in the low-responding adult lines, and implicate reduced activity at two candidate enhancers in the IL1A/IL1B locus. In WI38hTERT cells, deleting either enhancer blunts the inflammatory response to IR, and FOXF1 knockout produces a similar effect on both inflammatory and interferon-stimulated genes. Overall, this addresses a genuinely important question for the field: how well do inflammatory conclusions from the standard fetal/neonatal fibroblast models generalize to adult tissue?

      Major comments

      This is an important question, and the manuscript contains several genuinely interesting observations. The suggestion that many adult primary fibroblasts respond far more weakly than the fetal/neonatal lines that dominate the literature could be a significant finding for the field. The IL-1 rescue data are convincing and support the idea that failure to engage the IL-1 amplification loop underlies the low-response phenotype. That said, the manuscript currently overreaches in a few of its conclusions, and one part of the experimental foundation needs firmer support before the central claims can stand. One concern, especially at the beginning of the manuscript, is whether the key low-responding adult strains are convincingly senescent under the conditions used for the main comparisons. This matters because Figure 1 functions as the entry point for the paper's central argument, but the M168 IR-treated cells shown there don't display obvious senescent morphology in the DAPI images; I don't see clear SAHF-like chromatin reorganization. The supplementary evidence for reduced EdU incorporation is relevant, but given how much weight M168 carries in the paper's logic, burying this characterization in the supplement isn't sufficient. Additionally, the picture for SA-β-gal positivity is not convincing. Either additional senescence characterization for M168 moved into the main figures, or a tighter, more integrated presentation of what's already there would bring more confidence. As it stands, the paper doesn't clearly rule out that M168 reflects incomplete senescence induction rather than a "low-inflammatory" senescent state, and that distinction is fairly central to the whole argument. Second, the manuscript still frames its central finding largely as fetal/neonatal versus adult, but the data themselves point to tissue origin as a major axis of variation. There's clear heterogeneity across fibroblast types, but developmental stage, tissue identity, and donor-to-donor variation aren't cleanly separated in this cohort. The claim best supported by the current data is that many adult fibroblasts in this panel are weaker inflammatory responders relative to the fetal/neonatal lines commonly used in the field, and not that developmental stage per se is the driver. Related to this, the enhancer and FOXF1 data are interesting and plausible, but the manuscript should be more careful about the line between "identified a contributing mechanism" and "explained the phenomenon." The enhancer deletions and FOXF1 knockout give clean, strong phenotypes in WI38hTERT cells. The low-response phenotype in primary adult fibroblasts is never directly perturbed at the mechanistic level. Therefore, a more explicitly that these experiments identify plausible contributors to differential inflammatory output, rather than presenting them as a settled explanation for why adult fibroblasts behave this way.

      Minor comments

      1. IL-1α activity in senescence is known to depend on post-transcriptional and post-translational regulation, not simply on IL1A transcript levels,so using "pro-IL1" in the figures is potentially meaningful, but this isn't addressed in the text. Please clarify what's actually being measured and discuss how it relates to prior work on IL-1α processing, localization, and activation state.
      2. The HOX/positional identity section is interesting on its own terms but doesn't currently tie back to the inflammatory phenotype that's the paper's central focus. Either strengthen that connection or move most of it to the supplement.
      3. Cluster definitions and gene-grouping logic are scattered across several supplementary figures, which makes the argument harder to follow than it needs to be. At least the key cluster summaries should be brought into the main figures or text.
      4. State the criteria for classifying fibroblasts as high-, mid-, and low-responding explicitly, either in the main text or in figure legends.
      5. The RELA-target versus ISG distinction is one of the more compelling aspects of the paper and would benefit from being introduced earlier in the Results.
      6. The rationale for using 40 Gy is covered in the Methods, but a brief comment in the main text on whether the conclusions would be expected to hold at lower, more physiologically relevant IR doses would help readers judge generalizability.

      Significance

      This study has the potential to make an important conceptual contribution by challenging the assumption that a strong inflammatory SASP is a universal feature of fibroblast senescence. Instead, it suggests that many adult primary fibroblasts mount a substantially weaker RELA-dependent inflammatory response to IR-induced senescence than the fetal/neonatal lines most commonly used in the field. If this holds up and is framed appropriately, it has real implications for how broadly current fibroblast-SASP models can be extrapolated. The manuscript also proposes a plausible mechanistic framework linking this heterogeneity to weak IL1A/IL1B induction, differential IL1-locus enhancer activity, and FOXF1-dependent regulation. Some of these mechanistic claims need to be presented with more caution than they currently are, but the underlying conceptual advance is still solid and should interest a fairly broad readership.

      This work should interest researchers working on cellular senescence,SASP regulation and inflammation, chromatin/enhancer control of inflammatory gene programs, DNA damage /therapy and atissue microenvironments

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      Referee #1

      Evidence, reproducibility and clarity

      In this paper, "Differential Enhancer Activity and FOXF1 Levels Contribute to Higher Inflammatory Gene Expression of Fetal/Neonatal Versus Adult Fibroblasts in IR-induced Senescence" the authors compared the inflammatory response of fetal vs adult fibroblasts in response to irradiation induced-senescence. They found that fetal fibroblasts express higher levels of pro-inflammatory genes compared to the adults' ones, notably on IL1A and IL1B genes expression. They suggest that this difference of response could be linked to chromatin accessibility and epigenetic regulation in enhancer region around the IL1 genes. And that also could be explained by different levels of FOXF1 transcription factor. Interestingly, even if adult fibroblast shows lower induction of pro-inflammatory gene expression, that level can be rescue by IL1a treatment. Showing that the cells didn't lack the capacity of inducing pro-inflammatory genes expression but is lower in DNA damage induce senescence compared to fetal fibroblasts.

      Some major comments in this manuscript could be:

      • Authors compare fetal and adult fibroblast from 2 different tissues of interest (lung vs mammary) for their first observation and the basis of this research. However, it is known and author also shows in figure 2 that different fibroblasts can be and behave differently from their tissues of origin. It is possible that the first statement could have a second bias on the tissue specificity.
      • Moreover, two different fetal fibroblast cell lines can also have different levels of expression of pro-inflammatory factors (MRC5 vs IMR90 for example). The author mainly focuses on comparing 1 fetal line to some adult ones. The question here is : is it truly a foetal phenotype or cell specificity ? Author used a second fetal cell line in their RNAseq comparison, but it was from dataset already available that could also bring some technical difference. Other fetal fibroblast could be used such as MRC5 or IMR90.

      Other comment:

      • Rationale for the HOX and non HOX TF graph?
      • Figure 5C hard to understand
      • Figure 6 : Will be easier to follow if all the cells are sorted the same way from ATACseq and H3K27ac.
      • Could add correlation curve between inflammatory gene response and nuclear translocation of RELA
      • Quantification of gH2AX foci in S3 is it different between cell lines?
      • Figure S22 doesn't seem to add more information. Rationale?
      • It is interesting that the fibroblast has been isolated from young and older adults. Would be interesting to see if aging impact in the same way the induction of senescence than fetal vs adult.

      Significance

      I found that this study leads to nice and interesting observations and highlights the potential differences between studies in fetal fibroblasts and the response that could be found adult fibroblast that we could find in adult tissues.

      However, I find the story sometime difficult to follow, as some information and graphs don't seem to be useful for the purpose of the story. It also feels that sometime there is a lack of consistency in the use of cells lines or models in the different paragraphs. The authors wanted to explore lot of topics in this story, but the realization seems confused. The manuscript could benefit from more clarity in the explanation of the different experiments used and analysis and could also be more focused.

      This study could be used in the senescence field for people who study mechanistic study or pathways related to NFKb or ISG pathways. It is interesting to know that adult fibroblast might have lower response compared to the fetal fibroblast usually used in the mechanistic study.

      My expertise as reviewer was in the senescence field and mechanistic study in the SASP production in fibroblast.

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      Reply to the reviewers

      __Response to Reviewers __

      Please see below detailed responses to all comments of the four reviewers:

      Reviewer #1

      Line 39: Remove comma from "therefore, keeping"

      Response: Comma was removed.

      Line 102: Remove comma from "drivers, that"

      Response: Comma was removed.

      Line 107: Change from "dependent temperature" to "temperature dependent"

      Response: It was changed to “temperature dependent”.

      Line 115: Change from "be used avoid severe" to "be used to avoid severe"

      Response: It was changed to “be used to avoid severe”.

      Line 258: Change "of QS is complicated... " to "of QF activity is complicated..."

      Response: It was changed to “of QF activity is complicated”.

      Line 263-264. Adoption of GAL4 in mosquitoes is also hampered by the lack of GAL4 activity when expressed using pan-neuronal drivers. Consider adding "or lack of effectiveness in neurons" to this sentence and cite the following paper: Zhao,Development of a pan-neuronal genetic driver in Aedes aegypti mosquitoes, Cell Report Methods, 2021.

      Response: We included this important reference in the revised version in the 2nd paragraph of the introduction when highlighting the limitations of GAL4.

      Discussion. It would be helpful to add discussion of which temperature sensitive intein variant the investigators recommend to choose for different situations and why. For the pan neuronal nsyb line, the investigators chose TS19, but for Orco, the investigators chose J1. This choice appears to reflect the severity of the temperature dependence, but it would be helpful if the investigators discuss this decision more explicitly. This would be helpful information for when researchers are considering implementing these variants into their experiments.

      __Response: __We appreciate this suggestion and provided additional context in the revised manuscript. This comment also promoted us to add a paragraph to the discussion, where we state in more detail about the advantages of having QF2C86_INTts with different temperature profiles.

      Briefly, we choose nSyb-QF2 C86_INTTS19 for larval experiments because the on/off threshold occurs at lower temperatures, at which development is slower, allowing analysis throughout larval development. Behavioral experiments, on the other hand are easier and more robust at somewhat higher temperature (~ 25°C) when flies are more active, which is why Orco-QF2 C86_INTJ1 was used.

      In addition, the investigators might want to mention that the HACK system (Lin and Potter, Genetics, 2016) could be a genetic strategy used with these QF2-intein^ts variants to convert existing GAL4/GAL80/LexA/QF2 lines to QF2-intein^ts lines.

      Response: That again is an excellent point, and the notion that standard GAL4 and QF/QF2 lines could be converted into temperature sensitive driver lines was included in the discussion.

      The authors should deposit their QF2-intein plasmids to Drosophila Genomics Resource Center and/or Addgene and include this information into their manuscript (ideally as a table for easy identification). This will help expedite adoption of these new reagents by other research groups.

      Response: We will deposit following plasmids to Addgene and include this information as a supplementary table 3.

      pCaSpeR4-Orco-QF2C86_INTWT, pCaSpeR4-Orco-QF2C86_INTJ1, pCaSpeR4-Orco-QF2C86_INTJ2, pCaSpeR4-Orco-QF2C86_INTTS19, pCaSpeR4-Orco-QF2C86_INTF1, pCaSpeR4-nSyb-QF2C86_INTWT and pCaSpeR4-nSyb-QF2C86_INTTS19

      Figure 1. Consider re-organizing these panels (and corresponding text) so that panels B,C come before A. That is, introduce QF2 and intein first, and then show how these are put together in the schematic.

      Response: Thank you, we agree. Figure 1 has been re-organized accordingly.

      Figure 1B. Check spelling of "Dimerization".

      Response: Thank you, corrected.

      Figure 1D. The images of the palps in J1/30C (and other examples) appear to show autofluorescence in palp sensilla. The authors may wish to mention this in the manuscript or legend to clarify that this is not induced GFP reporter expression.

      Response: Has now been noted in the figure legend.

      Figure 1A Legend. "QF2 containing the Sce VMA1 intein at immediately upstream of indicated cysteine (C) residue driven by the Orco promoter were combined with a QUAS-reporter," Awkward phrasing; not clear what this means. Is QF2-N the N-terminus of QF2, and QF2-C the C-terminus of QF2 in the schematic? This might be clearer if Figure 1 panel A comes after panels B, C as mentioned above.

      Response: We have re-ordered Figure 1 as suggested and made clarified the text of Figure legend 1A (now 1C); Temperature-sensitive inteins were inserted at C86 of the QF2 coding sequence. Self-splicing of the intein at permissive temperature connects the two QF2 fragments and reconstitute a functional QF2 protein, while at restrictive temperature, the intein is retained within the DNA binding domain, abolishing QF2 activity.”

      Figure 1B Legend, line 603. What is "QF2s" ? Plural of QF2? If so, I think you can do without the 's'.

      Response: ‘s” was removed.

      Figure 1B Legend, line 605. "removal of the middle domain of the original QF to avoid lethality caused by broad expression of". Awkward phrasing. Consider changing to "the location of the middle domain removed in original QF to avoid ...."

      __Response: __It was changed to The dashed line indicates the location of the middle domain removed in the original QF to avoid toxicity caused by broad expression of QF.”

      General question. Does the intein get completely removed from the protein, or is there some of it left behind? This information could be incorporated into the Introduction or associated with Figure 1.

      Response: Western analysis indicates self-spicing of the VMA1 intein from GAL4 restores appropriate host protein size. Lack of a QF antibody does not provide an opportunity to check for that. However, given that spacing of the cysteine residues in zinc finger motifs is crucial for DNA binding, it is highly likely that precise removal of intein must occur to restore transcriptional activity of QF2. A comment regarding this issue was added in the first paragraph of the result section. “Analysis of GAL4containing temperature-sensitive VMA1 inteins by Western blot analysis indicate that self-splicing results in expected reduction to the size of the host protein (Zeidler et al. 2004; Tan et al. 2009). Whether the VMA1 intein is precisely removed from QF2 cannot be assessed due to lack of an antibody. Regardless, the key goal of our study is to assess whether temperature induced VMA1 splicing can restore QF2 functionality by assessing reporter gene expression.

      Figure 2 (and 4 and 5). I found the temperature shift schematics confusing and not clear. Please update. Is D1 day 1? If so, then how can it be at 30°C for D1 to D6 (red bar) and also at lower temperatures (D1-D4 green and blue bars)? Consider using absolute days; ie D1-D6 indicates at that temperature for 6 days after eclosion, and then D7-D10 would be at that following temperature for 3 more days. Also, the colored red and green bars start before Day1-- does that mean animals are shifted to those temperatures as pupae before adult eclosion? Or immediately upon eclosion? Maybe instead of using D1, D4, D6, the authors would use hours at that temperature; ie, D3 = 72 hrs. Overall, I found these schematics of the time shifts very hard to follow.

      Response: Thank you for pointing this ambiguity out. This was corrected and will be in the revised Figure 2.

      Figure 2 legend, line 631. "taster" should be "faster".

      Response: corrected.

      Figure 3. The larval expression experiments in this transparent system are excellent. It would be more rigorous if the investigator should quantify these results. It would be useful to have a graph showing increase in fluorescence over time; ie, 6, 12, 18, 24, 36,48 hours, 96 hrs after shift to permissive temperature. This would provide some guidelines in how quickly the intein splicing process works and also when it maxes out. I realize larval development might limit the extend of this quantification. These would be helpful experiments to add quantification to their methods, but I realize this is somewhat addressed in Figure 5B using a different experimental paradigm.

      Response: We performed additional downshift experiments and present images and quantification at the suggested timepoints. These data are presented in Figure 3B. The findings show that induction occurs within 6 hours and reaches steady state level within less than 24 hours after downshift to permissive temperature of 18°C.

      Figure 4A. The direction arrows are hard to see on the airflow schematics.

      Response: Will be corrected in new Figure 4.

      Figure 4 legend, line 672. "Bars with different letters are significantly." Awkward sentence, please revise.

      Response: Revised to “Bars indicated by different letters are significantly different”.

      Reviewer #2

      Please quantify all expression data (e.g. Fig 1D, 2B, 3) in terms of GFP signal, and ideally also in terms of GFP gene expression level.

      Response: Due to the limited expression of Orco in olfactory sensory neurons, it is challenging to accurately quantify gene expression levels. However, we measured mean fluorescence by quantifying GFP protein expression levels using Fiji software (Shihan et al, 2021, Biochem. Biophys. Rep.; Jain et al, 2023, Front. Ecol. Evol.) and included these quantified data in the revised manuscript (see Figures 1D, 2C and 3A and 3B)

      1. The imaging has been conducted through larval or adult cuticle, without the use of antibodies. The imaging results are thus very crude, and the concern is that low level of GFP expression remains undetected. Please conduct experiments where you dissect and immunostain a tissue (brain, etc) and then quantify the GFP signal in various conditions.

      As suggested by the reviewer, we also repeated GFP expression analysis expression using immunostaining of the antennae and the maxillary palps of flies reared across all three temperatures for wild type and two temperature-sensitive inteins (Supplementary Figure 2). While somewhat more sensitive, the results of these antibody staining experiments align with the live imaging data (compare quantification in Figure 1 (live imaging) and Supplementary Figure 2 (antibody staining).

      For some experiments you use TS19 lines, and for others - J1. The data is thus hard to compare. Please use one or both lines throughout, or at least explain your choice of lines.

      __Response: __We appreciate this suggestion (also raised by reviewer 1) and will provide introductory sentences to each of these experiments. This comment also promoted us to add a paragraph to the discussion, where we state in more detail about the advantages of having QF2C86_INTts with different temperature profiles.

      Briefly, we choose TS19 for larval experiments since the on/off switch occurs at lower temperatures, providing more time to conduct temperature-shift experiments during larval development (Figure 3B). Behavioral experiments, on the other hand are somewhat easier to carry out at higher temperature when flies overall are more active, which is why J1 was used in olfactory assays (Figure 4).

      Please map your insertions at least to a chromosome, and ideally - more precisely than that.

      __Response: __We mapped the insertion of Orco-QF2_INTts and nSyb-QF2C86_INTts transgenes both genetically and by conducting inverse PCR analysis and cloning DNA adjacent to the insertion sites. This information is included in the revised manuscript in Supplementary Table 2.

      The authors raise the question about the equivalency of QS repression and the inactive form of QF2 the made, but they never address this question properly. Please demonstrate, by quantifying gene expression and GFP (or another reporter) fluorescence levels that QF2_INT at 25/30C leads to the same or lower expression at the most effective QS line (on the X).

      __Response: __We appreciate this suggestion but think it to be beyond the scope of this paper. Additionally, based on published data by C. Potter and colleagues, who have developed and expanded the use of the Q system, and the data presented in this work, we think that the QINT based system is superior to turn on/off expression of QF/QF2 compared to QS/quinic acid. First, QS inhibition by feeding larvae quinic acid showed minimal rescue in the CNS, compared to of larvae controls lacking (lacking QS; Riabinina et al 2019) [https://pmc.ncbi.nlm.nih.gov/articles/PMC6499530]; Second, expression of QUAS-mCD8 reporter expression in the CNS is only partially restored and only in a subset of neurons in tub-QS/quinic acid fed larvae fed, and in many neurons, it is not restored at all (Riabinina et al., 2015; see attached Supplementary Figure 3 of that paper). Lastly, these authors noted that attempts to restore expression in the adult CNS were not successful, presumably due to lack of efficient BBB crossing of quinic acid (Riabinina et al., 2015, and Riabinina et al., 2019). Lastly, even in non-neuronal cells, restoring expression of QUAS-reporters of tub-QS, quinic acid fed flies was poor after one day, needing five days to reach saturation levels that were similar controls (Potter et al., 2010). Given these published data and our findings, showing that QINT allows full recovery of both expression (Figures 2 and 3) and function (Figure 4 and 5) in larvae and adults within 24 to 72 hours, we believe that the suggested experiments would be of little value to the current study.

      Please properly and systematically characterise time courses of activation/inactivation of J1/TS19 lines in different assays.

      Response: Figure 2 show activation/deactivation time course in OSNs for Orco-QF2C86_INTJ1S19. In addition, new data shows time course of activation in the CNS (brain/ventral nerve cord) and PNS for nsyb-QF2C86_INTTS19 (Figure 3B).

      Reviewer #3

      While the modified temperature-dependent regulatory system seems promising, the authors do not address leakiness of the system at higher "permissive" temperatures (i.e. 25 {degree sign}C) that are common in routine fly work. Moreover, temperature shifts can influence developmental processes so controls showing that the temperature changes alone do not affect reporter expression in a standard QF2 driver (without intein) would be useful to include.

      Response: “Leaky expression” is a fair concern, which could arise due to residual splicing of the INTts at higher temperatures__. __Quantification of our expression analysis indicates that with the exception of INTJ2, which shows some, but highly reduced levels of expression at 30°C, none of the other three constructs show evidence of leakiness at 25°C (INTTS19 and INTF1) or 30 °C (INTJ1) (See Figures 1D, 2B and 3A and 3B).

      Data on the effectiveness of reporter labeling are presented qualitatively (i.e., fluorescence images). Measurements of reporter intensity across temperatures and time points (e.g., mean fluorescence per neuron) would strengthen claims of "robust" vs. "no" expression and allow more rigorous comparisons of splicing efficiencies among the four intein variants. The quantification will also require appropriate statistical tests.

      __Response: __Yes, this follows the potential concern of leakiness raised above, which we have addressed, using Fiji ImageJ software (Shihan et al, 2021, Biochem. Biophys. Rep.; Jain et al, 2023, Front. Ecol. Evol.) for quantifying GFP expression across temperatures.

      3.Divergent temperature profiles are shown for certain inteins (e.g., INTTS19 shows activity only at 18{degree sign}C, whereas in yeast it was active up to 25{degree sign}C). Providing biochemical evidence (e.g., Western blot showing spliced vs. unsplicedQF2) would clarify whether the observed phenotypes stem from splicing efficiency or altered protein stability.

      __Response: __A similar point was raised by reviewer 1, and for convenience, we paste our response here again:

      Western analysis indicates self-spicing of the VMA1 intein from GAL80/GAL4 restores appropriate host protein size. Lack of a QF antibody does not provide an opportunity to check for that. However, given that spacing of the cysteine residues in zinc finger motifs is crucial for DNA binding, it is highly likely that precise removal of intein must occur to restore transcriptional activity of QF2. A note regarding this issue was added in the first paragraph of the result section. “Analysis of GAL4containing temperature-sensitive VMA1 inteins by Western blot analysis indicate that self-splicing results in expected reduction to the size of the host protein (Zeidler et al. 2004; Tan et al. 2009). Whether the VMA1 intein is precisely removed from QF2 cannot be assessed due to lack of an antibody. Regardless, the key goal of our study is to assess whether temperature induced VMA1 splicing can restore QF2 functionality by assessing reporter gene expression.

      Reviewer #4

      Temperature stress at 30{degree sign}C

      The restrictive temperature used (30{degree sign}C) is known to be stressful for Drosophila, including increased larval mortality. This raises the possibility that some effects may be influenced by temperature rather than solely by the genetic system. Please address this explicitly. Additional controls or discussion would strengthen confidence that 30{degree sign}C is appropriate. Could intermediate temperatures (e.g., 28-29{degree sign}C) provide a more physiological compromise?

      __Response: __Temperature affects physiology by mainly increasing metabolism. The temperature range used in the study (18 to 30 °C ) has no impact on health of Drosophila. When raised at any temperature between 18 °C and 30°C develop and mate normally and are fertile, with the key difference having accelerated development (and presumably somewhat accelerated aging) as temperature increases. We agree with the reviewer that any experiment with changing temperature conditions requires adequate controls. We included such a control by using the standard QF2 driver, as well as QF2_INTwt whose activity, and hence reporter expression, are not dependent on temperature per se for comparison (Figure 1D, Figures 3A, 4B and 5A). Furthermore, the functional olfactory assay shows that olfactory performance is indistinguishable in control flies kept at 25 °C and 30°C (Figures 4B and 4C).

      Terminology clarity (Abstract)The wording around "permissive" and "restrictive" temperature ranges should be clarified, as these ranges partly overlap depending on the construct.

      __Response: __We think that “permissive” and “restrictive” are clear descriptors for “supporting/allowing” activity and “preventing/hindering” activity to occur, and for the following reasons, we think it to be fair to use these terms in the abstract. First, when considering temperature-sensitive mutations, a precise cut-off point generally does not exist, but there is a small range of temperatures, over which transition from permissive to restrictive occurs. Second, since the mutations have distinct profiles, with some being restrictive, while other being permissive at 25°C, it is not possible to define the specific range for each INTts in the abstract due to space limitation. By using the terms “permissive” and “restrictive”, we allow for a context dependent interpretation.

      Figure 4 legend clarity.

      "Left/right" should be replaced with "top/bottom" (or equivalent) to match the figure layout. The legend requires more detail: what exactly do the green (or red) bars represent? The meaning of labels such as "a" and "b" should be explicitly defined.

      __Response: “__Left/right" was replaced with "top/bottom".

      The green bars indicate the response of flies reared at 25°C to ethyl acetate and the red bars are the response of flies reared at 30°C.

      Labels such as “a” and “b” indicate significantly different fly response to ethyl acetate based on ordinary one-way ANOVA with Tukey’s multiple comparison tests (p

      1. Generality to other systems (Discussion)

      The Discussion highlights potential application in systems such as zebrafish. It would strengthen this section if the authors could comment on:

      expected temperature ranges in these organisms so whether specific intein variants might be better suited for such systems

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      Referee #4

      Evidence, reproducibility and clarity

      Summary

      This study engineers temperature-sensitive intein insertions into the QF2 transcription factor to create a simplified Q-system that enables reversible, temperature-dependent control of gene expression without the need for the QS/quinic acid suppressor. Using Drosophila, the authors demonstrate robust, tunable, and functional temporal regulation of gene expression and neuronal activity across development and in adults. This is a well-executed and practically valuable toolkit paper introducing a clever intein-based simplification of the Q-system. The manuscript is well written, clear, and easy to follow.

      Minor concerns

      1. Temperature stress at 30{degree sign}C The restrictive temperature used (30{degree sign}C) is known to be stressful for Drosophila, including increased larval mortality. This raises the possibility that some effects may be influenced by temperature rather than solely by the genetic system.
        • Please address this explicitly.
        • Additional controls or discussion would strengthen confidence that 30{degree sign}C is appropriate.
        • Could intermediate temperatures (e.g., 28-29{degree sign}C) provide a more physiological compromise?
      2. Terminology clarity (Abstract) The wording around "permissive" and "restrictive" temperature ranges should be clarified, as these ranges partly overlap depending on the construct.
      3. Figure 4 legend clarity
        • "Left/right" should be replaced with "top/bottom" (or equivalent) to match the figure layout.
        • The legend requires more detail: what exactly do the green (or red) bars represent?
        • The meaning of labels such as "a" and "b" should be explicitly defined.
      4. Generality to other systems (Discussion) The Discussion highlights potential application in systems such as zebrafish. It would strengthen this section if the authors could comment on:
        • expected temperature ranges in these organisms
        • whether specific intein variants might be better suited for such systems

      Significance

      Good paper well written. It is a niche target audience, which is researchers who use drosophila. So clearly a tool paper. Needs to address the temperatures needed, as I feel 30oC is too warm for most fly work. Just needs to be addressed in the text.

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      Referee #3

      Evidence, reproducibility and clarity

      While the modified temperature-dependent regulatory system seems promising, the authors do not address leakiness of the system at higher "permissive" temperatures (i.e. 25 {degree sign}C) that are common in routine fly work. Moreover, temperature shifts can influence developmental processes so controls showing that the temperature changes alone do not affect reporter expression in a standard QF2 driver (without intein) would be useful to include.

      Data on the effectiveness of reporter labeling are presented qualitatively (i.e., fluorescence images). Measurements of reporter intensity across temperatures and time points (e.g., mean fluorescence per neuron) would strengthen claims of "robust" vs. "no" expression and allow more rigorous comparisons of splicing efficiencies among the four intein variants. The quantification will also require appropriate statistical tests.

      Divergent temperature profiles are shown for certain inteins (e.g., INTTS19 shows activity only at 18{degree sign}C, whereas in yeast it was active up to 25{degree sign}C). Providing biochemical evidence (e.g., Western blot showing spliced vs. unspliced QF2) would clarify whether the observed phenotypes stem from splicing efficiency or altered protein stability.

      Significance

      The manuscript by Ahn and Amrein describes a modification of the binary Q transcriptional regulatory system to introduce temperature control through inclusion of a temperature sensitive intein. The authors demonstrated, robust, cell type specific QUAS reporter expression in Drosophila olfactory sensory neurons regulated via temperature shifts and rescue of a lethal phenotype under QF/QUAS control. With further validation this could be another useful tool for the Drosophila research community. This work would have broader significance, if the new intein-regulated QF2 transcription factor had been demonstrated to function for other models, as was suggested by the authors.

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      Referee #2

      Evidence, reproducibility and clarity

      This paper presents a novel, interesting and valuable contribution to the genetic tool development for model and non-model organisms. The work is conceptually novel and should be shared with the research community. However, the work is not rigorous enough and raises several important major comments.

      Major comments:

      1. Please quantify all expression data (e.g. Fig 1D, 2B, 3) in terms of GFP signal, and ideally also in terms of GFP gene expression level.
      2. The imaging has been conducted through larval or adult cuticle, without the use of antibodies. The imaging results are thus very crude, and the concern is that low level of GFP expression remains undetected. Please conduct experiments where you dissect and immunostain a tissue (brain, etc), and then quantify the GFP signal in various conditions.
      3. For some experiments you use TS19 lines, and for others - J1. The data is thus hard to compare. Please use one or both lines throughout, or at least explain your choice of lines.
      4. Please map your insertions at least to a chromosome, and ideally - more precisely than that
      5. The authors raise the question about the equivalency of QS repression and the inactive form of QF2 the made, but they never address this question properly. Please demonstrate, by quantifying gene expression and GFP (or another reporter) fluorescence levels that QF2_INT at 25/30C leads to the same or lower expression at the most effective QS line (on the X).
      6. Please properly and systematically characterise time courses of activation/inactivation of J1/TS19 lines in different assays.

      Minor comment:

      Please cite the articles that implemented QF2 in mosquitoes, C.elegans, zebrafish etc - you can find relevant references in this book chapter: https://link.springer.com/protocol/10.1007/978-1-0716-2541-5_2.

      Significance

      This paper presents a novel, interesting and valuable contribution to the genetic tool development for model and non-model organisms. The work is conceptually novel and should be shared with the research community. However, the work is not rigorous enough and raises several important major comments.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In the manuscript by Ahn and Amrein, the investigators optimize and introduce temperature sensitive inteins into the transcription factor QF2 to create a temperature-sensitive version of QF2. At restrictive temperatures (25C or 30C depending on which intein variant is used), QF2 is non-functional. At the permissive temperature, the intein protein self-splices out of QF2 and generates a fully functional QF2. The investigators test the efficacy of their new system using two different QF2 expression patterns: 1) orco, which expresses in most olfactory neurons, and 2) nsyb, which expresses pan-neuronaly. They perform temperature shifts with both systems and examine induction of GFP reporters, as well as perform behavioral experiments to demonstrate loss of olfactory responses (Orco) or lethality (nsyb) at permissive temperatures.

      Major Comments

      This is an exciting update to the Q-system toolbox, and solves many challenges with implementation of the Q-system; most notably, temporal control of QF activity. This new QF2-intein^ts system could replace the use of QS and also the need for quinic acid suppression of QS. It could also become the default QF version to use when generating new constructs. Overall, the experiments are well performed and the investigators demonstrate convincingly the effectiveness of their new temperature sensitive versions of QF2. This new genetic tool opens up many new experimental strategies in Drosophila, and could also be extended for use in other model systems like fish and mosquitoes.

      Minor Comments

      Line 39: Remove comma from "therefore, keeping"

      Line 102: Remove comma from "drivers, that"

      Line 107: Change from "dependent temperature" to "temperature dependent"

      Line 115: Change from "be used avoid severe" to "be used to avoid severe"

      Line 258: Change "of QS is complicated... " to "of QF activity is complicated..."

      Line 263-264. Adoption of GAL4 in mosquitoes is also hampered by the lack of GAL4 activity when expressed using pan-neuronal drivers. Consider adding "or lack of effectiveness in neurons" to this sentence and cite the following paper: Zhao, Development of a pan-neuronal genetic driver in Aedes aegypti mosquitoes, Cell Report Methods, 2021.

      Discussion. It would be helpful to add discussion of which temperature sensitive intein variant the investigators recommend to choose for different situations and why. For the pan neuronal nsyb line, the investigators chose TS19, but for Orco, the investigators chose J1. This choice appears to reflect the severity of the temperature dependence, but it would be helpful if the investigators discuss this decision more explicitly. This would be helpful information for when researchers are considering implementing these variants into their experiments.

      In addition, the investigators might want to mention that the HACK system (Lin and Potter, Genetics, 2016) could be a genetic strategy used with these QF2-intein^ts variants to convert existing GAL4/GAL80/LexA/QF2 lines to QF2-intein^ts lines.

      The authors should deposit their QF2-intein plasmids to Drosophila Genomics Resource Center and/or Addgene and include this information into their manuscript (ideally as a table for easy identification). This will help expedite adoption of these new reagents by other research groups.

      Figure 1. Consider re-organizing these panels (and corresponding text) so that panels B,C come before A. That is, introduce QF2 and intein first, and then show how these are put together in the schematic.

      Figure 1B. Check spelling of "Dimerization".

      Figure 1D. The images of the palps in J1/30C (and other examples) appear to show autofluorescence in palp sensilla. The authors may wish to mention this in the manuscript or legend to clarify that this is not induced GFP reporter expression.

      Figure 1A Legend. "QF2 containing the Sce VMA1 intein at immediately upstream of indicated cysteine (C) residue driven by the Orco promoter were combined with a QUAS-reporter," Awkward phrasing; not clear what this means. Is QF2-N the N-terminus of QF2, and QF2-C the C-terminus of QF2 in the schematic? This might be clearer if Figure 1 panel A comes after panels B, C as mentioned above.

      Figure 1B Legend, line 603. What is "QF2s" ? Plural of QF2? If so, I think you can do without the 's'.

      Figure 1B Legend, line 605. "removal of the middle domain of the original QF to avoid lethality caused by broad expression of". Awkward phrasing. Consider changing to "the location of the middle domain removed in original QF to avoid ...."

      General question. Does the intein get completely removed from the protein, or is there some of it left behind? This information could be incorporated into the Introduction or associated with Figure 1.

      Figure 2 (and 4 and 5). I found the temperature shift schematics confusing and not clear. Please update. Is D1 day 1? If so, then how can it be at 30C for D1 to D6 (red bar) and also at lower temperatures (D1-D4 green and blue bars)? Consider using absolute days; ie D1-D6 indicates at that temperature for 6 days after eclosion, and then D7-D10 would be at that following temperature for 3 more days. Also, the colored red and green bars start before Day1-- does that mean animals are shifted to those temperatures as pupae before adult eclosion? Or immediately upon eclosion? Maybe instead of using D1, D4, D6, the authors would use hours at that temperature; ie, D3 = 72 hrs. Overall, I found these schematics of the time shifts very hard to follow.

      Figure 2 legend, line 631. "taster" should be "faster".

      Figure 3. The larval expression experiments in this transparent system are excellent. It would be more rigorous if the investigators could quantify these results. It would be useful to have a graph showing increase in fluorescence over time; ie, 6, 12, 18, 24, 36, 48 hours, 96 hrs after shift to permissive temperature. This would provide some guidelines in how quickly the intein splicing process works and also when it maxes out. I realize larval development might limit the extend of this quantification. These would be helpful experiments to add quantification to their methods, but I realize this is somewhat addressed in Figure 5B using a different experimental paradigm.

      Figure 4A. The direction arrows are hard to see on the airflow schematics.

      Figure 4 legend, line 672. "Bars with different letters are significantly." Awkward sentence, please revise.

      Significance

      This work represents a significant technical advance in the genetic control of transgene expression in Drosophila, especially in relation to improving the Q-system toolbox. The authors used a self-splicing intein to develop a temperature-sensitive QF2 variant. This has immediate experimental applications, enabling temporal control of a binary expression system through simple temperature changes. It could also simplify use of the Q-system by keeping it off at a restrictive temperature, possibly replacing the need for a QS suppressor. Temperature shifts to control QF2 activity are also more efficient than the current method of using QS suppression coupled with quinic acid feeding. An additional advantage of this QF2-intein^ts system is its reversibility: QF2 activity can now be turned on and off without additional components. This work opens up many new experimental avenues.

      A temperature-controlled QF2 variant could also simplify long-term maintenance of widely expressed QF2 lines, such as pan-neuronal lines, by keeping animals at restrictive temperatures and shifting temperatures as needed to induce QF2 activity. This will be especially useful in non-Drosophila insects, such as Aedes and Anopheles mosquitoes, where any health defects caused by pan-neuronal QF2 expression can complicate rearing and line maintenance.

      This work will be of interest to those working in any genetic system in which temperature shifts are possible, with immediate interest to Drosophila researchers. It will also be of great interest also to those working in C.elegans, zebrafish, and mosquitoes where QF2 reagents have proven useful but have room for improvement.

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      Reply to the reviewers

      We thank the reviewers for identifying several areas for improvement. We

      have addressed all of them in the new version. We detail below a point-by-point

      response. The list of substantial changes made in the manuscript includes;

      • Revamped Figure 1 to remove superfluous panels, discussion of hemag-

      glutination assay (HA assay) and edited the caption to more accurately

      reflect the work done.

      • The Materials and Methods section has been thoroughly revised with new

      sections and more explanations;

      – Improved discussion of virus used in the study. Added details for:

      ∗ Justifications for use of H3N2 virus strain in this study

      ∗ L194P mutation in the strain that allows infection of chicken

      ∗ HA assay and its use in our study

      ∗ Prior studies that detail methods used here

      ∗ Experiments to determine dosage

      ∗ Infection protocol

      – Expanded details for the collection of genomic information (gene se-

      quences and names) used in our study

      – Detailed the mRNAseq analysis

      • We added tissue-identity marker-gene validation (PAX6/SIX3/STMN3 for

      brain, SULT/FGG/FGB for kidney) to address the possibility of bad dis-

      sections at E10.

      • We added data from plaque-assay/RT-qPCR-of-allantoic-fluid experiments.

      • The corrected/replaced citation of the old H5 HPAIV review that didn’t

      support our claim.

      1• Improved the legibility of all figures (heatmaps/plots) by increasing fonts

      and removing margins

      • Better justified our claims of infection clearance in kidney/lungs using

      viral sequences detected in our samples.

      • Toned down discussion to indicate this study is not exhaustive, but is

      indicative of a roadmap for further investigations.

      • Provided more context to the RIG-I claims, citing alternate explanations

      for our findings.

      • Reworded the abstract to emphasize that if even one virus, H3N2, is

      cleared by the innate immune system of the embryo without needing

      RIG-I, then the centrality of RIG-I as an essential component is called

      into question.

      • Expanded discussion of role of microglia in the brain and their response

      to the infection in embryonated eggs

      • The web-based tool has been redesigned from the ground up with several

      enhancements and new features allowing better exploration

      • Added a download link for raw data and underlying genomic resources

      developed for this analysis

      • Moved microglia heatmap to main text and added more markers and in-

      formation.

      • Added a figure to show tissue-specific expression as well as PCA plot to

      demonstrate integrity of kidney and brain dissections.

      • Added a concluding section on limitations and future directions in the

      discussion.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): This

      manuscript by Gurses et al. uses mRNA-seq profiling of brain, kidney,

      and lung tissues from H3N2-infected 10-day embryonated chicken

      eggs to investigate how the developing avian innate immune system

      responds to influenza infection in the absence of RIG-I. The authors

      report that viral transcripts are concentrated in the brain whereas

      kidney and lung exhibit strong innate immune activation with little

      detectable viral RNA. From these observations, they propose that

      peripheral tissues successfully clear infection through innate mech-

      anisms involving MDA5, TLR, and IRF-mediated signaling, while

      the embryonic brain acts as an immune-low compartment that is not

      able to effectively contain the virus through innate mechanisms. The

      study further argues that the absence of RIG-I does not compro-

      mise antiviral defense in this model and that M2-like macrophage

      programs dominate the post-infection response. 1. The evidence

      2for immune privilege rather than neurotropism is still suggestive but

      not definitive. They do provide nice supportive data, but they may

      want to include a limitation of the study to highlight the need for

      validation.

      Response: We agree, and have added an explicit limitations subsection to

      the Discussion and softened the corresponding statements in the Conclusions.

      Immune privilege is not explicitly proven by our data, the evidence indicates this

      might be the simplest, consistent explanation. The data showed abundant viral

      RNA indicative of active infection in the brain, while kidney and lung showed

      only trace viral RNA alongside an M2-macrophage signature consistent with a

      waning infection. We now state clearly that this pattern is consistent with, but

      does not conclusively prove an immune-privilege mechanism, and other forms

      of validation, including time-series data between 0h and 48h is needed.

      1. The manuscript concludes that M2-polarized macrophages are

      the primary effectors responsible for viral clearance and that these

      macrophages are excluded from the brain. This is a nice finding

      especially in lieu of the fact that microglia are embryonic derived and

      may not polarize to M2 state easily. The supporting data is inferred

      so this limitation could also be highlighted.

      Response: We thank the reviewer for this insightful point. We have added

      a paragraph to the Discussion noting that microglia are yolk-sac/embryonically

      derived and, unlike monocyte-derived macrophages, are not known to readily

      adopt an M2 polarization state, which offers a plausible cell-intrinsic explana-

      tion for the absence of the M2 signature in brain and complements our tissue-

      access/immune-privilege interpretation. We are explicit that this remains an

      inference from transcriptomic data rather than a directly demonstrated cellular

      mechanism.

      1. The data show that a strong innate response can occur in

      embryonated chicken eggs despite the absence of RIG-I. However, the

      manuscript occasionally extends this observation into broader claims

      that RIG-I loss does not impair antiviral defense or that previous

      hypotheses regarding RIG-I-mediated susceptibility are unsupported.

      RIG-I’s role may be quite virus specific so it should be made clear

      that this relates to the flu Condition.

      Response: We agree with these cautionary points about RIG-I’s rols in an-

      tiviral defense. Our main point is that H3N2 does get cleared from peripheral

      tissues by the innate immune system, despite the lack of RIG-I. We have re-

      vised the manuscript to scope our claims explicitly to this H3N2/embryonated-

      egg model and to the implications for RIG-I centrality more broadly. Despite

      the absence of RIG-I, we observe robust activation of downstream antiviral

      gene expression, consistent with functional compensation by MDA5 and other

      pattern-recognition pathways (chMDA5/chCARDIF/chLGP2 signaling), in line

      with prior transcriptomic evidence of MDA5-driven responses to HPAI strains

      H7N9, H7N1, H5N1, and H9N2[1, 2, 3, 4]. We have softened the manuscript’s

      language accordingly: rather than stating that RIG-I loss does not impair an-

      tiviral defense broadly, we now state that, in this model and for this virus, the

      3innate response is not impaired by the absence of RIG-I, while explicitly noting

      that this lack of RIG-I might be permissive for other viruses or strains.

      1. Several figures contain heatmaps with very small labels, making

      them difficult to see. Increasing label size would improve readability.

      Response: We have redrawn the heatmaps with bigger labels to address this

      concern.

      1. The authors should clarify whether viral transcripts were nor-

      malized across tissues in a way that permits quantitative comparison

      of viral burden between organs.

      Response: Viral transcript abundance

      was normalized to transcripts per million mapped reads (TPM) within each li-

      brary, which allows a relative comparison of viral burden within a tissue-type,

      between 48h post-infection and uninfected controls. We have added this detail

      to the Methods. We note, however, that because brain, kidney, and lung differ

      substantially in cellular composition and total RNA content, this normalization

      supports qualitative and relative comparisons of viral burden rather than an ab-

      solute, tissue-independent quantitation; we now state this limitation explicitly

      in the Methods.

      1. The manuscript cites relevant avian innate immunity literature,

      although discussion of prior transcriptomic studies examining MDA5-

      mediated influenza responses in chickens could be expanded.

      Response: We have expanded the discussion of prior transcriptomic studies

      of MDA5-mediated influenza responses in chickens, citing [1], [2], [3], and [4]

      (see also our response to point 3 above), and situate our findings relative to this

      body of work.

      Reviewer #1 (Significance (Required)): This is an interesting and

      informative transcriptomic study that addresses an important ques-

      tion in avian immunology: how chickens compensate for the evolu-

      tionary loss of RIG-I and how innate antiviral responses are organized

      during embryonic development. The cross-tissue comparative design

      is a strength, and the manuscript provides a valuable resource de-

      scribing transcriptional responses to H3N2 infection in embryonated

      eggs. The data demonstrate clear innate immune activation and dif-

      ferential tissue responses following infection. The manuscript’s cen-

      tral mechanistic conclusions are inferred primarily from transcrip-

      tomic signatures rather than directly demonstrated, but the func-

      tional roles of many of these genes is well enough established that the

      correlation is compelling. The claim that macrophages are the prin-

      cipal effectors responsible for clearance in the periphery and their

      absence is responsible for outcome in the brain is speculative, but

      this does seem plausible and is interesting. It is especially inter-

      esting that microglia do not compensate and this may reflect their

      embryonic origin. Monocyte-derived macrophages may be required

      and not present in the chick embryo brain. The study is valuable as

      a hypothesis-generating transcriptomic analysis. Some conclusions

      would benefit from qualification or additional validation but overall

      it is an informative analysis and should be published.

      Response: We have toned down some of our conclusions and expanded dis-

      cussion of the mRNA seq analyses.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This study investigates the innate immune response to H3N2 in-

      fluenza virus infection in the developing chicken embryo using tran-

      scriptomic profiling across multiple tissues. The authors report that,

      despite the absence of adaptive immunity at this developmental stage,

      peripheral tissues exhibit a robust innate immune response associated

      with efficient viral clearance, whereas viral persistence is largely con-

      fined to the brain. Based on these findings, the authors propose a

      model in which MDA5/TLR-mediated antiviral signaling, macrophage-

      associated immune responses, and tissue repair programs contribute

      to peripheral viral clearance, while the relative immune privilege of

      the developing brain contributes to localized viral persistence. Over-

      all, the manuscript addresses an important biological question and

      provides novel insights into antiviral immunity during embryonic de-

      velopment. The study is well designed, the analyses are comprehen-

      sive, and the findings represent a valuable contribution to the field.

      The manuscript is of high quality, and the comments below are in-

      tended only to further strengthen its presentation. I recommend

      publication after minor revision.

      Response: We have addressed the concerns raised here.

      Major Comments I have no major comments to address.

      Minor Comments The Discussion concludes with several interest-

      ing perspectives that place the findings in a broader biological con-

      text. To further strengthen these sections, the authors may consider

      framing some of these ideas as potential future directions inspired by

      the current findings. This would further emphasize the distinction

      between the experimental observations and their broader biological

      implications while preserving the overall message of the manuscript.

      Response: We planned for this study to inspire further investigations and

      framing some of the speculations as potential future directions is a great sug-

      gestion, we have incorporated this into a new final section on limitations and

      future directions in the discussions.

      Reviewer #2 (Significance (Required)): This manuscript addresses

      an important and timely question regarding the capacity of the em-

      bryonic innate immune system to control influenza virus infection in

      the absence of adaptive immunity. Using comprehensive transcrip-

      tomic analyses across multiple tissues, the authors provide a valuable

      dataset and propose a coherent model of tissue-specific antiviral re-

      sponses during embryonic development. The findings extend current

      knowledge of avian innate immunity and offer new perspectives on

      the mechanisms underlying peripheral viral clearance and viral per-

      sistence within the developing brain.

      Response: Thanks for highlighting the significance of our findings.

      The study fills an important gap in our understanding of host antiviral

      responses during early development by characterizing how in-

      nate immune pathways alone respond to systemic influenza infection.

      The work provides meaningful conceptual advances by challenging

      existing assumptions regarding the requirement for adaptive immu-

      nity and the role of RIG-I deficiency in avian influenza responses. In

      addition, the proposed model of immune privilege contributing to vi-

      ral persistence in the developing brain provides a valuable framework

      for future mechanistic studies.

      Response: This comment is very encouraging.

      The manuscript will be of interest to a broad audience of re-

      searchers working in innate immunity, developmental immunology,

      host-pathogen interactions, virology, avian biology, and comparative

      immunology. While the immediate clinical implications are limited,

      the findings may also be of interest to investigators studying neu-

      rotropic viral infections and early-life immune responses.

      Response: We hope to spur further studies along these lines, as indicated in

      this comment. Thank you.

      The proposed biological model is compelling and well supported

      by the data. A few refinements to the wording in the Discussion to

      further distinguish experimental observations from broader mecha-

      nistic interpretations would further strengthen an already excellent

      manuscript.

      Response: Thanks for highlighting this point. We have reworded the discus-

      sion to address this, clearly distinguishing experimental observations from the

      broader interpretations to help delineate and highlight areas that need further

      work.

      Overall, I find this to be a well-executed and thoughtfully pre-

      sented study that makes a valuable contribution to the field. I believe

      the manuscript is suitable for publication after minor revision.

      Response: We are grateful for these encouraging comments.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary

      This manuscript investigates the innate antiviral response to H3N2

      influenza virus infection in embryonated chicken eggs, focusing on

      how chickens compensate for the absence of the viral RNA sen-

      sor RIG-I. Using RNA sequencing of brain, kidney, an lung tis-

      sues, the authors propose that viral infection is effectively controlled

      in the lungs and kidneys but persists in the brain. They propose

      that brain-specific viral persistence reflects immune privilege and re-

      stricted immune-cell (macrophage) access rather than enhanced viral

      neurotropism. Their transcriptomic analyses are further interpreted

      to suggest that, despite lacking adaptive immunity and RIG-I, chicken

      embryos mount a robust innate antiviral response through alterna-

      tive sensing pathways. Overall, the study addresses a valid question

      in avian influenza biology and provides a small transcriptomic dataset

      that might be useful. The comparative analysis across different or-

      6gans could, with some validation, provide a strength. However, all

      conclusions are inferred from a single set of transcriptomic data and

      would benefit from additional functional validation. Furthermore,

      the central claim over the absence of RIG-I alone explaining (or not)

      influenza susceptibility in chickens is not supported, or particularly

      novel.

      Response: We thank the reviewer for this thorough and critical evaluation.

      The points raised, particularly regarding virus choice and the strength of evi-

      dence for infection, clearance, and tissue identity, prompted a substantial revi-

      sion of the manuscript, and we address each point below.

      Major comments 1. Choice of virus. If the primary question be-

      hind the study is the interplay between host innate immunity and

      virus, why choose a human seasonal strain rather than an avian

      one? Furthermore, the authors chose an H3N2 virus from the era

      when the H3 HA evolved to specialise in binding long chain 2,6

      sialic acids and consequently stopped haemagglutinating chicken red

      blood cells and growing well in the allantoic cavity of eggs. See

      https://doi.org/10.1006/viro.2000.0679 [5] and doi:10.1128/JVI.00058-

      10 [6] for instance, but there is a very substantial body of literature

      on this because of the problems it caused for surveillance and vaccine

      production. The papers the authors cite to say that their virus is

      known to grow in avian systems does not support this claim (it’s an

      old review primarily on H5 HPAIV).

      Response: We thank the reviewer for this detailed and critical assessment; it

      prompted us to re-examine and better justify our choice of virus, which has sub-

      stantially strengthened the manuscript. We address the specific points below

      and have expanded the corresponding sections of the Discussion and Meth-

      ods. In brief, the virus used in this study is not a wild-type human seasonal

      strain but an egg-adapted derivative, A/Uruguay/716/2007 (NYMC X-175C),

      that carries the well-characterized HA substitution L194P, which shifts receptor-

      binding specificity toward the avian-type α2,3-linked sialic acids found in chicken

      tissue [7, 8]. This virus haemagglutinates chicken red blood cells, which would

      not occur if the virus were unable to bind the avian-type receptor; this is direct

      evidence of productive receptor engagement, not merely an assumption carried

      over from its use in vaccine production.

      The choice of virus weakens this study, not least because there is

      little evidence provided to show that infection really took in the eggs,

      despite a very high inoculum of ¿ 1E5 pfu. More detail on the virus

      itself would help - what inoculum was used to grow it in eggs has it

      adapted to growing in that substrate? If it does agglutinate chicken

      RBCs this suggests it has. If the EID50 really is 1E5 pfu, then

      there is a high chance the prep might be full of defective interfering

      particles.

      Response: We have addressed this above, and modified the caption to figure

      1 and the discussion to specifically address these points. Any response to inter-

      fering particles would be uniform across tissues, whereas we see sharp differences

      7between the tissues, suggestive of differential infections.

      The reviewer’s concern about the plausibility of our titer prompted us to

      recheck our calculations, in the course of which we identified and corrected an

      error in how the stock plaque titer had been computed. The 50% egg infectious

      dose (EID50), the dilution producing detectable HA activity in 50% of inocu-

      lated eggs (6/12) was 10−5, corresponding to a titer of 105 EID50/mL. Based

      on the stock’s plaque titer, this dilution corresponds to a plaque-equivalent con-

      centration of ≈ 150 PFU/mL, or approximately 15 PFU delivered per egg in

      the 100 µL inoculum volume. We have also explained clearly the sequence of

      experiments for characterizing the virus stock.

      To address the question of is it just detection of passive inoculom ? Detection

      specifically of viral mRNA (rather than residual genomic RNA) requires active

      viral transcription within the tissue, which is evidence of productive engagement

      with host cells rather than passive carryover of inoculum and the differential

      detection of viral mRNA is evidence in favor of viral infection being the primary

      driver of the response.

      why choose a human seasonal strain rather than an avian one?

      Response: The choice of virus reflects both practical and scientific consider-

      ations, and we have added both to the manuscript rather than leaving this im-

      plicit. Practically, A/Uruguay/716/2007 was readily available in the lab under

      existing biosafety and institutional approvals, since it is used there for vaccine

      seed-virus production. Scientifically, this strain is not a naive human isolate:

      it was egg-adapted through multiple passages in the allantoic cavity of embry-

      onated chicken eggs (ECE) specifically to bind avian-type sialic acid receptors,

      and it has a previously reported neurotropic phenotype in this system, which

      made it well suited to our goal of comparing peripheral (kidney and lung) versus

      central (brain) innate immune responses to a virus capable of both peripheral

      and CNS replication in ECE.

      We agree that an avian-origin strain would avoid any ambiguity about host

      adaptation, and we now note this explicitly as a limitation and a natural next

      step for follow-up work using a wild-type avian influenza virus.

      The papers the authors cite to say that their virus is known to

      grow in avian systems does not support this claim (it’s an old review

      primarily on H5 HPAIV).

      Response: We thank the reviewer for catching this; the citation did not

      support the claim as stated, and we have corrected it. We address this point

      by (a) replacing the inappropriate reference, (b) citing the substantial prior

      literature establishing that this strain is egg-adapted for growth in chicken eggs,

      which is also the basis on which it is used for vaccine seed-virus production,

      and (c) adding the specific molecular details of the adaptation below. As noted

      above, the hemagglutination assay is itself direct evidence that the virus binds

      the avian-type (α2,3-linked) sialic acid receptor present in chicken tissue; the

      assay would not produce agglutination otherwise. Further mechanistic detail

      follows.

      The H3N2 subtype used here (A/Uruguay/716/2007, NYMC X-175C) in-

      fects ECE due to egg-adaptive substitutions of virus hemagglutinin (specifically

      8L194P), to increase conformational dynamics to bind the avian-type (short and

      are predominantly α 2-3 linked) receptor[7]. The egg-adaptive mutation, L194P,

      has been extensively characterized both structurally and antigenically[8].

      1. Validation of tissue extraction. As the authors hint, getting

      individual organs out of 10 day old embryos is not always easy. The

      picture of a whole embryo in Fig 1C does not show that this was

      successfully achieved and nor do the transcriptomic data in Fig 2

      and subsequent figures. Was any histology and/or transcriptomics

      for organ-specific genes done?

      Response: This is an important point, and we agree that organ dissection

      at E10 is technically demanding. Rather than histology, which we did not

      perform, we used tissue-specific marker transcripts as a check on sample identity

      and purity: robust expression of brain markers (PAX6, SIX3, STMN3) and the

      kidney markers (SULT, FGG, FGB ) across control and infected tissues confirms

      that the brain and kidney samples are relatively free of cross-contaminating

      tissue. We also used PCA to show that lung samples do not cluster with each

      other, while kidney and brain samples do. These data are now shown in Fig.

      3 with three panels, one each for the kidney-specific genes, brain-specific genes

      and the PCA plot. We are more cautious about the lung samples, there are not

      many genes that are consistently up across control and infected tissues, because

      they likely represent a mixture of respiratory and adjacent tissue; we now state

      explicitly that we treat ”lung” as a proxy for peripheral, non-CNS tissue rather

      than a molecularly pure lung sample, and we have added this caveat, along with

      the marker-gene analysis, to the Methods and Discussion.

      1. Lack of evidence for viral growth and/or clearance. The con-

      clusion that the virus is completely cleared from the embryo lungs

      and kidneys is based primarily on the near absence of viral tran-

      scripts detected by RNA sequencing set against a clear signal in brain.

      However, the study relies on small numbers of samples (2 control, 2

      infected) from a single time point and the data do not distinguish

      clearance from lack of infection.

      Response: We agree that a single time point with two replicates per condition

      cannot, on its own, incontrovertibly distinguish clearance from a lower level of

      productive infection that never reached the burden seen in brain. However,

      we do not believe the data are consistent with simple lack of infection; kidney

      samples show low but reproducible viral transcript counts (indicating some level

      of viral infection occurred), the sialic acid receptors required for viral entry are

      expressed across all three tissues which are all in contact with allantoic fluid

      where the virus was injected, and both an M2-macrophage signature and a

      strong ISG response are present specifically in the tissues with little viral RNA,

      which is a pattern more consistent with an active immune response having acted

      on the virus than with the virus never having been present. We have revised the

      manuscript to present this as convergent, but still circumstantial, evidence for

      a waning/cleared infection rather than a proven time course, and we explicitly

      flag that a longitudinal design with additional time points and larger sample

      sizes would be needed to establish clearance kinetics directly; we note this as

      9a priority for follow-up work given the resource and labor constraints of the

      current study.

      The transcriptomic data in Fig 3 suggest the presence of replicat-

      ing virus in the embryo brain, but it’s difficult to judge how much.

      Additional virological validation, such as infectious virus quantifi-

      cation (e.g., plaque assay or TCID50), viral antigen detection by

      immunohistochemistry or immunofluorescence, or independent viral

      RNA quantification by RT-qPCR, would strengthen the conclusion

      that peripheral tissues undergo true infection, while viral clearance

      would require some form of time course analysis. This should also in-

      clude measurements of virus in allantoic fluid, as many strains of IAV

      grow well in the cells lining the cavity but do not infect the embryo.

      Response: We agree that we do not have an independent, tissue-level con-

      firmation of infection in brain, kidney, and lung beyond the mRNA-seq data

      itself. The differential recovery of viral transcripts by mRNA-seq across tissues

      remains our primary evidence for active, tissue-specific infection: detecting viral

      mRNA (rather than residual genomic RNA from the inoculum) requires active

      viral transcription within that tissue, and the sharp differences between tissues

      argue against uniform carryover of inoculum as an alternative explanation. We

      have clarified this logic in the Discussion.

      To address the reviewer’s concern about the virus preparation itself, we have

      added two additional assays to the Materials and Methods, though we are careful

      to note what they do and do not establish. Plaque assays in MDCK-SIAT1 cell

      cultures confirmed the titer and viability of the virus stock used for inoculation.

      Separately, RT-qPCR for HA and PB1 transcripts in allantoic fluid harvested

      48 h post-inoculation demonstrated productive viral replication in the chorioal-

      lantoic membrane, confirming that the inoculation protocol successfully estab-

      lished an active infection in the egg. These assays confirm that a competent,

      replicating virus was delivered to the embryo via the allantoic route; they do

      not independently verify infection of the internal organs profiled by mRNA-seq,

      which remains supported by the transcriptomic evidence alone. We view this as

      an important distinction and have flagged the need for organ-level orthogonal

      validation (e.g., immunostaining or RT-qPCR of dissected brain/kidney/lung

      tissue) as a priority in the Limitations section.

      1. General overinterpretation of limited data. The authors should

      be careful not to overinterpret the data they have, without any forms

      of orthogonal validation. For instance, while the transcriptomic data

      do show increased expression of various ISGs, consistent with acti-

      vation of MDA5- and TLR-mediated antiviral pathways, they do not

      demonstrate that these pathways are functionally required for viral

      control or that they compensate for the absence of RIG-I. Similarly,

      the interpretations over macrophage polarisation, access and brain

      immune privilege remains inferential, and the statement that the

      virus ”simply replicates where macrophages cannot reach” is stronger

      than the current evidence supports. Direct assessment of immune-cell

      infiltration, for example by immunostaining, flow cytometry, or other

      10imaging-based approaches, would help determine whether viral per-

      sistence is primarily driven by immune-cell exclusion or by intrinsic

      differences in tissue susceptibility.

      Response: We agree that our transcriptomic data alone cannot establish

      functional requirement, and we have revised the manuscript to distinguish clearly

      between what the data show (ISG induction consistent with MDA5- and TLR-

      mediated signaling, and an M2-macrophage transcriptional signature restricted

      to peripheral tissues) and what remains inferential (that these pathways are

      causally responsible for viral control, and that macrophage exclusion, rather

      than intrinsic tissue susceptibility, explains brain persistence). Specifically, we

      have removed the phrase “simply replicates where macrophages cannot reach”

      and replaced it with more qualified language that flags this as one plausible

      interpretation consistent with the data rather than a demonstrated mechanism.

      We have also added a concluding Limitations and future directions section in

      the Discussion explicitly listing the orthogonal validation (immunostaining or

      flow cytometry for immune-cell infiltration, and functional pathway perturba-

      tion) that would be needed to move from correlation to causation, and note this

      as a priority for follow-up work. Given the resource constraints of this study,

      we believe the transcriptomic evidence supports these interpretations as a well-

      motivated hypothesis rather than an established mechanism, and we have edited

      the manuscript throughout to reflect that distinction.

      Minor comments 1. The manuscript would benefit from a clearer

      description of the RNA-sequencing analysis, including the sequencing

      depth, quality-control criteria, statistical thresholds for differential

      expression, and any batch correction methods used. Providing these

      details would improve the transparency and reproducibility of the

      study.

      Response: Our revamped Materials and Methods section now includes de-

      tails of the mRNA-seq analysis. We have also revamped the browser-based tool

      to allow exploration of different methods of filtering the data. Our results rely

      on robust changes (¿ 2-fold upregulation of affected genes, low variability across

      samples), which renders them less sensitive to changes in methodology. We

      have also made the raw sequencing data and processed files available for down-

      load, and readers can additionally reproduce or extend the analysis themselves

      through our web-based tool.

      1. Figure 1 should be improved or cut. Images of opened eggs

      are not particularly useful, or (as already noted) an image of a whole

      embryo by itself. The cartoons and description of how an HA assay

      works are incorrect (clotting factors should have been washed away)

      and 1F does not show data that demonstrate an EID50 value.

      Response: We thank the reviewer for these specific and valid criticisms. We

      have simplified Figure 1: the image of the opened egg and the whole-embryo

      have been removed. We have moved the description of the HA assay to the

      methods, and made clear how we used the assay in our experiments. The

      reviewer is correct that non-specific agglutinins/clotting factors should be, and

      were, removed by washing prior to the assay. We have also explained better in

      the methods how we determined EID50, which was then used to infect the eggs

      in our study.

      1. As the study was performed in embryonic day 10 chicken em-

      bryos, the authors should discuss the extent to which these findings

      can be extrapolated to adult chickens. It would be helpful to comment

      on how immune maturation may influence the antiviral pathways de-

      scribed and whether the proposed mechanisms are likely to extend

      beyond the embryonic stage.

      Response: We agree this deserves explicit discussion. A key advantage of

      the E10 embryo model is that the innate immune system is largely developed

      while the adaptive system has not yet come online, allowing us to isolate the

      innate contribution to viral control without confounding effects from adaptive

      immunity. Also because the mothers have not been exposed to the virus, there

      are no mother-derived antibodies to consider, the response is purely innate. Be-

      cause the initial response to influenza infection in adult chickens is also innate

      (adaptive responses typically take one to two weeks to mature, even in adults) ,

      the innate mechanisms we describe here plausibly extend to the earliest response

      to infection in adults as well. We are careful, however, not to claim this extrap-

      olation is established: immune maturation could alter the relative contributions

      of specific pathways (e.g., a fully developed adaptive system, more mature mi-

      croglia, or a different tissue-resident macrophage repertoire in adults), and we

      now state explicitly in the Discussion that extending these findings to adult or

      post-hatch birds will require direct validation in that setting.

      Reviewer #3 (Significance (Required)):

      Strengths

      The manuscript presents a small transcriptomics data set that

      might be of interest to the field if better described and validated.

      Limitations

      Lack of depth and adequate validation are main problems. The

      only data are from a single transcriptomics run done with very small

      numbers of samples, using a virus strain that may skew the results

      and which is not validated by any measurements of virus replication

      other than RNAseq. Comparison with Existing Knowledge The study

      potentially adds a little to our knowledge of how Galliformes respond

      to influenza virus without RIG-I and its activating ubiquitin ligase

      Appropriate Audience

      If strengthened, this work would be of interest to researchers

      in avian immunology and influenza biology. It may also appeal to

      investigators studying comparative immunity and systems-level ap-

      proaches to host-pathogen interactions.

      Response: We thank the reviewer for this candid assessment. We agree with

      the underlying concern about depth and validation and have addressed it as fully

      as our resources allow, primarily through the changes described above: correct-

      ing and strengthening the virological justification for the strain used, adding

      tissue-identity marker analysis to support our organ assignments, expanding

      the Methods to improve reproducibility, and revising the Discussion throughout

      12to distinguish direct observations from inferred mechanisms and to flag vali-

      dation experiments (immunostaining, flow cytometry, additional time points,

      and an avian-strain comparison) as clear priorities for future work. We do not

      believe these revisions resolve every limitation the reviewer raises, and we are

      not in a position to generate substantial new experimental data at this time; we

      do believe, however, that the manuscript now represents its findings as a well-

      supported, hypothesis-generating resource rather than a definitive mechanistic

      study, which we hope addresses the reviewer’s core concern about overinterpre-

      tation of a single, small dataset.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      This manuscript investigates the innate antiviral response to H3N2 influenza virus infection in embryonated chicken eggs, focusing on how chickens compensate for the absence of the viral RNA sensor RIG-I. Using RNA sequencing of brain, kidney, and lung tissues, the authors propose that viral replication is effectively controlled in the lungs and kidneys but persists in the brain. They propose that brain-specific viral persistence reflects immune privilege and restricted immune-cell (macrophage) access rather than enhanced viral neurotropism. Their transcriptomic analyses are further interpreted to suggest that, despite lacking adaptive immunity and RIG-I, chicken embryos mount a robust innate antiviral response through alternative sensing pathways.

      Overall, the study addresses a valid question in avian influenza biology and provides a small transcriptomic dataset that might be useful. The comparative analysis across different organs could, with some validation, provide a strength. However, all conclusions are inferred from a single set of transcriptomic data and would benefit from additional functional validation. Furthermore, the central claim over the absence of RIG-I alone explaining (or not) influenza susceptibility in chickens is not supported, or particularly novel.

      Major comments

      1. Choice of virus. If the primary question behind the study is the interplay between host innate immunity and virus, why choose a human seasonal strain rather than an avian one? Furthermore, the authors chose an H3N2 virus from the era when the H3 HA evolved to specialise in binding long chain 2,6 sialic acids and consequently stopped haemagglutinating chicken red blood cells and growing well in the allantoic cavity of eggs. See https://doi.org/10.1006/viro.2000.0679 and doi:10.1128/JVI.00058-10 for instance, but there is a very substantial body of literature on this because of the problems it caused for surveillance and vaccine production. The papers the authors cite to say that their virus is known to grow in avian systems does not support this claim (it's an old review primarily on H5 HPAIV). The choice of virus weakens this study, not least because there is little evidence provided to show that infection really took in the eggs, despite a very high inoculum of > 1E5 pfu. More detail on the virus itself would help - what inoculum was used to grow it in eggs has it adapted to growing in that substrate? If it does agglutinate chicken RBCs this suggests it has. If the EID50 really is ~1E5 pfu, then there is a high chance the prep might be full of defective interfering particles.
      2. Validation of tissue extraction. As the authors hint, getting individual organs out of 10 day old embryos is not always easy. The picture of a whole embryo in Fig 1C does not show that this was successfully achieved and nor do the transcriptomic data in Fig 2 and subsequent figures. Was any histology and/or transcriptomics for organ-specific genes done?
      3. Lack of evidence for viral growth and/or clearance. The conclusion that the virus is completely cleared from the embryo lungs and kidneys is based primarily on the near absence of viral transcripts detected by RNA sequencing set against a clear signal in brain. However, the study relies on small numbers of samples (2 control, 2 infected) from a single time point and the data do not distinguish clearance from lack of infection. The transcriptomic data in Fig 3 sugest the presence of replicating virus in the embryo brain, but it's difficult to judge how much. Additional virological validation, such as infectious virus quantification (e.g., plaque assay or TCID50), viral antigen detection by immunohistochemistry or immunofluorescence, or independent viral RNA quantification by RT-qPCR, would strengthen the conclusion that peripheral tissues undergo true infection, while viral clearance would require some form of time course analysis. This should also include measurements of virus in allantoic fluid, as many strains of IAV grow well in the cells lining the cavity but do not infect the embryo.
      4. General overinterpretation of limited data. The authors should be careful not to overinterpret the data they have, without any forms of orthogonal validation. For instance, while the transcriptomic data do show increased expression of various ISGs, consistent with activation of MDA5- and TLR-mediated antiviral pathways, they do not demonstrate that these pathways are functionally required for viral control or that they compensate for the absence of RIG-I. Similarly, the interpretations over macrophage polarisation, access and brain immune privilege remains inferential, and the statement that the virus "simply replicates where macrophages cannot reach" is stronger than the current evidence supports. Direct assessment of immune-cell infiltration, for example by immunostaining, flow cytometry, or other imaging-based approaches, would help determine whether viral persistence is primarily driven by immune-cell exclusion or by intrinsic differences in tissue susceptibility.

      Minor comments

      1. The manuscript would benefit from a clearer description of the RNA-sequencing analysis, including the sequencing depth, quality-control criteria, statistical thresholds for differential expression, and any batch correction methods used. Providing these details would improve the transparency and reproducibility of the study.
      2. Figure 1 should be improved or cut. Images of opened eggs are not particularly useful, or (as already noted) an image of a whole embryo by itself. The cartoons and description of how an HA assay works are incorrect (clotting factors should have been washed away) and 1F does not show data that demonstrate an EID50 value.
      3. As the study was performed in embryonic day 10 chicken embryos, the authors should discuss the extent to which these findings can be extrapolated to adult chickens. It would be helpful to comment on how immune maturation may influence the antiviral pathways described and whether the proposed mechanisms are likely to extend beyond the embryonic stage.

      Significance

      Strengths

      The manuscript presents a small transcriptomics data set that might be of interest to the field if better described and validated.

      Limitations

      Lack of depth and adequate validation are main problems. The only data are from a single transcriptomics run done with very small numbers of samples, using a virus strain that may skew the results and which is not validated by any measurements of virus replication other than RNAseq.

      Comparison with Existing Knowledge

      The study potentially adds a little to our knowledge of how Galliformes respond to influenza virus without RIG-I and its activating ubiquitin ligase

      Appropriate Audience

      If strengthened, this work would be of interest to researchers in avian immunology and influenza biology. It may also appeal to investigators studying comparative immunity and systems-level approaches to host-pathogen interactions.

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      Referee #2

      Evidence, reproducibility and clarity

      This study investigates the innate immune response to H3N2 influenza virus infection in the developing chicken embryo using transcriptomic profiling across multiple tissues. The authors report that, despite the absence of adaptive immunity at this developmental stage, peripheral tissues exhibit a robust innate immune response associated with efficient viral clearance, whereas viral persistence is largely confined to the brain. Based on these findings, the authors propose a model in which MDA5/TLR-mediated antiviral signaling, macrophage-associated immune responses, and tissue repair programs contribute to peripheral viral clearance, while the relative immune privilege of the developing brain contributes to localized viral persistence. Overall, the manuscript addresses an important biological question and provides novel insights into antiviral immunity during embryonic development. The study is well designed, the analyses are comprehensive, and the findings represent a valuable contribution to the field. The manuscript is of high quality, and the comments below are intended only to further strengthen its presentation. I recommend publication after minor revision.

      Major Comments

      I have no major comments to address.

      Minor Comments

      The Discussion concludes with several interesting perspectives that place the findings in a broader biological context. To further strengthen these sections, the authors may consider framing some of these ideas as potential future directions inspired by the current findings. This would further emphasize the distinction between the experimental observations and their broader biological implications while preserving the overall message of the manuscript.

      Significance

      This manuscript addresses an important and timely question regarding the capacity of the embryonic innate immune system to control influenza virus infection in the absence of adaptive immunity. Using comprehensive transcriptomic analyses across multiple tissues, the authors provide a valuable dataset and propose a coherent model of tissue-specific antiviral responses during embryonic development. The findings extend current knowledge of avian innate immunity and offer new perspectives on the mechanisms underlying peripheral viral clearance and viral persistence within the developing brain.

      The study fills an important gap in our understanding of host antiviral responses during early development by characterizing how innate immune pathways alone respond to systemic influenza infection. The work provides meaningful conceptual advances by challenging existing assumptions regarding the requirement for adaptive immunity and the role of RIG-I deficiency in avian influenza responses. In addition, the proposed model of immune privilege contributing to viral persistence in the developing brain provides a valuable framework for future mechanistic studies.

      The manuscript will be of interest to a broad audience of researchers working in innate immunity, developmental immunology, host-pathogen interactions, virology, avian biology, and comparative immunology. While the immediate clinical implications are limited, the findings may also be of interest to investigators studying neurotropic viral infections and early-life immune responses.

      The proposed biological model is compelling and well supported by the data. A few refinements to the wording in the Discussion to further distinguish experimental observations from broader mechanistic interpretations would further strengthen an already excellent manuscript. Overall, I find this to be a well-executed and thoughtfully presented study that makes a valuable contribution to the field. I believe the manuscript is suitable for publication after minor revision.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Gurses et al. uses mRNA-seq profiling of brain, kidney, and lung tissues from H3N2-infected 10-day embryonated chicken eggs to investigate how the developing avian innate immune system responds to influenza infection in the absence of RIG-I. The authors report that viral transcripts are concentrated in the brain whereas kidney and lung exhibit strong innate immune activation with little detectable viral RNA. From these observations, they propose that peripheral tissues successfully clear infection through innate mechanisms involving MDA5, TLR, and IRF-mediated signaling, while the embryonic brain acts as an immune-low compartment that is not able to effectively contain the virus through innate mechanisms. The study further argues that the absence of RIG-I does not compromise antiviral defense in this model and that M2-like macrophage programs dominate the post-infection response.

      1. The evidence for immune privilege rather than neurotropism is still suggestive but not definitive. They do provide nice supportive data, but they may want to include a limitation of the study to highlight the need for validation.
      2. The manuscript concludes that M2-polarized macrophages are the primary effectors responsible for viral clearance and that these macrophages are excluded from the brain. This is a nice finding especially in lieu of the fact that microglia are embryonic derived and may not polarize to M2 state easily. The supporting data is inferred so this limitation could also be highlighted.
      3. The data show that a strong innate response can occur in embryonated chicken eggs despite the absence of RIG-I. However, the manuscript occasionally extends this observation into broader claims that RIG-I loss does not impair antiviral defense or that previous hypotheses regarding RIG-I-mediated susceptibility are unsupported. RIG-I's role may be quite virus specific so it should be made clear that this relates to the flu condition.
      4. Several figures contain heatmaps with very small labels, making them difficult to see. Increasing label size would improve readability.
      5. The authors should clarify whether viral transcripts were normalized across tissues in a way that permits quantitative comparison of viral burden between organs.
      6. The manuscript cites relevant avian innate immunity literature, although discussion of prior transcriptomic studies examining MDA5-mediated influenza responses in chickens could be expanded.

      Significance

      This is an interesting and informative transcriptomic study that addresses an important question in avian immunology: how chickens compensate for the evolutionary loss of RIG-I and how innate antiviral responses are organized during embryonic development. The cross-tissue comparative design is a strength, and the manuscript provides a valuable resource describing transcriptional responses to H3N2 infection in embryonated eggs. The data demonstrate clear innate immune activation and differential tissue responses following infection. The manuscript's central mechanistic conclusions are inferred primarily from transcriptomic signatures rather than directly demonstrated, but the functional roles of many of these genes is well enough established that the correlation is compelling. The claim that macrophages are the principal effectors responsible for clearance in the periphery and their absence is responsible for outcome in the brain is speculative, but this does seem plausible and is interesting. It is especially interesting that microglia do not compensate and this may reflect their embryonic origin. Monocyte-derived macrophages may be required and not present in the chick embryo brain. The study is valuable as a hypothesis-generating transcriptomic analysis. Some conclusions would benefit from qualification or additional validation but overall it is an informative analysis and should be published.

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      Reply to the reviewers

      We sincerely thank the Editor and all three reviewers for their careful evaluation of our manuscript and constructive comments. We appreciate their positive assessment of the conceptual framework and the breadth of the experimental design, imaging, and quantitative analyses. We have substantially revised the manuscript in response to their comments. In particular, we have moderated several mechanistic conclusions, clarified the distinction between promoter-defined sites of TCP4 production and the resulting effect, added temporal induction and matched-cell osmotic analyses, revised the interpretation of cell enlargement and the pectin and auxin data, and reorganised the Discussion and figures. Our detailed responses are provided below.

      REVIEWER #1

      Major Comment 1: Interpretation of cell enlargement as growth compensation

      Reviewer comment: When characterising the impacts of epidermis-specific TCP4 expression, the authors interpret larger cells in DEX-treated leaves as growth compensation for reduced cell division. However, this appears inconsistent with the lower growth rates in DEX-treated cells in Fig. 4C. If compensation occurs, one might expect higher growth rates. The larger cells may simply result from cells stopping division earlier.

      Response: We thank the reviewer for this important distinction and agree that our original interpretation of the increased cell area as evidence of enhanced compensatory growth was too strong. The live-imaging data show reduced growth rates following PDF1-driven TCP4 induction; therefore, the larger final cell area in DEX-treated leaves cannot be interpreted as evidence that these cells expand faster. Rather, the increased final cell size is consistent with premature exit from the cell cycle, in line with TCP4’s established role in promoting differentiation (Nath et al., 2003; Palatnik et al., 2003). Cells stop dividing earlier and subsequently attain larger final sizes despite lower instantaneous growth rates.

      We have revised the manuscript throughout to distinguish final cell size from instantaneous or interval-specific growth rate. Enlarged cells are now interpreted as being consistent with earlier exit from proliferation and accumulation of larger, non-dividing cells. We have removed statements implying that TCP4 actively increases cell expansion rates as a compensatory response and now describes the phenotype as cell enlargement associated with reduced proliferative capacity or precocious proliferative exit.

      Major Comment 2: Epidermal versus subepidermal positional effects may be confounded by transgene expression levels

      Reviewer comment: The authors show a stronger phenotype following TCP4 expression in the epidermis than in the subepidermis, but it is unclear whether this reflects tissue position or differences in expression levels between PDF1;GR and AN3;GR lines.

      Response: We agree with the reviewer. Because the abundance of TCP4 was not quantitatively matched between the PDF1;GR and AN3;GR lines, the present experiments do not permit a strict quantitative comparison of the intrinsic responses of epidermal and subepidermal tissues to equivalent TCP4 levels. In addition, the inducible constructs express a miR319-resistant version of TCP4, and differences in promoter activity, transgene abundance, insertion site, and spatial distribution may all contribute to the observed phenotypic differences.

      We have therefore revised the manuscript to avoid attributing the stronger phenotype of PDF1-driven TCP4 induction solely to tissue position. We now describe the empirical observation that PDF1-driven TCP4 induction produced the most severe phenotypes among the transgenic systems examined, while explicitly acknowledging that a direct comparison with AN3-driven expression would require quantitative matching of TCP4 abundance across the two tissue domains.

      Importantly, the phenotypic trends associated with both PDF1- and AN3-driven TCP4 induction were reproducible across multiple independent insertion lines (Figs. S1 and S3), indicating that the observed responses are not specific to a single transgenic insertion. However, these data do not resolve the relative contributions of tissue position and TCP4 abundance. Quantitative measurements of TCP4 levels within the respective tissue domains would be required to distinguish these possibilities.

      Major Comment 3: Matched-cell quantification of osmotic shrinkage

      Reviewer comment: In the sorbitol treatment, the authors compare cell area before and after treatment at the population level. It would be more convincing to quantify shrinkage for each individual cell in MorphoGraphX.

      Response: We thank the reviewer for this suggestion. We have now performed a matched-cell analysis in which the same individual epidermal cells were tracked before and after hyperosmotic treatment using MorphoGraphX.

      For each matched cell, the relative decrease in cell area was calculated as:

      Relative area decrease (%) = [1 − (A_sorbitol/ A_water] × 100,

      where A_water and A_sorbitol represent the cell areas measured in water (turgid state) and after treatment with 0.6 M sorbitol, respectively. This paired analysis directly quantifies the deformation of individual cells and avoids confounding arising from comparisons between different cell populations.

      The matched-cell analysis confirmed that PDF1-driven TCP4 induction is associated with a smaller decrease in cell area following sorbitol treatment than in MOCK-treated primordia. The corresponding data, statistical analyses, figure panels, and methodological description have been added to the revised manuscript and Supplementary Information.

      We now interpret these results as evidence of reduced osmotic deformation under the experimental conditions__,__ consistent with altered epidermal mechanical behaviour. As osmotic deformation depends on both cell-wall properties and turgor, we have avoided interpreting this assay as a direct measurement of cell-wall stiffness.

      Major Comment 4: Number of samples for pectin staining

      Reviewer comment: Please indicate the number of samples for the pectin staining experiment.

      Response: We thank the reviewer for identifying this omission. We have now added the number of independent biological samples analysed for the LM19 and LM20 immunolabelling experiments to the corresponding Methods section and figure legend. Four leaves were analysed per genotype and antibody (n=4).

      Major Comment 5: Continuous induction from germination and need for temporal induction

      Reviewer comment: The manuscript states that the inducible approach eliminates confounding effects associated with constitutive expression, but TCP4 was induced continuously from germination. Would it be better to induce TCP4 in leaves 2, 3, or 4 days after initiation to assess the immediate consequences of TCP4 activation, including auxin signalling?

      Response: We agree with the reviewer that our original statement that the GR system “eliminates” developmental compensation was too strong. Continuous DEX treatment from germination provides inducible control relative to constitutive expression but does not distinguish early developmental effects from later responses.

      To address the temporal sensitivity of the phenotype, we performed reciprocal transfer experiments in jaw-D;GR and PDF1;GR seedlings. Seedlings were maintained under continuous MOCK or DEX conditions or transferred between MOCK and DEX at 4 days after stratification (DAS), generating four treatment regimes: MOCK-to-MOCK (MtM), DEX-to-DEX (DtD), MOCK-to-DEX (MtD), and DEX-to-MOCK (DtM).

      In jaw-D;GR, induction at 4 DAS was sufficient to produce a substantial reduction in leaf area and cell number, whereas removal of DEX at 4 DAS permitted partial recovery. In PDF1;GR, induction at 4 DAS similarly produced a strong reduction in leaf area and cell number, while removal of DEX after early exposure did not restore leaf development. These results show that TCP4 activation within this early developmental window is sufficient to strongly alter subsequent leaf growth and that strong PDF1-driven induction produces a particularly persistent developmental response. The transfer experiments and corresponding cellular analyses have been added to Figs. S8 and S9 and described in the Results and Methods.

      We have accordingly removed the claim that the inducible system “eliminates” developmental compensation and now state that it provides temporal control of TCP4 activation and treatment-matched comparisons__. __We also agree that acute reporter imaging immediately following TCP4 induction would be required to establish the direct kinetics of auxin-response activation. Our current DR5 time-course demonstrates sustained auxin response under continuous epidermal TCP4 induction but does not establish immediate auxin activation. We have clarified this limitation in the revised manuscript.

      Minor Comment 1: PIN1 and AUX1 are not ideal examples of layer-specific expression

      Reviewer comment: Line 164: examples other than PIN1 and AUX1 could be used because these genes are expressed in both epidermal and inner tissues.

      Response: We agree and have revised this sentence. PIN1 and AUX1 have been removed as examples of strictly layer-specific regulatory components. The revised text uses examples with more appropriate tissue-domain specificity and avoids __describing __broadly distributed proteins as layer-restricted factors.

      Minor Comment 2: SEM samples appear dried, and the benefit over confocal imaging is unclear

      Reviewer comment: The samples in Fig. 4A appear dried out. It is unclear what benefit SEM provides over confocal imaging for the visualisation of leaf primordia.

      Response: We thank the reviewer for this observation. SEM preparation requires fixation, dehydration, critical-point drying, and coating, and therefore does not preserve the hydrated state of the tissue. We have clarified this limitation and no longer use the SEM images to infer tissue hydration or mechanical properties.

      The SEM images were included to provide complementary high-resolution visualisation of overall primordium morphology and developmental outgrowth at defined stages. Quantitative conclusions regarding growth dynamics and tissue geometry are based on live confocal imaging and MorphoGraphX analyses rather than SEM morphology alone.

      Minor Comment 3: “71 hours” should be “72 hours”

      Reviewer comment: The time point “71 hours” appears several times and should perhaps be “72 hours”.

      Response: We thank the reviewer for identifying this inconsistency. The time point has been corrected to 72 h throughout the manuscript, figures, and Supplementary Information.

      Minor Comment 4: Principal growth-direction lines are too short and thin

      Reviewer comment: The principal growth-direction lines in Fig. S7B are difficult to visualise and should be scaled up in MorphoGraphX.

      Response: We agree and have regenerated the principal growth-direction visualisations with longer, more visible vectors. Vector scaling was increased for presentation purposes without altering the underlying quantitative measurements. The __displayed vectors __now correspond specifically to the maximum principal direction of growth (PDGmax).

      Minor Comment 5: Apparent contradiction between isotropic and anisotropic expansion

      Reviewer comment: Lines 450-451 state that DEX-treated cells show more isotropic expansion, whereas lines 454-458 state that they grow highly anisotropically. Please clarify.

      Response: We thank the reviewer for identifying this ambiguity. The original wording conflated two distinct properties of growth: the magnitude of growth anisotropy and the orientation of the principal growth direction relative to the organ axis.

      We have rewritten this section to distinguish these parameters explicitly and to describe the quantitative measurement shown in each panel. In particular, DEX-treated primordia show a more coherent alignment of the principal growth direction with the proximodistal axis. We now no longer describe this directional coherence as equivalent to increased cell-level anisotropy, and the terms “isotropic” and “anisotropic” are now used only with reference to the corresponding anisotropy measurement.

      Minor Comment 6: Inconsistent supplementary figure numbering

      Reviewer comment: The paper has eight supplementary figures, but sometimes refers to Fig. S10. Please check all main and supplementary figure references.

      Response: We thank the reviewer for identifying this inconsistency. The Supplementary Information has been reorganised and expanded to incorporate the additional experiments performed during revision, and all supplementary figure citations have been systematically checked and corrected.

      The temporal transfer experiments are now presented in Fig. S8, the corresponding cellular analyses in Fig. S9, and the pectin and auxin-response analyses in Fig. S10. All in-text cross-references have been updated accordingly.

      Minor Comment 7: Auxin figures should follow pectin staining

      Reviewer comment: Figure S8: the auxin figures should be placed after the pectin staining figure.

      Response: We agree. The Supplementary Information has been reorganised so that the pectin data precede the auxin-response data, matching their order of presentation in the Results. Both datasets are now presented in Fig. S10.

      Minor Comment 8: Discussion is too long, and the intrinsically disordered region section is not closely related to the data

      Reviewer comment: The Discussion is too long and should be shortened. The section on intrinsically disordered regions is not closely related to the data.

      Response: We agree and have substantially refocused the Discussion. The extended speculation concerning intrinsically disordered regions and their potential role in TCP4 movement has been removed. The revised Discussion focuses more closely on conclusions supported by the present experiments and clearly distinguishes experimental observations from working hypotheses.

      Minor Comment 9: “Each step is causally linked” is an overstatement

      Reviewer comment: Line 795: The statement that “each step is causally linked” is an overstatement.

      Response: We agree and have deleted this statement. Our measurements of auxin response, pectin epitopes, cortical microtubules organisation, apparent Young's modulus, osmotic deformation, and growth geometry do not establish a linear temporal or causal sequence.

      The revised Discussion explicitly acknowledges that these responses may occur sequentially, in parallel, or through interacting biochemical and mechanical feedback processes. Their proposed relationships are therefore presented as a working model rather than an established causal pathway__.__

      Minor Comment 10: Figures from Zhao et al. should be clearly attributed

      Reviewer comment: Lines 830-832: The authors should clearly indicate that these figures are from the Zhao et al. paper.

      Response: We agree. The relevant text has been revised to explicitly attribute these findings to Zhao et al. (2020) and to clearly distinguish the predictions and observations reported in that study from the data generated in the present work.

      Minor Comment 11: The same scale should be used for AFM stiffness comparison

      Reviewer comment: Figure 5C: the same scale should be used for a better comparison of cell wall stiffness.

      Response: We agree and have regenerated the AFM heat maps using the same apparent Young’s modulus scale for the MOCK and DEX conditions. This allows direct visual comparison of the magnitude and spatial distribution of AFM-derived apparent Young's modulus between treatments.

      Minor Comment 12: Figure text is too small

      Reviewer comment: Text in some figures, for example Fig. 5F, is very small and should be increased.

      Response: We agree. Font sizes have been increased throughout the revised figures, particularly for axis labels, panel annotations, statistical labels, and legends. Figure readability was also checked at the intended publication dimensions.

      Minor Comment 13: Two graphs can be fused for better flow

      Reviewer comment: Two graphs from 104-116 can be fused for better flow.

      Response: We thank the reviewer for this suggestion. The corresponding graphs have been reorganised within the revised figure to reduce redundancy and improve the flow of the data presentation.

      Minor Comment 14: Plant-line nomenclature is inconsistent

      Reviewer comment: Names of plant lines should be more consistent, for example PDF1;GR and PDF1; GR.

      Response: We agree. Plant-line nomenclature has been standardised throughout the manuscript, figures, figure legends, Methods, and Supplementary Information. We consistently use PDF1;GR, AN3;GR, jaw-D;GR, PDF1-mVENUS, and PDF1-3×GFP.

      Minor Comment 15: There is no Fig. 5I

      Reviewer comment: Line 603: There is no Figure 5I.

      Response: We thank the reviewer for identifying this incorrect citation. The reference to Fig. 5I has been corrected to the appropriate panel in the revised manuscript.

      Minor Comment 16: Figure 5 should be rearranged for better flow

      Reviewer comment: Figure 5 should be rearranged for better flow.

      Response: We thank the reviewer for this suggestion. We have reorganised Fig. 5 to improve the progression from cortical microtubule organisation to mechanical measurements and osmotic deformation. The corresponding Results text has also been reorganised to follow the same sequence as the revised figure.

      REVIEWER #2

      Major Comment 1: The title overstates the findings

      Reviewer comment: The title “Interlayer Communication Integrates Genetic and Mechanical Signals for Robust Leaf Morphogenesis” overstates the findings because the manuscript does not directly demonstrate that interlayer communication integrates genetic and mechanical signals.

      Response: We agree with the reviewer and have revised the title to __more closely __reflect the findings directly supported by our data.

      The revised title is:

      “Control of leaf shape through non-cell autonomous TCP4 regulation”

      The revised title focuses on the role of TCP4 in leaf shape regulation and the non-cell-autonomous effects demonstrated in our experiments, while avoiding the broader claim that interlayer communication integrates genetic and mechanical signals to ensure robust morphogenesis. Corresponding mechanistic statements in the Abstract and Discussion have also been moderated.

      Major Comment 2: Loss of proximodistal expression asymmetry and apparent abaxial localisation

      Reviewer comment: The layer-specific constructs appear to lose the native proximodistal TCP4 expression asymmetry. To what extent is broader expression responsible for the phenotypes rather than tissue-layer identity? In addition, TCP4 appears localised only to the abaxial epidermis in Fig. 3. If so, how can this explain rescue and the proposed stiffening phenotype?

      Response: We thank the reviewer for highlighting these two important limitations. We agree that PDF1- and AN3-driven rTCP4 does not reproduce the native proximodistal TCP4 expression pattern. The phenotypes obtained with these constructs therefore reflect TCP4 activity within the promoter-defined expression domains together with the ectopic spatial distribution imposed by the heterologous promoter. We have revised the manuscript accordingly and no longer attribute these phenotypes solely to tissue-layer identity.

      Regarding the apparent abaxial enrichment of the PDF1-driven rTCP4-mVENUS signal in Fig. 3, fluorescence was heterogeneous and most readily detected in the abaxial epidermis in the representative primordium shown. However, rTCP4-mVenus signal was also detected in both the adaxial and abaxial epidermis in other samples (now shown in Fig. S6E). Weak adaxial fluorescence was more difficult to resolve consistently because of imaging depth and signal-to-background limitations. We have therefore revised the text to avoid implying uniform or exclusively abaxial expression of the PDF1-driven construct.

      Importantly, our conclusions do not require equivalent TCP4 accumulation on the two epidermal surfaces. The mobility restricted PDF1::rTCP4-3xGFP experiments show that TCP4 activity originating within the PDF1 expression domain can be associated with cellular responses in underlying tissue without detectable movement of the TCP4 fusion into those cells. Together with the live-imaging data, these observations support non-cell-autonomous effects of PDF1-driven TCP4 activity, but they do not establish whether these effects are mediated mechanically, biomechanically, or by a combination of both. We have made this distinction explicit throughout the revised manuscript.

      Major Comment 3: Quantification of early developmental arrest and why epidermal expression appears stronger

      Reviewer comment: Did early developmental arrest occur in lines other than the epidermal lines? The manuscript should quantify this. Why might expression in one cell layer produce a stronger phenotype than constitutive expression?

      Response: We thank the reviewer for raising this point. Severe early developmental phenotypes were observed most prominently in the PDF1-driven TCP4 lines. Constitutive PDF1::rTCP4-mVenus expression frequently resulted in germination failure or severe embryonic/seedling phenotypes, whereas the surviving lines available for subsequent analysis represent the weakest viable insertions. In the inducible system, strong PDF1;GR lines (#1 and #2) developed normally in the absence of DEX but showed severe post-germination leaf arrest following TCP4 induction. Comparable complete developmental arrest was not observed in the viable AN3;GR or endogenous-domain induction lines under the conditions examined. These observations and their quantification have been clarified in the revised manuscript.

      We agree, however, that these differences cannot be attributed solely to epidermal tissue identity because TCP4 was not quantitatively matched across the different expression systems. We have therefore revised the manuscript to describe the empirical observation that the strongest PDF1-driven lines produced the most severe developmental phenotypes, while acknowledging differences in promoter activity, transgene abundance, insertion site, and spatial expression as potential contributors.

      One possible explanation for the difference between constitutive and inducible PDF1-driven expression is their different developmental timing. Because the PDF1 promoter is active during embryonic development, strong constitutive TCP4 activity may interfere with development sufficiently early to prevent recovery of the strongest lines. By contrast, the GR system allows plants carrying strong insertions to develop in the absence of DEX before TCP4 is activated post-germination, thereby revealing severe post-embryonic phenotypes that would not be recoverable in the corresponding system.

      We now discuss this as a possible explanation rather than evidence that epidermal domain is intrinsically more potent than expression from other domains.

      Major Comment 4: TCP4 mobility undermines early conclusions about the dominant role of the epidermis

      Reviewer comment: At the point where PDF1;GR and AN3;GR lines are compared, it is not known whether TCP4 remains in the intended layer or moves. Therefore, conclusions about the dominant role of the epidermis should be made cautiously.

      Response: We agree. In the original manuscript, conclusions regarding tissue-layer-specific effects were drawn before directly __establishing the distribution and mobility ofTherefore, we could not perform the reciprocal mobility-restriction experiment __ TCP4 protein.

      We have therefore revised the Fig. 2 Results section to clarify that PDF1- and AN3-driven induction identifies the promoter-defined domain of TCP4 production, but does not establish confinement of the TCP4 protein to that tissue layer. At this stage of the analysis, direct intercellular movement of TCP4 could therefore contribute to the cross-layer phenotypes observed in these lines.

      Statements attributing a dominant role to the epidermis have consequently been removed from this section. The subsequent protein-localisation and mobility-restriction experiments are now presented as necessary tests to distinguish direct TCP4 movement from movement-independent and non-cell-autonomous responses.

      Major Comment 5: Mobility-restricted TCP4 in the mesophyll would be an informative experiment

      Reviewer comment: Expressing a mobility-restricted 3×GFP-TCP construct in the mesophyll would be the ultimate experiment to test whether downstream signals can propagate in both directions. Is such an experiment underway? This is optional.

      Response: We agree that a mobility-restricted subepidermal construct would provide a valuable complementary test of whether movement-independent downstream responses can propagate from the subepidermal domain. We attempted to generate the corresponding pAN3::rTCP4-3xGFP line but were unable to recover viable lines despite repeated attempts. We therefore could not perform the reciprocal mobility-restriction experiment within the present study. The pAN3::rTCP4-GR lines provide evidence for the effects of TCP4 produced in the subepidermal domain, but because TCP4 mobility is not restricted in these lines, they cannot address whether the resulting cross-layer responses occur independently of direct TCP4 movement. We now __acknowledge __this limitation explicitly in the revised manuscript.

      Major Comment 6: Pectin labelling is unclear and mechanistic conclusions are unsupported

      Reviewer comment: The pectin labelling is unclear, particularly in the PDF1;GR line. Better images and clearer resolution would be beneficial. The data are insufficient to support conclusions such as “ectopic demethylesterified pectin in the subepidermis propagates mechanical constraints non-cell-autonomously to inner layers.”

      Response: We agree with the reviewer that the original interpretation extended beyond what could be concluded from the LM19 and LM20 immunolabelling data.

      We have revised the pectin section to describe these results as changes in pectin epitope distribution associated with PDF1-driven TCP4 induction.

      The revised text reports the observed LM19 and LM20 labelling patterns without assigning a specific mechanical consequence to either epitope. We have also replaced the representative images with clearer examples and added the number of independent leaf samples analysed.

      The pectin data are now presented as evidence of altered cell-wall composition or organisation associated with TCP4 induction. We explicitly acknowledge that LM19 and LM20 labelling alone does not establish whether the corresponding walls are stiffened or loosened, or whether these changes causally contribute to the AFM, osmotic-deformation, or growth phenotypes.

      Major Comment 7: Why use PDF1;GR rather than epidermis-confined TCP4-3×GFP?

      Reviewer comment: Several experiments investigating TCP4 in the epidermis use PDF1;GR#1 rather than TCP4-3×GFP. The latter is confined to the epidermis, whereas TCP4 in PDF1;GR can move between layers. Why was the more restricted line not used?

      Response: We thank the reviewer for raising this important distinction. The PDF1;GR#1 and PDF1-3×GFP lines were used for complementary experimental purposes.

      The mobility-restricted PDF1-3×GFP construct was generated specifically to test whether direct TCP4 protein movement is required for cross-layer cellular responses. The confinement of the TCP4-3xGFP fusion to the epidermis, together with changes in cell number and cell area in the underlying tissue, shows that at least some non-cell-autonomous responses can occur without detectable movement of the TCP4 fusion into those cells. However, the viable PDF1-3xGFP lines displayed relatively weak phenotypic effects and were therefore not suitable for examining the severe growth and mechanical phenotype produced by strong epidermal TCP4 induction.

      In contrast, PDF1;GR#1 was used for live-growth analysis, reciprocal transfer experiments, CMT imaging, AFM, osmotic perturbation, and auxin-reporter experiments because the GR system provides temporal control of TCP4 activity and enables genetically matched MOCK- and DEX-treated samples. This was particularly important for experiments requiring defined treatment conditions or developmental windows.

      We agree that this distinction was not sufficiently clear in the original manuscript. Because TCP4 produced from the PDF1 domain is not confined to the epidermis in PDF1;GR#1, experiments using this line are now described as effects of PDF1-driven TCP4 induction, rather than effects of strictly epidermis-confined TCP4. The mobility-restricted PDF1-3xGFP line is used specifically to address whether direct TCP4 movement is required for the observed cross-layer response.

      Major Comment 8: The double-loop feedback model is presented as established fact

      Reviewer comment: The “double-loop feedback” section presents a hypothetical sequence as an established fact. The data do not resolve whether microtubule rearrangement occurs before or after wall stiffening or auxin signalling. The authors also do not show that neighbouring cells are mechanically constrained rather than responding to a secondary messenger.

      Response: We agree with the reviewer and have substantially revised this section.

      We have removed the statement that “each step is causally linked” and no longer present the observed changes in auxin response, pectin epitope, cortical microtubule organisation, apparent Young's modulus, osmotic deformation, and growth geometry as a defined linear sequence.

      The “double-loop feedback” model has been replaced by a working model describing potential biochemical and mechanical routes through which TCP4 activity may influence growth across layers__. __The revised Discussion explicitly states that our measurements do not establish the temporal or causal order of these responses. CMT reorganisation, wall modification, altered mechanical behaviour, and auxin signalling may occur sequentially, in parallel, or through interacting feedback processes.

      We also agree that the present experiments to not distinguish mechanical coupling from signalling through a secondary messenger. The mobility-restricted PDF1::rTCP4-3×GFP experiment shows that direct TCP4 movement is not required for all cross-layer cellular responses under the conditions tested, but it does not identify the mechanism by which those responses are transmitted. We therefore now discuss biochemical signalling, including altered auxin response, and mechanical coupling as alternative, non-mutually exclusive possibilities rather than as established components of a causal pathway.

      Accordingly, the revised Discussion presents these relationships as hypotheses arising from the combined observations and identifies their temporal and causal resolution as an important question for future work.

      Minor Comment 9: Where is Fig. S10?

      Reviewer comment: Where is Figure S10? Do the authors mean S8?

      Response: We thank the reviewer for identifying this inconsistency. The Supplementary Information has been reorganised and expanded to incorporate the additional experiments performed during revision, and all supplementary figure citations have been systematically checked and corrected.

      The temporal transfer experiments are now presented in Fig. S8, the corresponding cellular analyses in Fig. S9, and the pectin and auxin-response analyses in Fig. S10. All in-text cross-reference have been updated accordingly.

      REVIEWER #3

      Major Comment 1: What do the weak transgenic lines represent, and can weak insertions alter spatial expression patterns?

      Reviewer comment: Strong TCP4 expression arrests leaf development or causes embryonic lethality, whereas many experiments use weak lines. What do these weak lines represent? The underlying assumption is that weakness does not interfere with the spatial expression pattern imposed by the promoter. How valid is this assumption?

      Response: We thank the reviewer for highlighting this important limitation of our experimental system. We agree that the terms “strong” and “weak” require clarification.

      In the revised manuscript, these terms refer specifically to phenotypic severity across independent transgenic insertions and are not intended to represent quantitative measurements of TCP4 abundance.

      Because the most severe PDF1-driven lines exhibited early developmental arrest or embryonic defects, viable lines with less severe phenotypes were necessarily used for several cellular, growth, cytoskeletal, and mechanical analyses.

      We therefore interpret these lines as providing an experimental window into TCP4-associated developmental responses without complete early arrest, rather than as proxies for endogenous TCP4 dosage.

      We also agree that insertion-dependent differences in spatial expression cannot be excluded. Although the PDF1 and AN3 promoters define predominantly epidermal and subepidermal expression domains, respectively, promoter identity alone does not guarantee identical expression levels or complete spatial uniformity among independent transgenic insertions. Consistent with this limitation, detectable rTCP4-mVENUS fluorescence showed some heterogeneity across the PDF1 expression domain.

      To minimise reliance on any single insertion, we analysed multiple independent PDF1;GR and AN3;GR lines spanning a range of phenotypic severities and based our conclusions on trends reproduced across independent lines. Where fluorescent fusion constructs were available, protein localisation was directly assessed.

      We have accordingly removed statements attributing heterogeneous fluorescence simply to “weak insertion strength” and replaced claims of a quantitative “dose-dependent response” with the more precise description “graded phenotypic severity across independent insertions”, because TCP4 abundance was not quantitatively measured across these lines.

      Major Comment 2: If epidermis-confined TCP4 is sufficient, does TCP4 movement into other layers have biological significance?

      Reviewer comment: TCP4 moves into approximately three layers, but epidermal-specific expression has the strongest effect, and epidermis-confined TCP4 can produce non-cell-autonomous phenotypes. Does TCP4 movement into other layers have biological significance? Can the authors reconcile why both cell-autonomous and non-cell-autonomous activities are biologically relevant?

      Response: We thank the reviewer for raising this important conceptual point. We agree that the original Discussion did not clearly distinguish TCP4 protein mobility from movement-independent non-cell-autonomous responses.

      Our localisation experiments show that rTCP4-mVENUS produced from the PDF1 expression domain can extend into approximately 2-3 layers beyond its predominant site of production. In contrast, the mobility-restricted rTCP4-3×GFP fusion remains confined to the epidermis but is nevertheless associated with changes in cell number and cell area in the underlying tissue. These results indicate that, under the PDF1-driven expression conditions tested, direct TCP4 protein movement is not strictly required for all cross-layer cellular responses.

      Importantly, we do not interpret this result to mean that TCP4 mobility is biologically redundant. The mobility-restriction experiment uses ectopic PDF1-driven rTCP4 in the jaw-D background and therefore does not directly test the function of TCP4 movement from its endogenous expression domain. Under native conditions, limited TCP4 mobility could contribute to the spatial range, timing, or quantitative distribution of TCP4 activity across neighbouring tissue layers.

      We have therefore revised the Discussion to distinguish three potentially complementary modes of TCP4 action:

      (i) Local TCP4 activity within cells containing the protein;

      (ii) Extension of direct TCP4 action into neighbouring cells through limited protein mobility;

      (iii) Movement-independent non-cell-autonomous responses initiated downstream of TCP4 activity. The latter could involve biochemical signalling, mechanical coupling, or both.

      These mechanisms are not mutually exclusive. Our experiments demonstrate the existence of movement-independent cross-layer responses under PDF1-driven expression, but to date establish that direct TCP4 mobility is dispensable during endogenous leaf development. We have revised the manuscript accordingly and reduced speculation concerning both auxin as the mobile relay and plasmodesmata as the route of TCP4 movement.

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      Referee #3

      Evidence, reproducibility and clarity

      This paper addresses the question of how anisotropic growth is achieved through cell-cell communications during leaf development. Transcription factor TCP4 plays a critical role in this process and authors addressed this question by perturbing TCP4 expression by restricting or inducing in specific cell layers and assessing the impacts across all cell layers in the meristem. Authors suggest that spatial distribution of TCP4 is essential for mechanical coupling required for the anisotropic growth and they suggest TCP4 is the central integrator in this process.

      Authors achieve this by looking first at the 3D asymmetry in TCP4 expression as opposed to earlier studies on 2D spatial expression differences that do not capture the nuanced layer based differences in gene expression.

      Overall, this is a very nice piece of work with lot of microscopy and quantifications. Although the manuscript reads a bit dry with varied expression profiles and repetitive quantifications, I think the paper is of interest to the community and it has an extensive array of data that provides interesting insights into developmental mechanisms.

      There is one major question that kept coming back. The strong expression has arrested leaf development of the leaves or embryonic lethality, most experiments used weak lines. The question then becomes what do these weak lines represent? The underlying assumption is that the weakness of the lines does not interfere with their spatial expression patterns from their respective promoters. How valid is this assumption? I think the authors should present their thoughts on this. I do not think this compromises their findings, but it is something that is an inherent to their entire paper that might warrant a discussion.

      Second aspect relates to cell autonomous and non-cell autonomous activities. Authors show that there is movement of TCP4 in to three layers. Authors also show that layer-specific expression especially in the epidermal layer has the strongest effect. One question remains is whether this movement of TCP4 into other layers has any biological significance. The data from epidermal specific expression will suggest otherwise. Can the authors reconcile and articulate why both cell autonomous and non-cell autonomous activities are of biological relevance.

      Overall, this manuscript makes a valuable contribution to understanding leaf development, and addressing these points would further enhance its impact.

      Significance

      This is an interesting paper addressing the cell-cell communications during plant development. This paper uses microscopy and cell type specific perturbations to draw inferences about cell-cell communication and how it shapes leaf development. This work is significant in addressing how growth anisotropy in leaf development involves interactions between transcription factors, hormonal pathways and cell wall stiffness mediators to finally arrive at a shape.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: This paper investigates the role of the growth-repressing transcription factor TCP4 in Arabidopsis leaf development. In particular, the authors test the role of TCP4 in different cell layers, and whether intercellular movement is necessary for the observed non-cell autonomous effects.

      General assessment: the authors nicely visualise the expression pattern of TCP4 and its microRNA in 3D and show that even when TCP4 is physically restricted to the epidermis it has non-cell autonomous effects on the whole of leaf development. They attempt to link layer-specific TCP4 expression to growth via pectin isoforms and microtubules, but this section is less convincing, and the pectin images in particular are hard to interpret and need to be clearer. They also overinterpret some findings, for example in early figures, where TCP4 is expressed in individual cell layers but likely diffuses broadly and later in the discussion

      Overall, it is an interesting paper and clearly advances our understanding of how cell layers interact during leaf development. It will be of broad interest in the leaf development field once some minor changes have been made. I have some questions/ suggestions below:

      Major comments:

      1. The title is somewhat overstating the findings. I am not convinced that they show "Interlayer Communication Integrates Genetic and Mechanical Signals for Robust Leaf Morphogenesis". They show that TCP4 has non-cell autonomous effects that are not accounted for by intercellular movement, and may be accounted for by reducing growth in the epidermis. But nowhere do they show that inter layer communication integrates genetic and mechanical signals (see points below). The title should be toned down.
      2. The authors ectopically express TCP4 in different cell layers, but how these effects interplay with the wild type expression differences along the proximo-distal axis are unclear. It looks like in the layer-specific lines, the P/D expression asymmetry is lost (eg. Fig. 3 I and J). To what extent is the broader expression responsible for phenotypic differences, rather than the cell layer differences? In addition, figure 3 seems to show that in the epidermal-specific lines TCP4 is localised only to the abaxial epidermis. Is this correct? If so, how does this fit with its function and ability to rescue? Does this impact the authors interpretation of the epidermal stiffening data? Surely stiffening on one side wouldn't cause a radial structure, but rather make the leaf curve? A discussion of this would benefit the paper.
      3. The authors state that epidermal expression of TCP4 had greatest phenotypic effects as some lines did not germinate when it was induced (lines 189-201). Did this ever happen in other lines? It seems surprising (and interesting!) if it is the case that expression specifically in the epidermis has stronger effects than constitutive expression. It would be good to see some sort of quantification since this point is an important one that the authors come back to. At the moment we must simply take their word that no early developmental arrest happened in other lines. In addition, do the authors have a hypothesis that could explain why expression in a single cell layer (with intercellular movement) gives a stronger phenotype than constitutive expression?
      4. The authors state "Altogether, the consistent rescues with either PDF1:GR or AN3:GR lines suggest that the epidermis is not limiting for growth at first order. However, when overexpressed, TCP4 expression in the epidermis appears to play a dominant role in leaf development, suggesting a stronger role of TCP4 in the epidermis than in subepidermal layers." (line 206-209) With these lines it is not known (at least at this point in the paper) whether TCP4 is restricted to each cell layer or moves. Therefore, making conclusions about which layer is important for growth must be carefully done, and the authors should make clear that it is possible that intercellular movement of TCP4 could mean that all lines have a similar expression, which would fit with their similar phenotypes (in fig. 2). This intercellular movement may undermine the strong conclusion (at this point) that TCP4 in the epidermis has a dominant role. Ideally the authors should show which layer TCP4 ends up in in each of the lines, but failing that they could simply make the caveat clear in the text.
      5. The 3xGFP-TCP in the epidermis nicely shows that the non-cell autonomous effects are due to either downstream messenger movement or mechanical signals. Expressing a similar construct in the mesophyll would be the ultimate experiment to confirm which layer is important, or whether these downsteam signals can go both ways. Do the authors have such an experiment underway?

      Optional 6. The pectin labelling (fig. S8) is unclear, especially in the PDF1::GR line. Better images with clearer resolution showing the epidermal-specific alternations more clearly would be beneficial. At the moment the data is not sufficient to support thteir interesting conclusions (eg. "ectopic demethylesterified pectin in the subepidermis propagates mechanical constraints non-cell-autonomously to inner layers." (lines 655-6) 7. In several cases where the authors investigate the role of TCP4 in the epidermis they use the PDF1;GR#1 line rather than the TCP4-3xGFP line. Why is this? The 3xGFP line is restricted to the epidermis, whereas TCP4 in the other line moves between layers. If the authors are testing the role of TCP4 in the epidermis the more restricted line would surely be a better choice. 8. In the section "Double-loop feedback: differentiation-to-mechanics and mechanics-to-differentiation." (line 791-808) the authors propose some interesting hypotheses that may explain their data. However, the way it is currently written reads as if it is all established fact rather than a hypothesis based on the data. The authors should rewrite to make it clear that this is a hypothesis. Currently the authors are in danger of overinterpreting their findings and inferring an order of events that are not clear from the data. For example, TCP4 likely has many effects, and it is not clear if microtubule rearrangement comes before or after cell wall stiffening or auxin signalling. In addition, the authors do not definitively show that TCP4 acts via mechanically constraining the neighbouring cell layer rather than by a secondary messenger. The authors should discuss this possibility and why they have ruled it out.

      Minor Comments 9. Where is figure S10? Do the authors mean S8?

      Significance

      Overall, it is an interesting paper and clearly advances our understanding of how cell layers interact during leaf development. It will be of broad interest in the leaf development field once some minor changes have been made

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript entitled "Interlayer Communication Integrates Genetic and Mechanical Signals for Robust Leaf Morphogenesis" Mane et al. characterized the impacts of tissue-specific expression of TCP4 on leaf morphogenesis from different angles. For that, they expressed TCP4 specifically in the epidermis or in the sub-epidermis in the mutant background jaw-D where TCPs are down-regulated. Key findings include: (i) expression of TCP4 in either epidermis or sub-epidermis can rescue the jaw-D phenotype, although the effect is stronger for epidermal expression, (ii) the effects of TCP4 are both cell-autonomous and non-cell-autonomous, and (iii) continuous expression of TCP4 leads to finger-like leaf development, which is associated with a host of changes in cell division and growth pattern, microtubule alignment, pectin composition and cell wall stiffness, and auxin signaling. I think the manuscript is well structured, the experiments are logical and well described with all materials carefully listed, and the results are properly quantified and overall consistent with each other.

      Major comments:

      • When characterizing the impacts of epidermis-specific expression of TCP4 on leaf growth and development, the experiments and interpretations are generally logical and convincing. However, I believe the interpretation that TCP4 expression leads to growth compensation (line 425-440) is less so. The authors argued that in DEX-treated leaves cells are larger, suggesting that cell enlargement is more pronounced in this condition to compensate for reduced cell division. However, more pronounced cell enlargement means higher growth rates, which is contradictory to Figure 4C showing that growth rates in DEX-treated cells are much lower than in MOCK-treated cells. If compensation happens, one would expect growth rates in DEX-treated cells to be higher to fuel more pronounced cell enlargement as the authors claimed. It is likely that DEX-treated cells are larger simply because they stop dividing early.
      • The authors show that expression of TCP4 in the epidermis leads to a stronger phenotype than the expression in the sub-epidermis, but it is not clear whether this is due to the positional effect (epidermis vs. subepidermis) or because PDF1;GR and AN3;GR lines don't have comparable expression levels.
      • In the sorbitol treatment, the authors showed cell area before and after treatment in the two genotypes at the population level. However, it is possible (and better) to quantify cell shrinkage (reduction in surface area) for each cell in MorphoGraphX. I think this data will be more convincing.
      • Please indicate the number of samples for the pectin staining experiment.
      • Line 178-179: The authors stated that their approach "eliminates confounding effects of developmental compensation associated with constitutive expression", but if I understand it correctly they induced TCP4 expression continuously from germination. Would it be better to induce TCP4 expression on leaves of 2, 3, or 4 days after initiation and see what happens? This way, it would be more direct to see if auxin signalling is activated upon TCP4 induction and so on.

      Minor comments:

      • Line 164: examples other than PIN1 and AUX1 can be taken, because these two are expressed in both the epidermis and inner tissue?
      • Line 371-372 (Figure 4A): the samples seem to be dried out. It is not clear to me the benefits of a SEM image over a confocal image to show the leaf primordia.
      • The timepoint "71 hours" appear several times (line 430 for example), while it should be "72 hours"?
      • The lines showing principal directions of growth are too short and thin preventing visualization (Figure S7B, line 452). In MGX, the length of these lines can be scaled up by a factor of 5 or 10 to make them clearer.
      • Line 450-451: DEX-treated cells show more isotropic expansion. However, in line 454-458: DEX-treated cells grow highly anisotropically. Please clarify.
      • The paper has 8 Sup. Figures but sometimes Figure S10 is referred to. Please check all the references to main and supl. Figures.
      • Figure S8: should put the auxin figures after the pectin staining figure.
      • The discussion is too long and should be shortened. For example, the part on "intrinsically disordered regions" is not closely related to the data in the manuscript.
      • Line 795: the authors said that "each step is casually linked" but I think this is an overstatement.
      • Line 830-832: the authors should indicate clearly that these figures are from Zhao et al., paper.
      • Figure 5C: The same scale should be used for a better comparison of cell wall stiffness.
      • Text on some figures are really small (e.g. 5F) and should be increased in font size.
      • Two graphs from 104-116 can be fused for a better flow.
      • Names of plant lines should be more consistent, e.g. PDF1;GR and PDF1; GR (line 234)
      • Line 603: there is no Figure 5I.
      • Figure 5 should be rearranged for a better flow. My suggestion as below. This one has better flow than the original figure.

      Referees cross-commenting

      All three reviewers agree that this paper is well structured, logical and would be a valuable contribution to the plant research community. Different points were raised by the reviewers, so I believe the paper would be much better after addressing all the comments. I agree with the other reviewer that the authors should change the title to better reflect the concretely supported findings of the paper. It also better to not overstate and add references where needed, especially in the discussion. Overall, a revision is expected for the paper.

      Significance

      The study shows for the first time the impacts of TCP4 expression in specific cell layers (epidermis and subepidermis) on leaf growth and development from a biomechanical point of view. It shows that expression of TCP4 either in the epidermis or subepidermis can restrict growth of the whole leaf. It then shows how TCP4 expression leads to changes in growth dynamics, microtubule orientation, CW composition and stiffness and auxin signalling, hence providing a relatively complete picture to explain mechanistically how TCP4 controls leaf growth and development. This basic research would be of interest to a broad audience as it deals with the mechanics of development. <br /> Strengths: (i) good question and subject of study, (ii) solid amount of data with proper quantifications coming from different transgenic lines, (iii) relatively complete as mentioned above.

      Further investigation: I was expecting more data from the sub-epidermal expression of TCP4, probably to see if the effects on microtubules and CWs are comparable to the epidermal expression of TCP4.

      About the reviewer: I have some research experience on biomechanics of growth and development of aerial organs in Arabidopsis. I have used most of the techniques in this research (gene expression analysis, morphometric analyses, growth dynamics analysis in MorphoGraphX, analysis of microtubule dynamics and CW stiffness...) in my own published papers.

  3. Aug 2026
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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      The last several years have seen major advances in our understanding of the basic cell biology of the set of single cell animal relatives, led by the authors and their colleagues. These groups have developed several as models and pioneered remarkable microscopy approaches to examine their cell biology. Here the authors extend this work to the ichthyosporean Sphaeroforma arctica, exploring the fascinating process by which a syncytial life stage cellularizes, and seeking to define the role of microtubules and Golgi trafficking. Their new expansion microscopy delivers impressive images of membrane and the cytoskeleton during this process and has the promise of answering important questions about the roles of microtubules and membrane trafficking. I thus went into my review quite excited. However, as I detail below, while some aspects of the process are carefully quantified, the current manuscript draws multiple broad conclusions that do not seem fully supported by the limited data provided. This substantially reduced my enthusiasm. I'd also note, though I did not take this into account in my evaluation, that many of the images are presented at a size that was difficult to interpret, without my electronically enlarging them, and two of the Figures were mis-labeled.

      The general points raised here are dealt with below. All panels in the main figures are enlarged, zoom-ins are added where they help, and figures that are no longer fitted have been moved to the supplementary figures. Panel labelling has been checked throughout, the two mislabelled figures are corrected, and the labelling is now consistent across the whole figure set. The additional quantification and the associated framework is set out under comment 2.

      1. Interpreting most of their Figures requires understanding the basics of cellularization in this organism. Comparing the diagram in Fig. 1B and the images in 1C left me confused. First, are all the images in 1C and similar images later cross sections? Are nuclei dispersed throughout the cytoplasm at the start or restricted to a region near the cortex. If the former, how do more central nuclei get cellularized? The transition from the unperturbed 20 and 40 minute timepoints left me unclear on the normal process. Panel G may have been helpful in this regard but only shows the treated embryo and no untreated one.

      This comment identified a real shortcoming in how we presented the system, and it prompted the largest single change to the text of the revised manuscript. In writing the original version we took the basic description of cellularization in S. arctica largely for granted, since it has been built up across several previous studies (Dudin et al. 2019, Ondracka et al. 2018, Olivetta et al. 2023 and Shah et al. 2024), and we did not restate it for a reader arriving at the organism for the first time. We have addressed this on three fronts: the imaging planes, the referee's question about nuclei, and the introduction.

      On the images themselves (Figure 1), these panels and the equivalent images throughout the manuscript are equatorial mid-sections. The imaging plane is now explicitly stated in every figure legend, so that single slices, equatorial mid-sections, and maximum-intensity projections can no longer be confused with one another. More usefully, we have added Figure EV1A, which shows an untreated cellularizing coenocyte as both a top view and an equatorial mid-section, at the onset of invagination and 10 minutes later, each at low and high contrast. Figure EV1A makes the two geometries directly comparable, rather than leaving the reader to reconcile the schematic in Figure 1C with a single plane, and it also provides the untreated counterpart to Figure 1G that the referee requested at the end of this comment. All parameters of cellularization measure in the study are now indicated on the sketch in Figure 1C.

      Figure Legend EV1A: (A) Cellularizing control S. arctica coenocytes labelled with FM4-64, shown as a top view and an equatorial mid-section at the onset of invagination (0 min) and 10 min later, each at low and high contrast display. Nuclei are visible as dye-excluded regions (asterisks). Scale bar, 10 µm.

      We would add that an untreated comparison was in fact already available, since Movie EV1 runs a control and an MBC-treated coenocyte side by side through the whole of cellularization at 10-minute intervals. We accept that this was not where the referee would naturally have looked for it, and Figure EV1A now places the comparison in the figures themselves.

      On the referee's central question, we can be unambiguous. Nuclei are not dispersed throughout the cytoplasm From the coenocytic phase through to cellularization, they remain associated with the cortex, and this has been established consistently across the previous work cited above. There are therefore no central nuclei in controls, and the question of how they would be enclosed does not arise. We recognize, however, that the original manuscript never stated this outright, and that a reader had no way of knowing it, and the referee was right to ask. The revised text now says it explicitly and follows it with the geometric consequence.

      Line 70-72: Nuclei divide synchronously and rather than occupying the interior of the coenocyte, associate with the cortex, evenly spacing out along the surface.

      Line 78-82: Recent work has shown that S. arctica cellularization is regulated by the nucleocytoplasmic (N/C) ratio, with cellularization timing tightly coupled to nuclear content relative to cytoplasmic volume. Invaginations initiate at the cell periphery, between adjacent cortical nuclei, and advance inward, carving compartments from the cortex around each nucleus rather than assembling them from the cell interior.

      Finally, the introduction has been substantially expanded so that the process is defined prior to the figures. It now sets out the coenocytic phase, the arrangement of nuclei at the cortex, the direction in which invaginations progress, and the stages through to the end of cellularization, drawing explicitly on the previous characterisations that the original manuscript had assumed the reader would already know. This also addresses the referee's difficulty with the transition between the unperturbed timepoints, which we had described without first establishing what the normal sequence is.

      The authors make a number of conclusions in Figure 1. Some are carefully quantified but others are not. For example, they state "producing uncoordinated ingression with variable rates, diagonal trajectories, and occasional bifurcations, in contrast to the uniform, perpendicular furrows observed in controls". I was not convinced by the single images provided that these were different-for example, spacing in the unperturbed 10 minute time point seems variable and furrows are not "uniformly perpendicular" in the unperturbed 20 minute time point. I also was puzzled by the lack of change in nuclear spacing while furrow spacing was altered. Later on in this section they state "MBC-treated coenocytes displayed significantly decreased and irregular furrow spacing, resulting in some compartments lacking nuclei entirely (Figure 1G & H)", but none of these images visualize nuclei directly-Perhaps the lower level background in H is supposed to indicate this but no parallel wildtype image is shown. Finally, while their TEM images (in the panels that are either I or J due to mislabeling) are lovely, I was not sure what to conclude from them- only the unperturbed images show the embryo surface for orientation and the lefthand unperturbed image is not as straight as they suggest-I certainly don't think these few images support their strong, detailed conclusions here: "MBC treatment resulted in aberrant membrane architecture, with furrows exhibiting bifurcated and convoluted morphology and abnormal fusion at the base of invagination, compared to the smooth, organized structure of control furrows".

      We have considerably expanded both the range and the amount of quantification in the revised manuscript, and every quantity we measure is now sketched out in Figure 1C. These measurements are organised around the four properties of cellularization that the revised manuscript is built on:

      1. Nuclear organisation, measured as inter-nuclear distance and its variability within a coenocyte (Figure EV1G and Figure 1F).
      2. Furrow positioning, measured as furrow spacing, its variability within a coenocyte, and the deviation of the invagination axis from perpendicular to the cortex (Figure 1H, Figure EV1H and Figure EV1I).
      3. Furrow ingression dynamics, measured as ingression rate, its variability, the maximum length furrows reach, its variability, and the duration of cellularization from the onset of invagination to Flip (Figure 1E and Figure EV1B-F).
      4. Developmental outcome, measured as the variability of released cell size (Figure EV1J). The same set of measurements is now applied to every perturbation in the manuscript, so that microtubule depolymerization, centrifugal displacement of nuclei and disruption of membrane trafficking are each characterised against the same quantities rather than described in their own terms. Applied across the whole dataset, these measurements answer the referee's objections directly.

      The descriptors they quote were asserted from single images rather than measured. We have deleted "diagonal trajectories", "occasional bifurcations" and "uniform, perpendicular" from the manuscript, and what remains is carried by measurement.

      Line 113-115: MT loss disrupted furrow ingression dynamics, producing uncoordinated ingression - in contrast to the consistent furrows observed in controls (Fig. 1D, Movie EV1).

      Coordination is now measured within single coenocytes, so that furrows are compared against their own neighbours rather than across the population. This is the comparison the referee implies when looking at a single image and asking whether the furrows within it differ. It is now Figure EV1D.

      Figure Legend EV1D: (C) Variability of furrow ingression rate within a coenocyte, from the rates in (B). DMSO 0.053, MBC 0.111, p = 0.014.

      Perpendicularity is measured in Figure EV1L, on ten furrows per coenocyte at matched ingression depth between conditions. This concedes the referee's specific point: control furrows are not uniformly perpendicular, and deviate by 5.51 degrees on average. We claim only that treated coenocytes deviate roughly twice as far.

      Figure Legend EV1I: (I) Deviation of the invagination axis from perpendicular to the cortex, |angle - 90|, averaged per coenocyte. DMSO 5.51 degrees (n=5), MBC 10.83 degrees (n=4), Ten furrows per coenocyte; p = 0.016. Ingression depth did not differ between conditions.

      On the referee's puzzlement about nuclear spacing, they have identified something we had underplayed. Mean inter-nuclear spacing genuinely does not change, and we do not claim that it does. What changes is its regularity, which roughly doubles. The same pattern recurs for furrow spacing, for the length furrows reach, for ingression rate and for the size of the cells finally released, and it is why the manuscript always argued that microtubules sustain the fidelity of cellularization rather than its execution. However, now it's strengthened by numerical data.

      Line 156-159: While mean inter-nuclear spacing was not significantly different between conditions (6.15 vs 6.22 μm, Fig. EV1G), MBC-treated coenocytes exhibited dramatically increased variability in nuclear positioning (0.143 vs 0.268, Fig. 1F), with nuclei ranging from properly positioned to severely mislocalized.

      On the visualisation of nuclei, we would push back in part. Nuclei are directly visible in FM4-64 as regions from which the dye is excluded, and this is how they were scored throughout; the approach is not introduced here and was used in this same organism in Olivetta and Dudin (2023). We accept that this was never explained and that the display contrast made it difficult to see. The Methods now state the scoring explicitly; Figure EV1A and Figure EV3D show coenocytes at both low and high display contrast, and nuclei are marked with asterisks.

      Figure legend EV1: (A) Cellularizing control S. arctica coenocytes labelled with FM4-64, shown as a top view and an equatorial mid-section at the onset of invagination (0 min) and 10 min later, each at low and high contrast display. Nuclei are visible as dye-excluded regions (asterisks). Scale bar, 10 µm.

      Line 272-274: Throughout these experiments nuclei are resolved in single optical sections as regions from which FM4-64 is excluded and are visible as such at both low and high display contrast (Fig. EV3D).

      The claim about compartment contents has also been moved to data where nuclei are directly labelled rather than inferred. The statement that some compartments lacked nuclei is removed from the furrow spacing sentence, and the observation is now made on U-ExM with a DNA stain, in Figure EV2C and Movie EV5, where it is made on a single section and on the full volume rather than on a projection.

      Figure EV2C: (C) Two MBC-treated coenocytes: a maximum intensity projection of 50 consecutive z-sections (left) and a single z-section of a second coenocyte (right). Tubulin signals persist around a subset of nuclei after MT depolymerization, and individual compartments enclose more than one nucleus. This observation is made on the single section and on the full volume in Movie EV5, not on the projection, since nuclei at different depths overlap in projection. Scale bar, 10 µm. Scale bars are adjusted for expansion factors.

      The mislabelled panels have been corrected throughout, and we thank the referee for catching them.

      TEM images of furrows with visible cell surface marked as yellow asterisks are now included in the main figure. Additional images of furrows (furrow 3 and 4) and zoom-ins of the convoluted membrane at the tip are now included in the supplementary Figure EV1K,L. These are representative images selected from DMSO (n = 25 furrows from 34 tomograms, with cell surface seen in 19), MBC (n = 67 furrows from 72 tomograms, with cell surface visible in 37), total 106 tomograms imaged across different cellularization stages and conditions. This information is now included in the legend of Figure 1J. TEM images of DMSO and MBC-treated full cells are provided in EV1K to provide an overview of furrow consistency in the two conditions. Nuclei are labelled with N to indicate nuclei per compartment.

      The images in Fig. 2B are remarkable and very informative, though as I note above they are presented at such a small size that they require considerable enlargement to appreciate. The surprising accumulation of actin at the invagination front, presumably long before membrane closure begins, was striking, as were the microtubule baskets. However, conclusions drawn again seemed too strong. The authors state "High magnification views further supported that these bundles closely tracked the advancing furrow fronts, with longer MT extensions associated with deeper furrows during later stages (Figure 2D, arrowheads)" (BTW once again this Figure was not labeled in parallel with the text-should be 2C). I did not think the NHS staining provided sufficient resolution of advancing furrow fronts to draw this conclusion. They end this section with some more detailed conclusions, which did not seem to me to be well supported by the single image shown: "Furrows were often misaligned, and compartments frequently enclosed multiple nuclei or, conversely, lacked nuclei entirely. In several cases, nuclei were observed trailing between furrows or located beneath partially formed compartments, suggesting that improper nuclear positioning may interfere with furrow progression and sealing (Figure 2F)." The latter conclusion also seemed to leave me wondering about cause and effect. The final sweeping conclusions in the paragraph on p. 7 top (next time please include page numbers) thus seemed much too broad.

      The descriptive claim has been replaced by a measurement made across the whole dataset, and the causal claim has been removed altogether.

      On presentation, all panels in Figure 2 are enlarged in the revised version. We have also added zoom-ins on a forming compartment in Figure EV2A, and a second late-stage example shown as single channels and merge in Figure EV2B, so that the relationship between the microtubule network and the invaginating membrane can be inspected at a useful magnification rather than inferred from a small panel.

      The sentence the referee quotes has been deleted. Their objection is well founded, since we were reading furrow fronts off the pan-labelling and then drawing a quantitative conclusion about depth from it. In its place, the relationship between microtubule length and furrow depth is now measured directly and reported as a correlation across 68 coenocytes, and we state explicitly what that correlation does not establish.

      Line 209-214: Quantitative analysis of MT networks showed that -MT length scaled with furrow depth (Spearman rho = 0.709, p = 1.3 × 10⁻¹¹, n = 68 coenocytes), consistent with MTs elongating in coordination with plasma membrane invagination (Figs. 2E,F). While this correlation suggests a role for MTs in furrow progression, it remains unclear whether this involves active polymerization at the furrow front or utilization of pre-formed MT tracks.

      On the second part quoted, the speculation that improper nuclear positioning may interfere with furrow progression and sealing has been removed. This addresses the point about cause and effect directly, since we cannot separate the two from these data and we no longer imply that we can. As set out in our response to comment 2, the claim that compartments lacked nuclei is also removed, and what remains of that observation now rests on Figure EV2C and Movie EV5 rather than on a single image, so we do not repeat it here.

      Line 218-221: Furrows were often misaligned, and individual compartments were observed to enclose more than one nucleus (Fig. EV2C, Movie EV5). Nuclei were also seen trailing between furrows or lying beneath partially formed compartments (Movie EV5).

      The panel that should have been cited as Figure 2C is corrected, as part of the labelling pass described under comment 2, and the callouts have been checked so that each panel is cited in the order of its lettering. Line numbers are included in this revised version, as the referee requests.

      Finally, in the closing paragraph of the section, we accept that it drew broader conclusions than the data in that section carried. It has been rewritten so that each claim is tied either to a specific measurement or to a named comparison with another system, and the section now ends on the comparison between our two titratable perturbations across ten measurements in Figure 4F rather than on a general statement about cytoskeletal coordination.

      I thought the use of centrifugation to move nuclei was clever. However, it also is moving many other things-for example it moves whatever organelles are labeled by BODIPY and apparently nuclear associated MTOCs, leading to some caveats and calling into question their claim that it "doesn't disrupt MTs". Once again, broad conclusions were drawn based on an n=1 image: "furrow ingression proceeded with kinetics comparable to controls, confirming that the core machinery for membrane trafficking and actin-driven invagination remained functional (Figure 3B). However, furrow spatial patterning was dramatically altered: in nuclear-depleted cortical regions, furrow initiations were more frequent and closely clustered, but failed to progress deeply. In nuclear-enriched regions, furrows progressed with kinetics comparable to controls but were misaligned, frequently enclosing multiple nuclei per compartment rather than the single nucleus observed in controls" Only one thing was quantified-furrow ingression rate-and this must have been done on the selected set of furrows that progressed, and not, for example, of ones like those at the bottom of the image series presented. None of the other conclusions about spatial patterning were quantified-for example, nuclei are not even visualized in Fig 3C. Finally, they do not even mention the results of the MT perturbation presented in this Figure, and the fact that few differences are apparent calls into question their conclusion that that "nuclei (and their associated MTOCs) serve as spatial landmarks that pattern membrane invagination".

      This comment is well taken on every count, and the centrifugation experiment has been reanalysed accordingly. Every claim in that section is now quantified. The comparisons are paired within single coenocytes (nuclei depleted vs enriched regions) so that each coenocyte serves as its own control. Moreover, the microtubule part of the experiment is analysed and reported rather than left aside. The new quantifications are the following:

      1. Nuclear displacement itself, as the percentage of nuclei in the enriched region per coenocyte before and after centrifugation (Figure EV3B).
      2. The state of the microtubule network after centrifugation, as the length of the longest microtubule of each nuclear aster (Figure EV3C).
      3. Furrow spacing in the nuclei-enriched and nuclei-depleted regions of the same coenocyte and its variability (Figure 3E and Figure EV3F).
      4. Furrow length by region and its ratio (Figure EV3G).
      5. Ingression rate by region and across spin conditions, and the invagination angle by region (Figure 3C, Figure 3D and Figure EV3E). On MTs , we agree that the original claim was not supportable and it has been removed. We now measure the network rather than assert that it is intact. Centrifugation does not leave MTs undisturbed. It relocates them together with the nuclei, and the same sentence reports what is lost from the depleted cortex.

      Line 269-276: Microtubules were relocated with the nuclei as every nucleus retained the associated MTOC and MT network, and the length of the longest microtubule of each network was unchanged by centrifugation (5.97 against 5.08 µm, Fig. EV3C). Throughout these experiments nuclei are resolved in single optical sections as regions from which FM4-64 is excluded and are visible as such at both low and high display contrast (Fig. EV3D). In the nuclei-depleted cortex of the same coenocytes, tubulin was present only as some short fragments, a median of three per coenocyte with a median length of 0.95 µm (Data EV1).

      On the broader point that centrifugation moves more than nuclei, the referee is right and we do not present it as a clean perturbation. The manuscript states that displacement is only partially penetrant (Olivetta et al. 2023), gives the previously reported figures for irregular invagination and lysis under identical conditions, and restricts the analysis to coenocytes with clear nuclear displacement.

      Line 257-263: This approach builds on previous work where we used centrifugation to perturb the spatial relationship between nuclei and the cortex and demonstrate that cellularization in S. arctica is sensitive to local nucleocytoplasmic ratios.26 Centrifugal displacement is partially penetrant, with approximately 40% of centrifuged coenocytes showing irregular plasma membrane invaginations and about 10% undergoing lysis.26 Analyses were therefore restricted to coenocytes showing clear nuclear displacement.

      The observation about selection is also correct, and we have made it explicit rather than leaving it implicit. Measurements were indeed made on furrows that progressed far enough to be traced, and the nuclei-depleted cortex carries many additional small indentations that never sustain ingression. We now state this in the Methods and in the legend of Figure 3C. The failure of those small indentations to progress is part of the phenotype itself.

      Line 308-317: Furrow spatial patterning was dramatically altered: in nuclear-depleted cortical regions, furrow initiations were more frequent and closely clustered but failed to progress deeply (Figs. 3B and EV3D). Among the furrows that did progress, spacing was more variable in the nuclear-free region than in the nuclear-enriched half of the same coenocyte. (Fig. 3E). Since furrow spacing is reliably quantifiable only for furrows that progress, the effective furrow spacing in this nuclear depleted region appeared wider and was abolished upon MBC treatment (Fig. EV3F). Taking into account the high number of furrow initials in the nuclear-depleted region (Figs. 3B and EV3D), and furrow separation in the region enriched with nuclei suggests a minimum furrow exclusion zone around individual nuclei and their MT networks.

      On the visibility of nuclei, and as set out in our response to comment 2, nuclei are resolved in single optical sections as regions from which FM4-64 is excluded. In this figure they are now marked with asterisks * in the time-lapse panel, Figure 3B, and Figure EV3D shows a centrifuged coenocyte at both low and high display contrast, so we do not repeat the general point here.

      The final point is the most important one, and the revised analysis answers it directly. The microtubule part is now reported, and far from showing few differences it reverses the relationship between nuclear position and furrow spacing. In control coenocytes the nuclei-depleted region carries wider gaps than the enriched one, and after MT depolymerization it carries narrower gaps.

      Line 317-322: Furrows in nuclear-depleted regions reached shorter lengths (2.14 against 7.50 µm, Fig. EV3G), forming asymmetrically longer compartments on the nuclear-enriched side. This advantage in nuclear-enriched regions was lost upon MBC treatment confirming the role of nucleus-associated MT networks in maintaining the synchronous invagination and uniform cellular partitioning.

      We have also weakened the conclusion the referee quotes, so that it claims a relationship rather than a mechanism.

      Line 322-326: With centrifugation, though the nucleus-associated MT networks also migrate, the regular spacing of the cytoskeletal network at the cortex was disrupted, and furrows no longer exhibited the uniform spacing characteristic of control coenocytes, suggesting indeed that MT-defined nuclear territories serve as spatial landmarks that define the coordinates of membrane invagination.

      Figure 4 is surprisingly described in a single short paragraph. While some things were quantified, I was not convinced that they could conclude that they observed furrows “mispositioned like those seen upon MT depolymerization”. More broadly, what do we really learn from this?

      This section has been substantially expanded, and the comparison the referee doubted is now made statistically. The section now opens with what is known about membrane supply during cellularization in other systems, including the link between the Golgi and microtubules that makes this perturbation informative in the first place, and it reports a dose series rather than a single condition.

      Line 363-375: While these experiments define how cellularization is spatially patterned, the cellular machinery driving membrane invagination itself remained to be identified. The previous experiments show that furrow invagination proceeds even when furrows are mispositioned. This indicates that the processes of new membrane addition and furrow positioning are controlled independently. To identify the cellular processes driving furrow invagination, we examined the role of membrane trafficking. In early Drosophila embryos, the membrane expands from a reservoir of microvilli localized at the apical cortex.33,34 The first phase of this membrane invagination is supplemented partly by Golgi-derived vesicles. These vesicles are transported in a MT-dependent manner and are stalled with colcemid or colchicine treatment which eventually prevented furrow invagination.18,23,35 It is unclear if such a membrane reservoir is present at the S. arctica cortex where the furrows are first initiated. Brefeldin A treatment in Drosophila embryos inhibits furrow progression in the final stage of cellularization.15,18

      On the dose series, a high dose of Brefeldin A at 3 µg/ml interferes with and in some coenocytes blocks cellularization, which establishes that Golgi-mediated trafficking is required for the process and is shown in Figure EV4A to Figure EV4C. All quantitative measurements are made at 1.5 µg/ml, a dose that leaves ingression intact, and the Methods now state this separation explicitly so that no measurement is read as belonging to the blocking dose.

      On the resemblance to MTs depolymerization, the referee is right that this could not be concluded from the images, and we have therefore tested it. Furrow spacing variability was measured identically in the experiments. They are statistically indistinguishable, and so are their two controls, which is what makes the comparison meaningful.

      Figure 4C: (C) Variability of furrow spacing within a coenocyte, from the same measurements. Methanol 0.212, BfA 0.433, p = 0.0047. Measured the same way in both experiments, the two perturbations are indistinguishable from one another (MBC 0.446 versus BfA 0.433, p = 0.66) and so are the two controls (DMSO 0.213 versus methanol 0.212, p = 0.67).

      On what is learned from the experiment, the answer is that the two perturbations dissociate, and this is now the organising result of the manuscript. Ten measurements are compared between them in Figure 4F, each as a log2 fold change against its own control and grouped by the property it belongs to, and Figure 4G summarises which machinery contributes to which property.

      Line 389-395: Nuclear positioning, by contrast, was not detectably affected as neither inter-nuclear distance nor its variability differed from controls (Figs. EV4E, F). Ingression rate (Fig. 4E), its variability (Fig. EV4G) and the maximum length reached by furrows (Figs. EV4H, EV4I) were likewise comparable to controls. Cellularization nonetheless took longer to complete (from 40 to 50 min, Fig. EV4J), and the cells released at the end were of more variable size (0.140 to 0.266, Fig. EV4K).

      Trafficking is therefore required for cellularization to proceed, since the high dose blocks it, yet at a dose that leaves ingression rate untouched what it contributes is where furrows form and whether cellularization finishes on time. Nuclear organisation is unaffected at that dose, in Figure EV4E and Figure EV4F, which is precisely where the two perturbations part company and is the reason the resemblance in furrow positioning is informative rather than trivial.

      The closing paragraph of this section has been rewritten as described in our response to comment 3, and it now ends on this comparison rather than on a general statement.

      Significance

      As I note in detail in the previous section, Here the authors extend this work to the ichthyosporean Sphaeroforma arctica, exploring the fascinating process by which a syncytial life stage cellularizes, and seeking to define the role of microtubules and Golgi trafficking. Their new expansion microscopy delivers impressive images of membrane and the cytoskeleton during this process and has the promise of answering important questions about the roles of microtubules and membrane trafficking. I thus went into my review quite excited. However, as I detail below, while some aspects of the process are carefully quantified, the current manuscript draws multiple broad conclusions that do not seem fully supported by the limited data provided. This substantially reduced my enthusiasm.

      We are grateful that the referee finds the ExM compelling. The concern that broad conclusions outran the data has driven most of this revision, and every conclusion in the manuscript is now either carried by a measurement made across the dataset or has been removed.

      Reviewer #2

      Evidence, reproducibility and clarity

      Summary

      This interesting paper is a follow-up from Dudin et al.'s seminal 2019 eLife paper describing cellularization in the ichthyosporean Sphaeroforma arctica. In that earlier story, a role for microtubules (MTs) in cellularization was supported by treatment with the MT-depolymerizing drug MBC, which deeply affected nuclear spacing and the regularity of cellularization. However, the difficulty of imaging microtubules at the time had prevented more in-depth functional studies. This technical barrier has now been lifted by ultrastructural expansion microscopy (U-ExM), and this paper thus picks up where the earlier study left off.

      The study combines live imaging, drug treatments, electron microscopy and ultrastructural electron microscopy to support a role for microtubules, nuclei, and membrane trafficking in sustaining the fidelity of S. arctica cellularization. An extensive live imaging dataset and careful image quantifications reinforce and expand the previously published observation that microtubules, while dispensable for cellularization to occur at all, are necessary for it to occur with proper timing and spacing. UEx-M images support the idea that actin and microtubules guide plasma membrane invaginations, and centrifugation experiments support a role for nuclear positioning in ensuring fidelity of cellularization. Finally, Brefeldin A treatment followed by live imaging and U-ExM supports a role for membrane trafficking in furrow positioning and elongation.

      Major comments

      The conclusions are adequately supported by the data, and their limitations are transparently acknowledged: notably, it is not fully clear by what mechanisms nuclei guide cellularization, and whether those mechanisms depend on microtubules or not. Below are a few points where I feel additional data (or better visualization, or additional verbal caveats) could improve the manuscript.

      We are grateful that the referee reads the work as picking up where the 2019 study left off, and that they find the conclusions supported and the limitations transparently stated. Their four points are addressed below.

      1) After 12,000rpm centrifugation, the authors point out that furrows "were misaligned, frequently enclosing multiple nuclei per compartment". This is not obvious in Figure 3C, where nuclei are only visible as empty spaces and only labelled (by asterisks) at a relatively early stage of furrow ingression. This point could be better supported by more explicit images (with nuclei more evident at late stages, even if only as blank spaces), or maybe by co-staining DNA and either membrane or F-actin in centrifuged samples.

      The co-staining the referee suggests is now provided. Figure 3A and Figure EV3A show centrifuged coenocytes by U-ExM with DNA, membrane, actin and tubulin labelled together, so nuclei are seen directly rather than as gaps. Asterisks now mark nuclei throughout the time-lapse in Figure 3B, and Figure EV3D shows a centrifuged coenocyte at both low and high display contrast. As set out in our response to Ref 1-2, nuclei are scored in live imaging as regions from which FM4-64 is excluded.

      2) In centrifuged samples (Figure 3A), some staining is visible in tubulin channel of the nuclei-free part, but does not correspond to discrete, observable microtubules. Could the authors comment on this? Do they think these represent diffuse, perhaps damaged microtubules (nucleated independently of nuclei?), or perhaps free tubulin, or mere background?

      We have measured it rather than interpreted it. These objects are short tubulin-positive fragments, clearly distinct from nuclear asters in both number and length, and we report them separately for that reason. We do not claim to know whether they are remnants of displaced MTs or independently nucleated, and the manuscript says so by describing them rather than assigning them an origin.

      Figure EV3C: In the nuclei-depleted cortex of the same coenocytes, tubulin was present only as short fragments, a median of three per coenocyte and 0.95 µm long (see Data EV1), which are different objects from an aster and are not tested against it.

      3) Similarly: in centrifuged samples Fig. 3C, the nuclei-free part of the cell does seem to cellularize, albeit slower and forming smaller compartments than the nucleated part. This suggests that nuclei (and microtubules?) guide cellularization, but are perhaps not necessary for it. Could the authors comment?

      We agree, and this is now quantified. In the nuclei-depleted region furrows are more widely and more variably spaced, reach shorter lengths and ingress more slowly than in the nuclei-enriched region of the same coenocyte, yet they form and progress. Nuclei therefore guide cellularization without being required for it, which is also what Referee 3 takes from the same experiment.

      Line 308-317: Furrow spatial patterning was dramatically altered: in nuclear-depleted cortical regions, furrow initiations were more frequent and closely clustered but failed to progress deeply (Figs. 3B and EV3D). Among the furrows that did progress, spacing was more variable in the nuclear-free region than in the nuclear-enriched half of the same coenocyte. (Fig. 3E). Since furrow spacing is reliably quantifiable only for furrows that progress, the effective furrow spacing in this nuclear depleted region appeared wider and was abolished upon MBC treatment (Fig. EV3F). Taking into account the high number of furrow initials in the nuclear-depleted region (Figs. 3B and EV3D), and furrow separation in the region enriched with nuclei suggests a minimum furrow exclusion zone around individual nuclei and their MT networks.

      4) The bottom row of Figure 3C presents a series of experiments combining MBC treatment with 12,000rpm centrifugation, but if I am not mistaken these are not discussed at all in the text. This is unfortunate, as it would be interesting to know what the authors want to conclude from these experiments (whose results are however not quantified, perhaps limiting their scope). In my view, one reason to be interested in this dataset is that it could in principle inform epistatic relationships between nuclei positioning and microtubules, that are otherwise left open: do these guide furrow ingression independently of each others (in which case their effects should be additive) or does one act purely through the other (in which case the combined treatment should not be worse than individual treatments)? In any case, I would suggest either commenting on these data explicitly (perhaps with additional quantification guiding interpretations) or removing them.

      The referee is right that these data were not discussed, and they are now quantified and interpreted. As set out in Ref 1-4, combining centrifugation with MT depolymerization does not add to the effect of nuclear displacement but reverses it, so that the region depleted of nuclei carries narrower gaps rather than wider ones. This is the epistatic test the referee proposes, and it argues against two contributions acting independently and in parallel. The nuclear contribution appears to run through the MT network.

      Minor comments

      • Figure 1 contains two distinct panel H's.
      • Typo p. 6: "actomyosing"
      • Page 7: "longer MT extensions associated with deeper furrows during later stages (Figure 2D, arrowheads)" rather seems to refer to Figure 2B or C.
      • Line and page numbers are missing
      • All corrected.

        Significance

      This paper will be of broad interest to readers interested in the evolution of multinucleated cells and cellularization, and the recurrent involvement of actin and microtubules in these processes (see notably recent papers by the Brugués lab). While it could appear incremental in an ichthyosporean-centric perspective (notably compared to the 2019 eLife paper that had anticipated some of the conclusions), its comparative implications gives it an additional scope in my view. Perhaps this is something the authors themselves could emphasize a bit more in their own conclusion. Finally, although most techniques had been established in earlier papers by the same authors, this study further confirms their robustness and the power of S. arctica as an emerging cell biology model - another nice touch.

      We have followed this suggestion. The closing section now sets our results against cellularization in Drosophila and in chytrids. Also, it compares it against recent work showing that partitioning by MT asters is intrinsically unstable, so that losing its control broadens the distribution of compartment sizes rather than shifting it. That is the pattern we observe in our study. We have hinted at this in the discussion, so that the comparative implications the referee points to are stated rather than left to the reader.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In their manuscript, Araújo et al. described their investigation into the cellularisation process in Sphaeroforma artica during the multicellular stage of its life cycle. The coenocytes are relatively small at 50 µm in diameter, but the authors developed expansion microscopy and used a new actin probe to provide a detailed description of the ingression of the membrane furrow between adjacent nuclei. They compared the formation of the furrow in the presence or absence of microtubules, as well as in conditions involving regularly spaced or clustered nuclei. They concluded that, while microtubules were not essential to the formation and ingression of the furrow, they were required for the fidelity of the process, as stated in the title of the manuscript.

      A better quantification of the ingression pattern would have improved the data. The centrifugation experiment is particularly interesting, as it forces the accumulation of nuclei on one side of the coenocytes. Surprisingly, this only partially perturbs the position of the furrow. This showed, on the one hand, that nuclei contribute to the positioning of the furrow close to them, and, on the other hand, that an additional mechanism exists independently of them. The analysis does not clarify whether the absence of microtubules in these centrifuged states affects the position of the furrow. Figure 3C seems to suggest partial rescue (suggesting that the contribution of nuclei is microtubule-dependent, but that the peripheral membrane has its own partitioning mechanism), but this has not been quantified.

      The quantification of ingression is considerably expanded, and the framework it now detailed in Ref 1 - 2.

      On the centrifuged coenocytes lacking microtubules, this is now quantified. As set out in Ref 1 - 4, the relationship between nuclear position and furrow spacing does not merely weaken when microtubules are removed, it reverses, which supports their inference that the contribution of nuclei is microtubule-dependent. In the same experiment, the local slowing of ingression where nuclei are absent persists whether or not MTs are present, which is consistent with the second half of their reading, that the cortex retains a partitioning mechanism of its own.

      One minor concern is that it remains unclear whether microtubule disruption affects the positioning of the nuclei, as stated in the text but not confirmed by the quantification (Figure 1F).

      Figure 1F now reports the variability of inter-nuclear distance within each coenocyte, which roughly doubles upon microtubule depolymerization, while the mean is unchanged and is shown separately in Figure EV1G. The claim in the text is therefore about regularity rather than about mean spacing, and it is now matched by the panel that supports it. This is set out in full in Ref 1 - 2.

      Reviewer #3 (Significance (Required)):

      The work is primarily descriptive, and the processes determining the position of the furrow and driving its ingression remain unknown. Nevertheless, the work provides beautiful images of a poorly described yet potentially informative system. These are distant relatives of animals with interesting common characteristics, such as the cellularisation process. The conservation of this process may reveal some key fundamental properties of multicellularity. Despite its limited conceptual advances, the manuscript is thus innovative and interesting.

      We thank the referee for their assessment. The revision strengthens what the paper claims. Comparing our two titratable perturbations across ten measurements shows that furrow positioning and furrow ingression dynamics can be disturbed independently of one another, and that neither prevents cellularization from completing (Figure 4F and Figure 4G). What is degraded is fidelity rather than execution of cellularization.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      In their manuscript, Araújo et al. described their investigation into the cellularisation process in Sphaeroforma artica during the multicellular stage of its life cycle. The coenocytes are relatively small at 50 µm in diameter, but the authors developed expansion microscopy and used a new actin probe to provide a detailed description of the ingression of the membrane furrow between adjacent nuclei. They compared the formation of the furrow in the presence or absence of microtubules, as well as in conditions involving regularly spaced or clustered nuclei. They concluded that, while microtubules were not essential to the formation and ingression of the furrow, they were required for the fidelity of the process, as stated in the title of the manuscript.

      A better quantification of the ingression pattern would have improved the data. The centrifugation experiment is particularly interesting, as it forces the accumulation of nuclei on one side of the coenocytes. Surprisingly, this only partially perturbs the position of the furrow. This showed, on the one hand, that nuclei contribute to the positioning of the furrow close to them, and, on the other hand, that an additional mechanism exists independently of them. The analysis does not clarify whether the absence of microtubules in these centrifuged states affects the position of the furrow. Figure 3C seems to suggest partial rescue (suggesting that the contribution of nuclei is microtubule-dependent, but that the peripheral membrane has its own partitioning mechanism), but this has not been quantified. One minor concern is that it remains unclear whether microtubule disruption affects the positioning of the nuclei, as stated in the text but not confirmed by the quantification (Figure 1F).

      Significance

      The work is primarily descriptive, and the processes determining the position of the furrow and driving its ingression remain unknown. Nevertheless, the work provides beautiful images of a poorly described yet potentially informative system. These are distant relatives of animals with interesting common characteristics, such as the cellularisation process. The conservation of this process may reveal some key fundamental properties of multicellularity. Despite its limited conceptual advances, the manuscript is thus innovative and interesting.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This interesting paper is a follow-up from Dudin et al.'s seminal 2019 eLife paper describing cellularization in the ichthyosporean Sphaeroforma arctica. In that earlier story, a role for microtubules (MTs) in cellularization was supported by treatment with the MT-depolymerizing drug MBC, which deeply affected nuclear spacing and the regularity of cellularization. However, the difficulty of imaging microtubules at the time had prevented more in-depth functional studies. This technical barrier has now been lifted by ultrastructural expansion microscopy (U-ExM), and this paper thus picks up where the earlier study left off. The study combines live imaging, drug treatments, electron microscopy and ultrastructural electron microscopy to support a role for microtubules, nuclei, and membrane trafficking in sustaining the fidelity of S. arctica cellularization. An extensive live imaging dataset and careful image quantifications reinforce and expand the previously published observation that microtubules, while dispensable for cellularization to occur at all, are necessary for it to occur with proper timing and spacing. UEx-M images support the idea that actin and microtubules guide plasma membrane invaginations, and centrifugation experiments support a role for nuclear positioning in ensuring fidelity of cellularization. Finally, Brefeldin A treatment followed by live imaging and U-ExM supports a role for membrane trafficking in furrow positioning and elongation.

      Major comments

      The conclusions are adequately supported by the data, and their limitations are transparently acknowledged: notably, it is not fully clear by what mechanisms nuclei guide cellularization, and whether those mechanisms depend on microtubules or not. Below are a few points where I feel additional data (or better visualization, or additional verbal caveats) could improve the manuscript.

      1) After 12,000rpm centrifugation, the authors point out that furrows "were misaligned, frequently enclosing multiple nuclei per compartment". This is not obvious in Figure 3C, where nuclei are only visible as empty spaces and only labelled (by asterisks) at a relatively early stage of furrow ingression. This point could be better supported by more explicit images (with nuclei more evident at late stages, even if only as blank spaces), or maybe by co-staining DNA and either membrane or F-actin in centrifuged samples.

      2) In centrifuged samples (Figure 3A), some staining is visible in tubulin channel of the nuclei-free part, but does not correspond to discrete, observable microtubules. Could the authors comment on this? Do they think these represent diffuse, perhaps damaged microtubules (nucleated independently of nuclei?), or perhaps free tubulin, or mere background?

      3) Similarly: in centrifuged samples Fig. 3C, the nuclei-free part of the cell does seem to cellularize, albeit slower and forming smaller compartments than the nucleated part. This suggests that nuclei (and microtubules?) guide cellularization, but are perhaps not necessary for it. Could the authors comment?

      4) The bottom row of Figure 3C presents a series of experiments combining MBC treatment with 12,000rpm centrifugation, but if I am not mistaken these are not discussed at all in the text. This is unfortunate, as it would be interesting to know what the authors want to conclude from these experiments (whose results are however not quantified, perhaps limiting their scope). In my view, one reason to be interested in this dataset is that it could in principle inform epistatic relationships between nuclei positioning and microtubules, that are otherwise left open: do these guide furrow ingression independently of each others (in which case their effects should be additive) or does one act purely through the other (in which case the combined treatment should not be worse than individual treatments)? In any case, I would suggest either commenting on these data explicitly (perhaps with additional quantification guiding interpretations) or removing them.

      Minor comments

      • Figure 1 contains two distinct panel H's.
      • Typo p. 6: "actomyosing"
      • Page 7: "longer MT extensions associated with deeper furrows during later stages (Figure 2D, arrowheads)" rather seems to refer to Figure 2B or C.
      • Line and page numbers are missing

      Referees cross-commenting

      After reading through the two other reviewer comments, I feel we are in substantial agreement that (1) the paper deals with an interesting issue and has potential; (2) it can be substantially improved by better quantifications and better visualization of nuclei across several experiments, which would be key to support some of the conclusions. Given the relatively short life cycle of S. arctica, I still believe that revisions satisfying all reviewers should be achievable within a reasonable time frame - I would suggest 1 to 3 months.

      Significance

      This paper will be of broad interest to readers interested in the evolution of multinucleated cells and cellularization, and the recurrent involvement of actin and microtubules in these processes (see notably recent papers by the Brugués lab). While it could appear incremental in an ichthyosporean-centric perspective (notably compared to the 2019 eLife paper that had anticipated some of the conclusions), its comparative implications gives it an additional scope in my view. Perhaps this is something the authors themselves could emphasize a bit more in their own conclusion. Finally, although most techniques had been established in earlier papers by the same authors, this study further confirms their robustness and the power of S. arctica as an emerging cell biology model - another nice touch.

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      Referee #1

      Evidence, reproducibility and clarity

      The last several years have seen major advances in our understanding of the basic cell biology of the set of single cell animal relatives, led by the authors and their colleagues. These groups have developed several as models and pioneered remarkable microscopy approaches to examine their cell biology. Here the authors extend this work to the ichthyosporean Sphaeroforma arctica, exploring the fascinating process by which a syncytial life stage cellularizes, and seeking to define the role of microtubules and Golgi trafficking. Their new expansion microscopy delivers impressive images of membrane and the cytoskeleton during this process and has the promise of answering important questions about the roles of microtubules and membrane trafficking. I thus went into my review quite excited. However, as I detail below, while some aspects of the process are carefully quantified, the current manuscript draws multiple broad conclusions that do not seem fully supported by the limited data provided. This substantially reduced my enthusiasm. I'd also note, though I did not take this into account in my evaluation, that many of the images are presented at a size that was difficult to interpret, without my electronically enlarging them, and two of the Figures were mis-labeled.

      1. Interpreting most of their Figures requires understanding the basics of cellularization in this organism. Comparing the diagram in Fig. 1B and the images in 1C left me confused. First, are all the images in 1C and similar images later cross sections? Are nuclei dispersed throughout the cytoplasm at the start or restricted to a region near the cortex. If the former, how do more central nuclei get cellularized? The transition from the unperturbed 20 and 40 minute timepoints left me unclear on the normal process. Panel G may have been helpful in this regard but only shows the treated embryo and no untreated one.
      2. The authors make a number of conclusions in Figure 1. Some are carefully quantified but others are not. For example, they state "producing uncoordinated ingression with variable rates, diagonal trajectories, and occasional bifurcations, in contrast to the uniform, perpendicular furrows observed in controls". I was not convinced by the single images provided that these were different-for example, spacing in the unperturbed 10 minute time point seems variable and furrows are not "uniformly perpendicular" in the unperturbed 20 minute time point. I also was puzzled by the lack of change in nuclear spacing while furrow spacing was altered. Later on in this section they state "MBC-treated coenocytes displayed significantly decreased and irregular furrow spacing, resulting in some compartments lacking nuclei entirely (Figure 1G & H)", but none of these images visualize nuclei directly-Perhaps the lower level background in H is supposed to indicate this but no parallel wildtype image is shown. Finally, while their TEM images (in the panels that are either I or J due to mislabeling) are lovely, I was not sure what to conclude from them- only the unperturbed images show the embryo surface for orientation and the lefthand unperturbed image is not as straight as they suggest-I certainly don't think these few images support their strong, detailed conclusions here: "MBC treatment resulted in aberrant membrane architecture, with furrows exhibiting bifurcated and convoluted morphology and abnormal fusion at the base of invagination, compared to the smooth, organized structure of control furrows".
      3. The images in Fig. 2B are remarkable and very informative, though as I note above they are presented at such a small size that they require considerable enlargement to appreciate. The surprising accumulation of actin at the invagination front, presumably long before membrane closure begins, was striking, as were the microtubule baskets. However, conclusions drawn again seemed too strong. The authors state "High magnification views further supported that these bundles closely tracked the advancing furrow fronts, with longer MT extensions associated with deeper furrows during later stages (Figure 2D, arrowheads)" (BTW once again this Figure was not labeled in parallel with the text-should be 2C). I did not think the NHS staining provided sufficient resolution of advancing furrow fronts to draw this conclusion. They end this section with some more detailed conclusions, which did not seem to me to be well supported by the single image shown: "Furrows were often misaligned, and compartments frequently enclosed multiple nuclei or, conversely, lacked nuclei entirely. In several cases, nuclei were observed trailing between furrows or located beneath partially formed compartments, suggesting that improper nuclear positioning may interfere with furrow progression and sealing (Figure 2F)." The latter conclusion also seemed to leave me wondering about cause and effect. The final sweeping conclusions in the paragraph on p. 7 top (next time please include page numbers) thus seemed much too broad.
      4. I thought the use of centrifugation to move nuclei was clever. However, it also is moving many other things-for example it moves whatever organelles are labeled by BODIPY and apparently nuclear associated MTOCs, leading to some caveats and calling into question their claim that it "doesn't disrupt MTs". Once again, broad conclusions were drawn based on an n=1 image: "furrow ingression proceeded with kinetics comparable to controls, confirming that the core machinery for membrane trafficking and actin-driven invagination remained functional (Figure 3B). However, furrow spatial patterning was dramatically altered: in nuclear-depleted cortical regions, furrow initiations were more frequent and closely clustered, but failed to progress deeply. In nuclear-enriched regions, furrows progressed with kinetics comparable to controls but were misaligned, frequently enclosing multiple nuclei per compartment rather than the single nucleus observed in controls" Only one thing was quantified-furrow ingression rate-and this must have been done on the selected set of furrows that progressed, and not, for example, of ones like those at the bottom of the image series presented. None of the other conclusions about spatial patterning were quantified-for example, nuclei are not even visualized in Fig 3C. Finally, they do not even mention the results of the MT perturbation presented in this Figure, and the fact that few differences are apparent calls into question their conclusion that that "nuclei (and their associated MTOCs) serve as spatial landmarks that pattern membrane invagination".
        1. Figure 4 is surprisingly described in a single short paragraph. While some things were quantified, I was not convinced that they could conclude that they observed furrows "mispositioned like those seen upon MT depolymerization". More broadly, what do we really learn from this?

      Referees cross-commenting

      Unfortunately I remain convinced that this manuscript has significant issues with the match between the data presented and the claims made--I laid these issues out clearly and stand by them

      Significance

      As I note in detail in the previous section, Here the authors extend this work to the ichthyosporean Sphaeroforma arctica, exploring the fascinating process by which a syncytial life stage cellularizes, and seeking to define the role of microtubules and Golgi trafficking. Their new expansion microscopy delivers impressive images of membrane and the cytoskeleton during this process and has the promise of answering important questions about the roles of microtubules and membrane trafficking. I thus went into my review quite excited. However, as I detail below, while some aspects of the process are carefully quantified, the current manuscript draws multiple broad conclusions that do not seem fully supported by the limited data provided. This substantially reduced my enthusiasm.

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      Reply to the reviewers

      We provide below a point-by-point response to reviewers’ comments, describing changes that were made to the manuscript. We also uploaded a "Full Revision" file that contains a general statement in addition to these responses to reviewers. We sincerely thank both reviewers for their thorough reading and suggestions that we believe contributed to a better concision and clarity of the revised manuscript.

      Reviewer #1

      Major Comments

      1. TDH3 has been a major model in the study of the evolution of gene expression as developed by the authors. While most conclusions derived from this system relate to genetic mechanisms, this study attempted to identify the molecular/cellular mechanisms. Although I greatly appreciate the author's comprehensive efforts, I would suggest that conclusions regarding molecular/cellular mechanisms should be made with greater caution, especially avoiding over-generalized conclusions that may be specific to P_TDH3. Thus, I suggest going over the manuscript again and adjusting some statements if they are over-generalizing, as well as adding an explicit discussion of this limitation.

      As suggested by the reviewer, we went over the manuscript and made sure that we specifically mentioned the TDH3 promoter in conclusions that may be specific to this promoter. In addition, we further addressed this limitation by including new results of an experiment where we measured the effects of yme2, chs1 and msh1 mutations on expression noise of three additional yeast promoters (PFBA1, PACT1, PTEF1). We found that the three mutations had similar effects on expression noise driven by PTDH3, PACT1 and PTEF1, but different or no effect on PFBA1 expression noise. Therefore, some of our conclusions may not be specific to the TDH3 promoter, such as the importance of mitochondrial state in regulating expression noise of nuclear genes. We described these findings in Results, in the new Figure 7 and in associated supplements. We also added a paragraph in the Discussion and we included new co-authors who performed these additional experiments. We believe these new results will increase the impact of the study.

      1. On the surface, it may seem reasonable to ask for changed noise by unchanged mean in order to distinguish independent regulators. However, from a mechanistic perspective, this is quite demanding. In the current prevailing model (transcriptional burst) of noise origination, mutations are not assumed to be noise-specific without affecting the mean. See e.g. PMID: 31320634. If noise and mean are intrinsically coupled, looking for noise-only regulatory mechanisms would imply something very different. This may mean, for example, that we are seeking one single mutation that changes noise and mean through one mechanism, while at the same time reverting mean to its wildtype value through another mechanism. It is likely that this is one of the reasons why causal mutations are difficult to identify.

      This is a very sensible comment. We agree that in the model of noise originating from transcriptional bursts, cis-acting mutations are not expected to alter noise without affecting the mean. However, trans-acting mutations may affect transcriptional bursts of a given gene via different mechanisms that compensate each others at the level of mean expression but not at the level of expression noise. Even though mutations impacting noise without altering mean expression may be less common than mutations affecting both mean and noise, they do exist as we showed here. It is possible that we did not identify mutations in transcription factors known to regulate TDH3 promoter activity because such mutations would affect both mean expression and noise. We added two sentences in the discussion to mention this hypothesis. In addition, while the transcriptional burst model can explain the coupling between intrinsic noise and mean expression, it does not apply to mutations impacting extrinsic noise.

      Regarding the difficulty to identify mutations altering only expression noise, we found a causal mutations in three out of five mutants analyzed. Interestingly, the three successes corresponded to mutations that increase noise, and the two failures to mutations that decrease noise, suggesting that mutations decreasing noise may be particularly difficult to identify. Finally we note that expression noise is a complex genetic trait in natural populations. An interesting case is shown in Fehrmann et al. 2013, where three trans-acting alleles (loci on chr7, 8 & 13) had a strong individual contribution on noise, partially coupled to mean changes. When combined together, their cumulative effect was very strong on both mean and noise, although the wild strain from which these alleles originate showed an elevated noise but no remarkable change in mean as compared to a reference strain. This implies that other (unmapped) loci “buffer” expression mean in this wild strain against the action of the three noise-acting loci, a scenario comparable to the one suggested by the reviewer.

      1. L209-214. The authors state that they selected 254 mutants with the largest noise changes from a library of 1,241 strains (L209-214). However, I cannot match this statement when contrasting figure1-figure supplement 1 (254 mutants) and Figure 1a (1,241 strains). Is this caused by experiments conducted in different labs ? Are there any intuitive ways for the authors to show the strains (and their parameter distribution) that produced consistent results across the two batches of data? E.g. using gray/black dots for un-/repeatable strains. Also, are mean expression levels similarly (un-)repeatable compared to noise ?

      Both fluorescence screens were performed in the same laboratory, using the same instrument and following the same protocol. We clarified in the text and figures how the 254 mutants were selected for the secondary screen. In particular, we colored dots on Figure 1 and on Figure 1 – figure supplement 1 to highlight strains with reproducible or non-reproducible change in expression noise in both assays. The apparent lack of reproducibility between the first screen of 1241 strains and the second screen of 254 mutants is not surprising because the 254 strains were not picked randomly among the 1241 initial strains: they were picked because they showed the largest expression changes (either for mean or noise) in the first screen. Statistically, the most extreme effects on mean expression and expression noise observed among the 1241 strains are expected to be over-estimated on average. This phenomena is similar to the “Winner’s curse” in economy. When measuring a quantitative trait for a large number of samples with a certain degree of uncertainty, values that fall in the tails of the distribution (the most extreme values) are statistically expected to be less accurately estimated than values falling near the mean of all samples. To address the last question of the reviewer, mean expression levels were found to show better repeatability than expression noise, probably because error bars (variation among replicate samples) tended to be much smaller (one order of magnitude) for mean expression than for expression noise.

      1. How were the five strains analyzed chosen? Are they the only strains fulfilling the criteria on L211-214?

      The five strains were picked arbitrarily among nine strains that matched the criteria mentioned in the text. We added a sentence to mention this point. We moved to Supplementary File 5 the section describing how the five strains were chosen to make the main text shorter and easier to read.

      1. L243-247. I didn't understand the logic why m2 is included, please elaborate.

      We modified the text to clarify why we picked mutation m2 as a candidate. The logic is that there was no strong statistical evidence to exclude the mutation (because of lower statistical power relative to other mutations).

      1. The equation for extrinsic noise (L899) seem to be slightly differently from that in Fu and Pachter 2016. The product of mean(RFP) and mean(YFP) is multiplied by 2 here, but not in Fu and Pachter 2016.

      We made a typo in the text and corrected it in the revised version. We verified in our R scripts that we used the correct version from Fu and Pachter (with product of mean(RFP) and mean(YFP) not multiplied by 2), which was the case. We are particularly thankful to the reviewer for the thorough proofreading of the manuscript.

      1. The experimental design to exclude noise from partitioning for yme2 is really nice. It would have been great if we had gotten to the bottom of this. (This is not a question so no response is needed)

      No response requested.

      1. The authors demonstrate that a nonsense mutation in CHS1 increases extrinsic noise via impaired chitin septum reparation in daughter cells. However, glucosamine treatment itself alters cell size (Figure 5-figure supplement 3a), which correlates with noise levels. This raises the question: do the observed changes in extrinsic noise stem from glucosamine-induced changes in cell size or from the impaired chitin repair caused by the CHS1 mutation itself? To disentangle these effects, an alternative approach to modulating chitin synthesis that does not alter cell size should be employed.

      We do not think that glucosamine-induced changes in cell size can explain the effect of glucosamine treatment on extrinsic noise in chs1 mutant. We observed that glucosamine treatment had a stronger impact on cell size in WT cells than in chs1(G1752a) mutant cells (Figure 5 – figure supplement 3a). However, glucosamine treatment had a much stronger impact on extrinsic noise of chs1(G1752a) mutant cells than WT cells (Figure 5g). Therefore, there is no direct correspondence between the effect of glucosamine on cell size and the effect of glucosamine on extrinsic noise. Even though glucosamine drastically reduced cell size in WT cells, it had almost no impact on expression noise in these cells. We added a sentence in the revised Results to clarify this point.

      Our hypothesis is that glucosamine increases chitin synthesis not only during repair of the chitin septum, but more globally at all stages of the cell cycle (as shown by Bulik et al., 2003), which may reduce cell size. The global impact of glucosamine on cell wall chitin levels could rescue defects caused by chs1 mutation on chitin septum repair. Previous studies showed that CHS1 was not involved in global chitin synthesis, but only in the repair of chitin septum in daughter cells.

      1. Why did glucosamine doses not significantly impact cell growth rates during the first phase after addition (Figure5 -figure supplement 4) ? Additionally, I can seem to find the experimental details for glucosamine dosing in the Methods.

      We specified the dose of glucosamine in the revised Methods. We did not observe a significant impact of glucosamine on growth rates during exponential growth either in the first growth phase or in the second growth phase after addition. However, glucosamine increased the duration of the lag phase in the second phase of growth. We do not know exactly why, but we speculate it is because the chitin cell wall becomes thicker after diauxic shift in presence of glucosamine, leading to a delay to resume cell division after cells are exposed to fresh medium with glucose.

      1. Figure 2-figure supplement 2g-l are missing.

      We included the missing panels in the revised figure.

      1. The current manuscript is a bit lengthy (although nicely comprehensive). After deciding the journal, I suggest it would need to be more concised and logically streamlined.

      We agree that the main text is lengthy, with methodological explanations sometime disrupting the main message. For this reason, we included in the revised version a new Supplementary File 5 where we moved these explanations that were important yet not essential for the reader to understand the main conclusions.

      Minor points

      1. P12,L345, "may not only by caused by" should be "may not only be caused by", ,and "YFP an RFP" should be "YFP and RFP" in the same sentence

      We corrected these mistakes.

      1. P19,L565, "sensitivite" is misspelled and should be "sensitive".P21,L630, "mitochondria dysfunction" should be "mitochondrial dysfunction."

      We corrected these mistakes.

      1. Typo in Figure 3's legend "** 0.001 > P {greater than or equal to} 0.001", which should read "0.01 > P {greater than or equal to} 0.001." This error appears again in Figure 4's legend.

      We corrected these mistakes.

      1. P9, L219 "Table 1" should be "Supplementary File 1"?

      We added the number of mutations per strain in Table 1.

      1. Inconsistent tetrad numbers: methods state 22 tetrads (L844) , results mention 21 tetrads ( L262 ) , and figure( figure1 -figure supplement 3) legends indicate 20 tetrads. Please clarify the correct number.

      Thank to the reviewer for mentioning this inconsistency. In fact, we dissected 22 tetrads but only included 21 tetrads that showed the expected segregation of all genetic markers in the fluorescence assay. Finally, we reported fluorescence measurements for 20 tetrads due to a possible contamination for the remaining tetrad. We clarified this in the Methods.

      1. The speculated retrograde response pathway is interesting. Can the authors propose some specific experiments to test that ?

      To test the involvement of the retrograde pathway in PTDH3 intrinsic noise, one could mutate negative or positive regulators of the retrograde signaling in wild-type cells or in chs1 and msh1 mutant cells and quantify the effect on intrinsic noise. We proposed this experiment in the revised discussion. In previous studies, null alleles of rtg1, rtg2 or rtg3 were shown to impair the retrograde response, while specific mutations in rtg2 and deletion of mks1 were shown to activate the retrograde pathway (Garrigos et al., 2024; Jazwinski and Krete, 2012). We expect to observe an elevated intrinsic noise in wild-type cells, but not necessarily in yme2 and msh1 mutant cells, when we activate the retrograde signaling. Conversely, we expect yme2 and msh1 mutations to not alter intrinsic noise anymore when the retrograde pathway activity is impaired by mutation.

      Reviewer #2

      The manuscript by Martin et al. titled 'Trans-acting mutations reveal non-nuclear modulators of both intrinsic and extrinsic gene expression noise in a eukaryote.' identifies genetic mutations in yeast that can has regulate gene expression noise in trans. The manuscript is well written, and the experiments have been performed in replicates. The authors also clearly highlight the experiments where the replicates do not agree in their outcomes.

      However, there are some issues that the authors need to address:

      1. Introduction is too long and needs to be concise

      We have shorten the introduction in the revised version. We have also moved parts of the main text in Supplementary File 5 to be more concise.

      1. Lines 150-153: Do we have enough studies yet for generalizations?

      We do not know other studies/examples that compared the effects of cis-acting and trans-acting mutations on mean expression and expression noise of a target gene. Therefore, we cannot generalize the results obtained for the TDH3 promoter. However, these results show that cis- and trans-acting mutations can significantly differ in their effects on expression noise (but we do not know for how many genes it is the case).

      1. Line 209 - Why 254 strains? Please justify

      We clarified why and how we chose these 254 strains for the secondary screen. We also added colors on Figure 1 to highlight these 254 strains.

      1. Why are the authors choosing median expression and not mean expression (which is usually the norm)?

      We used the median to quantify the average expression among cells as we did in previous studies with the same fluorescent reporter system because median is more robust than mean to rare outliers. However, we found the difference between median and mean fluorescence to be really small. We included a new figure (Figure 2 – figure supplement 4) showing that the effect of yme2, chs1 and msh1 mutations on expression noise were almost identical when using mean and median to calculate the noise. This is because we measured fluorescence from large number of cells for each sample (~5000) and because the distributions of fluorescence levels among cells are always unimodal with very rare outliers (as showed in Figure 3 – figure supplement 1; Figure 5 – figure supplement 1 and Figure 6 – figure supplement 1).

      1. Do EMS mutants have intra-population genetic heterogeneity? This should be discussed in the text.

      We sequenced the genome of the 5 EMS mutants at a coverage of ~100x, but did not find evidence of genetic heterogeneity in these strains: all mutations detected were found at a frequency near 1. In another project, we sequenced the genomes of 288 EMS mutants and detected genetic heterogeneity in 3 strains: mutations were not fixed in these strains, but found at a frequency near 0.75. We therefore expect the number of mutants with genetic heterogeneity from the collection analyzed in figure 1 to be very small. In addition, genetic heterogeneity cannot impact our conclusions because no genetic heterogeneity was detected in the 5 EMS mutants analyzed and because we constructed two independent clones to investigate the effects of each mapped mutation. We added a sentence in the main text mentioning that no genetic heterogeneity was detected in the 5 EMS mutants.

      1. Line 238-240: Shouldn't the change in frequency in low, mid and high- subpopulations be tested relative to the expected distribution from the wild-type strain? This could also alter how mutations are chosen for validation. This should be mentioned in the results section and the text should be rephrased to reflect this point, although it is mentioned in the methods section

      We are not completely sure to understand what statistical test the reviewer has in mind. We could not easily compare the observed frequency of mutant and wild-type alleles in low, mid and high subpopulations to expected frequencies, because these frequencies depend not only on the effect of the mutation on fluorescence among cells but also on the effect of the mutation on growth rate (which is unknown). Our strategy to compare mutation frequency in medium bulk vs low and high bulk was designed to detect mutations changing expression noise independently from their potential effect on mean expression or growth rate.

      1. Line 243: The mutant name YPW2162 suddenly appears in the text - where did this strain come from?

      This is the name of one of five mutants analyzed, as mentioned in Table 2 referenced in the same sentence. We modified the sentence to make it clearer: “For a fourth mutant (YPW2162), ...”

      1. Line 286: 'increase' instead of 'increased' .

      We corrected this error.

      1. Could genomic rearrangements/copy number variation alter expression noise? For example, for m4, m5 and m6 mutants. The authors have genome data of these strains, so this can be checked.

      According to the reviewer’s comment, we performed additional analyses showing that CNVs and rearrangements did not contribute to variation of expression noise in the five EMS mutants included in the mapping experiments. To detect large CNVs and aneuploidies, we computed sequencing depth in 1-kb sliding windows along the genome for each mutant. The profiles were uniform and similar to coverage profiles obtained for the reference strain. To detect rearrangements, we analyzed sequencing data using the GRIDSS module that can detect junctions between non-contiguous parts of the genome from the mapping location of paired-end reads. Using this tool, we only detected 5 rearrangements that were previously known to be present in the genome of all mutant strains relative to the reference genome (deletions at ho and ura3 loci and duplications of TDH3 promoter, CYC1 terminator as well as 41 bases from chromosome I in the PTDH3-YFP transgene inserted at the ho locus). None of these rearrangements can explain variation of expression noise among mutants. Results from GRIDSS analysis are included in Supplementary File 4 and reported in the main text.

      1. Figure 2 - y-axis: What is the measure of expression noise used here? This should be mentioned in the figure captions throughout to avoid confusion.

      We used the same measure of expression noise for all figures, as mentioned in the Methods. We added it in the figure captions as suggested by the reviewer to avoid confusion.

      1. Line 359 - please mention the effect size here and wherever possible throughout the manuscript

      In the sentence mentioned by the reviewer, we used the forward scatter signal (FSC.A) as a relative measure of cell size. A difference of FSC.A between two samples is known to reflect a difference of cell size. However, we cannot estimate the effect size on cell size because the relationship between FSC.A and cell size depends on the instrument and settings, and we have not characterized this relationship empirically. Therefore, we removed “a small effect” from the sentence and we instead only mentioned that the effect on cell size was statistically significant. Indeed, we cannot be sure that the small (and significant) reduction of FSC.A we observed corresponded to a small reduction of cell size. 12. Figure 6 - figure supplement 2 - Please mention the strains represented by grey and orange boxes

      To make it more visible, we moved this information from an inset in panel b to the top of the figure.

      1. One could envisage that there are many more genetic regulators of expression noise which may have not been discovered yet. This point perhaps could be discussed.

      Absolutely. We added a sentence in the discussion to acknowledge that many genetic modulators of noise may still remain unknown.

      1. The figure captions are too long - they should be made concise

      We reduced the length of the longest figure legends. In particular, some of the text from Figure 4 legend was moved to Supplementary File 5.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Martin et al. titled 'Trans-acting mutations reveal non-nuclear modulators of both intrinsic and extrinsic gene expression noise in a eukaryote.' identifies genetic mutations in yeast that can has regulate gene expression noise in trans. The manuscript is well written, and the experiments have been performed in replicates. The authors also clearly highlight the experiments where the replicates do not agree in their outcomes.

      However, there are some issues that the authors need to address:

      1. Introduction is too long and needs to be concise
      2. Lines 150-153: Do we have enough studies yet for generalizations?
      3. Line 209 - Why 254 strains? Please justify
      4. Why are the authors choosing median expression and not mean expression (which is usually the norm)?
      5. Do EMS mutants have intra-population genetic heterogeneity? This should be discussed in the text.
      6. Line 238-240: Shouldn't the change in frequency in low, mid and high- subpopulations be tested relative to the expected distribution from the wild-type strain? This could also alter how mutations are chosen for validation. This should be mentioned in the results section and the text should be rephrased to reflect this point, although it is mentioned in the methods section.
      7. Line 243: The mutant name YPW2162 suddenly appears in the text - where did this strain come from?
      8. Line 286: 'increase' instead of 'increased'
      9. Could genomic rearrangements/copy number variation alter expression noise? For example, for m4, m5 and m6 mutants. The authors have genome data of these strains, so this can be checked.
      10. Figure 2 - y-axis: What is the measure of expression noise used here? This should be mentioned in the figure captions throughout to avoid confusion.
      11. Line 359 - please mention the effect size here and wherever possible throughout the manuscript
      12. Figure 6 - figure supplement 2 - Please mention the strains represented by grey and orange boxes
      13. One could envisage that there are many more genetic regulators of expression noise which may have not been discovered yet. This point perhaps could be discussed.
      14. The figure captions are too long - they should be made concise

      Significance

      Although the effect of cis-acting mutations on expression noise has been studied, there remains a significant gap in understanding whether and how trans-acting mutations could alter protein expression noise. This is where the manuscript provides interesting and important insights into the role of trans mutations on expression noise through careful experimental dissection. The manuscript also elucidates an influence of mitochondria on expression noise and therefore, on phenotypic plasticity. The manuscript will be of interest to the researchers working on gene expression regulation and gene expression noise.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This manuscript investigates how rare trans-acting mutations can alter the cell-cell variability ("noise") of gene expression in yeast without affecting mean expression. Using a Saccharomyces cerevisiae strain carrying a PTDH3-YFP reporter, the authors screened 1241 EMS-mutagenized clones by flow cytometry and identified five candidate mutants with significantly altered fluorescence noise but unchanged mean expression. They crossed each mutant to a wild-type mapping strain and performed bulk segregant analysis to identify candidate causative mutations. Ultimately, three single-nucleotide substitutions were confirmed to reproducibly affect noise: a CHS1(G1752A) nonsense mutation, a YME2(G1234A) mutation, and an MSH1(G1262A) mutation. Site-directed reconstitution of each mutation (in a clean background with dual reporters) showed that CHS1(G1752A) increased extrinsic noise primarily in small daughter cells, YME2(G1234A) increased intrinsic noise, and MSH1(G1262A) increased intrinsic noise in a growth-phase-dependent manner. Notably, all three genes encode non-nuclear functions: CHS1 (chitin synthase I, cell-wall repair), YME2 (inner mitochondrial membrane protein), and MSH1 (mitochondrial DNA repair ATPase). The authors conclude that mitochondrial state and cell-wall integrity can modulate expression noise of a nuclear gene, highlighting novel trans-acting noise regulators. The experimental approach (random mutagenesis + BSA + targeted validation) is sound, and the conclusions - that these three mutations each increase noise in specific ways - are generally well supported by the data. This is an excellent work. It is thought-provoking and very comprehensive. It is also very well written (but a bit lengthy in its current form) and mostly technically sound. I would ultimately recommend publication. Yet, if I am to contribute to the strength of the paper and the robustness of the conclusion, here are some important points.

      Major Comments

      1. TDH3 has been a major model in the study of the evolution of gene expression as developed by the authors. While most conclusions derived from this system relate to genetic mechanisms, this study attempted to identify the molecular/cellular mechanisms. Although I greatly appreciate the author's comprehensive efforts, I would suggest that conclusions regarding molecular/cellular mechanisms should be made with greater caution, especially avoiding over-generalized conclusions that may be specific to P_TDH3. Thus, I suggest going over the manuscript again and adjusting some statements if they are over-generalizing, as well as adding an explicit discussion of this limitation.
      2. On the surface, it may seem reasonable to ask for changed noise by unchanged mean in order to distinguish independent regulators. However, from a mechanistic perspective, this is quite demanding. In the current prevailing model (transcriptional burst) of noise origination, mutations are not assumed to be noise-specific without affecting the mean. See e.g. PMID: 31320634. If noise and mean are intrinsically coupled, looking for noise-only regulatory mechanisms would imply something very different. This may mean, for example, that we are seeking one single mutation that changes noise and mean through one mechanism, while at the same time reverting mean to its wildtype value through another mechanism. It is likely that this is one of the reasons why causal mutations are difficult to identify.
      3. L209-214. The authors state that they selected 254 mutants with the largest noise changes from a library of 1,241 strains (L209-214). However, I cannot match this statement when contrasting figure1-figure supplement 1 (254 mutants) and Figure 1a (1,241 strains). Is this caused by experiments conducted in different labs ? Are there any intuitive ways for the authors to show the strains (and their parameter distribution) that produced consistent results across the two batches of data? E.g. using gray/black dots for un-/repeatable strains. Also, are mean expression levels similarly (un-)repeatable compared to noise ?
      4. How were the five strains analyzed chosen? Are they the only strains fulfilling the criteria on L211-214?
      5. L243-247. I didn't understand the logic why m2 is included, please elaborate.
      6. The equation for extrinsic noise (L899) seem to be slightly differently from that in Fu and Pachter 2016. The product of mean(RFP) and mean(YFP) is multiplied by 2 here, but not in Fu and Pachter 2016.
      7. The experimental design to exclude noise from partitioning for yme2 is really nice. It would have been great if we had gotten to the bottom of this. (This is not a question so no response is needed)
      8. The authors demonstrate that a nonsense mutation in CHS1 increases extrinsic noise via impaired chitin septum reparation in daughter cells. However, glucosamine treatment itself alters cell size (Figure 5-figure supplement 3a), which correlates with noise levels. This raises the question: do the observed changes in extrinsic noise stem from glucosamine-induced changes in cell size or from the impaired chitin repair caused by the CHS1 mutation itself? To disentangle these effects, an alternative approach to modulating chitin synthesis that does not alter cell size should be employed.
      9. Why did glucosamine doses not significantly impact cell growth rates during the first phase after addition (Figure5 -figure supplement 4) ? Additionally, I can seem to find the experimental details for glucosamine dosing in the Methods.
      10. Figure 2-figure supplement 2g-l are missing.
      11. The current manuscript is a bit lengthy (although nicely conprehensive). After deciding the journal, I suggest it would need to be more concised and logically streamlined.

      Minor points

      1. P12,L345, "may not only by caused by" should be "may not only be caused by", ,and "YFP an RFP" should be "YFP and RFP" in the same sentence
      2. P19,L565, "sensitivite" is misspelled and should be "sensitive".P21,L630, "mitochondria dysfunction" should be "mitochondrial dysfunction."
      3. Typo in Figure 3's legend "** 0.001 > P {greater than or equal to} 0.001", which should read "0.01 > P {greater than or equal to} 0.001." This error appears again in Figure 4's legend.
      4. P9, L219 "Table 1" should be "Supplementary File 1"?
      5. Inconsistent tetrad numbers: methods state 22 tetrads (L844) , results mention 21 tetrads ( L262 ) , and figure( figure1 -figure supplement 3) legends indicate 20 tetrads. Please clarify the correct number.
      6. The speculated retrograde response pathway is interesting. Can the authors propose some specific experiments to test that ?

      Significance

      The study represents a conceptually interesting attempt to move beyond cis-acting noise modulators by identifying non-nuclear proteins that regulate nuclear gene expression randomness. A significant advance is the identification of mitochondria as a possible regulator of intrinsic noise. This study provides evidence that mitochondrial integrity can independently affect the stochastic synthesis of a nuclear gene. The results of this study will be of interest to evolutionary biologists and geneticists interested in the regulation of gene expression and the origins of phenotypic heterogeneity. Furthermore, phenotypic heterogeneity is of considerable significance when discussing realistic issues such as drug and antibiotic resistance. This manuscript provides critical mechanistic insight, and showcases a very comprehensive effort to reveal those mechanisms. I am an evolutionary geneticist more familiar with computational and theoretical considerations of phenotypic heterogeneity.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Common reply to all reviewers.

      We sincerely thank the reviewers for their thoughtful evaluation of our work and apologize for the long delay in submitting our response. This delay resulted from the unexpected departure of the post-doc leading the study and the time required to re-establish the necessary expertise.

      In light of these circumstances, and taking the reviewers' comments into account, we substantially revised the manuscript to provide a more focused and coherent story.

      In this revised version we have reorganized original data and added new experiments to support the conclusion that the WASp/Arp2/3 axis controls nuclear resistance to mechanical compression, limiting inflammatory responses arising from nuclear rupture. This study provides the first demostration of an enhanced inflammatory signature related to nuclear mechanics in a WAS disease model and in human patients. Notably, we show that this inflammatory signature is corrected in our own cohort of gene therapy treated patients.

      Efforts to better define the mechanistic link between actin dynamics and nuclear instability did not yield conclusive results, reflecting the complexity of this question, which extends beyond the scope of the present manuscript. As detailed in individual replies and reflected in the text of the revised version, we discuss the potential mechanisms that our data suggest at this stage.

      Reviewer 1

      1.In Figure 1a the authors describe the changes in nuclear area of WKO macrophages. However, in the figure legend it is not mentioned whether the images presented are single z-planes or maximum z-projections. If the former is true, then I would suggest the authors present the maximum z-projections. If the latter is the case, I would suggest the authors to include this information on the figure legend to improve reader's comprehension.

      1. In Line 135 the authors write that the WASp null macrophages display a higher frequency of micronuclei formation and that this is an indication of nuclear disruption. However, the lab of Kenneth Campellone (10.1371/journal.pgen.1010045) has demonstrated that in the absence of Arp-2/3 micronuclei arise from defective chromosomal segregation. I believe the authors should distinguish whether this is the case or it is indeed the nuclear envelope rupture events that produce the micronuclei. The authors could add a sentence in the result and discussion section discussing this possibility.

      2. In figure 1C the authors claim that depletion of WASp results in reduced levels of Lamin-A/C. However, this is very hard to see any difference in the Western Blot provided.

      3. In line 141 the authors mention that loss of WASp results in Lamin-A/C wrinkles and "nuclear irregularities". What are the irregularities mentioned here? Maybe the authors should point them out in the imaging provided in Figure 1 to improve reader comprehension.

      An entire new Figure (revised Figure 2) now presents key nuclear parameters (nuclear areas, Lamin A/C gaps and nuclear protrusions) in confined and unconfined cells. The data show that nuclear envelope ruptures and protrusions are specifically occurring in confined WASp null macrophages.

      Other data that were not conclusive or could not be collected for every condition (micronuclei, DNA damage, Lamin A/C levels by WB) have been excluded from the new version. The possible role of aberrant chromosomal segregation on the nuclear phenotype based on Campellone’s work is at page 11, Discussion.

      In line 177-178 the authors write that there is an increase in nuclear deformation after transwell migration. However, this is not shown. I suggest the authors include a circularity index quantification in the same dataset to support that claim.

      Transwell assay is presented in revised Supplementary Figure 3D and only shows, the rate of cell transmigration. We did not develop further analysis of circularity and DNA damage post-passage as cells were overall too damaged to allow robust quantifications.

      In addition, the difference in the γH2AX foci in post-3µm in wt and WKO conditions appear to be almost identical. I believe it is the post migration difference in DNA damage that should be stressed here. Finally, the authors claim that the WKO macrophages are more nuclear envelope rupture-prone and this results in DNA damage accumulation as has been already described. However, the lab of Jan Lammerding has also demonstrated that nuclear deformation alone can also induce DNA damage accumulation in a cell cycle specific manner via stalled replication forks. The authors should distinguish between the two possibilities with live cell imaging by expressing a DNA damage marker (e.g. 53BP1, coupled with either NLS-GFP or cGAS to assess whether DNA damage accumulates in response to mechanical deformation or nuclear envelope rupture).

      We have not performed additional experiments to document DNA damage in WT and WKO macrophages at steady state or upon confinement. For consistency we excluded the preliminary data on DNA damage as not conclusive at this stage. We do agree that discriminating what comes first in this system is an important question that we have discussed it in the revised version (Page 11).

      In line 204 the authors show that reintroduction of WASp-GFP reduces the frequency in which "Lamin-A/C rupture" events are observed. Then they conclude WASp is important to maintain nuclear stability. While this Lamin-A/C is a good marker for nuclear blebs, I believe that more "classic" nuclear envelope rupture markers should be used to assess the potential compromised nuclear integrity (e.g. NLS-GFP or cGAS). Hence, I would suggest the authors to attempt to replicate this finding with live cell imaging of cells under strong (3µm) confinement and quantify the frequency in which they detect NLS-GFP leakage or perinuclear accumulation of cGAS. Another experiment would be to try and rescue the phenotype observed in the fabricated microchannels used in Figure 2 and observe whether the nuclear envelope rupture events are reduced.

      We agree that showing rescue of NLS cytosolic release upon WASp rescue would be strong. Attempts in this direction were unsuccessful due to limitations in expressing two different exogenous proteins (WASp and NLS) in primary macrophages. We performed rescue experiments in microchannels. Interestingly, overexpression of WASp–GFP in WKO cells strongly inhibited entry into the microchannels, likely because WASp expression exceeded endogenous WT levels (Revised Figure 3E). This finding further supports the conclusion that WASp increases resistance to deformation. However, the marked reduction in the ability to enter channels in cells overexpressing WASp precluded analysis of nuclear ruptures inside microchannels.

      In Figure 4 the authors describe the potential role of WASp in the formation of actin patches in the vicinity of the nucleus. In both Fig4C and Fig4E, the mildly confined (6 µm) condition is missing. I would suggest the authors to quantify the distribution of the different actin structures and the colocalization of WASp/Phalloidin signal also in 6µm confinement. Additionally, in the lines 220-222 the authors claim that the formation of the perinuclear actin ring is absent in wt macrophages, however we still see the perinuclear actin ring in the images

      The actin patches phenotype is now presented in the revised Supplementary Figure 2B, showing only the 3 µm cell height condition, where the phenotype is more pronounced. As we were unable to obtain conclusive evidence linking the presence of actin patches to events of nuclear rupture at the single cell level, these data are now presented as a correlative observations.

      Adding on my previous comment, if the perinuclear actin patches are not present in the absence of confinement, can the authors display the formation of these patches through live cell imaging utilizing probes such as LifeAct or the Actin Chromobody in wild type macrophages?

      As noted above, actin patches, their mechanism of formation, and their causal relationship to nuclear instability were not investigated further and are no longer a primary focus of the revised manuscript.

      While it is intuitive that if WASp is important for the formation of perinuclear actin patches, then this has to be through Arp-2/3 this should be tested. I would suggest the authors to either pharmacologically inhibit Arp2/3 with CK-666 or genetically manipulating the system by either overexpressing a dominant negative version of the Arp2/3 subunit or silencing an essential subunit.

      The actin patches forming under compression are Arp2/3 dependent in dendritic cells, as demonstrated in DCs by a joined effort with our collaborators (Alraies et al, Nature Immunology 2025). We have performed preliminary experiments indicating that this is the case also in macrophages, however these data are not presented in the new version as explained in the previous comments.

      The authors interestingly discovered the upregulated expression of certain inflammatory cytokines. While the authors nicely show that the mRNA levels increase, they do not show whether this translates also in increased protein levels. I would suggest the authors to include an experiment in which they assess the protein levels of a few of the upregulated chemo-/cytokines via immunoblotting or ELISAs. This is also the case for IL-6 mRNA levels showed in Figure 5.

      We agree that measuring protein levels would strengthen the mRNA data. However, the experimental setup does not allow for this. First, the number of cells recovered after confinement is not sufficient for downstream protein analysis. Second, RNA is collected after short periods of confinement (1-4 hrs), as longer confinement cause damage to the cells.

      In FigS1 the authors display that in WKO cells have increased "cellular stiffness" when WASp is depleted from macrophages in the absence of confinement. However, since the cantilever is placed on top of the nuclear area, I am not sure whether the authors were measuring nuclear rigidity or cortical rigidity. In any case, in Fig5 they show that in the absence of confinement factors responsible for branched actin nucleation are downregulated (If I am not wrong, Arp2/3 is a major factor contributing to cortical actin nucleation - along with mDia1, although there might be a compensation mechanism), while the expression of factors related to nuclear mechanics is not altered. This is not in line with the result from the AFM experiment presented in FigS1. I am not sure what does this AFM experiment add to the message of the manuscript. The authors could possibly add a sentence to make the connection to the rest of the manuscript clearer.

      We do agree that this initial attempt to characterize stiffness is not sufficiently developed and we excluded it from the revised version.

      The authors beautifully show that WKO macrophages and AMO's show an upregulation of inflammation related genes. However, I believe it is important to assess whether the upregulation of these signature genes upon confinement can be rescued by the re-introduction of wild-type WASp (and possibly a mutant that is not able to activate Arp-2/3 - to display that is indeed the Arp-2/3 mediated nucleation of these actin patches that limit inflammation).

      We carefully considered performing these experiments. However, both general and study-specific considerations ultimately prevented us from doing so. We are working with primary cells in which transduction efficiency is approximately 50% and positive cells cannot be enriched by sorting, as the procedure induces cellular activation. Therefore, all experiments must be performed on bulk cell populations, in which only around half of the cells are expected to display a rescued phenotype. Given that the increase in IL-6 expression observed in WASp-deficient cells is relatively modest, achieving any significant conclusion would have required a substantial number of biological replicates. This is still challenging with the confiner approach because of inherent variability of the system and limited access to reagents to assemble the device. Overall, while the confiner approach is highly informative for single-cell imaging and can be used for RNA isolation, as we have done here, it is not yet sufficiently developed to allow high-content downstream analyses across multiple experimental conditions.

      The finding that CRISPR/Cas9-mediated depletion of WASp in wild-type cells recapitulates the increase in IL-6 expression (Figure 5I) is consistent with a causal link between WASp deficiency and inflammatory activation.

      In the discussion (line 333-335), the authors mention that the WASp (and I presume the produced actin patches) act as a barrier to prevent nuclear envelope rupture events. From the images provided in the manuscript I get the impression that both WASp and the perinuclear actin patches display a polarized nature. For example, in Figure 4C the actin patch is located at the left of the nucleus. WASp in general is more abundant at the trailing side of the cytoplasm in the migrating macrophage displayed in Fig S4B. Finally, WASp has higher abundancy in a specific perinuclear area in Figure 4E. How these seemingly polarized structures act as barriers? Do they stop nuclear blebs from protruding through lamin B1 gaps? Are nuclear envelope rupture events in migrating macrophages happening specifically in the migrating or trailing end of the nucleus?

      These are all pertinent and logical questions. As explained, being unable to further document the relation between actin patches and NE stability at this stage, the

      occurrence of actin patches in WT is now presented in revised Supplementary Figure 2B, as a correlative observation.

      There has been a report by the lab of Andrea Ablasser (10.1126/science.aaw6421) in which loss of nucleocytoplasmic compartmentalization does not activate the expression of cytokines, as BAF can compete against cGAS for binding of leaked chromatin thus limiting inflammation. If there is not a cell-type specific (or lack of) expression of BAF on BMDMs, how do the authors explain their results in relation to the aforementioned publication?

      We thank the reviewer for raising this important point. We believe the different outcomes are most likely explained by differences in both the experimental model and the confinement protocol. Ablasser and colleagues performed their experiments in HeLa cells, whereas our study uses primary bone marrow-derived macrophages. In addition, their analysis was performed after recovery from confinement, whereas we isolate RNA immediately after release to capture the early transcriptional response to mechanical compression. Consistent with the importance of these kinetics, we find that extending confinement from 1 to 4 hours largely abolishes the inflammatory response in BMDMs. Finally, as discussed in relation to the STING-deficient experiments, we cannot exclude that additional inflammatory pathways contribute to the phenotype observed in WASp-deficient macrophages but are not engaged in HeLa cells.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: This study from Roberto Amadio and coworkers significantly expands our understanding of WASp by identifying its role in nuclear mechanotransduction, linking actin defects to nuclear instability and inflammation, specifically for macrophages. Although there are several studies related to Dendritic Cells and T cells for example, linking the role of the protein to pro-inflammatory pathways and expression of cytokines, this is the first study that thoroughly links WASp, nuclear instability and downstream pro-inflammatory activation. These findings have broader implications for immune disorders and actinopathies, highlighting WASp as a key regulator of mechanosensing and inflammatory control. Future research should validate these results in vivo, further investigate the actin-based nuclear support system, and explore therapeutic strategies targeting nuclear integrity or cGAS-STING signaling in WAS and related conditions.

      The study provides strong evidence supporting a new role for WASp in nuclear integrity and inflammation control in macrophages. The findings are backed by multiple experimental approaches, including high-resolution imaging, mechanotransduction assays, gene expression profiling, and live-cell tracking, which consistently show increased nuclear deformation, rupture, and inflammatory activation in WASp-deficient macrophages. Reproducibility is reinforced through the use of different experimental models, including genetic reconstitution (WASp-GFP rescue), CRISPR-Cas9 knockout validation, and transcriptomic analysis in both murine and human macrophages, as well as PBMC data from WAS patients pre- and post-gene therapy. The study is clearly structured, with systematic quantification of nuclear defects, inflammation markers, and transcriptional changes. However, some dense data presentation (e.g., transcriptomic heatmaps) and limited discussion of alternative pathways could make certain sections more accessible. Despite these minor limitations, the study's conclusions are well-supported, reproducible, and provide clear mechanistic insights into how WASp regulates nuclear stability and inflammatory signalling.

      Minor comments: For better readability of text, can the authors include the following editions into their main text: 1) Please define BMDM at its earliest reference (page 5, line 187) 2) Can the authors provide information regarding how the image processing was performed for heterochromatin content, its distribution and chromocenter analysis? 3) It would be great if they can explain how the roundedness of nuclei was characterized 4) Rather than calling it cell mechanics, I would be more comfortable to read it cell stiffness or cell elasticity on line 150, page 4. 5) For more clarity and quantitative information, can the authors provide numbers everywhere in the main text where comparisons between two datasets are cited to? For example: WKO cells were 20 +/- 10% stiffer than WT ones, etc.

      We thank the reviewer for the positive assessment of our work. As outlined in the introductory comments, we substantially reorganized the manuscript to improve its overall flow while maintaining the main message as highlighted by this reviewer “A new role for WASp in nuclear integrity and inflammation control in macrophages”.

      We have added new data that strengthen the link between WASp, the Arp2/3 pathway, and nuclear stability in confined macrophages. At the same time, we have reduced the emphasis on the role of actin patches in regulating nuclear stability, as the current evidence is not yet sufficiently conclusive.

      Reviewer 3

      In this manuscript cells of WASp null mice bone marrow derived macrophages. They find that lamin A/C levels and RNA decreased while lamin A/C wrinkles increased and chromatin measured indirectly did not change. Next WKO cell were more likely to migrate through constricted channels and had more nuclear rupture events as well as those cells/nuclei undergoing multiple ruptures. Under low artificial confinement WT and WKO nuclei had similar low percentage of nuclear blebbing but upon high artificial confinement WKO nuclei measured a drastic increase in nuclear blebbing and ruptures while blebbing could be partially rescued by expression of WASp-GFP. The manuscript then suggests that perinuclear actin dependent on WASp is the mechanism due the occurrence of what appear to be random patches of actin. Finally, the paper reports upregulation of a subset of genes different from WT and WKO due to confinement. The data are largely of interest to the field but there remains no conclusive evidence to support a clear mechanism for why WKO nuclei undergo nuclear blebbing, rupture, and differential gene expression under artificial confinement.

      The manuscript does not do a sufficient job detailing the cause of the nuclear mechanical changes between lamin A/C and actin. There is data that lamin A/C decreases (Figure1) why is the mechanism not just loss of lamin A/C? Instead random actin patches protect the nucleus? However, the manuscript does not disrupt these actin patches in another manner to show they are indeed important to resisting artificial confinement. An actin depolymerizer with compression might show this? Alternatively, it might not be about actin structures but acto-myosin contraction known to be essential to causing nuclear blebbing and rupture in artificially confinement experiments (Mistriotis et al., 2019 JCB). The fact that lamin A/C is upregulated in WT upon confinement and not in WKO suggests mechanotransduction is not occurring properly as lamin A/C are upregulated under tension which is lost in WKO. This suggests that actin incorporation with the nucleus might be broadly flawed through possibly the LINC complex. The wrinkling of the nuclear lamina in WKO suggests it is actually under less tension, possibly supporting this idea. This continues to point to the fact that this paper, while it has a lot of interesting data, does not appear to have a conclusive understanding of the mechanism occurring in WKO.

      We thank the reviewer for this well-focused review.

      In this revised version, we present novel data adding to the original findings in a format that we believe conveys a more coherent message. Specifically, our data support the conclusion that the nuclei of WASp-deficient macrophages are less resistant to mechanical compression, resulting in the activation of inflammatory pathways in both mouse and human systems.

      We also attempted to define the mechanistic link between actin dynamics and nuclear instability. However, these efforts did not yield conclusive results, reflecting the complexity of this question, which extends beyond the scope of the present manuscript. Accordingly, for consistency, data on actin patches are now presented as correlative evidence (Supplementary Figure 2B) rather than a causal mechanism. Lamin A/C instability and acto-myosin contractility as potential mechanism underlying nuclear fragility are discussed ad proposed by the reviewer (Results page 8, Discussion page 10,11).

      An entire new Figure (revised Figure 2) now presents key nuclear parameters (nuclear areas, Lamin A/C gaps and nuclear protrusions) in confined and unconfined WT and WASp null cells and in cells treated with Arp2/3 inhibitor. Results show that nuclear envelope ruptures and protrusions are specifically occurring in confined WASp null macrophages and even more in cells treated with Arp2/3 inhibitors. Rescue with WASp overexpression corrects the phenotype, reinforcing that the WASp-Arp2/3 axis controls nuclear integrity under mechanical challenge.

      Revised Figure 4 presents results in microchannels, now complemented by new data showing that Arp2/3 inhibitors increase entry rate (phenocopying WASp deficiency) and that rescue with WASp-GFP brings back entry levels below those of WT cells (likely due to overexpression).

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      1. Amadio and co-authors propose a mechanism that links the immune-specific actin regulator WASp with nucleus integrity/mechanosensing and the proinflammatory phenotype observed in WASp-null cells. This work arises from the following three points/previous findings:
      2. the WAS syndrome, caused by mutations of the WAS protein (WASp), is associated with autoimmune and autoinflammatory manifestations. WASp is an activator of the famous Arp2/3 complex which controls branched actin polymerization.
      3. immune cells need to migrate in confined and challenging environments and therefore mechanisms of correct mechanosensing are required for their survival and to ensure a working immune system.
      4. nucleus integrity loss and DNA damage upon external force, sensed by cGAS-STING in the cytosol, can trigger senescence, death or autoimmunity.

      Therefore, the question of how WASp connects mechanosensing to the inflammatory response appears natural and it is indeed a very interesting question. This study would clearly help the understanding of the WAS autoimmune syndrome and explain processes beyond it, as other members of the WASp family might act in similar ways in other contexts such as cancer.

      The paper is well written and the figures are nicely presented. The proposed mechanism is intriguing, however it is not fully clear and not entirely supported by the presented data. Some functional experiment would be needed to demonstrate it, or the statements adapted to what the data support. Also, sometimes statistics are quite weak and some data could be further analyzed.

      The authors suggest the following mechanism, depicted in Figure 5J, here summarized with percentages of cells. The majority (90%) of WT cells sustain mechanical compression without visible nuclear blebs (Figure 3B) and low (but unknown, to be provided) percentage of NE rupture (Figure 3D), thanks to the appearance of an actin-WASp-rich patch in the cytoplasm, close to the cell nucleus, visible in the 30% of the cells (Figure 4B). On the other hand, cells that lack WASp, show nuclear blebs in the 35% of the case (Figure 3B), three times more NE rupture compared to WT (Figure 3D) and the actin patch only in 10% of the cells (Figure 3B, or less? Figure 4D). Therefore, it looks like only a small minority of cells shows what the model proposes as a general working mechanism. Considering that statistical tests are often poor and rescue experiments do not show any clear (or statistically different) result, the authors should support the work with additional experiments and discussion.

      First, physically, how does the presence of an intracellular (stiff?) actin patch (observed in 30% of WT cells) prevent a nucleus from damage? Could the author further explain/comment on this point? More detailed quantifications could be provided, together with a clearer discussion or some key experiment (see later) to demonstrate this idea.

      For example, is the mechanoresponse of cells with/without patch different? Do the nuclei of cells with patch show a different area increase, less blebbing and rupture? This would suggest less "force" transmitted to the nuclei.

      Otherwise, as many WT cells do not show the patch, but the nucleus deals fine with the compression, could a global cytosolic/cortical stiffening explain the mechanism? The patch could be the extreme outcome of this stiffening, therefore observed in fewer cells. In this second option, cytosolic stiffening should be quantified (see AFM point below) and the model explained better.

      We fully recognize the validity of the reviewer's comments and acknowledge the limitations of the original version of the manuscript. As commented in reply to Reviewer 3, we now present a deeply revised version to provide a more coherent message, that is supported by the data.

      The central message of the revised manuscript is that, for the first time, we demonstrate that nuclei of WASp-deficient macrophages are less resistant to mechanical compression, leading to the activation of inflammatory pathways in both mouse and human systems. While we sought to define the mechanism by which altered actin dynamics gives rise to nuclear instability, these efforts were not conclusive at this stage, reflecting the complexity of the underlying biology.

      For consistency, data on actin patches are now presented as correlative evidence (Supplementary Figure 2B) rather than a mechanism.

      Related to both options, functional experiments to prove that the expression of WASp is sufficient to prevent NE rupture, are required. The authors already perform a rescue experiment (that is a very elegant way to prove a mechanism), by over-expressing WASp-GFP in WKO cells, but statistics and numbers are too low. Figure 3F-G shows a reduction in blebbing nuclei in confined cells upon WASp rescue (see below comments about this plot), but it is not clear if it statistically reduces NE ruptures (Figure 3I too weak, low N, no statistical difference shown), if it rescues the formation of the actin patch or if it has any further effect.

      In the rescue experiment (now revised Figure 3A) data show a robust rescue of the blebbing phenotype upon reconstitution (data on n=83 WKO control and n=99 WKO reconstituted cells in N=3 independent experiments). Rescue of Lamin A/C rupture in rescued cells (Revised 3B) was analyzed in fewer cells because of variable reconstitution efficiencies, labeling resolution and confinement efficiency. We now comment this set of data as a trend and not as conclusive evidence.

      It is also not obvious why the authors discarded a more central role of nuclear mechanics in this entire process. It is widely accepted and shown in many studies (Lomakin et al. 2020, Earle et al. 2020 https://doi-org.sire.ub.edu/10.1038/s41563-019-0563-5 ; Cho et al. 2019 https://doi.org/10.1016/j.devcel.2019.04.020) that nuclear envelope composition in general controls nuclear mechanosensing and the ability of nuclei to sustain mechanical force. The authors show a statistically significant downregulation of LaminA/C, but not LaminB, in WKO compared to WT cells. Therefore, WKO nuclei should be softer, as further supported by the higher levels of H3K9me2. This interpretation, that could be supported by AFM indentation of the nuclei, explain why WKO cells enter more easily into the microchannels and potentially the entire mechanism.

      To exclude a direct role of nuclear mechanics in preventing NE break, some experiments could be done. For example, is LaminA/C over-expression in WKO sufficient to rescue the WT phenotype in terms of nuclear blebbing under confinement and NE break? Or, on the other hand, would LaminA/C silencing in WT, lead to increase nucleus blebbing/NE break upon mechanical compression? This set of experiments, together with the ones suggested before, would be key to support the hypothesis proposed in the manuscript and clarify the mechanism, and the over-expression of LaminA/C with transient transfection is not a complicated task.

      We thank the reviewer for raising the role of nuclear mechanics in the process, which we have not excluded. Indeed, we have discussed more extensively this hypothesis in the text of the revised version (Discussion page 10), considering new data showing Lamin A/C rupture (Revised 2C,E) and the original data showing Lamin A/C reduction at steady state and in response to mechanical confinement in WKO (Revised Figure 5F). We have not directly addressed rescue by Lamin A/C overexpression, which will be the objective of future investigations.

      Other major points:

      • Related to Figure1: images and quantifications of cell shape are missing. Differences in cell spreading are expected when interfering with actin regulators. As nucleus shape depends on both cell spreading and on the ability of the actin to pull onto the nucleus and flattened it (especially when cells are plated in extremely stiff environments like glass), quantification of cell area and images of the actin cortex would elucidate better the cell phenotype. If the authors already have phalloidin stainings, this analysis is straight forward and does not require additional experiments. An entire new Figure (revised Figure 2) now presents key nuclear parameters (nuclear areas, Lamin A/C gaps and nuclear protrusions) in confined and unconfined WT and WASp null cells, and cells treated with Arp2/3 inhibitor. Results show that nuclear envelope ruptures and protrusions are specifically occurring in confined WASp null macrophages and Arp2/3 treated cells. Rescue with WASp overexpression corrects the phenotype, reinforcing that a WASp-Arp2/3 axis controls nuclear integrity under mechanical challenge. Supplementary Figure 2A shows that cell areas under confinement are not statistically different pointing to a nucleus specific process.

      Revised Figure 4 presents results in microchannels, now complemented by new data showing that Arp2/3 inhibitors increase entry rate (phenocopying WASp deficiency) and that rescue with WASp-GFP brings back entry levels to those of WT cells.

      • The nuclei of WKO macrophages do not appear to be much different from WT ones (Figure 1), as claimed in the text, considering a pPresentation of data on nuclear parameters has been deeply revised in new Figure 2 providing different representative images in line with statistical quantifications of key parameters.

      As the authors acquired 3D stacks of nuclei, it would be nice to know if nuclei are different in volume and 3D shape parameters. This would further connect to the first question and clarify if nuclei have a slightly larger area because of a bigger volume or if they are less flattened by the actin cortex. ROBERTO?

      AFM experiments: by indenting a cell of 500nm on top of its nucleus, are cytosolic mechanics being measured or rather cortical/perinuclear ones? Changes in cortical mechanics are expected by knocking-out WASp or any other actin regulator. It is not clear whether the authors are measuring cytosolic mechanics or what. this must be defined better and further discussed. Moreover, as cells are not elastic materials, the physical parameter measured is an "apparent young modulus" and the axis title could be changed. We agree that this assay is not conclusive and in line with the manuscript claim and it has been excluded.

      Regarding the front/back localization of herniations: how do the authors define front/back when imaging only the Hoechst channel in fixed cells? Could it be that some cells start migrating back within the channel? This could be expected during migration. If this was done using live data, nothing has to be changed otherwise the analysis is not robust. We agree with this point. Data are collected from fixed cells and we cannot exclude that cells migrated back. Thus, we excluded this result from the present version.

      • Regarding the NLS-GFP timelapses and kymographs (Figure 2F): why is the NLS-GFP intensity increasing in time, in both WT and WKO, in the first time frames? Shouldn't it be constant in time and change its localization only upon rupture? If the signal increases because the transfection is not at a saturation level, the authors could wait to have a constant signal. To avoid confusion, as the NLS is used as a qualitative tool only and nuclear rupture events should be evident anyway, it would be better to show only parts of the kymographs with a constant baseline intensity. Also, the time scales of images/kymographs are not specified anywhere, this information should be added. This increase in signal is a technical issue we have observed over experiments, linked to instability of the signal acquired by the microscope. As this graphical representation does not add to the message and may create confusion we removed from the present version.

      • Related to the mechanical compression experiments (Figure 3), data regarding the response of WT and WKO nuclei to confinement are missing. Is the area of both population increasing in the same way upon confinement or not (quantify nucleus area before and after confinement)? Are these nuclei unruffling in the same way? These data could be obtained from the already acquired datasets and enriched with some LaminA/C experiment or actin-patch ones (see above).

      Revised Figure 2B shows responses to confinement in WT and WKO with measures showing larger increase in the nucleus area in confined cells WASp ko cells and In Arp2/3 treated cells. The same Figures shows Lamin A/C gaps and ruptures. Ruffling parameters were not captured well in confined cells by technical issues that we could not solve, so the ruffling parameter is no longer part of the revised version.

      • Regarding the RNAseq experiments. Why there are three repeats for confined data and two for unconfined ones? Do two confined datasets correspond to the same control or how were the samples acquired? Are the sample batch-corrected? As we were not expecting major differences in the unconfined cells, we kept only two replicates for the control conditions. Samples were not batch-corrected, as were processed together, PCA shows they cluster according to biological conditions, even though confined samples, especially WKO showed greater variability; which is intrinsic of the assay and can be appreciated also in qPCR experiments

      The NER inflammatory score (Figure 5F): the last repeat of WKO 3um shows very high score values, much higher than the other two, clearly affecting global statistics. Also, in Supplementary Figure 5B one point of WKO confined appears quite far from the other two, is this the same one of Figure 5F? Please comment on this.

      The sample showing higher score values is the same that is separated in the PCA. Overall, confiners are difficult to control and inherent variability is expected, especially in bulk downstream approaches. Before running bulk RNA seq, we selected 3 samples with varied Il6 upregulation by RT-PCR. Nevertheless, when looking at the profile of individual genes of the branched actin scores, nuclear mechanical score, NER score and RhoA, the trend is seen in the 3 samples, although to different extents. We validated the prototype inflammatory gene IL-6 in several replicates, finding a robust statistic.

      Overall, even if we acknowledge the intrinsic variability of this assay, we believe this reflect that actual biological variation and does not affect our conclusion. Moreover, we do observe a similar trend in gene corrected WAS patients.

      The fact that WASp shows a milder effect than the complete blockade of Arp2/3 or LaminA/C (discussion lines 372 and following), could be due by the overexpression of other regulators of branched actin? It would not be surprising if cells over-express other actin regulators to compensate the loss of WASp, either to promote branched actin via Arp2/3 or filamentous structures via formins. As the authors performed RNA sequencing, it would be interesting and not complicated to check for the expression of these factors in WKO vs WT cells. We show upregulation of Rac2/RhoA related genes in RNA-seq (Revised Supplementary Figure 4D) and we have discussed that overexpression of compensatory mechanism may be causal to the altered phenotype in WASp null cells.

      Regarding the human dataset analysis, wasn't it known that WKO cells or people with the WAS, have a proinflammatory signature? Could the author clarify the novelty of the analysis? As the reviewer correctly highlights, it is well known that WAS patients show enhanced production of inflammatory cytokines in their peripheral blood.

      However, to the best of our knowledge, this is the first dataset to report a complete transcriptomic analysis of PBMCs data in WAS patients. Most importantly we show a unique cohort of patients before and after gene therapy.

      Minor comments:

      Replies are only for the data that are part of the revised version

      • In the figures is not always clear if the representative images and the associated quantifications come from live or fixed cells. This must be clarified for reproducibility.
      • Line 81, is the citation of Thiam et al. 2016 correct?
      • Line 99 missing citations.
      • Line 134: "nuclei are more irregular": apart from the discussion above, an explanation such as "as quantified by nucleus roundness" could be added.
      • In Figure 1 and in the text, why do the authors focus on Emerin? This point is not explained at all. Is it only to visualize the nuclear envelope or for any specific reason? A small sentence of explanation should be added.
      • As stated in lines 164-165: "the entrance rate of WKO macrophages was consistently higher, especially at smaller constrictions". This is not precisely true, it is rather at the intermediate/larger ones (6-5um), but not at 4 or 3um (at least not statistically significant, or p-values are missing). OK correct in text

      • Figure 2E: fixation and staining for what? Aren't the experiments and analysis done on live cells? This has been corrected

      • Supplementary Figure 2B missing statistical test or all not significant?

      • Supplementary Figure 2D why the pre-conditions have such long bars in the upper values? Is it due to a single outlier cell?
      • Line 185 "to more faithfully recapitulate deformations events experienced by macrophages in tissues, we next applied a vertical confiner device". The confinement tool is certainly a great system in mechanobiology and can mimic certain types of confinement cells experience in vivo, but it is probably not the most "faithful recapitulation" of an in vivo tissue, right? I would downgrade/rephrase this sentence. Corrected

      • Figure 3E: provide for times (minutes, minutes after confinement is applied) instead of frame numbers in images. Higher magnification would allow to visualize rupture/blebbing better.

      • Figure 4D missing statistics? Error bars (doing % in each experiment, mean and standard deviation..)
      • Percentages in panel B and D of Figure 4 are not exactly the same or consistent, it is not clear why.
      • Supplementary Figure 4B: Why is the average intensity first shown with the three blocks (back-center-front) and then shown across the cell (lower panel)? If the lower panel is an average of n=42 cells, the first panel is not needed. Otherwise, if the lower cell is a single representative cell, what does the label "Avg WASp intensity" refer to? Finally, the plot of the normalized intensity, is done for 1 cell or is it done from the average? Clarify.
      • The nuclear mechanics score includes many genes/proteins, whose regulation would affect nuclear mechanics in different ways. Could the author comment on this or on some specific finding? The fact that nuclear mechanics are altered by confinement, and that this depends on WASp, is very interesting but not discussed. We added discussion of nuclear mechanics at page 10, 11.

      • Figure 5 and supplementary Figure 5: color scale miss numbers in several panels.

      • Line 333 cite.
      • Line337 in vitro tools mimic, do not recapitulate forces experienced by cells in vivo.
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      Referee #4

      Evidence, reproducibility and clarity

      R. Amadio and co-authors propose a mechanism that links the immune-specific actin regulator WASp with nucleus integrity/mechanosensing and the proinflammatory phenotype observed in WASp-null cells. This work arises from the following three points/previous findings:

      • the WAS syndrome, caused by mutations of the WAS protein (WASp), is associated with autoimmune and autoinflammatory manifestations. WASp is an activator of the famous Arp2/3 complex which controls branched actin polymerization.
      • immune cells need to migrate in confined and challenging environments and therefore mechanisms of correct mechanosensing are required for their survival and to ensure a working immune system.
      • nucleus integrity loss and DNA damage upon external force, sensed by cGAS-STING in the cytosol, can trigger senescence, death or autoimmunity.<br /> Therefore, the question of how WASp connects mechanosensing to the inflammatory response appears natural and it is indeed a very interesting question. This study would clearly help the understanding of the WAS autoimmune syndrome and explain processes beyond it, as other members of the WASp family might act in similar ways in other contexts such as cancer.

      The paper is well written and the figures are nicely presented. The proposed mechanism is intriguing, however it is not fully clear and not entirely supported by the presented data. Some functional experiment would be needed to demonstrate it, or the statements adapted to what the data support. Also, sometimes statistics are quite weak and some data could be further analyzed.<br /> The authors suggest the following mechanism, depicted in Figure 5J, here summarized with percentages of cells. The majority (90%) of WT cells sustain mechanical compression without visible nuclear blebs (Figure 3B) and low (but unknown, to be provided) percentage of NE rupture (Figure 3D), thanks to the appearance of an actin-WASp-rich patch in the cytoplasm, close to the cell nucleus, visible in the 30% of the cells (Figure 4B). On the other hand, cells that lack WASp, show nuclear blebs in the 35% of the case (Figure 3B), three times more NE rupture compared to WT (Figure 3D) and the actin patch only in 10% of the cells (Figure 3B, or less? Figure 4D). Therefore, it looks like only a small minority of cells shows what the model proposes as a general working mechanism. Considering that statistical tests are often poor and rescue experiments do not show any clear (or statistically different) result, the authors should support the work with additional experiments and discussion.

      First, physically, how does the presence of an intracellular (stiff?) actin patch (observed in 30% of WT cells) prevent a nucleus from damage? Could the author further explain/comment on this point? More detailed quantifications could be provided, together with a clearer discussion or some key experiment (see later) to demonstrate this idea. For example, is the mechanoresponse of cells with/without patch different? Do the nuclei of cells with patch show a different area increase, less blebbing and rupture? This would suggest less "force" transmitted to the nuclei.

      Otherwise, as many WT cells do not show the patch, but the nucleus deals fine with the compression, could a global cytosolic/cortical stiffening explain the mechanism? The patch could be the extreme outcome of this stiffening, therefore observed in fewer cells. In this second option, cytosolic stiffening should be quantified (see AFM point below) and the model explained better. Related to both options, functional experiments to prove that the expression of WASp is sufficient to prevent NE rupture, are required. The authors already perform a rescue experiment (that is a very elegant way to prove a mechanism), by over-expressing WASp-GFP in WKO cells, but statistics and numbers are too low. Figure 3F-G shows a reduction in blebbing nuclei in confined cells upon WASp rescue (see below comments about this plot), but it is not clear if it statistically reduces NE ruptures (Figure 3I too weak, low N, no statistical difference shown), if it rescues the formation of the actin patch or if it has any further effect.<br /> It is also not obvious why the authors discarded a more central role of nuclear mechanics in this entire process. It is widely accepted and shown in many studies (Lomakin et al. 2020, Earle et al. 2020 https://doi-org.sire.ub.edu/10.1038/s41563-019-0563-5 ; Cho et al. 2019 https://doi.org/10.1016/j.devcel.2019.04.020) that nuclear envelope composition in general controls nuclear mechanosensing and the ability of nuclei to sustain mechanical force. The authors show a statistically significant downregulation of LaminA/C, but not LaminB, in WKO compared to WT cells. Therefore, WKO nuclei should be softer, as further supported by the higher levels of H3K9me2. This interpretation, that could be supported by AFM indentation of the nuclei, explain why WKO cells enter more easily into the microchannels and potentially the entire mechanism. To exclude a direct role of nuclear mechanics in preventing NE break, some experiments could be done. For example, is LaminA/C over-expression in WKO sufficient to rescue the WT phenotype in terms of nuclear blebbing under confinement and NE break? Or, on the other hand, would LaminA/C silencing in WT, lead to increase nucleus blebbing/NE break upon mechanical compression? This set of experiments, together with the ones suggested before, would be key to support the hypothesis proposed in the manuscript and clarify the mechanism, and the over-expression of LaminA/C with transient transfection is not a complicated task.

      Other major points:

      • Related to Figure1: images and quantifications of cell shape are missing. Differences in cell spreading are expected when interfering with actin regulators. As nucleus shape depends on both cell spreading and on the ability of the actin to pull onto the nucleus and flattened it (especially when cells are plated in extremely stiff environments like glass), quantification of cell area and images of the actin cortex would elucidate better the cell phenotype. If the authors already have phalloidin stainings, this analysis is straight forward and does not require additional experiments.
      • The nuclei of WKO macrophages do not appear to be much different from WT ones (Figure 1), as claimed in the text, considering a p<0.05 with more than 2000 cells for the area plot and a not significant plot for roundness, while claiming "more irregular nuclei" in the text. Claims in the text must follow the statistical difference of the data as well as representative images (instead of a very elongated nucleus in WKO when the plot says another thing). If 3 images are needed to show 3 types of nuclei, it is maybe recommended to divide the nuclei in 3 categories like round/elongated (roundness <0.7?)/with micronuclei, quantify the % of each category in WT vs WKO and their area separately. Also, are nuclei with micronuclei smaller than the ones without, as it looks from the image? If this is the case, quantifying nuclear area and roundness for each category might support the statistics. As the authors acquired 3D stacks of nuclei, it would be nice to know if nuclei are different in volume and 3D shape parameters. This would further connect to the first question and clarify if nuclei have a slightly larger area because of a bigger volume or if they are less flattened by the actin cortex.
      • AFM experiments: by indenting a cell of 500nm on top of its nucleus, are cytosolic mechanics being measured or rather cortical/perinuclear ones? Changes in cortical mechanics are expected by knocking-out WASp or any other actin regulator. It is not clear whether the authors are measuring cytosolic mechanics or what. this must be defined better and further discussed. Moreover, as cells are not elastic materials, the physical parameter measured is an "apparent young modulus" and the axis title could be changed.
      • Regarding the front/back localization of herniations: how do the authors define front/back when imaging only the Hoechst channel in fixed cells? Could it be that some cells start migrating back within the channel? This could be expected during migration. If this was done using live data, nothing has to be changed otherwise the analysis is not robust.
      • Regarding the NLS-GFP timelapses and kymographs (Figure 2F): why is the NLS-GFP intensity increasing in time, in both WT and WKO, in the first time frames? Shouldn't it be constant in time and change its localization only upon rupture? If the signal increases because the transfection is not at a saturation level, the authors could wait to have a constant signal. To avoid confusion, as the NLS is used as a qualitative tool only and nuclear rupture events should be evident anyway, it would be better to show only parts of the kymographs with a constant baseline intensity. Also, the time scales of images/kymographs are not specified anywhere, this information should be added.
      • Related to the mechanical compression experiments (Figure 3), data regarding the response of WT and WKO nuclei to confinement are missing. Is the area of both population increasing in the same way upon confinement or not (quantify nucleus area before and after confinement)? Are these nuclei unruffling in the same way? These data could be obtained from the already acquired datasets and enriched with some LaminA/C experiment or actin-patch ones (see above).
      • Regarding the RNAseq experiments. Why there are three repeats for confined data and two for unconfined ones? Do two confined datasets correspond to the same control or how were the samples acquired? Are the sample batch-corrected? The NER inflammatory score (Figure 5F): the last repeat of WKO 3um shows very high score values, much higher than the other two, clearly affecting global statistics. Also, in Supplementary Figure 5B one point of WKO confined appears quite far from the other two, is this the same one of Figure 5F? Please comment on this.
      • The fact that WASp shows a milder effect than the complete blockade of Arp2/3 or LaminA/C (discussion lines 372 and following), could be due by the overexpression of other regulators of branched actin? It would not be surprising if cells over-express other actin regulators to compensate the loss of WASp, either to promote branched actin via Arp2/3 or filamentous structures via formins. As the authors performed RNA sequencing, it would be interesting and not complicated to check for the expression of these factors in WKO vs WT cells.
      • Regarding the human dataset analysis, wasn't it known that WKO cells or people with the WAS, have a proinflammatory signature? Could the author clarify the novelty of the analysis?

      Minor comments:

      • In the figures is not always clear if the representative images and the associated quantifications come from live or fixed cells. This must be clarified for reproducibility.
      • Line 81, is the citation of Thiam et al. 2016 correct?
      • Line 99 missing citations.
      • Line 134: "nuclei are more irregular": apart from the discussion above, an explanation such as "as quantified by nucleus roundness" could be added.
      • In Figure 1 and in the text, why do the authors focus on Emerin? This point is not explained at all. Is it only to visualize the nuclear envelope or for any specific reason? A small sentence of explanation should be added.
      • As stated in lines 164-165: "the entrance rate of WKO macrophages was consistently higher, especially at smaller constrictions". This is not precisely true, it is rather at the intermediate/larger ones (6-5um), but not at 4 or 3um (at least not statistically significant, or p-values are missing).
      • Figure 2E: fixation and staining for what? Aren't the experiments and analysis done on live cells?
      • Supplementary Figure 2B missing statistical test or all not significant?
      • Supplementary Figure 2D why the pre-conditions have such long bars in the upper values? Is it due to a single outlier cell?
      • Line 185 "to more faithfully recapitulate deformations events experienced by macrophages in tissues, we next applied a vertical confiner device". The confinement tool is certainly a great system in mechanobiology and can mimic certain types of confinement cells experience in vivo, but it is probably not the most "faithful recapitulation" of an in vivo tissue, right? I would downgrade/rephrase this sentence.
      • Figure 3E: provide for times (minutes, minutes after confinement is applied) instead of frame numbers in images. Higher magnification would allow to visualize rupture/blebbing better.
      • Figure 4D missing statistics? Error bars (doing % in each experiment, mean and standard deviation..)
      • Percentages in panel B and D of Figure 4 are not exactly the same or consistent, it is not clear why.
      • Supplementary Figure 4B: Why is the average intensity first shown with the three blocks (back-center-front) and then shown across the cell (lower panel)? If the lower panel is an average of n=42 cells, the first panel is not needed. Otherwise, if the lower cell is a single representative cell, what does the label "Avg WASp intensity" refer to? Finally, the plot of the normalized intensity, is done for 1 cell or is it done from the average? Clarify.
      • The nuclear mechanics score includes many genes/proteins, whose regulation would affect nuclear mechanics in different ways. Could the author comment on this or on some specific finding? The fact that nuclear mechanics are altered by confinement, and that this depends on WASp, is very interesting but not discussed.
      • Figure 5 and supplementary Figure 5: color scale miss numbers in several panels.
      • Line 333 cite.
      • Line337 in vitro tools mimic, do not recapitulate forces experienced by cells in vivo.

      Referees cross-commenting

      Agree with the questions of other reviewers, especially on the points needed to prove the mechanisms such as the point on nuclear envelope break vs DNA damage, or the role of Arp2/3 raised by Reviewer #1 and the LaminA/C vs actin patch question raised by Reviewer #3. In my opinion, not all the suggestions made by the 4 reviewers are strictly required to consider this work for publication, but the mechanism has to be supported by more experiments, including some functional ones.

      Significance

      R. Armadio and co-authors propose a cellular mechanism that controls, at the single cell level, nuclear integrity in confined environments and, at the tissue level, inflammation response. To show this new role of WASp in controlling nucleus shape and integrity, the authors used various tools to compress cells in vitro and provide an extensive RNA profile of both WT and WKO cells, with or without compression. This work fits together with other recent papers, cited by the authors, like Delgado et al. 2024, that bridge extremely relevant mechanobiology findings, with more physiological problems and diseases. Therefore the mechanism, if demonstrated properly, can be interesting for the biophysics community, the general mechanobiology field as well as for people with a more medical background (because of the implications in the syndrome). In mechanobiology this work opens new questions regarding the role of other members of the WASp family in controlling cellular mechano-sensing and nucleus integrity in other tissues or different DNA damage sensing mechanisms. From a physical perspective, it is very intriguing to think a (stiff?) patch in the cytosol of cells could protect the nucleus from damage and to think at possible other implications of this idea. As evident from the comments, this is written from a biophysics/mechanobiology background.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript cells of WASp null mice bone marrow derived macrophages. They find that lamin A/C levels and RNA decreased while lamin A/C wrinkles increased and chromatin measured indirectly did not change. Next WKO cell were more likely to migrate through constricted channels and had more nuclear rupture events as well as those cells/nuclei undergoing multiple ruptures. Under low artificial confinement WT and WKO nuclei had similar low percentage of nuclear blebbing but upon high artificial confinement WKO nuclei measured a drastic increase in nuclear blebbing and ruptures while blebbing could be partially rescued by expression of WASp-GFP. The manuscript then suggests that perinuclear actin dependent on WASp is the mechanism due the occurrence of what appear to be random patches of actin. Finally, the paper reports upregulation of a subset of genes different from WT and WKO due to confinement. The data are largely of interest to the field but there remains no conclusive evidence to support a clear mechanism for why WKO nuclei undergo nuclear blebbing, rupture, and differential gene expression under artificial confinement.

      The manuscript does not do a sufficient job detailing the cause of the nuclear mechanical changes between lamin A/C and actin. There is data that lamin A/C decreases (Figure1) why is the mechanism not just loss of lamin A/C? Instead random actin patches protect the nucleus? However, the manuscript does not disrupt these actin patches in another manner to show they are indeed important to resisting artificial confinement. An actin depolymerizer with compression might show this? Alternatively, it might not be about actin structures but acto-myosin contraction known to be essential to causing nuclear blebbing and rupture in artificially confinement experiments (Mistriotis et al., 2019 JCB).

      The fact that lamin A/C is upregulated in WT upon confinement and not in WKO suggests mechanotransduction is not occurring properly as lamin A/C are upregulated under tension which is lost in WKO. This suggests that actin incorporation with the nucleus might be broadly flawed through possibly the LINC complex. The wrinkling of the nuclear lamina in WKO suggests it is actually under less tension, possibly supporting this idea. This continues to point to the fact that this paper, while it has a lot of interesting data, does not appear to have a conclusive understanding of the mechanism occurring in WKO.

      Measuring cytoplasm stiffness makes no sense in Sup Fig 1.

      Significance

      This study provides unique evidence that WASp is important in nuclear mechanobiology though the mechanism is not clear.

      This data will be a great interest to the field of mechanobiology, but revisions will be required to clarify the underlying mechanism of WASp action in maintaining nuclear integrity.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: This study from Roberto Amadio and coworkers significantly expands our understanding of WASp by identifying its role in nuclear mechanotransduction, linking actin defects to nuclear instability and inflammation, specifically for macrophages. Although there are several studies related to Dendritic Cells and T cells for example, linking the role of the protein to pro-inflammatory pathways and expression of cytokines, this is the first study that thoroughly links WASp, nuclear instability and downstream pro-inflammatory activation. These findings have broader implications for immune disorders and actinopathies, highlighting WASp as a key regulator of mechanosensing and inflammatory control. Future research should validate these results in vivo, further investigate the actin-based nuclear support system, and explore therapeutic strategies targeting nuclear integrity or cGAS-STING signaling in WAS and related conditions.

      The study provides strong evidence supporting a new role for WASp in nuclear integrity and inflammation control in macrophages. The findings are backed by multiple experimental approaches, including high-resolution imaging, mechanotransduction assays, gene expression profiling, and live-cell tracking, which consistently show increased nuclear deformation, rupture, and inflammatory activation in WASp-deficient macrophages. Reproducibility is reinforced through the use of different experimental models, including genetic reconstitution (WASp-GFP rescue), CRISPR-Cas9 knockout validation, and transcriptomic analysis in both murine and human macrophages, as well as PBMC data from WAS patients pre- and post-gene therapy. The study is clearly structured, with systematic quantification of nuclear defects, inflammation markers, and transcriptional changes. However, some dense data presentation (e.g., transcriptomic heatmaps) and limited discussion of alternative pathways could make certain sections more accessible. Despite these minor limitations, the study's conclusions are well-supported, reproducible, and provide clear mechanistic insights into how WASp regulates nuclear stability and inflammatory signalling.

      Minor comments: For better readability of text, can the authors include the following editions into their main text:

      1. Please define BMDM at its earliest reference (page 5, line 187)
      2. Can the authors provide information regarding how the image processing was performed for heterochromatin content, its distribution and chromocenter analysis?
      3. It would be great if they can explain how the roundedness of nuclei was characterized
      4. Rather than calling it cell mechanics, I would be more comfortable to read it cell stiffness or cell elasticity on line 150, page 4.
      5. For more clarity and quantitative information, can the authors provide numbers everywhere in the main text where comparisons between two datasets are cited to? For example: WKO cells were 20 +/- 10% stiffer than WT ones, etc.

      Significance

      Strengths: This study uncovers a novel function of WASp in nuclear integrity maintenance and inflammation control, expanding its known role beyond actin polymerization at the cell cortex. The findings are supported by rigorous experimental approaches, including high-resolution imaging, live-cell tracking, gene expression profiling, and mechanotransduction assays. Additionally, human data from WAS patients validate the clinical relevance, showing that gene therapy can partially reverse inflammation. The study's multi-model approach, including genetic reconstitution (WASp-GFP rescue) and CRISPR validation, ensures reproducibility and robustness.

      Limitations: Despite strong in vitro evidence, alternative pathways contributing to nuclear rupture, such as RhoA/ROCK signaling, are not fully explored. Finally, while transcriptomic data from WAS patients reinforce key findings, sample variability limits generalization.

      This study provides a conceptual shift in our understanding of WASp's function, demonstrating that it is not only an actin regulator but also a key protector of nuclear stability under mechanical stress. It links mechanosensing to immune activation, showing that nuclear rupture in WASp-deficient macrophages triggers cGAS-STING-mediated inflammation. This establishes a new connection between cytoskeletal defects and nuclear-driven inflammatory pathways, offering a mechanistic explanation for the chronic inflammation seen in Wiskott-Aldrich Syndrome.

      This work will be cruicial for biologists, in particular from the field of oncology and immunology, and biophysicists to explore the link between nuclear mechanics and WAS protein expression in health and disease.

      I am a biophysicist and experimental physicist with background in cell mechanics and immunology.

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript entitled "WASp activity in macrophages prevents mechano-induced inflammation by 1 protecting the nuclear envelope" Amadio and colleagues demonstrate that WASp-mediated actin polymerization at perinuclear areas protect against nuclear envelope rupture events and subsequent upregulation of inflammation signature genes.

      I found this study intriguing but I do have some concerns and some missing experiments that if addressed by the authors, I would recommend the paper for publication.

      Comments:

      1. In Figure 1a the authors describe the changes in nuclear area of WKO macrophages. However, in the figure legend it is not mentioned whether the images presented are single z-planes or maximum z-projections. If the former is true, then I would suggest the authors present the maximum z-projections. If the latter is the case, I would suggest the authors to include this information on the figure legend to improve reader's comprehension.
      2. In Line 135 the authors write that the WASp null macrophages display a higher frequency of micronuclei formation and that this is an indication of nuclear disruption. However, the lab of Kenneth Campellone (10.1371/journal.pgen.1010045) has demonstrated that in the absence of Arp-2/3 micronuclei arise from defective chromosomal segregation. I believe the authors should distinguish whether this is the case or it is indeed the nuclear envelope rupture events that produce the micronuclei. The authors could add a sentence in the result and discussion section discussing this possibility.
      3. In figure 1C the authors claim that depletion of WASp results in reduced levels of Lamin-A/C. However, this is very hard to see any difference in the Western Blot provided.
      4. In line 141 the authors mention that loss of WASp results in Lamin-A/C wrinkles and "nuclear irregularities". What are the irregularities mentioned here? Maybe the authors should point them out in the imaging provided in Figure 1 to improve reader comprehension.
      5. In line 177-178 the authors write that there is an increase in nuclear deformation after transwell migration. However, this is not shown. I suggest the authors include a circularity index quantification in the same dataset to support that claim. In addition, the difference in the γH2AX foci in post-3µm in wt and WKO conditions appear to be almost identical. I believe it is the post migration difference in DNA damage that should be stressed here. Finally, the authors claim that the WKO macrophages are more nuclear envelope rupture-prone and this results in DNA damage accumulation as has been already described. However, the lab of Jan Lammerding has also demonstrated that nuclear deformation alone can also induce DNA damage accumulation in a cell cycle specific manner via stalled replication forks. The authors should distinguish between the two possibilities with live cell imaging by expressing a DNA damage marker (e.g. 53BP1, coupled with either NLS-GFP or cGAS to assess whether DNA damage accumulates in response to mechanical deformation or nuclear envelope rupture).
      6. In line 204 the authors show that reintroduction of WASp-GFP reduces the frequency in which "Lamin-A/C rupture" events are observed. Then they conclude WASp is important to maintain nuclear stability. While this Lamin-A/C is a good marker for nuclear blebs, I believe that more "classic" nuclear envelope rupture markers should be used to assess the potential compromised nuclear integrity (e.g. NLS-GFP or cGAS). Hence, I would suggest the authors to attempt to replicate this finding with live cell imaging of cells under strong (3µm) confinement and quantify the frequency in which they detect NLS-GFP leakage or perinuclear accumulation of cGAS. Another experiment would be to try and rescue the phenotype observed in the fabricated microchannels used in Figure 2 and observe whether the nuclear envelope rupture events are reduced.
      7. In Figure 4 the authors describe the potential role of WASp in the formation of actin patches in the vicinity of the nucleus. In both Fig4C and Fig4E, the mildly confined (6 µm) condition is missing. I would suggest the authors to quantify the distribution of the different actin structures and the colocalization of WASp/Phalloidin signal also in 6µm confinement. Additionally, in the lines 220-222 the authors claim that the formation of the perinuclear actin ring is absent in wt macrophages, however we still see the perinuclear actin ring in the images.
      8. Adding on my previous comment, if the perinuclear actin patches are not present in the absence of confinement, can the authors display the formation of these patches through live cell imaging utilizing probes such as LifeAct or the Actin Chromobody in wild type macrophages?
      9. While it is intuitive that if WASp is important for the formation of perinuclear actin patches, then this has to be through Arp-2/3 this should be tested. I would suggest the authors to either pharmacologically inhibit Arp2/3 with CK-666 or genetically manipulating the system by either overexpressing a dominant negative version of the Arp2/3 subunit or silencing an essential subunit. This would make it clear that these actin patches are indeed Arp-2/3 driven.
      10. The authors interestingly discovered the upregulated expression of certain inflammatory cytokines. While the authors nicely show that the mRNA levels increase, they do not show whether this translates also in increased protein levels. I would suggest the authors to include an experiment in which they assess the protein levels of a few of the upregulated chemo-/cytokines via immunoblotting or ELISAs. This is also the case for IL-6 mRNA levels showed in Figure 5.
      11. In FigS1 the authors display that in WKO cells have increased "cellular stiffness" when WASp is depleted from macrophages in the absence of confinement. However, since the cantilever is placed on top of the nuclear area, I am not sure whether the authors were measuring nuclear rigidity or cortical rigidity. In any case, in Fig5 they show that in the absence of confinement factors responsible for branched actin nucleation are downregulated (If I am not wrong, Arp2/3 is a major factor contributing to cortical actin nucleation - along with mDia1, although there might be a compensation mechanism), while the expression of factors related to nuclear mechanics is not altered. This is not in line with the result from the AFM experiment presented in FigS1. I am not sure what does this AFM experiment add to the message of the manuscript. The authors could possibly add a sentence to make the connection to the rest of the manuscript clearer.
      12. The authors beautifully show that WKO macrophages and AMO's show an upregulation of inflammation related genes. However, I believe it is important to assess whether the upregulation of these signature genes upon confinement can be rescued by the re-introduction of wild-type WASp (and possibly a mutant that is not able to activate Arp-2/3 - to display that is indeed the Arp-2/3 mediated nucleation of these actin patches that limit inflammation).
      13. In the discussion (line 333-335), the authors mention that the WASp (and I presume the produced actin patches) act as a barrier to prevent nuclear envelope rupture events. From the images provided in the manuscript I get the impression that both WASp and the perinuclear actin patches display a polarized nature. For example, in Figure 4C the actin patch is located at the left of the nucleus. WASp in general is more abundant at the trailing side of the cytoplasm in the migrating macrophage displayed in Fig S4B. Finally, WASp has higher abundancy in a specific perinuclear area in Figure 4E. How these seemingly polarized structures act as barriers? Do they stop nuclear blebs from protruding through lamin B1 gaps? Are nuclear envelope rupture events in migrating macrophages happening specifically in the migrating or trailing end of the nucleus?
      14. There has been a report by the lab of Andrea Ablasser (10.1126/science.aaw6421) in which loss of nucleocytoplasmic compartmentalization does not activate the expression of cytokines, as BAF can compete against cGAS for binding of leaked chromatin thus limiting inflammation. If there is not a cell-type specific (or lack of) expression of BAF on BMDMs, how do the authors explain their results in relation to the aforementioned publication?

      Significance

      In the manuscript entitled "WASp activity in macrophages prevents mechano-induced inflammation by 1 protecting the nuclear envelope" Amadio and colleagues demonstrate that WASp-mediated actin polymerization at perinuclear areas protect against nuclear envelope rupture events and subsequent upregulation of inflammation signature genes. the idea is interesting but the evidence is somewhat preliminary. see my suggestions.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript presents a comparative genomic analysis of chemosensory systems across the order Vibrionales. By combining phylogenetic analyses, genomic context, MCP repertoires, and structural comparisons, the authors investigate the diversity and evolution of chemosensory systems within Vibrionales. The study addresses an interesting question and assembles a substantial genomic dataset. The manuscript is generally well illustrated and contains several potentially useful observations regarding the distribution and organization of chemosensory systems across Vibrionales genomes. However, there are a number of conceptual, methodological, and presentation-related issues that should be addressed before the evolutionary conclusions can be fully supported.

      The claim that F8 represents a novel chemosensory system appears to be incorrect Lines 407-424, as well as several other places throughout the manuscript, describe F8 as a "previously uncharacterized lineage" and a "newly identified" system. However, F8 chemosensory systems were previously described by Wuichet and Zhulin and have been part of the established chemosensory system classification framework for more than a decade. Furthermore, F8 systems are already annotated as such in MiST. For example, the genome GCF_003390675.1 (Vibrio anguillarum), which is included in this study, contains CheA, CheR, and CheB proteins assigned to an F8 system in MiST.

      Consequently, describing F8 as a novel, newly identified, or newly designated system appears inappropriate. This issue requires substantial revision throughout the manuscript. The authors should clearly distinguish between the previously established F8 chemosensory class and any novel observations reported here. If the novelty lies in the distribution of F8 systems within Vibrionales, their genomic organization, their evolutionary history, or some other aspect, this should be stated explicitly.

      __Response: __We thank the reviewer for appreciating our work and further commenting on the points to improve this study. As correctly mentioned by you, F8 CSS has been discovered by Wuichet and Zhulin and already annotated in the MiST database. Since this study focuses on experimentally studied CSS clusters within Vibrio organisms, where F6, F7, and F9 have already been studied very well; however, the F8 is not studied experimentally at least in Vibrio cholerae in any literature. We have revised the statement about F8 CSS cluster being novel throughout the manuscript. We agree that MiST database reports different Che proteins, such as CheA, CheR, and CheB, assigned to F8 class, but the information about the entire F8 gene cluster in those Vibrio species has not been provided. Following this, we have identified this entire F8 gene cluster in our study and discussed it as an ‘experimentally unexplored CSS’ in the manuscript.

      The conclusions regarding horizontal gene transfer and vertical inheritance are not sufficiently supported. Lines 429-445 contain evolutionary interpretations that appear internally inconsistent. The manuscript interprets the sporadic distribution of F7 as evidence of vertical inheritance coupled with lineage-specific adaptation, whereas several lines later the similarly patchy distribution of F8 is interpreted as evidence of horizontal gene transfer. Similar distribution patterns should not be used to support contrasting evolutionary scenarios without additional supporting evidence.

      More broadly, patchy phylogenetic distributions alone are generally insufficient evidence for horizontal gene transfer. Alternative explanations, including differential gene loss, genome reduction, incomplete sampling, or rapid sequence divergence, should also be considered. Furthermore, the conclusions regarding vertical inheritance and HGT imply reconstruction of deep evolutionary history across broad bacterial groups. Based on the methods presented, these inferences appear to rely primarily on CheA phylogenies and analyses of homologous sequences. While informative, these analyses may not be sufficient to confidently infer ancestral origins. The authors should either provide additional phylogenetic evidence supporting these conclusions or moderate the language throughout the manuscript. In their current form, the proposed evolutionary scenarios would be more appropriately presented as hypotheses rather than demonstrated conclusions.

      __Response: __We thank the reviewer for this insightful comment. We agree that the evolutionary interpretations presented in the original manuscript need additional supporting evidence. Since our inferences regarding vertical inheritance and horizontal gene transfer were primarily based on phylogenetic distribution patterns and CheA-based phylogenetic analyses. In response, we have carefully revised the relevant sections of the manuscript and moderated the language throughout. The previously presented statements as a conclusion have been reformulated as hypotheses or possible evolutionary scenarios. It must be noted that comparative analysis of protein architecture of F9 CSS, along with phylogeny, revealed a high degree of similarity between Vibrionales F9 protein architecture and their Alphaproteobacterial counterparts, putatively suggesting the HGT event-based acquisition of these CSS within order Vibrionales.

      References are frequently missing, incomplete, or potentially inappropriate. One major concern is the quality and completeness of referencing throughout the manuscript. Multiple statements either lack references altogether or appear to cite sources that do not directly support the associated claims. For example, lines 76-78 cite Ulrich et al. (2005) in support of the statement that two-component systems constitute a dominant signaling paradigm in prokaryotes, whereas the cited article is entitled "One-component systems dominate signal transduction in prokaryotes." The text and/or citation should therefore be reconsidered. Similarly, the statement in lines 100-101 that 17 classes of flagellar chemosensory systems have been designated should be accompanied by an appropriate reference. In addition, numerous ecological, physiological, and evolutionary statements throughout the Introduction and Results sections either lack citations or would benefit from more precise supporting references. I recommend that the authors carefully review all references and ensure that each citation directly supports the corresponding statement.

      __Response: __We thank the reviewer for this comment. We have added the correct reference at the place of Ulrich et al 2005. Along with this we have further checked the references throughout the manuscript and corrected them wherever it is necessary.

      The manuscript would benefit from substantial restructuring and shortening The manuscript is considerably longer than necessary, and several sections appear only loosely connected to the central biological question. In particular, the Introduction contains extensive discussions of Vibrio ecology, virulence, motility, host interactions, and general signal transduction. While these topics are relevant, the overall narrative currently reads more like a broad review article than an introduction to comparative genomics study. I recommend substantially shortening and restructuring the Introduction so that the central biological question and the specific objectives of the study become more apparent to the reader.

      Similarly, the section entitled "Multipartite genome and extensive RNA gene repertoire reflect niche adaptation in Vibrionales" (lines 302-342) contains several observations regarding genome size, tRNA counts, and rRNA copy numbers. However, it remains unclear how these analyses contribute to the primary conclusions regarding chemosensory system evolution. This section should either be shortened substantially and more explicitly connected to the central theme of the manuscript or moved to supplementary material. The manuscript would also benefit from substantial language editing. Numerous grammatical and stylistic issues are present throughout the text, including awkward phrasing, subject-verb agreement errors, and overly long sentences. A thorough language revision would improve readability and help the reader focus on the scientific content.

      __Response: __We thank the reviewer for this comment. We agree that this manuscript is considerably larger and we have shortened several parts in the introduction such as Vibrio ecology, virulence, motility, host interactions and focused more on study objectives. We have also moved the result tilted as “Multipartite genome and extensive RNA gene repertoire reflect niche adaptation in Vibrionales” in the supplementary part. We have carefully revised the entire manuscript to address grammatical errors, improve sentence structure, correct subject–verb agreement issues, and eliminate awkward phrasing. We have also streamlined several lengthy sentences and paragraphs to enhance clarity, readability, and the overall presentation of the scientific content.


      Specific comments:

      Line 20: The phrase "28 Vibrio clades" requires clarification. It is not clear what these clades represent, how they were defined, or whether "clades" is the most appropriate term. Please define these groups more clearly. Perhaps the term "genera" would be more appropriate.

      __Response: __We thank the reviewer for this comment. We agree that the terms clade and genera can be confusing. Jiang et al., 2022 have given this clade classification for Vibrionaceae family members based on phylogenetic analysis of 8 core genes. We have adopted this classification for our study and cited it wherever it is needed. We have explained this in the introduction section.

      Lines 23 and 39: F8 is described as a "novel lineage" and a "previously uncharacterized system." As discussed above, F8 systems have already been described and are annotated in MiST. Please revise these statements.

      __Response: __We thank the reviewer for this comment. We have revised this sentence throughout the manuscript.

      Lines 35-36: The statement could be interpreted as implying that the involvement of chemosensory systems in host colonization is unique to Vibrio. Since chemosensory systems are broadly distributed across bacteria and frequently contribute to host interactions, I suggest rephrasing this sentence to avoid overstatement.

      __Response: __We thank the reviewer for this comment. We rephrased this sentence.

      Lines 41-42: The conclusion that CheA and MCP proteins represent promising drug targets is not directly supported by the analyses presented in this manuscript. The study does not evaluate essentiality, druggability, inhibition, or therapeutic feasibility. I recommend removing this statement.

      __Response: __We thank the reviewer for this comment. We agree that this study does not directly evaluate the essentiality of CheA/MCP proteins as a therapeutic target. Therefore, we removed this part from the manuscript.

      Line 66: The sentence describing responses to environmental cues via "chemotaxis, quorum sensing, and response to nutrients" is awkwardly phrased, since chemotaxis itself often represents a response to nutrients. Please revise for clarity.

      __Response: __We have revised this sentence.

      Lines 69-70: The statement that signal transduction in Vibrio species is "highly precise" and senses chemical gradients "very accurately" require both a reference and a clearer explanation. Relative to what system or organism is this precision being evaluated?

      __Response: __We removed this sentence.

      Lines 76-78: Please reconsider the citation to Ulrich et al. (2005) and revise the associated statement accordingly.

      __Response: __We have corrected this part.

      Lines 100-101: Please provide a reference supporting the statement that 17 classes of flagellar chemosensory systems have been designated.

      __Response: __We have added reference for this sentence.

      Line 106: The manuscript states that our understanding of CSS architecture and function derives primarily from a limited number of model organisms, particularly Escherichia coli. However, later sections highlight the extensive literature on Vibrio cholerae chemotaxis. Since V. cholerae itself is one of the better-characterized organisms in this field, the rationale for emphasizing E. coli alone is unclear.

      __Response: __We agree with your point. We have removed the part of chemosensory system of Escherichia coli and focused on Vibrio cholerae.

      Lines 278-286: This paragraph appears to contain contradictory statements regarding the number of genera included in the analysis. Please clarify how many genera were included, which were excluded, and the criteria used for inclusion.

      __Response: __We have clarified the criteria for inclusion of genera in our study.

      Lines 289-291: Please provide a reference supporting the statement regarding the mutualistic association between Aliivibrio fischeri and squid.

      __Response: __We thank the reviewer for this comment. We have provided the necessary reference for this statement.

      Lines 291 onward: Several ecological and physiological statements in this section require appropriate references.

      __Response: __We have provided the necessary references for these statements. Also, we have shortened some parts of it.

      Lines 302-342: This section would benefit from substantial shortening or a clearer connection to the central theme of chemosensory system evolution.

      __Response: __We have moved this part into the supplementary material, since it is not directly related to the central theme of the manuscript.

      Lines 369-371: This statement is difficult to interpret. MCPs are generally much more variable in abundance than core chemotaxis proteins, and conservation of abundance alone does not demonstrate essentiality. Please clarify and revise this conclusion.

      __Response: __We agree with the reviewer. We have revised this statement in the manuscript. MCP protein shows more abundance than other che proteins.

      Lines 429-445: The contrasting interpretations of F7 and F8 distributions require additional supporting evidence or a more cautious presentation.

      __Response: __We thank the reviewer for this insightful observation. We have rephrased the statement in the manuscript.

      Lines 538-541: This sentence is difficult to follow and would benefit from reformulation. In addition, CheA domain architectures, including those associated with Vibrionales F6, F7, F8, and F9 systems, have recently been described in detail by Berry et al., 2023. The authors should discuss their observations within the context of this work.

      __Response: __We thank the reviewer for this insightful observation. We have reformulated this result part in accordance with the Berry et al., 2023 paper. We have mentioned the possible reasons behind the absence of P2 domain in Che-F6 as well as extra structured insertion between F8 and F9.

      Line 545: The phrase "additional insertion domain" is not well defined. If this region corresponds to a recognized domain, it should be identified explicitly. If it represents an insertion or a poorly structured region, more appropriate terminology should be used.

      __Response: __We thank the reviewer for this comment. We have used the terminology “an insertion” as it is not a recognizable domain.

      Figure 4: It would be helpful to include the identifiers of the proteins used in the structural comparisons.

      __Response: __We thank the reviewer for this comment. We have added the identifiers for the CheA proteins.

      __

      __

      __ __

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This article has to be completely rewritten because it is now very confused and difficult to read, starting with the title: landscape, architecture, and bipartite organization are difficult terms to employ when describing the "chemosensory system" of bacteria. Systems, in my opinion, does not refer to assemblies, and chemosensing in bacteria refers to anything that involves quorum sensing. Nevertheless, not all bacteria use quorum sensing for flagellar motility, and if these Che-As (and MCPs) are found in "non-chemosensory" systems and are mapped in the abstract without much explanation, this should probably be thoroughly further discussed before debating the organization of the genome and the division of "chemosensory" genes.

      __Response: __We thank the reviewer for these comments regarding terminology and conceptual framing. We agree that bacterial signalling encompasses a wide range of signaling mechanisms beyond those involved in flagellar motility. In our manuscript, use of chemosensory system refers specifically to CheA/CheW/CheY/MCP-based signalling pathways as defined by Wuichet & Zhulin, 2010. We have revised the introduction to clarify the difference between chemosensory systems and other bacterial sensory systems such as quorum sensing. We also agree that not all CheA-containing pathways are necessarily associated with flagellar motility and because of this, we have used chemosensory array and chemostaxis in different contexts. Accordingly, we have moderated several statements throughout the manuscript and mentioned that some chemosensory pathways may regulate other cellular functions. We also agree that here wording “driven by bipartite genome architecture” should be changed to distribution of chemosensory systems across different replicons. Overall, following your suggestions, we have significantly changed the title, introduction, results and discussion.

      There are often few attempts to take into account the "modern" literature to describe the evolution of bacteria and vibrionales and their capacity for quorum sensing, particularly in the introduction and discussion. Such two lengthy, in-depth paragraphs about the biology and diversity of Vibrionales that span more than two pages and only include six references are rather inappropriate for a research article, particularly when the topic is chemosensing and genetics. The description of the Che family, whose abbreviation is never explained, is highly ambiguous and very confusing in comparison to very basic understanding about vibrionales. Despite their significance intracellularly, we typically gain little from reading this section of Ches. Without making a distinction between chemotaxis and quorum sensing, the third paragraph is intended to a lesson about one component systems, two component systems, and chemosensory systems. Which kind of audience do the authors hope to reach? Microbiologists? Examples of these systems that are better characterized can be found throughout the literature.

      Bacterial chemosensory systems are then abruptly introduced. "Chemosensory systems are extremely modular (?) and are typically organized as gene clusters within bacterial genomes". Which sensory genes? Which clusters? What families of bacteria? Which references? When it comes to the classification of chemosensory systems, only Gumerov et al. 2021 is cited. However, what role do other groups play in the study of bacterial Ches and their distribution among the vast diversity of bacteria, such as proteobacteria, actinomycetes, and firmicutes? In order to give a more pertinent Introduction, there are fewer acronyms to employ and undoubtedly more efforts to thoroughly analyze the literature.

      __Response: __We thank the reviewer for this detailed and constructive comment. We agree that the earlier introduction was giving more space to the general biology and diversity of Vibrionales and did not provide sufficient context on the development, diversity, and evolution of bacterial CSS. We have therefore substantially revised and shortened the introductory sections describing Vibrionales biology and diversity, while expanding and restructuring the section on bacterial signal transduction and chemosensory systems. Specifically, we have

      • reduced the number of acronyms and simplified the description of OCSs, TCSs, and CSSs;
      • explicitly distinguished chemosensory signaling from quorum sensing, emphasizing that quorum sensing is primarily a population-dependent signaling process, whereas chemosensory systems detect environmental cues and regulate cellular behaviors such as motility;
      • defined the Che (stands for Chemosensory) proteins and introduced the major components of the chemosensory machinery before discussing their organization; and
      • expanded the discussion of the diversity and evolutionary distribution of bacterial chemosensory systems beyond Vibrionales, incorporating additional literature covering their occurrence and diversification across major bacterial lineages. We have also revised the text describing chemosensory systems as “modular” to clarify that this refers to the combinatorial organization and evolutionary diversification of core signaling proteins, accessory components, and sensory receptors, rather than simply the co-occurrence of che genes within a genomic region. We have further added relevant references to provide a broader and more contemporary overview of bacterial chemosensory system diversity and evolution.

      It learns a little bit more about the chemosensory clusters (whose genes?) in V. cholera, but this information is not really helpful for the study's objective, which at the very end of this incredibly long and badly worded introduction is still ambiguous and essentially unknown. The non-chemosensory/chemotactic motile Vibrionale mutants (line 143) were built by which research team? The authors? Every sentence the authors utilize in the introduction, such as "motility and chemotaxis directly or indirectly contribute to the pathogenicity of bacteria, is somewhat disorganized without a citation (lines 152-155). "The complex (?) interaction between chemotaxis and virulence gene expression in pathogenic Vibrio species" is not better to consider. There are no references included, even while discussing the last three decades of V. cholera research. This is not really appropriate for publication. After a lengthy introduction to the many proteins that mediate chemosensing (quorum sensing) in bacteria, the study is restricted to informatics work, see material and methods, and finally limited on CheAs, which is fairly harmful. For correlation analysis, phylogeny, and structure modeling, the authors use previously available information. Therefore, what distinguishes all of these tables and figures from what is currently understood about bacterial genetics and evolution?

      Response: We thank the reviewer for this important comment. We agree that the original introduction contained excessive discussion of well-characterized chemosensory systems in Vibrio cholerae. We have therefore shortened this section, clarified the study objectives, and added appropriate references to statements concerning motility, chemotaxis, and virulence.

      Regarding the novelty of the study, our objective was not to rediscover individual chemosensory proteins, but to provide a comparative, order-wide analysis of chemosensory system evolution across Vibrionales. Using 116 representative genomes and ~10,000 additional genomes/MAGs, we resolved four distinct CSS lineages (F6-F9), characterized their contrasting genomic distributions and replicon localization, and identified F8 as an experimentally unexplored system. We further provide evolutionary evidence for distinct trajectories of these systems, including probable horizontal acquisition of F9, and reveal a conserved F6 core alongside more dynamic accessory system. We have also revised the introduction to make these objectives, findings, and the significance of the comparative analysis more explicit.


      Reviewer #2 (Significance (Required)):

      The majority of the figures are too little to make any sense. Additionally, there are some unexpected "surprises". For example, the authors' in silico data on Photobacterium toruni, E. coli, and Vibrio qinghaiensis while Introduction led us to anticipate or pick V. cholera as the primary target (see last part of introduction). Bootstrap analysis and a clear display of the clades are necessary for phylogenetic validation. A well-established protein structure (Che-A? Che-B? Other Ches?) is required as an unambiguous reference in order to validate protein structure modelling.

      I would also add that since Vibrionaceae is a family of g-proteobacteria in the order Vibrionales, it is not surprising that they are common traits in the genomes of proteobacteria and vibrionales. However, I'm not sure what the authors mean when they say that patchy and replicon-flexible groups are vertically inherited from g-bacteria. A set of genes (discrete CSS types?) that would be horizontally acquired from alphaproteobacteria are the subject of the same critical point. What is the duration of the convergence of Alphaproteobacteria and Vibrionales? Please refer to Sonnenberg and Haugen (2023) about "bipartite" genome and horizontal transfer.

      __Response: __We thank the reviewer for these suggestions. We have improved the readability of a few figures by increasing font size, clarifying clade annotations, and adding bootstrap support values to the phylogenetic trees. We have also revised the introduction to clarify that the study focuses on chemosensory systems across Vibrionales, rather than V. cholerae alone, and have highlighted Photobacterium toruni and Vibrio qinghaiensis because of their unusual CSS distribution across replicons. In order to have a well-established protein structure from AFDB for structures validation, we have used the pLDDT and pTM score for already available and predicted structures, respectively, and we have added this point in our methodology. Finally, we have revised our interpretation of vertical inheritance and horizontal acquisition, moderating the relevant claims. We have further refined the evolutionary interpretation of F6, F7, and F8 by placing their distribution within the broader Gammaproteobacteria context, while F9 is discussed separately based on its close phylogenetic association with Alphaproteobacteria and the evidence supporting its possible horizontal acquisition.

      __ Reviewer #3 (Evidence, reproducibility and clarity (Required)):__

      This manuscript by Rawool and Sharma is a comprehensive bioinformatics analysis of the chemosensory systems in Vibrionales, their chromosomal locations, likely evolution and acquisition, and differences in CheA architectures. The Abstract and Discussion sections are beautifully written, but the language in the rest of the paper, especially in the Introduction section, is difficult to follow at times (I have many comments below under minor points). An impressive amount of data was acquired and analyzed for this paper, including the identification of F8 systems in Vibrios, and I commend the authors for filling the knowledge gap with such a comprehensive data set. However, the paper overall is extremely long and very detailed, and it took me a very long time to plow through all of it.

      The Introduction section, for example, reads like a literature review from a dissertation, but without figures, and could be streamlined without losing impact. Lines 132-154, include many details on environmental sensing and virulence studies, but it reads like a long list of disconnected sentences with findings from different studies, rather than an integrated summary of current knowledge. The Methods section is also very detailed, and although I am not a bioinformatician, the level of detail often seems excessive. In the Results section, data sentences are often followed by discussion or qualification sentences, which makes the Results section even longer. So, while the science in this paper and its interpretation is sound, the paper needs reworking to make it more palatable for most readers. I think it will be difficult for most readers to remain engaged through the entire manuscript the way it is currently presented.

      Response: We thank the reviewer for their appreciation of our study. Following their suggestions, we have shortened the introduction part as well as the overall manuscript. In the methods part, we have included the detailed parameters used in our analysis, so that result can be reproducible for others. We agree about the lengthy result part, and we have shortened this part as well.


      Minor points:

      1. General - sometimes clades are written in capitals, sometimes not, and sometimes they are in italics and other times not. Was this intentional? Shouldn't the formatting be consistent throughout?

      __Response: __We thank the reviewer for noting down this inconsistency. There should not be variation in the formatting. We have carefully reviewed the entire manuscript and standardized the formatting of clade names throughout the text, figures, figure legends, and supplementary materials to ensure consistency.

      Line 35 - "moving towards nutrients and away from harm" is a very narrow interpretation of chemosensory systems, referring solely to chemotaxis, which only 1 of the 4 chemosensory systems in Vibrio likely supports. The statement here should be more inclusive.

      __Response: __We thank the reviewer for this important clarification. We agree that the original statement focused primarily on chemotaxis and did not adequately reflect the broader functional diversity of bacterial chemosensory systems. The sentence has been revised to emphasize that chemosensory systems mediate the detection of environmental cues and can regulate a variety of cellular behaviors, including but not limited to motility.

      Line 56 and line 702 - "V. cholerae" not "V. cholera" - a common victim of autocorrect

      __Response: __We thank the reviewer for identifying this typographical error. "V. cholera" has been corrected to "V. cholerae" at the indicated locations and throughout the manuscript.

      Line 64 - "marine sea"? Marine = of the sea. So, effectively "sea sea" = redundancy.

      __Response: __We thank the reviewer for noting this. We have corrected this part in the manuscript.

      Lines 67-68 - the English on these lines doesn't make sense to me. Perhaps "...reaching swimming speeds of 40-200 mm/sec, which requires 1-2 orders of magnitude more energy for propulsion than that required for Escherichia coli".

      __Response: __We thank the reviewer for noting this point. Since this sentence is not directly related to the chemosensory system, we have removed this line from the manuscript.

      Line 69 - Why mention V. alginolyticus here as an example of a Na+-driven flagellar motor when it's relevant for other Vibrios as well? Also, no reference is provided.

      __Response: __We thank the reviewer for noting this point. Since this sentence is not directly related to the chemosensory system, we have removed this line from the manuscript.

      Lines 79-95 - While introducing the proteins found in a chemosensory pathway, chemotaxis itself is given as the "pathway", whereas it should be indicated that it is an example pathway. Not all chemosensory pathways have CheYs that interact with FliM. And not all chemoreceptors in Vibrios have periplasmic sensing domains - some are cytoplasmic.

      __Response: __We agree with the reviewer on this point. We have revised the phrasing in the manuscript.

      Lines 91-93 - awkward sentence where the last clause reads like a non-sequitur.

      __Response: __We thank the reviewer for this observation. We have revised and streamlined the relevant paragraph to improve its clarity, organization, and overall flow.

      Lines 110-113 - The "function" of chemosensory systems are determined by their output, whereas the signals recognized determine their specificity. Please correct.

      __Response: __We thank the reviewer for this clarification. We agree that the signals recognized by chemoreceptors determine the specificity of a chemosensory system, whereas its function is defined by the downstream cellular response it regulates. Accordingly, we have revised the text to distinguish between signal specificity and system function.

      Line 111 - Aer is not an MCP as it is not a "methyl-accepting" receptor in E. coli. Change "MCP proteins" to "chemoreceptors" to be accurate.

      Response: We have changed this part.

      Line 115 = 43 MCPs; Line 149 = 45 chemoreceptors; Line 225 = 46 MCPs - there is variation in the total number of MCPS in Vibrios. Perhaps give a number range where appropriate (line 115, V. cholerae in general), and specific numbers where specific strains are mentioned.

      __Response: __We thank reviewer for noticing this variation in MCP gene numbers. We have revised this statement.

      Line 116 - "forms"

      __Response: __We have changed this part.

      Line 119 - Change "the" to "a" and what is meant by "double-layered membrane structure" since it isn't in the membrane? Please use a more accurate description.

      __Response: __We thank reviewer for this comment. We agreed that it is not a double-layered membrane, rather F9 CSS cluster form a double-layered appearance in the cytoplasm. We have rephrased this in the manuscript.

      Line 121 - Replace the comma with a semi-colon before "overall".

      __Response: __We have changed this part.

      Line 124 - You've already told us that F9 is a cytoplasmic array

      __Response: __We have changed this part.

      Line 226 - Change "during" to "via"

      __Response: __We have changed this part.

      1. Line 133 - "which is involved" and "indicating a link"

      __Response: __We have changed this part.

      Lines 137-139 - If V. cholerae shows a chemotaxis response to these chemicals, then it isn't clear why you would say that they sense the environment "through these CSS clusters" - only 1 cluster (F6) is known to be involved in chemotaxis.

      __Response: __We thank reviewer for this comment. We have corrected this statement.

      Line 140 - "for epithelial colonization"

      __Response: __We have changed this part.

      Line 151 - gene names should be in italics

      __Response: __We have changed this part throughout the manuscript.

      Line 153- "contributes"

      __Response: __We have changed this part.

      Line 156 - "are associated"

      __Response: __We have changed this part.

      Line 162 - "Studies" don't perform anything. It is an inappropriate subject. But kudos for using the word "lacuna" so eloquently!

      __Response: __We have changed this part.

      Line 179 - "from which a pie chart”!

      __Response: __We have changed this part.

      Line 187 - "using the ggsignif"

      __Response: __We have changed this part.

      Line 209 - "A total of 154"

      __Response: __We have changed this part.

      Line 219 - "using parameters the same"

      __Response: __We have changed this part.

      Line 225 - "the 46 MCP proteins were aligned"

      __Response: __We have changed this part.

      Line 240 - "was converted"

      __Response: __We have changed this part.

      Figure 1 - the labels under parts C, D and E are too small

      __Response: __We have increased the font size of the labels and revised the figures.

      Line 346 - "encodes"

      __Response: __We have changed this part.

      Line 349 - "with average values"

      __Response: __We have changed this part.

      Line 352 - defined HK and RR on lines 347-348

      __Response: __We have changed this part.

      Line 365 - replace "chemotaxis-associated" with "chemosensory-associated". Chemotaxis is not inclusive.

      __Response: __We have changed this part.

      Line 380 - shouldn't "cheA" be in italics?

      __Response: __We have changed this part.

      Line 281 - again, "chemosensory proteins" not "chemotaxis proteins"

      __Response: __We have changed this part.

      Line 382 - "in trends with" makes no sense

      __Response: __We have changed this part.

      Line 283 - "an average of 33"

      __Response: __We have changed this part.

      Lines 387-388 - I don't understand the comment in brackets - why was the protein count limited to 40?

      __Response: __We thank the reviewer for this comment. We agree that the rationale for the cutoff value was unclear. Therefore, we have revised the analysis and replaced the previously used cutoff of 40 MCP proteins with the average MCP protein count of 34 calculated from the dataset. The corresponding text has been updated in the revised manuscript.

      Lines 398-405 - It isn't clear which species have lost flagella, and did they also loose the F6 system? Did they retain the other systems? Lines 427-429 doesn't make it any clearer and I am interested to know.

      __Response: __We thank reviewer for this comment. The species from three clades namely Marinum, Rumoiensis, and Halioticoli don’t have any CSS proteins present in it and all these organisms are well-known to be non-motile, suggesting that they don’t have flagellar proteins. These species lost F6 as well as all other F classes.

      Another organism, Vibrio qinghaiensis Q67 does not encode F6 system, however it has F7 system.

      Line 423 - "HubP protein functions" makes no sense

      __Response: __We thank the reviewer for this comment. The text has been revised to clarify that HubP is a polar landmark protein that acts as an anchoring factor for the localization of chemotaxis arrays through its interaction with the ParC/ParP complex. We have given proper reference for this.

      Line 470 - why are your reporting 2 F7s and then explaining further on on Line 474 that this is a genome assembly artifact? The information is dislocated and should be streamlined.

      __Response: __We thank reviewer for this comment. As per our analysis, two F7 clusters are present in Vibrio qinghaiensis Q67 species. When we checked further, these clusters reside within nearly identical (~99% sequence identity) terminal regions spanning ~20.3 kb at both ends of the replicon. This is artifact during genome assembly process which involves duplicated terminal sequences. We have corrected the order of writing these details in the text.

      Line 499 - "with the smaller"

      __Response: __We have changed this part.

      Lines 536-538 - The F7 system in Vibrios isn't involved in chemotaxis signaling - please correct your language here.

      __Response: __We have revised the statement for clarification and added the figure number.

      Lines 550-551 - "Structural superpositions analyses similarity...." makes no sense. I don't understand what you are trying to say.

      __Response: __We have rephrased this sentence.

      Lines 612 and 630 report the same thing - sensing of bile and mucin. Are both instances necessary?

      Response: We have removed the redundant sentence.

      Figure 6B - "against" not "againts"

      __Response: __We have changed this text.

      Line 634 - "accounting for 69% of sensory inputs"

      __Response: __We have changed this part.

      Line 659 - Hiremath et al., 2015b isn't an appropriate reference for the sensory repertoire of PAS domains. Please reference a PAS domain review instead, e.g., Stuffle and Watts, 2021. PMID: 33647528; PMCID: PMC8169565.

      __Response: __We thank reviewer for this comment. We agreed that the reference cited was not completely based on PAS domain, but the authors have mentioned about PAS domain in their paper. However, we have also cited paper for PAS domain which you mentioned in the comment.

      Line 664 - "ligand" not "legend"?

      __Response: __We have changed this part.

      Line 740 = "chemosensory" instead of "chemotaxis"

      __Response: __We have changed this part.

      Line 744 - "between the extra domain..."

      __Response: __We have changed this part.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Rawool and Sharma is a comprehensive bioinformatics analysis of the chemosensory systems in Vibrionales, their chromosomal locations, likely evolution and acquisition, and differences in CheA architectures. The Abstract and Discussion sections are beautifully written, but the language in the rest of the paper, especially in the Introduction section, is difficult to follow at times (I have many comments below under minor points). An impressive amount of data was acquired and analyzed for this paper, including the identification of F8 systems in Vibrios, and I commend the authors for filling the knowledge gap with such a comprehensive data set. However, the paper overall is extremely long and very detailed, and it took me a very long time to plow through all of it.

      The Introduction section, for example, reads like a literature review from a dissertation, but without figures, and could be streamlined without losing impact. Lines 132-154, include many details on environmental sensing and virulence studies, but it reads like a long list of disconnected sentences with findings from different studies, rather than an integrated summary of current knowledge. The Methods section is also very detailed, and although I am not a bioinformatician, the level of detail often seems excessive. In the Results section, data sentences are often followed by discussion or qualification sentences, which makes the Results section even longer. So, while the science in this paper and its interpretation is sound, the paper needs reworking to make it more palatable for most readers. I think it will be difficult for most readers to remain engaged through the entire manuscript the way it is currently presented.

      Minor points:

      1. General - sometimes clades are written in capitals, sometimes not, and sometimes they are in italics and other times not. Was this intentional? Shouldn't the formatting be consistent throughout?
      2. Line 35 - "moving towards nutrients and away from harm" is a very narrow interpretation of chemosensory systems, referring solely to chemotaxis, which only 1 of the 4 chemosensory systems in Vibrio likely supports. The statement here should be more inclusive.
      3. Line 56 and line 702 - "V. cholerae" not "V. cholera" - a common victim of autocorrect
      4. Line 64 - "marine sea"? Marine = of the sea. So, effectively "sea sea" = redundancy.
      5. Lines 67-68 - the English on these lines doesn't make sense to me. Perhaps "...reaching swimming speeds of 40-200 m/sec, which requires 1-2 orders of magnitude more energy for propulsion than that required for Escherichia coli".
      6. Line 69 - Why mention V. alginolyticus here as an example of a Na+-driven flagellar motor when it's relevant for other Vibrios as well? Also, no reference is provided.
      7. Lines 79-95 - While introducing the proteins found in a chemosensory pathway, chemotaxis itself is given as the "pathway", whereas it should be indicated that it is an example pathway. Not all chemosensory pathways have CheYs that interact with FliM. And not all chemoreceptors in Vibrios have periplasmic sensing domains - some are cytoplasmic.
      8. Lines 91-93 - awkward sentence where the last clause reads like a non-sequitur.
      9. Lines 110-113 - The "function" of chemosensory systems are determined by their output, whereas the signals recognized determine their specificity. Please correct.
      10. Line 111 - Aer is not an MCP as it is not a "methyl-accepting" receptor in E. coli. Change "MCP proteins" to "chemoreceptors" to be accurate.
      11. Line 115 = 43 MCPs; Line 149 = 45 chemoreceptors; Line 225 = 46 MCPs - there is variation in the total number of MCPS in Vibrios. Perhaps give a number range where appropriate (line 115, V. cholerae in general), and specific numbers where specific strains are mentioned.
      12. Line 116 - "forms"
      13. Line 119 - Change "the" to "a" and what is meant by "double-layered membrane structure" since it isn't in the membrane? Please use a more accurate description.
      14. Line 121 - Replace the comma with a semi-colon before "overall".
      15. Line 124 - You've already told us that F9 is a cytoplasmic array
      16. Line 226 - Change "during" to "via"
      17. Line 133 - "which is involved" and "indicating a link"
      18. Lines 137-139 - If V. cholerae shows a chemotaxis response to these chemicals, then it isn't clear why you would say that they sense the environment "through these CSS clusters" - only 1 cluster (F6) is known to be involved in chemotaxis.
      19. Line 140 - "for epithelial colonization"
      20. Line 151 - gene names should be in italics
      21. Line 153- "contributes"
      22. Line 156 - "are associated"
      23. Line 162 - "Studies" don't perform anything. It is an inappropriate subject. But kudos for using the word "lacuna" so eloquently!
      24. Line 179 - "from which a pie chart"
      25. Line 187 - "using the ggsignif"
      26. Line 209 - "A total of 154"
      27. Line 219 - "using parameters the same"
      28. Line 225 - "the 46 MCP proteins were aligned"
      29. Line 240 - "was converted"
      30. Figure 1 - the labels under parts C, D and E are too small
      31. Line 346 - "encodes"
      32. Line 349 - "with average values"
      33. Line 352 - defined HK and RR on lines 347-348
      34. Line 365 - replace "chemotaxis-associated" with "chemosensory-associated". Chemotaxis is not inclusive.
      35. Line 380 - shouldn't "cheA" be in italics?
      36. Line 281 - again, "chemosensory proteins" not "chemotaxis proteins"
      37. Line 382 - "in trends with" makes no sense
      38. Line 283 - "an average of 33"
      39. Lines 387-388 - I don't understand the comment in brackets - why was the protein count limited to 40?
      40. Lines 398-405 - It isn't clear which species have lost flagella, and did they also loose the F6 system? Did they retain the other systems? Lines 427-429 doesn't make it any clearer and I am interested to know.
      41. Line 423 - "HubP protein functions" makes no sense
      42. Line 470 - why are your reporting 2 F7s and then explaining further on on Line 474 that this is a genome assembly artifact? The information is dislocated and should be streamlined.
      43. Line 499 - "with the smaller"
      44. Lines 536-538 - The F7 system in Vibrios isn't involved in chemotaxis signaling - please correct your language here.
      45. Lines 550-551 - "Structural superpositions analyses similarity...." makes no sense. I don't understand what you are trying to say.
      46. Lines 612 and 630 report the same thing - sensing of bile and mucin. Are both instances necessary?
      47. Figure 6B - "against" not "againts"
      48. Line 634 - "accounting for 69% of sensory inputs"
      49. Line 659 - Hiremath et al., 2015b isn't an appropriate reference for the sensory repertoire of PAS domains. Please reference a PAS domain review instead, e.g., Stuffle and Watts, 2021. PMID: 33647528; PMCID: PMC8169565.
      50. Line 664 - "ligand" not "legend"?
      51. Line 740 = "chemosensory" instead of "chemotaxis"
      52. Line 744 - "between the extra domain..."

      Referees cross-commenting

      I concur with Reviewer 1, major comment 3, and inadvertently omitted a similar comment from my review. There is absent or inappropriate referencing throughout much of the manuscript that should be addressed.

      Significance

      Strengths and limitations: Provides a comprehensive assessment of the chemosensory landscape of Vibrionales by analyzing 116 genomes and a large-scale RefSeq and MAG set of ~10,000 organisms. The major limitation of this study is in its writing, which is very detailed and long.

      Advance: Yes, this study fills the gap in our understanding of the chemosensory repertoire of Vibrionales.

      Audience: The major audience will be the bacterial chemosensing community.

      Expertise: I work in bacterial chemosensing and have worked on chemoreceptors from several Vibrio species.

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      Referee #2

      Evidence, reproducibility and clarity

      This article has to be completely rewritten because it is now very confused and difficult to read, starting with the title: landscape, architecture, and bipartite organization are difficult terms to employ when describing the "chemosensory system" of bacteria. Systems, in my opinion, does not refer to assemblies, and chemosensing in bacteria refers to anything that involves quorum sensing. Nevertheless, not all bacteria use quorum sensing for flagellar motility, and if these Che-As (and MCPs) are found in "non-chemosensory" systems and are mapped in the abstract without much explanation, this should probably be thoroughly further discussed before debating the organization of the genome and the division of "chemosensory" genes.

      There are often few attempts to take into account the "modern" literature to describe the evolution of bacteria and vibrionales and their capacity for quorum sensing, particularly in the introduction and discussion. Such two lengthy, in-depth paragraphs about the biology and diversity of Vibrionales that span more than two pages and only include six references are rather inappropriate for a research article, particularly when the topic is chemosensing and genetics. The description of the Che family, whose abbreviation is never explained, is highly ambiguous and very confusing in comparison to very basic understanding about vibrionales. Despite their significance intracellularly, we typically gain little from reading this section of Ches. Without making a distinction between chemotaxis and quorum sensing, the third paragraph is intended to a lesson about one component systems, two component systems, and chemosensory systems. Which kind of audience do the authors hope to reach? Microbiologists? Examples of these systems that are better characterized can be found throughout the literature.

      Bacterial chemosensory systems are then abruptly introduced. "Chemosensory systems are extremely modular (?) and are typically organized as gene clusters within bacterial genomes". Which sensory genes? Which clusters? What families of bacteria? Which references? When it comes to the classification of chemosensory systems, only Gumerov et al. 2021 is cited. However, what role do other groups play in the study of bacterial Ches and their distribution among the vast diversity of bacteria, such as proteobacteria, actinomycetes, and firmicutes? In order to give a more pertinent Introduction, there are fewer acronyms to employ and undoubtedly more efforts to thoroughly analyze the literature. It learns a little bit more about the chemosensory clusters (whose genes?) in V. cholera, but this information is not really helpful for the study's objective, which at the very end of this incredibly long and badly worded introduction is still ambiguous and essentially unknown. The non-chemosensory/chemotactic motile Vibrionale mutants (line 143) were built by which research team? The authors? Every sentence the authors utilize in the introduction, such as "motility and chemotaxis directly or indirectly contribute to the pathogenicity of bacteria, is somewhat disorganized without a citation (lines 152-155). "The complex (?) interaction between chemotaxis and virulence gene expression in pathogenic Vibrio species" is not better to consider. There are no references included, even while discussing the last three decades of V. cholera research. This is not really appropriate for publication. After a lengthy introduction to the many proteins that mediate chemosensing (quorum sensing) in bacteria, the study is restricted to informatics work, see material and methods, and finally limited on CheAs, which is fairly harmful. For correlation analysis, phylogeny, and structure modeling, the authors use previously available information. Therefore, what distinguishes all of these tables and figures from what is currently understood about bacterial genetics and evolution?

      Significance

      The majority of the figures are too little to make any sense. Additionally, there are some unexpected "surprises". For example, the authors' in silico data on Photobacterium oruni, E. coli, and Vibrio qinghaiensis while Introduction led us to anticipate or pick V. cholera as the primary target (see last part of introduction). Bootstrap analysis and a clear display of the clades are necessary for phylogenetic validation. A well-established protein structure (Che-A? Che-B? Other Ches?) is required as an unambiguous reference in order to validate protein structure modelling.

      I would also add that since Vibrionaceae is a family of g-proteobacteria in the order Vibrionales, it is not surprising that they are common traits in the genomes of proteobacteria and vibrionales. However, I'm not sure what the authors mean when they say that patchy and replicon-flexible groups are vertically inherited from g-bacteria. A set of genes (discrete CSS types?) that would be horizontally acquired from alphaproteobacteria are the subject of the same critical point. What is the duration of the convergence of Alphaproteobacteria and Vibrionales? Please refer to Sonnenberg and Haugen (2023) about "bipartite" genome and horizontal transfer.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript presents a comparative genomic analysis of chemosensory systems across the order Vibrionales. By combining phylogenetic analyses, genomic context, MCP repertoires, and structural comparisons, the authors investigate the diversity and evolution of chemosensory systems within Vibrionales. The study addresses an interesting question and assembles a substantial genomic dataset. The manuscript is generally well illustrated and contains several potentially useful observations regarding the distribution and organization of chemosensory systems across Vibrionales genomes. However, there are a number of conceptual, methodological, and presentation-related issues that should be addressed before the evolutionary conclusions can be fully supported.

      Major concerns

      1. The claim that F8 represents a novel chemosensory system appears to be incorrect Lines 407-424, as well as several other places throughout the manuscript, describe F8 as a "previously uncharacterized lineage" and a "newly identified" system. However, F8 chemosensory systems were previously described by Wuichet and Zhulin and have been part of the established chemosensory system classification framework for more than a decade. Furthermore, F8 systems are already annotated as such in MiST. For example, the genome GCF_003390675.1 (Vibrio anguillarum), which is included in this study, contains CheA, CheR, and CheB proteins assigned to an F8 system in MiST. Consequently, describing F8 as a novel, newly identified, or newly designated system appears inappropriate. This issue requires substantial revision throughout the manuscript. The authors should clearly distinguish between the previously established F8 chemosensory class and any novel observations reported here. If the novelty lies in the distribution of F8 systems within Vibrionales, their genomic organization, their evolutionary history, or some other aspect, this should be stated explicitly.
      2. The conclusions regarding horizontal gene transfer and vertical inheritance are not sufficiently supported Lines 429-445 contain evolutionary interpretations that appear internally inconsistent. The manuscript interprets the sporadic distribution of F7 as evidence of vertical inheritance coupled with lineage-specific adaptation, whereas several lines later the similarly patchy distribution of F8 is interpreted as evidence of horizontal gene transfer. Similar distribution patterns should not be used to support contrasting evolutionary scenarios without additional supporting evidence. More broadly, patchy phylogenetic distributions alone are generally insufficient evidence for horizontal gene transfer. Alternative explanations, including differential gene loss, genome reduction, incomplete sampling, or rapid sequence divergence, should also be considered. Furthermore, the conclusions regarding vertical inheritance and HGT imply reconstruction of deep evolutionary history across broad bacterial groups. Based on the methods presented, these inferences appear to rely primarily on CheA phylogenies and analyses of homologous sequences. While informative, these analyses may not be sufficient to confidently infer ancestral origins. The authors should either provide additional phylogenetic evidence supporting these conclusions or moderate the language throughout the manuscript. In their current form, the proposed evolutionary scenarios would be more appropriately presented as hypotheses rather than demonstrated conclusions.
      3. References are frequently missing, incomplete, or potentially inappropriate One major concern is the quality and completeness of referencing throughout the manuscript. Multiple statements either lack references altogether or appear to cite sources that do not directly support the associated claims. For example, lines 76-78 cite Ulrich et al. (2005) in support of the statement that two-component systems constitute a dominant signaling paradigm in prokaryotes, whereas the cited article is entitled "One-component systems dominate signal transduction in prokaryotes." The text and/or citation should therefore be reconsidered. Similarly, the statement in lines 100-101 that 17 classes of flagellar chemosensory systems have been designated should be accompanied by an appropriate reference. In addition, numerous ecological, physiological, and evolutionary statements throughout the Introduction and Results sections either lack citations or would benefit from more precise supporting references. I recommend that the authors carefully review all references and ensure that each citation directly supports the corresponding statement.
      4. The manuscript would benefit from substantial restructuring and shortening The manuscript is considerably longer than necessary, and several sections appear only loosely connected to the central biological question. In particular, the Introduction contains extensive discussions of Vibrio ecology, virulence, motility, host interactions, and general signal transduction. While these topics are relevant, the overall narrative currently reads more like a broad review article than an introduction to a comparative genomics study. I recommend substantially shortening and restructuring the Introduction so that the central biological question and the specific objectives of the study become more apparent to the reader. Similarly, the section entitled "Multipartite genome and extensive RNA gene repertoire reflect niche adaptation in Vibrionales" (lines 302-342) contains several observations regarding genome size, tRNA counts, and rRNA copy numbers. However, it remains unclear how these analyses contribute to the primary conclusions regarding chemosensory system evolution. This section should either be shortened substantially and more explicitly connected to the central theme of the manuscript or moved to supplementary material. The manuscript would also benefit from substantial language editing. Numerous grammatical and stylistic issues are present throughout the text, including awkward phrasing, subject-verb agreement errors, and overly long sentences. A thorough language revision would improve readability and help the reader focus on the scientific content.

      Specific comments

      Line 20: The phrase "28 Vibrio clades" requires clarification. It is not clear what these clades represent, how they were defined, or whether "clades" is the most appropriate term. Please define these groups more clearly. Perhaps the term "genera" would be more appropriate.

      Lines 23 and 39: F8 is described as a "novel lineage" and a "previously uncharacterized system." As discussed above, F8 systems have already been described and are annotated in MiST. Please revise these statements.

      Lines 35-36: The statement could be interpreted as implying that the involvement of chemosensory systems in host colonization is unique to Vibrio. Since chemosensory systems are broadly distributed across bacteria and frequently contribute to host interactions, I suggest rephrasing this sentence to avoid overstatement.

      Lines 41-42: The conclusion that CheA and MCP proteins represent promising drug targets is not directly supported by the analyses presented in this manuscript. The study does not evaluate essentiality, druggability, inhibition, or therapeutic feasibility. I recommend removing this statement.

      Line 66: The sentence describing responses to environmental cues via "chemotaxis, quorum sensing, and response to nutrients" is awkwardly phrased, since chemotaxis itself often represents a response to nutrients. Please revise for clarity.

      Lines 69-70: The statement that signal transduction in Vibrio species is "highly precise" and senses chemical gradients "very accurately" requires both a reference and a clearer explanation. Relative to what system or organism is this precision being evaluated?

      Lines 76-78: Please reconsider the citation to Ulrich et al. (2005) and revise the associated statement accordingly.

      Lines 100-101: Please provide a reference supporting the statement that 17 classes of flagellar chemosensory systems have been designated.

      Line 106: The manuscript states that our understanding of CSS architecture and function derives primarily from a limited number of model organisms, particularly Escherichia coli. However, later sections highlight the extensive literature on Vibrio cholerae chemotaxis. Since V. cholerae is itself one of the better-characterized organisms in this field, the rationale for emphasizing E. coli alone is unclear.

      Lines 278-286: This paragraph appears to contain contradictory statements regarding the number of genera included in the analysis. Please clarify how many genera were included, which were excluded, and the criteria used for inclusion.

      Lines 289-291: Please provide a reference supporting the statement regarding the mutualistic association between Aliivibrio fischeri and squid.

      Lines 291 onward: Several ecological and physiological statements in this section require appropriate references.

      Lines 302-342: This section would benefit from substantial shortening or a clearer connection to the central theme of chemosensory system evolution.

      Lines 369-371: This statement is difficult to interpret. MCPs are generally much more variable in abundance than core chemotaxis proteins, and conservation of abundance alone does not demonstrate essentiality. Please clarify and revise this conclusion.

      Lines 429-445: The contrasting interpretations of F7 and F8 distributions require additional supporting evidence or a more cautious presentation.

      Lines 538-541: This sentence is difficult to follow and would benefit from reformulation. In addition, CheA domain architectures, including those associated with Vibrionales F6, F7, F8, and F9 systems, have recently been described in detail by Berry et al., 2023. The authors should discuss their observations within the context of this work.

      Line 545: The phrase "additional insertion domain" is not well defined. If this region corresponds to a recognized domain, it should be identified explicitly. If it represents an insertion or a poorly structured region, more appropriate terminology should be used.

      Figure 4: It would be helpful to include the identifiers of the proteins used in the structural comparisons. Recommendation: Major Revision.

      Significance

      General assessment

      This manuscript presents a comparative genomic analysis of chemosensory systems across the order Vibrionales. The study addresses an interesting question and compiles a substantial genomic dataset. Its main strength lies in the large-scale comparative analysis of chemosensory system distribution, organization, and diversity across Vibrionales genomes, providing a useful resource for future studies of Vibrio signal transduction and evolution.

      The manuscript contains several potentially valuable observations regarding chemosensory-system diversity and generates hypotheses about their evolutionary history. However, some of the central evolutionary conclusions currently appear stronger than the evidence presented. In particular, the interpretation of vertical inheritance and horizontal gene transfer, as well as the treatment of F8 as a novel lineage, require clarification and revision. The manuscript would also benefit from substantial shortening and restructuring to improve focus and readability.

      Advance

      The primary advance of this study is a comprehensive comparative survey of chemosensory systems across Vibrionales. The work expands current knowledge of the distribution, genomic organization, and diversity of these systems and provides a useful synthesis of available genomic data.

      The study is primarily descriptive and comparative in nature. It generates interesting evolutionary hypotheses and provides a valuable dataset for future investigations. However, I do not believe that the manuscript currently establishes the existence of a novel chemosensory class, and several proposed evolutionary scenarios would benefit from additional supporting evidence.

      Audience

      This work will be of greatest interest to researchers studying bacterial signal transduction, chemotaxis, comparative genomics, microbial evolution, and Vibrio biology. It is primarily a basic research contribution and will likely serve as a resource for future studies of chemosensory-system evolution and function.

      Expertise

      My expertise includes bacterial signal transduction, chemosensory systems and chemotaxis, two-component systems, comparative and evolutionary genomics, and microbial genome annotation.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      TITLE The pore-forming toxin Monalysin contributes to infection-induced suppression of defecation in female Drosophila


      SUMMARY In this study, the authors investigate how Pseudomonas entomophila counteracts increased gut peristalsis, a known antibacterial defense mechanism in Drosophila. They demonstrate that a heat- and protease-sensitive factor mediates reduced defecation following bacterial exposure. Using bacterial mutants, they assess whether major virulence regulators modulate this phenomenon and identify GacA as being involved. A proteomic approach is then employed to identify factors underlying the defecation phenotype, leading the authors to focus on the pore-forming toxin Monalysin. They show that absence of this toxin severely impairs the reduction in defecation following P. entomophila exposure. While the study addresses an interesting and robust phenotype, the overall approach lacks sufficient rigor and openness toward alternative hypotheses. In its current form, the study is not suitable for publication. Furthermore, the absence of publication of the foundational study (currently available only as a bioRxiv preprint) represents a serious limitation for the present work.


      POSITIVE POINTS • The phenotype described is robust and reproducible. • The involvement of a heat- and protease-sensitive factor is clearly demonstrated. • The proteomic strategy and the comparative approach involving hfq and gacA mutants are informative.


      NEGATIVE POINTS • A major weakness lies in the reliance on a pivotal study that remains unpublished. The authors nevertheless use these unpublished data to support the current work.

      Response. Our work that identified inhibition of peristalsis by P. entomophila and laid foundations for a present manuscript has been published. Please see the updated reference 22.

      • Reduced defecation following P. entomophila exposure may simply reflect reduced food intake. This alternative hypothesis is neither addressed nor tested, either in the current manuscript or in the cited bioRxiv preprint.

      • We quantified food intake (new figures 1E, 4D) and did not find evidence that flies with reduced defecation have reduced intake.

      • Although increased peristalsis following pathogen exposure is presented as the entry point of the study, the authors do not directly assess whether peristalsis is indeed reduced under their experimental conditions

      • Direct quantification of intestinal peristalsis (Figure 4E) confirmed that it was reduced after P. entomophila infection.

      • The study would benefit from a qualitative analysis of gut morphology.

      • Was performed (Figure 5).

      • Overstatement of the conclusions regarding GacA-related data tends to undermine the credibility of the entire study.

      • Corrected.


      INTRODUCTION Minor Line 66: It is difficult to reconcile the benefit of this phenomenon if it is triggered irrespective of the ingested bacteria. Please clarify this section by detailing how, in adults, this response may benefit the host (e.g., pathogen clearance), while in larvae exposed to L. plantarum it may serve a different purpose.

      1. We added a sentence to clarify potential differences between pathogens and microbiota. L70-72.

        Major/Minor Reference 22 is a bioRxiv preprint and has not been peer reviewed. The authors should explicitly state that "a recent preprint suggests...". The data and conclusions from this preprint cannot be used as definitive evidence.

      2. Since the paper is published, we left the text unchanged.

        Minor Why was a 2 h incubation at 29{degree sign}C chosen? Please provide a rationale for this relatively high temperature.

      3. Experiments were performed following previously-established protocols and 29° C is the temperature close to the optimal growth temperature for P. entomophila.

        Major Reduced defecation may simply reflect reduced feeding. How do the authors control for food intake following bacterial exposure? This should be addressed both qualitatively and quantitatively, at least for the experimental conditions used in Figures 1B and 1C.

      4. Food intake was quantified (new figures 1E, 4D).

        Major Putative reductions in peristalsis should be directly tested. Dissected guts from infected adults could be monitored ex vivo in Schneider medium, where peristalsis is easily detectable and quantifiable.

      5. Direct quantification of intestinal peristalsis ex vivo was performed as suggested (Figure 4E).

        Major A positive control demonstrating the canonical increase in defecation following exposure to another pathogen (e.g., Ecc15) is necessary, at least in Figure 1B.

      6. We used Ecc15 as a positive control (Figure 1B) and indeed observed the expected increase in defecation.

        Minor Line 102: "although still lower than in control flies" - the statistics are non-significant; therefore, no difference should be claimed.

      7. The claim was removed.

        Minor Line 103: The experiment is described as testing whether P. entomophila must be alive, yet the results do not discriminate between the need for live bacteria and the requirement for a heat-sensitive product. Only Figure 1D addresses this point. Please rephrase accordingly.

      8. Was rephrased.

        Minor Figure 1 legends: Please clearly define the indices used and explain their relevance. For example, how does fold change compare to dots/fly, and how are these values calculated?

      Explained in lines 103-109. Dots/fly represents the absolute number of defecation spots per fly and therefore shows both treatment effects and variability among experimental days. Fold change represents defecation normalized to the corresponding mean sucrose control from the same experimental day, reducing day-to-day variability and facilitating comparison of relative changes between experimental conditions. Thus, the two representations provide complementary absolute and control-normalized measures of defecation.

      Minor The meaning of "N (days) = ..." is unclear. Please clarify.

      1. Clarified in the Figure 1 legend. N (days)” indicates the number of independent experimental days on which the experiment was repeated.

        Minor/Major ANOVA applies to parametric datasets, whereas the Mann-Whitney test does not. No tests are reported to justify the assumption of normality. Please verify whether the datasets are parametric and, if not, apply appropriate non-parametric multiple-comparison tests.

      2. We have added the normality test to the Methods section and reanalyzed all results using the appropriate statistical tests according to the distribution of the data.

        Major A qualitative analysis of adult gut morphology would substantially strengthen the study. Transmission light microscopy and actin staining could reveal gut alterations associated with P. entomophila infection and potentially specific effects of Monalysin. This analysis should accompany Figures 1B-1E, 2A-2B, and 4C.

      3. We would like to mention that these were technically challenging experiments to perform. Nevertheless, we managed to analyse the most important conditions (Figure 5). We observed that P. entomophila causes gut shrinkage and reduces the intensity of phalloidin staining in a monalysin-dependent manner.


      The GacS/GacA system controls secretion of the defecation-blocking factor Major Line 121: It is a clear overstatement to claim that "the ΔgacA mutant failed to suppress defecation," as Figure 2B shows a statistically significant ~2-fold decrease compared with sucrose controls. Furthermore, the assertion that "the GacA regulatory system governs secretion or production of" the factor is not supported by the data presented. Such conclusions, which are forced and not supported by the data, cast doubt-and may even discredit-the remainder of the study. Please revise this sentence and consider the entire manuscript with the same level of care.

      1. We agree with the reviewer and revised the manuscript accordingly.

      Monalysin is one of the factors inhibiting defecation Minor Overexpression of the mnl gene in an otherwise innocuous bacterial strain, and assessment of its effect on defecation, would be an informative addition to the study.

      R. We expressed monalysin in E. coli using an arabinose-inducible system and observed that such E. coli strain was able to reduce defecation in flies (Figure 4H).

      Reviewer #1 (Significance (Required)): POSITIVE POINTS • The phenotype described is robust and reproducible. • The involvement of a heat- and protease-sensitive factor is clearly demonstrated. • The proteomic strategy and the comparative approach involving hfq and gacA mutants are informative. __________ NEGATIVE POINTS • A major weakness lies in the reliance on a pivotal study that remains unpublished. The authors nevertheless use these unpublished data to support the current work. • Reduced defecation following P. entomophila exposure may simply reflect reduced food intake. This alternative hypothesis is neither addressed nor tested, either in the current manuscript or in the cited bioRxiv preprint. • Although increased peristalsis following pathogen exposure is presented as the entry point of the study, the authors do not directly assess whether peristalsis is indeed reduced under their experimental conditions. • The study would benefit from a qualitative analysis of gut morphology. • Overstatement of the conclusions regarding GacA-related data tends to undermine the credibility of the entire study. If fully corrected, the study will interest specialists in host-pathogens interactions with invertebrates as hosts.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): The manuscript by Rubinić et al. investigates how the Drosophila pathogen Pseudomonas entomophila (Pe) suppresses host defecation, a defense mechanism that normally promotes pathogen clearance. Building on prior observations that Pe induces a sex-specific blockade of defecation, the authors identify a secreted, thermosensitive protein factor regulated by the GacS/GacA two-component system. Through comparative proteomics and functional assays, they implicate the pore-forming toxin Monalysin as a contributor to infection-induced suppression of defecation. The experimental approach is well designed, combining genetic and proteomic analyses with a quantitative physiological readout. The data are convincing, clearly presented, and support the main conclusions. The proteomic analysis is thorough; however, the rationale for prioritizing Monalysin among the 146 candidate proteins could be made more explicit. While Monalysin is a logical choice given prior knowledge of its role in virulence and epithelial damage, briefly discussing why other prominent candidates were not pursued would improve transparency and guide future work.

      1. We thank the reviewer for constructive feedback. We provide additional justification for our selection of Monalysin. L190-193.

        The authors show that deletion of mnl significantly attenuates defecation suppression but does not fully abolish it, indicating that Monalysin is only a partial contributor to this phenotype. This point is appropriately acknowledged in the manuscript, but the interpretation would benefit from expanding on the discussion of additional candidate proteins highlighted in Figure 4. Ideally other candidates from that list should be tested.

      We extended the discussion on additional factors. L278-287.

      Although the identification of Monalysin is convincing, the mechanistic link between pore formation and reduced gut motility remains unclear. The study would benefit from further characterization of how Monalysin affects gut homeostasis, including potential connections to known pathways such as TRPA1 signaling.

      We agree that further characterization of how Monalysin inhibits gut peristalsis would be valuable. Given the numerous possible mechanisms, we consider a detailed investigation of this aspect to be beyond the scope of the present study and therefore leave it for future studies. We have expanded the Discussion to address potential mechanisms by which Monalysin may affect gut motility. L267-274.

      Moreover, linking defecation impairment more directly to virulence outcomes, for instance by including survival curves comparing infections with wild-type and Δmnl bacteria, would help clarify the physiological relevance of this phenotype.

      1. We included the survival curve and also pathogen load (Fig 4F, 4G).

        Several points of clarification would further improve reproducibility and clarity. The authors should specify the CFU amounts used in infection experiments and clarify how these relate to the preparation of bacterial supernatants. It would also be helpful to indicate whether the effect of the supernatant on defecation is dose dependent. In addition, while the fold-change panels included in several figures are informative, they are somewhat redundant, and briefly justifying their inclusion in the figure legends or explaining how they complement the absolute defecation counts would improve clarity.

      R. We have added the CFU counts in the methods. The relevance of fold change panels has been explained (see response to reviewer 1).

      Reviewer #2 (Significance (Required)):

      This work extends the known functions of Monalysin and provides a conceptual advance in understanding how pathogens can subvert host defenses by modulating intestinal transit rather than simply triggering inflammatory or cytotoxic responses. Impairment of defecation represents a broadly used pathogenic strategy. In this context, the manuscript would benefit from a broader comparative perspective, such as a genomic and/or genetic analysis of mnl to assess its conservation and variability across P. entomophila strains or related pathogens. Such an analysis would help place the findings within a wider evolutionary framework and strengthen the argument that suppression of gut motility is a common and adaptive microbial strategy. Overall, this study should be of broad interest to researchers working on host-microbe interactions, gut physiology, and bacterial pathogenesis, and it provides a strong foundation for future mechanistic and comparative work in this area.

      R. We thank the reviewer for the suggestions. To provide a broader comparative perspective, we performed a phylogenetic analysis of Monalysin-related proteins from multiple P. entomophila strains and related bacterial isolates. The analysis shows that Monalysin from P. entomophila strains cluster closely, whereas related proteins are also present in other Pseudomonas isolates and more distantly related bacteria. We have incorporated these findings into the revised manuscript (Fig. 6; lines 244–255).

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      The authors were interested in the mechanisms by which the entomopathogenic Pseudomonas entomophila bacterium can inhibit defecation in Drosophila. Using a differential proteomic approach, they identified Monalysin, a secreted pore-forming toxin, as a good candidate. They further showed thanks to the use of a P. entomophila mutant deleted for the mnl gene, that Mnl was indeed involved in suppressing host defecation.

      Major comments:

      Although overall the experiments were well designed and analyzed, one key experiment is lacking to fully support their conclusion: The authors claim (for instance lines 195-196) that "Monalysin as one of the bacterial factors required for suppressing defecation and bacterial clearance". The author have to demonstrate the bacterial clearance. They can perform CFU counting (bacterial load monitoring) in the gut infected with WT Pe vs Delta-mnl mutant. This experiment is essential to state that the blockage of defecation slow down bacterial clearance. Such data would strengthened their study.

      1. CFU counts are included and showed reduced persistence (Fig 4F).

      Minor comments: - Line 102: "...although still lower than in control flies (Fig. 1B, Fig. 1B')." Authors should add "although not significant".

      R. The sentence was rewritten.

      • It would be very useful for readers and future experimenters to know the amount (in CFU) of P. entomophila provided per fly. The authors indicate a bacterial concentration in OD600 (lines 273-275). However, an OD does not reflect the quantity/concentration of bacteria. For a given volume at a given OD600 value, each bacterium has a different concentration (mainly due to the size of the bacteria). Also, the number of flies varies from 10 to 20 per vial. Does the amount of bacteria provided vary according to the number of flies?
      1. We quantified colony-forming units (CFUs) of Pseudomonas entomophila and report these values in the Methods. We used the same infection mixture—and thus the same bacterial concentration—for vials containing either 10 or 20 flies. Given the high bacterial concentration, flies in both conditions ingested an equal number of bacteria, as shown in the graph below. Amount of ingested P. entomophila cells depending on the number of flies in the vial. 10 or 20 flies per vial were infected with *P. entomophila OD200 mixed 1:1 with sucrose for 0.5 h. CFUs were quantified in single flies by plating the serially diluted homogenate on LB plates. Data are shown as log10 CFU/fly. *
      • Lines 119-123 and figures 2B, B': how do the authors explain that in Delta-gacA Pe mutant, the defecation is only partially restored? The authors should mitigate their conclusion lines 122-123 as well as in the rest of the manuscript.
      1. We agree and have revised the manuscript accordingly.

        Moreover, they stated lines 182-186 that Delta-mnl mutant only partially suppress defecation. If we compare figure 4C-C' to figure 2 B-B', it seems that delta-mnl has a stronger phenotype than Delta-gacA. In other word, Mnl is likely only partially regulated by GacA since removing Mnl has a stronger effect on defecation rescue than removing GacA.

      2. We agree and mentioned this.

      • Lines 492-493: Incomplete reference.

      R. Corrected

      Reviewer #3 (Significance (Required)): Defecation has been previously described as participating to the elimination of pathogens. Understanding how pathogens hijack host defenses is of prime importance to combat them. In this study, the authors identified a secreted factor, Monalysin, implicated in the inhibition of the defecation in Drosophila. Monalysin has been previously characterized as a pore-forming toxin damaging the gut of Drosophila. The data presented here, unravel a "side" function of this toxin. Although Monalysin is probably not the only factor of P. entomophila involved in blocking defecation, the data presented provide information on virulence mechanisms and the versatility of a toxin and could help the "Host-Pathogen" community to better understand microbial virulence. My expertise: host-pathogen interaction using Drosophila as host model.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The authors were interested in the mechanisms by which the entomopathogenic Pseudomonas entomophila bacterium can inhibit defecation in Drosophila. Using a differential proteomic approach, they identified Monalysin, a secreted pore-forming toxin, as a good candidate. They further showed thanks to the use of a P. entomophila mutant deleted for the mnl gene, that Mnl was indeed involved in suppressing host defecation.

      Major comments:

      Although overall the experiments were well designed and analyzed, one key experiment is lacking to fully support their conclusion: The authors claim (for instance lines 195-196) that "Monalysin as one of the bacterial factors required for suppressing defecation and bacterial clearance". The author have to demonstrate the bacterial clearance. They can perform CFU counting (bacterial load monitoring) in the gut infected with WT Pe vs Delta-mnl mutant. This experiment is essential to state that the blockage of defecation slow down bacterial clearance. Such data would strengthened their study.

      Minor comments:

      • Line 102: "...although still lower than in control flies (Fig. 1B, Fig. 1B')." Authors should add "although not significant".
      • It would be very useful for readers and future experimenters to know the amount (in CFU) of P. entomophila provided per fly. The authors indicate a bacterial concentration in OD600 (lines 273-275). However, an OD does not reflect the quantity/concentration of bacteria. For a given volume at a given OD600 value, each bacterium has a different concentration (mainly due to the size of the bacteria). Also, the number of flies varies from 10 to 20 per vial. Does the amount of bacteria provided vary according to the number of flies?
      • Lines 119-123 and figures 2B, B': how do the authors explain that in Delta-gacA Pe mutant, the defecation is only partially restored? The authors should mitigate their conclusion lines 122-123 as well as in the rest of the manuscript. Moreover, they stated lines 182-186 that Delta-mnl mutant only partially suppress defecation. If we compare figure 4C-C' to figure 2 B-B', it seems that delta-mnl has a stronger phenotype than Delta-gacA. In other word, Mnl is likely only partially regulated by GacA since removing Mnl has a stronger effect on defecation rescue than removing GacA.
      • Lines 492-493: Incomplete reference.

      Referee cross-commenting

      @Reviewer #1: I did similar comments on the weak GacA phenotype and I therefore agree with Reviewer #1. The authors must mitigate their conclusion about GacA. I also agree that the founding article published in bioRxiv is a weakness.

      I suggest that the authors focus their study on Monalysin (this is the title of the article) by conducting additional experiments (gut CFU monitoring, complementation, dose-dependent experiments, food intake).

      Significance

      Defecation has been previously described as participating to the elimination of pathogens. Understanding how pathogens hijack host defenses is of prime importance to combat them. In this study, the authors identified a secreted factor, Monalysin, implicated in the inhibition of the defecation in Drosophila. Monalysin has been previously characterized as a pore-forming toxin damaging the gut of Drosophila. The data presented here, unravel a "side" function of this toxin. Although Monalysin is probably not the only factor of P. entomophila involved in blocking defecation, the data presented provide information on virulence mechanisms and the versatility of a toxin and could help the "Host-Pathogen" community to better understand microbial virulence.

      My expertise: host-pathogen interaction using Drosophila as host model.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Rubinić et al. investigates how the Drosophila pathogen Pseudomonas entomophila (Pe) suppresses host defecation, a defense mechanism that normally promotes pathogen clearance. Building on prior observations that Pe induces a sex-specific blockade of defecation, the authors identify a secreted, thermosensitive protein factor regulated by the GacS/GacA two-component system. Through comparative proteomics and functional assays, they implicate the pore-forming toxin Monalysin as a contributor to infection-induced suppression of defecation. The experimental approach is well designed, combining genetic and proteomic analyses with a quantitative physiological readout. The data are convincing, clearly presented, and support the main conclusions. The proteomic analysis is thorough; however, the rationale for prioritizing Monalysin among the 146 candidate proteins could be made more explicit. While Monalysin is a logical choice given prior knowledge of its role in virulence and epithelial damage, briefly discussing why other prominent candidates were not pursued would improve transparency and guide future work. The authors show that deletion of mnl significantly attenuates defecation suppression but does not fully abolish it, indicating that Monalysin is only a partial contributor to this phenotype. This point is appropriately acknowledged in the manuscript, but the interpretation would benefit from expanding on the discussion of additional candidate proteins highlighted in Figure 4. Ideally other candidates from that list should be tested. Although the identification of Monalysin is convincing, the mechanistic link between pore formation and reduced gut motility remains unclear. The study would benefit from further characterization of how Monalysin affects gut homeostasis, including potential connections to known pathways such as TRPA1 signaling. Moreover, linking defecation impairment more directly to virulence outcomes, for instance by including survival curves comparing infections with wild-type and Δmnl bacteria, would help clarify the physiological relevance of this phenotype. Several points of clarification would further improve reproducibility and clarity. The authors should specify the CFU amounts used in infection experiments and clarify how these relate to the preparation of bacterial supernatants. It would also be helpful to indicate whether the effect of the supernatant on defecation is dose dependent. In addition, while the fold-change panels included in several figures are informative, they are somewhat redundant, and briefly justifying their inclusion in the figure legends or explaining how they complement the absolute defecation counts would improve clarity.

      Referee cross-commenting

      I agree with my colleagues' comments; the manuscript requires further improvement. There are several points raised by all reviewers that should definitely be addressed.

      Significance

      This work extends the known functions of Monalysin and provides a conceptual advance in understanding how pathogens can subvert host defenses by modulating intestinal transit rather than simply triggering inflammatory or cytotoxic responses. Impairment of defecation represents a broadly used pathogenic strategy. In this context, the manuscript would benefit from a broader comparative perspective, such as a genomic and/or genetic analysis of mnl to assess its conservation and variability across P. entomophila strains or related pathogens. Such an analysis would help place the findings within a wider evolutionary framework and strengthen the argument that suppression of gut motility is a common and adaptive microbial strategy. Overall, this study should be of broad interest to researchers working on host-microbe interactions, gut physiology, and bacterial pathogenesis, and it provides a strong foundation for future mechanistic and comparative work in this area.

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      Referee #1

      Evidence, reproducibility and clarity

      The pore-forming toxin Monalysin contributes to infection-induced suppression of defecation in female Drosophila

      Summary

      In this study, the authors investigate how Pseudomonas entomophila counteracts increased gut peristalsis, a known antibacterial defense mechanism in Drosophila. They demonstrate that a heat- and protease-sensitive factor mediates reduced defecation following bacterial exposure. Using bacterial mutants, they assess whether major virulence regulators modulate this phenomenon and identify GacA as being involved. A proteomic approach is then employed to identify factors underlying the defecation phenotype, leading the authors to focus on the pore-forming toxin Monalysin. They show that absence of this toxin severely impairs the reduction in defecation following P. entomophila exposure. While the study addresses an interesting and robust phenotype, the overall approach lacks sufficient rigor and openness toward alternative hypotheses. In its current form, the study is not suitable for publication. Furthermore, the absence of publication of the foundational study (currently available only as a bioRxiv preprint) represents a serious limitation for the present work.


      Positive points

      • The phenotype described is robust and reproducible.
      • The involvement of a heat- and protease-sensitive factor is clearly demonstrated.
      • The proteomic strategy and the comparative approach involving hfq and gacA mutants are informative.

      Negative points

      • A major weakness lies in the reliance on a pivotal study that remains unpublished. The authors nevertheless use these unpublished data to support the current work.
      • Reduced defecation following P. entomophila exposure may simply reflect reduced food intake. This alternative hypothesis is neither addressed nor tested, either in the current manuscript or in the cited bioRxiv preprint.
      • Although increased peristalsis following pathogen exposure is presented as the entry point of the study, the authors do not directly assess whether peristalsis is indeed reduced under their experimental conditions.
      • The study would benefit from a qualitative analysis of gut morphology.
      • Overstatement of the conclusions regarding GacA-related data tends to undermine the credibility of the entire study.

      Introduction

      Minor Line 66: It is difficult to reconcile the benefit of this phenomenon if it is triggered irrespective of the ingested bacteria. Please clarify this section by detailing how, in adults, this response may benefit the host (e.g., pathogen clearance), while in larvae exposed to L. plantarum it may serve a different purpose. Major/Minor Reference 22 is a bioRxiv preprint and has not been peer reviewed. The authors should explicitly state that "a recent preprint suggests...". The data and conclusions from this preprint cannot be used as definitive evidence.


      A secreted proteinaceous factor from P. entomophila is sufficient to reduce defecation Minor Why was a 2 h incubation at 29{degree sign}C chosen? Please provide a rationale for this relatively high temperature. Major Reduced defecation may simply reflect reduced feeding. How do the authors control for food intake following bacterial exposure? This should be addressed both qualitatively and quantitatively, at least for the experimental conditions used in Figures 1B and 1C. Major Putative reductions in peristalsis should be directly tested. Dissected guts from infected adults could be monitored ex vivo in Schneider medium, where peristalsis is easily detectable and quantifiable. Major A positive control demonstrating the canonical increase in defecation following exposure to another pathogen (e.g., Ecc15) is necessary, at least in Figure 1B. Minor Line 102: "although still lower than in control flies" - the statistics are non-significant; therefore, no difference should be claimed. Minor Line 103: The experiment is described as testing whether P. entomophila must be alive, yet the results do not discriminate between the need for live bacteria and the requirement for a heat-sensitive product. Only Figure 1D addresses this point. Please rephrase accordingly. Minor Figure 1 legends: Please clearly define the indices used and explain their relevance. For example, how does fold change compare to dots/fly, and how are these values calculated? Minor The meaning of "N (days) = ..." is unclear. Please clarify. Minor/Major ANOVA applies to parametric datasets, whereas the Mann-Whitney test does not. No tests are reported to justify the assumption of normality. Please verify whether the datasets are parametric and, if not, apply appropriate non-parametric multiple-comparison tests. Major A qualitative analysis of adult gut morphology would substantially strengthen the study. Transmission light microscopy and actin staining could reveal gut alterations associated with P. entomophila infection and potentially specific effects of Monalysin. This analysis should accompany Figures 1B-1E, 2A-2B, and 4C.


      The GacS/GacA system controls secretion of the defecation-blocking factor Major Line 121: It is a clear overstatement to claim that "the ΔgacA mutant failed to suppress defecation," as Figure 2B shows a statistically significant ~2-fold decrease compared with sucrose controls. Furthermore, the assertion that "the GacA regulatory system governs secretion or production of" the factor is not supported by the data presented. Such conclusions, which are forced and not supported by the data, cast doubt-and may even discredit-the remainder of the study. Please revise this sentence and consider the entire manuscript with the same level of care.


      Monalysin is one of the factors inhibiting defecation Minor Overexpression of the mnl gene in an otherwise innocuous bacterial strain, and assessment of its effect on defecation, would be an informative addition to the study.

      Referee cross-commenting

      I totally agree with the comments from the other reviewers. Concerning the study of other potential virulence factors apart from Monalysin, I would require that either they test new ones or they focus more on monalysin with as proposed, CFU, complementation and survival. I'm surprised that none of the reviewers commented about the overstatement concerning GacA mutant phenotype that is not especially different from the wt P.e.

      Significance

      Positive points

      • The phenotype described is robust and reproducible.
      • The involvement of a heat- and protease-sensitive factor is clearly demonstrated.
      • The proteomic strategy and the comparative approach involving hfq and gacA mutants are informative.

      Negative points

      • A major weakness lies in the reliance on a pivotal study that remains unpublished. The authors nevertheless use these unpublished data to support the current work.
      • Reduced defecation following P. entomophila exposure may simply reflect reduced food intake. This alternative hypothesis is neither addressed nor tested, either in the current manuscript or in the cited bioRxiv preprint.
      • Although increased peristalsis following pathogen exposure is presented as the entry point of the study, the authors do not directly assess whether peristalsis is indeed reduced under their experimental conditions.
      • The study would benefit from a qualitative analysis of gut morphology.
      • Overstatement of the conclusions regarding GacA-related data tends to undermine the credibility of the entire study.

      If fully corrected, the study will interest specialists in host-pathogens interactions with invertebrates as hosts.

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      Reply to the reviewers

      Reviewer #1: Evidence, Reproducibility And Clarity

      General comment

      Reviewer comment: Dugourd et al. present COSMOS+, a computational framework that integrates multi-omics data with prior knowledge networks to generate mechanistic hypotheses connecting signaling, transcriptional regulation, and metabolism. The study addresses a relevant challenge in the field: the difficulty of moving beyond purely data-driven factor analysis toward biologically interpretable and causally grounded insights, introducing MOON, a scalable iterative network scoring algorithm as a practical alternative to computationally expensive optimization-based approaches such as CARNIVAL.

      Importantly, the method has the potential to highlight errors in prior knowledge networks. This will not palliate the incompleteness of the existing prior knowledge but, at least, it can identify inconsistencies and help 'correct' the databases. It is not entirely clear whether inconsistencies stem from specificities of cell lines/samples that are not reflected in the general databases, or simply mistakes in the databases.

      In any case, reconstructing mechanistic hypotheses based on (multi)omics datasets remains an important endeavour, necessary to understand biological processes and also with clear applications in medicine.

      The code is available and clearly documented ensuring reproducibility. The literature survey is comprehensive and useful to understand the need for developments in this field.

      While the framework is flexible and the applications span a diverse range of contexts from cell line collections to clinical cohorts, some aspects of the work require stronger justification and additional evaluation tests might increase the usefulness of the methods for the community.

      Response:

      We thank the reviewer for the careful and constructive assessment of COSMOS+, including the observation that the framework may expose limitations in current prior-knowledge networks. We agree that the original manuscript needed clearer justification of several methodological choices and a more explicit account of the scope and limitations of its conclusions.

      In response, we added targeted analyses and clarifications on score interpretation, parameter robustness, multiple testing, benchmark coverage and reasons for failure, and prior-knowledge-network context dependence. These revisions frame COSMOS+/MOON outputs as standardized network-consistency scores and testable hypotheses, rather than layer-invariant hypothesis-test statistics, definitive causal mechanisms, or validated biomarkers. The detailed analyses and manuscript changes are described under R1.1–R1.10 below.

      R1.1: MOON score distributions across layers

      Reviewer comment: MOON scores are ULM t-values at layer 1, but at deeper layers they are t-values computed from previous t-values. It is unclear whether the propagated scores follow the same distribution across layers, which is a requirement to apply the same threshold of |score| > 1.5 uniformly. The authors should either provide a justification for why the threshold remains meaningful at deeper layers, or characterize through simulation how score distributions change across layers and propose a layer-specific thresholding strategy.

      Response:

      We thank the reviewer for raising this point. We agree that propagated MOON scores should not be described as formal t-distributed p-values with identical null calibration at every propagation level. The initial activity layer is inferred from molecular signatures, whereas upstream MOON scores are obtained by applying signed linear models to already-inferred downstream scores. We therefore interpret MOON scores as standardized network-consistency scores used for mechanistic prioritization, rather than as exact layer-invariant hypothesis-test statistics.

      To assess whether propagation level nevertheless introduced systematic score inflation in the CytoSig benchmark, we added an empirical score-distribution check using the estimated CytoSig MOON score. For each perturbation where the applied ligand was scorable, we extracted the matching ligand's MOON score and propagation level. These 549 matching ligand-experiment scores covered 63 ligands and levels 1–4. The applied-ligand scores were not significantly associated with propagation level (Spearman rho = 0.052, p = 0.223; linear-regression slope = −0.134 score units per level, p = 0.353, R2 = 0.0016). Thus, the benchmark did not show evidence that matching ligand scores were artificially larger simply because the ligand was farther upstream in the network.

      We also summarized all MOON scores across the 1,359 CytoSig experiments by propagation level. Score medians remained close to zero across levels, and deeper propagated layers did not show systematically broader score distributions than the direct-input or shallow propagated layers. For example, the median absolute score across experiments was 0.956 at level 0, 0.722 at level 1, 0.676 at level 2, and ranged from 0.567 to 0.686 across levels 3–10. This supports the use of a fixed absolute-score threshold as a pragmatic prioritization boundary in this benchmark, while avoiding the stronger claim that the same numerical threshold represents an identical calibrated p-value at every propagation level.

      Because the CytoSig analysis represents the effect of propagating signals across a prior knowledge network across a wide variety of distinct conditions, with almost every node of the network being scored, we expect the MOON score behavior to be generalised to other contexts as well. We have placed the detailed methods, results, and interpretation described in this response in Supplementary Text S2: “Empirical assessment of MOON score distributions across propagation levels.”

      We addressed the separate questions of maximum reachability steps and benchmark coverage under R1.7 and R1.8, respectively. The distinct use of score thresholds as candidate-input boundaries is addressed under R1.5.

      We have modified the following sections of the manuscript accordingly:

      CytoSig results, Section 2.2:

      Original manuscript excerpt: “...COSMOS prior knowledge within the specified number of steps, therefore their score couldn’t be estimated).”

      Updated manuscript excerpt: “...COSMOS prior knowledge within the specified number of steps, therefore their score couldn’t be estimated). The MOON scores did not systematically increase with propagation level (see Supplementary Text S2), indicating that the same absolute score threshold can be used across levels in this benchmark.”

      R1.2: Asymmetric consistency pruning

      Reviewer comment: The transcriptional consistency check removes TF -> target edges where TF score is incoherent with target expression, but no analogous check is applied to kinase -> TF or receptor -> kinase edges. The upstream signaling layer is therefore only constrained by the upstream anchor comparison while the transcriptional layer (TF -> target gene) undergoes aggressive pruning. The authors should justify this asymmetry explicitly or extend the consistency check to upstream layers where phosphoproteomic data is available.

      We are aware that prior knowledge on signs of activation might also be lacking, often it will be hard to know what is the real ground truth without doing specific experiments in the right cell types/conditions. Some more discussion about this would clarify the difficulty of this problem.

      Response:

      We thank the reviewer for raising this point. The TF-target coherence step is an edge-level consistency check that is used only when the directly downstream molecular readout is available. For a signed TF-target edge, expression of the target gene is measured; for example, a positive interaction between an active TF and a down-regulated target gene is incoherent with the data. This does not establish that the prior-knowledge edge is universally incorrect, but it supports removing the edge from the context-specific network for that analysis.

      By contrast, the effect of a kinase-TF interaction can depend on a particular phosphosite that is not measured, while a measured phosphosite may not be the site through which the kinase regulates the TF. An inferred kinase activity or a discordant phosphosite measurement therefore does not generally provide an equivalent direct test of the individual edge. Such discordance can reflect incomplete site coverage or context-specific regulation rather than an incorrect prior-knowledge interaction or sign, so we do not apply the TF-target pruning rule broadly to upstream signaling layers.

      Following this comment, we added two clarifications: one sentence in the Results section that directs readers to the Methods rationale, and a short Methods explanation with the kinase-TF example above. No new analysis or supplementary text was required for this methodological clarification.

      We have modified the following sections of the manuscript accordingly:

      Location: Results, Section 2.1

      Original manuscript excerpt: “...(incoherence between sign of the TF activity score and the sign of the downstream measurement/factor weight input).”

      Updated manuscript excerpt: “...(incoherence between sign of the TF activity score and the sign of the downstream measurement/factor weight input). This check is specific to TF-target interactions because RNA data directly measure the expression of the target gene, whereas the available upstream measurements do not generally provide an equivalent direct readout of individual interactions (see Methods).”

      Location: Methods, Section 4.4.1

      Original manuscript excerpt: “If RNA data points (here, MOFA weights) are provided, a check can be performed to remove any interaction from the network that connects a TF and a downstream gene that has an incoherent expression sign with the TF MOON score. Thus, the moon function and the TF-target coherence check can be run in a loop until the output of the moon doesn’t contain any incoherence between TF scores and downstream targets. The algorithm to remove incoherent TF-target interactions is as follow:”

      Updated manuscript excerpt: “If RNA data points (here, MOFA weights) are provided, a check can be performed to remove any interaction from the network that connects a TF and a downstream gene that has an incoherent expression sign with the TF MOON score. Such a check can be performed for TF-target interactions because the expression of the direct target gene is measured: for example, a positive interaction between an active TF and a down-regulated target gene is incoherent with the data. In contrast, the effect of a kinase-TF interaction can depend on phosphorylation at a specific site that is not measured, and an inferred kinase activity or a measurement at a different phosphosite may therefore not provide an equivalent direct readout of that interaction. Thus, the moon function and the TF-target coherence check can be run in a loop until the output of the moon doesn’t contain any incoherence between TF scores and downstream targets. The algorithm to remove incoherent TF-target interactions is as follow:”

      R1.3: MOFA scale_views parameter

      Reviewer comment: For the MOFA section, the authors state they used the parameter viewsscale_views = False. This means the three omics layers were not variance-normalized before input to MOFA. Since transcriptomics, proteomics, and metabolomics have different features ranges, the omic layer with higher absolute variance will dominate the factor structure. The authors should either justify this choice explicitly or show that the factor structure is not dominated by a single omic layer.

      Response:

      We thank the reviewer for highlighting this issue. To assess whether the NCI60 MOFA factor structure depended on scale_views = False, we trained a sensitivity model using the same configuration as the selected analysis (a maximum of 10 factors, yielding 9 active factors), changing only scale_views to True.

      The view-scaled model also retained 9 active factors. The total variance explained by RNA, metabolomics, and proteomics was essentially unchanged (59.5999%, 18.7037%, and 23.2947% in the original model versus 59.5989%, 18.6754%, and 23.2887%, respectively, in the view-scaled model). Factor scores showed a one-to-one correspondence between the two models, with absolute Pearson correlations of at least 0.9993 across all nine factors; opposite correlation signs reflect the arbitrary orientation of latent factors.

      These results show that the selected factor structure is not materially affected by view-scale normalization. We therefore retain the original analysis and interpretation reported in the manuscript.

      R1.4: Multiple-testing correction

      Reviewer comment: There is no multiple testing correction in the clinical association analysis. The ULM-based clinical metadata association tests each factor against each clinical category independently. With 9 factors and the number of clinical categories available in NCI60 (tissue of origin alone has ~10 categories, plus age, pathology, and other variables), the number of simultaneous tests is large. The same issue applies to the Cox survival analysis in the Paloma3 section, where a separate Cox model is fitted for every node in the MOON network and results are reported without any correction for multiple comparisons. The authors should apply FDR correction at both stages to confirm that reported associations are not false positives arising from the large number of simultaneous tests performed.

      Response:

      We thank the reviewer for raising this important point. We have now applied Benjamini-Hochberg correction to the NCI60 metadata-factor and PALOMA3 Cox test families.

      For the NCI60 analysis, we corrected 297 metadata category-factor tests (33 categories x 9 factors). The principal associations used to interpret the factors remained significant: Factor 2 was associated with melanoma origin (nominal p-value = 3.67 x 10^-15, FDR = 5.45 x 10^-13), Factor 4 was negatively associated with leukemia origin (nominal p-value = 2.19 x 10^-9, FDR = 2.17 x 10^-7), and Factor 2 was negatively associated with epithelial origin (nominal p-value = 1.69 x 10^-6, FDR = 1.00 x 10^-4).

      For PALOMA3, we distinguished the two-arm treatment-by-biomarker interaction analyses from the separately evaluated treatment-arm node-wise Cox scans. The latter did not yield an individual MOON score with FDR

      In response to this comment, we now report FDR values beside the principal NCI60 nominal p-values, revise the PALOMA3 wording to report the RB1 interaction FDR and separate it from the node-wise scan, and describe the correction families and verified ULM/MOON settings in the Methods.

      We have modified the following sections of the manuscript accordingly:

      Location: Results, Section 2.3

      Original manuscript excerpt: “Factor 4 was showing a significant negative association with samples of Leukemic origin. We also saw that factor 2 was significantly associated with a Melanoma origin, and negatively associated with an Epithelial origin (Figure 3C).”

      Updated manuscript excerpt: “After Benjamini-Hochberg correction across the 297 metadata category-factor tests (33 metadata categories x 9 factors), Factor 4 was negatively associated with samples of leukemic origin (nominal p-value = 2.19 x 10^-9, FDR = 2.17 x 10^-7). Factor 2 was associated with melanoma origin (nominal p-value = 3.67 x 10^-15, FDR = 5.45 x 10^-13), and negatively associated with epithelial origin (nominal p-value = 1.69 x 10^-6, FDR = 1.00 x 10^-4; Figure 3C).”

      Location: Results, Section 2.6

      Original manuscript excerpt: “CDK2 and RB1 MOON scores were both found to be significantly associated with worse response in the treatment arm, but not at their expression level. Furthermore, RB1 coefficient relative direction is reversed between its expression and MOON score. A lower MOON score of RB1 in patients is significantly associated with worse patient response in the treatment arm, while a high expression was marginally associated with worse patient response (MOON interaction p-value = 0.008, RNA interaction p-value = 0.12). Since RB1 is a known inhibited target of CDK4 and 6 as well as a tumor suppressor (Knudsen et al, 2019), its MOON score direction is more consistent with expectation than its expression.”

      Updated manuscript excerpt:* “CDK2 and RB1 MOON scores showed nominal treatment-by-biomarker interaction associations with worse response in the treatment arm. Furthermore, the relative direction of the RB1 coefficient was reversed between its expression and MOON score. A lower MOON score of RB1 in patients was nominally associated with worse patient response in the treatment arm (MOON interaction p-value = 0.008, FDR = 0.743), whereas high expression was not (RNA interaction p-value = 0.12). In the separately evaluated treatment-arm node-wise Cox analyses, no individual MOON score remained significant after Benjamini-Hochberg correction (FDR *

      Location: Methods, Section 4.11

      Original manuscript excerpt: “Paloma3 cohort analysis Patient level RNA counts were z-transformed across patients. The ULM method of decoupleR was used with CollecTRI to estimate patient specific transcription factor activity signatures (XXX min target per TF). Patient specific COSMOS networks were generated using MOON with XXX steps up-stream from TFs. Using patient specific progression free survival values, COX survival models were computed for each gene of the one hand and each MOON score on the other hand, by splitting first patients by median gene expression or median MOON score, and then computing the COX hazard ratio between control and treatment group first, and second between high and low expression/moon scores.”

      Updated manuscript excerpt: “Paloma3 cohort analysis Patient level RNA counts were z-transformed across patients. The ULM method of decoupleR was used with CollecTRI to estimate patient specific transcription factor activity signatures, retaining TFs with at least 5 measured targets. Patient specific COSMOS networks were generated using MOON with a maximum of 10 upstream layers from the TF activities; for COX analyses, score matrices were restricted to nodes at levels 0–5. Using patient specific progression free survival values, COX survival models were computed for each gene of the one hand and each MOON score on the other hand, by splitting first patients by median gene expression or median MOON score, and then computing the COX hazard ratio between control and treatment group first, and second between high and low expression/moon scores. Nominal p-values from the interaction and treatment-arm Cox analyses were adjusted separately using the Benjamini-Hochberg method within the MOON-score and RNA-expression test families.”

      R1.5: Uniform t-value thresholds

      Reviewer comment: The threshold of |t| > 2 is applied uniformly across TF activity scoring, kinase scoring, LR scoring, and clinical associations without justification. A t-value of 2 corresponds approximately to p

      Response:

      We agree that the role and statistical interpretation of the score thresholds required clarification. The numerical threshold is not treated as a common, calibrated p-value cutoff across the analyses. Rather, its role is analysis-specific. In network construction, score thresholds restrict the set of candidate inputs considered for mechanistic hypothesis generation. In the NCI60 clinical association analysis, |t| > 2 screen was used for descriptive heatmap prioritization, whereas statistical inference is now based on Benjamini-Hochberg correction across the 297 metadata category-factor tests (R1.4).

      To test the candidate-input boundary directly, we have now performed a focused sensitivity analysis of the NCI60 factor 4 TF-to-ligand MOON branch. We repeated this branch using absolute upstream TF score thresholds of 1.5, 2 (the submitted setting), and 2.5, while retaining the same prior-knowledge network, downstream ligand scores, and MOON settings. The re-estimated TF-to-ligand result was combined in each case with the same stored receptor-to-TF/metabolite branch.

      The threshold changed the number of TF candidates retained after prior-knowledge-network filtering from 104 at 1.5 to 84 at 2 and 64 at 2.5. Relative to the threshold-2 result, the runs with these alternative thresholds had Spearman score correlations of 0.9990 and 0.9996 and score-sign agreement of 99.84% and 99.89%, respectively; furthermore for both alternative thresholds the top 50 absolute-score nodes were preserved.

      The pathway-control analysis results were similarly stable: all 20 highest-ranked pathways were retained at the permissive threshold and 19 of 20 at the stringent threshold; 19 of the top 20 node-pathway pairs were retained at both alternatives. The principal focal-adhesion, neurotrophin, MAPK, ERBB, and cancer-related pathway-control signals were recovered in each run.

      We added a statement to the NCI60 Results and placed the detailed methods, numerical results, interpretation, and scope limitation in Supplementary Text S3, “Sensitivity of the NCI60 factor 4 MOON analysis to the upstream TF candidate threshold.” This branch-specific, reassembled sensitivity analysis supports robustness of the NCI60 factor 4 TF-to-ligand findings to this candidate-input boundary, but does not claim that a score of 2 has an identical p-value interpretation across datasets.

      We have modified the following sections of the manuscript accordingly:

      Location: Results, Section 2.3

      Original manuscript excerpt: “To find which biological processes are captured in the moon network, we perform a pathway over-representation analysis with sets of nodes down-stream of top deregulated MOON score nodes (absolute MOON score > 1.5) Then, we can represent pathways that are significantly over-represented downstream of given top scoring nodes of the MOON thresholded network in a heatmap (Figure 3E). Reassuringly, this analysis found expected control mechanisms, such as JAK1 or IL6ST very significantly controlling the JAK-STAT signaling pathway or ITGB (integrins) family members controlling focal adhesion. It also allows us to propose chemicals that can potentially control signaling pathways such as Acetaminophen or 4-hydroxy-oestradiol controlling the MAPK pathway.”

      Updated manuscript excerpt: “To find which biological processes are captured in the moon network, we perform a pathway over-representation analysis with sets of nodes down-stream of top deregulated MOON score nodes (absolute MOON score > 1.5) Then, we can represent pathways that are significantly over-represented downstream of given top scoring nodes of the MOON thresholded network in a heatmap (Figure 3E). Reassuringly, this analysis found expected control mechanisms, such as JAK1 or IL6ST very significantly controlling the JAK-STAT signaling pathway or ITGB (integrins) family members controlling focal adhesion. It also allows us to propose chemicals that can potentially control signaling pathways such as Acetaminophen or 4-hydroxy-oestradiol controlling the MAPK pathway. These results appeared overall robust to changes in the initial threshold used to select candidate TFs for network construction (absolute score thresholds: 1.5, 2 [current setting], and 2.5; Supplementary Text S3).”

      R1.6: Cytosig minimum regulon size

      Reviewer comment: The methods section does not report what minimum regulon size was used for the Cytosig analysis, and no justification is provided for this parameter. It is not clear if it is still 10 or a different one. Cytosig signatures vary widely in how many genes were measured across experiments, meaning a uniform minimum threshold will exclude TFs not because they are inactive but simply because their targets were not measured in a given experiment. The authors should explicitly report the threshold used, assess whether using a lower threshold such as 5 recovers additional scorable signatures without substantially degrading TF activity reliability, and report the distribution of gene coverage across Cytosig signatures to contextualize how many signatures are affected by this limitation.

      Response:

      We thank the reviewer for pointing out that this parameter was not reported clearly enough. The CytoSig benchmark script calls decoupleR::run_ulm() without an explicit minsize (minimal number of measured downstream targets) argument. The previously computed TF-activities independently confirms that the effective setting in the submitted benchmark was minsize = 5: for each of the 1,359 signatures, the number of cached TF activities exactly matched the number of CollecTRI TFs with at least five measured targets (724,571 TF-signature activity estimates in total). Thus, the benchmark already used an effective five-target minimum, rather than a threshold of 10.

      To further explore how much the results would change with a higher cutoff, We have quantified the effect of comparing this setting with a hypothetical minsize = 10 cutoff. The 1,359 filtered CytoSig signatures contained a median of 18,423 measured genes per signature (IQR 16,720–19,073) and a median of 6,005 measured CollecTRI target genes (IQR 5,534–6,085). With the effective five-target minimum, a median of 539 TFs per signature were retained; at 10 targets, this would decrease to 440. Across all TF-signature pairs, the five-target setting retained 724,571 activity estimates, compared with 590,207 under the ten-target cutoff. The five-to-nine-target group therefore contributed 134,364 additional TF-signature estimates (18.5% of the TF-activity input layer).

      The minsize setting filters returned TFs rather than changing the ULM fit for TFs that pass both cutoffs. Scores for TFs with at least 10 measured targets would therefore be identical under the two settings. The added five-to-nine-target estimates had lower median absolute ULM scores than the >=10-target estimates (0.69 versus 0.95) and were less often above an absolute score of 2 (12.7% versus 21.9%). While this highlights an expected association between score magnitude and number of targets, it does not represent an independent validation of the reliability of low-coverage TF activities. We therefore describe the setting as a coverage tradeoff and do not claim that the comparison establishes unchanged reliability.

      We added the effective minimum to the Cytokine scoring Methods and placed the detailed implementation check, coverage distribution, score-magnitude context, and limitation in Supplementary Text S4, “Effect of the minimum CollecTRI target-set size on CytoSig TF-activity coverage.” The supplement also clarifies that this parameter controls the availability of TF inputs; applied-ligand scoring additionally depends on ligand mapping and prior-knowledge-network reachability.

      We have modified the following sections of the manuscript accordingly:

      Location: Methods, Section 4.5.3

      Original manuscript excerpt: “...referred to as the TF score. Then, for each resulting TF score profile, we filtered out specifically the COSMOS…”

      Updated manuscript excerpt: “...referred to as the TF score. Only TFs with at least 5 measured CollecTRI targets were retained for scoring (minsize = 5; see Supplementary Text S4). Then, for each resulting TF score profile, we filtered out specifically the COSMOS…”

      R1.7: MOON reachability step limit

      Reviewer comment: The maximum number of propagation steps for the reachability filtering is not reported or justified. In the case of the Cytosig analysis, the paper states that 31 ligands could not be scored because they were not reachable upstream of TFs within the allowed number of steps, but never states what that number was. This parameter directly determines which ligands can be benchmarked, but no sensitivity analysis is provided showing whether increasing the step limit recovers additional ligands or changes the benchmark results. The tool might be enhanced with a report of alternative values, discussion of optimal values and discussion of the tradeoff between reachability and the MOON score reliability at greater network distances.

      Response:

      We thank the reviewer for highlighting that the rationale and sensitivity of this parameter were not sufficiently clear. The Methods already stated that the CytoSig reachability filter used ten steps; the CytoSig benchmark script uses the same setting for MOON propagation (n_steps = 10, passed as n_layers = 10) and for the pre-MOON reachability filter. We use this value as a permissive upper bound, rather than as a biologically privileged path length: it avoids considering arbitrarily long prior-knowledge-network paths while allowing multi-step receptor, signaling, and TF routes.

      We assessed the observed depth sensitivity in the submitted benchmark. The 549 cleaned applied-ligand scores across 63 ligands occurred only at MOON levels 1–4 (205, 254, 83, and 7 entries, respectively); no scored applied ligands occurred at levels 5–10. Restricting the benchmark to levels 1, 2, 3 retained 205, 459, and 542, entries across 8, 45 and 60 ligands, respectively. Once levels 1–4 were included, the benchmark summary was unchanged through level 10: 47 of 63 ligands had a positive mean score, while 16 and 4 ligands had nominally significant positive and negative one-sample tests, respectively.

      We also performed a reachability-only check over ten steps for the 52 evaluable (as they are mapable on our network) experiments with missing scores, spanning 11 ligands. After the same expression and prior-knowledge-network membership filtering as in the original CytoSig analysis, none had a directed path from the applied ligand to the filtered TF-input layer at any finite distance. This indicates that simply increasing the propagation limit would not recover the missing applied-ligand scores.

      We have added clarification in the methods (pasted below), and a Supplementary Text S5, “Sensitivity of the CytoSig MOON benchmark to the propagation/reachability step limit.” The supplementary text presents the implementation setting, the level-restricted analysis and its limitation, the extended-horizon reachability-only check, and the limit of what these data can establish about deeper-score reliability. We have modified the following sections of the manuscript accordingly:

      Location: Methods, Section 4.5.3

      Original manuscript excerpt: “...within ten steps upstream of the TFs.”

      Updated manuscript excerpt: “...within ten steps upstream of the TFs. We used this ten-step maximum as a permissive upper bound; in the CytoSig benchmark, applied ligands that were scored by MOON occurred at levels 1–4 (Supplementary Text S5).”

      R1.8: Benchmark coverage

      Reviewer comment: The benchmark scores only 549 out of 1359 available signatures (40% of the data) due to PKN reachability and TF coverage limitations. Since both limiting parameters are neither reported nor optimized, it is unclear whether the excluded 60% represents a fundamental limitation of the method or an artifact of conservative parameter choices. The authors should explore alternative parameter values and report their effect on both benchmark coverage and performance jointly.

      Response:

      We agree that the parameter choices underlying this benchmark required clearer justification.

      The 549/1,359 value is the coverage of the direct applied-ligand recovery benchmark, rather than the fraction of CytoSig signatures for which MOON can generate a score table. This issue is addressed by the preceding analyses (see response R1.6, R1.7). Supplementary Text S4 documents that the CytoSig TF-activity input layer already used the effective permissive setting minsize = 5, and quantifies the reduction in TF-input coverage that would result from a ten-target minimum. Supplementary Text S5 documents the shared ten-step reachability/propagation setting and its sensitivity analysis: the observed benchmark endpoint was reached by level 4 in a post-hoc level-restricted analysis of the submitted MOON scores, while extending the reachability horizon did not recover scores in the evaluable missing-row subset. The limitations of these analyses—including that they are not full reruns at each alternative setting and do not validate reliability at unobserved deeper levels—are stated explicitly in the corresponding supplementary texts S4 and S5.

      Together, the epxloration of alternative parameters and exploration of filtering steps show that, for 549 signatures (across 61 ligands) both 1) the ligand is clearly identifiable and can be mapped onto the prior knowledge network and 2) the ligand is reachable (within any given number of steps) from downstream TFs. The remaining 810 signatures do not yield an appropriate result to benchmark the ability of MOON scores to capture applied ligands. We therefore do not propose an additional manuscript amendment for R1.8; the R1.6 and R1.7 revisions and their supplementary texts provide the relevant parameter reporting and sensitivity results.

      R1.9: Consistently negative ligands

      Reviewer comment: Four ligands received consistently negative MOON scores across experiments where they were applied (the opposite of the expected direction). The paper identifies and corrects the OSM annotation error but does not investigate or explain the remaining consistently negative ligands. The authors should identify the source of the systematic sign inversion for each of these ligands whether it reflects additional PKN annotation errors, missing interactions, or biological context specificity, and report whether correcting those errors changes the overall benchmark performance.

      Response:

      We thank the reviewer for raising this point. We further explored the four ligands with nominally (i.e. before multiple-hypothesis correction) significant negative mean applied-ligand MOON scores in the submitted CytoSig benchmark: OSM (n = 5, mean = -3.25), FGF10 (n = 9, mean = -1.04), IL6 (n = 39, mean = -0.68), and IGF1 (n = 6, mean = -0.43). These results were identified with the exploratory, unadjusted per-ligand one-sample test used in the benchmark summary. OSM, FGF10, and IGF1 were negative in every corresponding signature; IL6 was negative in 35 of 39 signatures. All four perturbations were annotated as activating treatments. This does not exclude experiment-specific context or data-quality effects, but it does not support a common reversal of the CytoSig treatment-label direction as the explanation for these cases.

      OSM remains the clearest localized PKN error: as reported in the manuscript, the erroneous inhibitory IL6ST annotation was corrected in OmniPath version 2024.03.19. A separate, broad endpoint/bridge PKN diagnostic also changed all five OSM scores from negative to positive (mean -3.25 to 3.63), but this is not the same network comparison as the 2024.03.19 update reported in the manuscript and is not presented as a replacement benchmark. IL6 was strongly rescued in this diagnostic (mean -0.68 to 1.89; negative scores 35/39 to 4/39). Its submitted local network included direct edges to the response genes CRP, A2M, and PTHLH that were absent from the updated representation. Since the diagnostic changes a broad PKN, this supports sensitivity to PKN representation or versioning without assigning the IL6 shift to one interaction.

      FGF10 and IGF1 did not reveal a similarly specific local annotation error. FGF10 remained weakly negative in the diagnostic (mean -1.04 to -0.15; 6/9 scores still negative), and its immediate FGF10–FGFR2 neighborhood was unchanged. All nine submitted FGF10 scores were computed at level 3 through the single preceding-layer node FGFR2. IGF1 was partly rescued (mean -0.43 to 0.49; 2/6 scores still negative), and each submitted score was computed at level 2 through INSR. These cases are compatible with sensitivity to the layer-wise MOON heuristic: an upstream node is scored when first reached through the immediately preceding layer, so evidence available only through more distant downstream paths does not directly re-enter that score. This is a plausible mechanism rather than proof that it is the sole cause of either result. The bounded analysis did not directly test cell-type, protocol, or other biological-context explanations.

      The endpoint/bridge comparison was intentionally treated as a diagnostic rather than a corrected replacement benchmark because it had slightly different coverage (547 scored entries across 61 ligands, compared with 549 entries across 63 ligands in the submitted cache). It contained one nominally significant negative ligand, CD40LG, while the number of nominally significant positive ligands remained 16. Since coverage and the identity of the nominally positive and negative ligands changed, we do not interpret this comparison as demonstrating a net improvement in overall benchmark performance. Instead, the analysis of these four representative ligands supports the conclusion that the negative cases have distinct potential reasons for failure, including PKN representation and layer-wise propagation sensitivity.

      The complete analysis and exploration of numerical results, and limitations of the diagnostic comparison are presented in proposed Supplementary Text S6. To keep the Results section concise, we propose one cross-reference immediately after the initial OSM/IL6 example.

      We have modified the following sections of the manuscript accordingly:

      Location: Results section 2.2

      Original manuscript excerpt: “...This erroneous annotation also explained the seemingly poor MOON score estimation of the IL6 ligand (Figure 2D), which has an average score of 2.0 in the newer version.”

      Updated manuscript excerpt: “...This erroneous annotation also explained the seemingly poor MOON score estimation of the IL6 ligand (Figure 2D), which has an average score of 2.0 in the newer version. Further analyses of the four ligands with nominally significant negative mean MOON scores are presented in Supplementary Text S6.”

      R1.10: Low positive-ligand reliability

      Reviewer comment: The paper reports only 16 out of 63 ligands (25%) show statistically significant positive MOON scores across experiments. The authors should investigate and discuss what drives this low reliability. Is the variability in scores across experiments for the same ligand explained by cell type specificity, experimental protocol differences (if there are any), PKN incompleteness, or limitations of the MOON scoring procedure itself? The use of a generic PKN like OmniPath, which contains interactions derived from many different cell types and conditions, may introduce context-irrelevant edges that add noise or produce sign inversions when scoring ligands in specific experimental contexts. Context-specific network inference approaches such as ARACNE, GENIE3, SCENIC applied directly to the transcriptomic data of each experiment or to other datasets in similar cell types/conditions, could potentially improve MOON's reliability by restricting propagation to interactions that are actually active in the biological context being analyzed.

      Without understanding the sources of failure it is unclear whether performance can be improved through such strategies or whether the low reliability reflects a more fundamental limitation of the prior knowledge network approach in diverse biological contexts.

      Response:

      The extended exploration reported in Supplementary Text S6 addresses the consistently negative mean-score cases individually: it identifies a localized OSM PKN error, supports sensitivity of IL6 to PKN representation or versioning, and identifies layer-wise propagation bottlenecks as plausible contributors for the unresolved FGF10 and partly IGF1 cases. That analysis does not directly test biological-context or protocol effects, but it shows that the negative-score cases are heterogeneous rather than attributable to a simple common sign reversal.

      To address the reviewer's suggestion of an inferred regulatory network, we performed a separate, bounded IFNA1 diagnostic with GENIE3. We used 104 CytoSig IFNA contrast-statistic profiles to infer a pooled IFNA response-context TF-target layer, while retaining the curated ligand/receptor/signaling PKN. This is not an experiment-specific or cell-type-specific network, and the same IFNA profiles were used for network inference and for evaluation; it is therefore a feasibility diagnostic rather than an independent validation.

      Replacing the curated CollecTRI TF-target layer with GENIE3 did not improve raw applied-ligand IFNA1 MOON scores. IFNA1 was scored in 76 of 104 contrasts in both the submitted CollecTRI/COSMOS cache and the GENIE3-only analysis, but the mean raw score decreased from 7.42 to 4.56 (only 10 of 76 paired scores were higher with GENIE3). A CollecTRI-priority merged layer partly recovered the GENIE3-only score reduction (mean 5.42), but remained below the submitted baseline and improved raw scores in only 18 of 76 paired contrasts. Mean within-experiment rank quantiles were slightly higher for the inferred and merged layers, but this observation is not independent evidence of improved prioritization because the same profiles were used to infer and assess the GENIE3 network.

      This analysis therefore does not show that altering the TF-target layer alone improves ligand-score recovery. It also cannot resolve the R1.9 reasons for failure directly, because the upstream PKN and the layer-wise MOON scoring heuristic were unchanged. Rather, the two analyses support a more specific interpretation: heterogeneous ligand scores can reflect several components of the workflow, including PKN representation, propagation behavior, and the regulatory layer.

      The complete GENIE3 comparison, its numerical results, and its limitations are presented in proposed Supplementary Text S7. To keep the Results section concise, we propose one cross-reference at the end of the existing IFNA1 example, alongside the proposed R1.9 failure-mode analysis.

      We have modified the following sections of the manuscript accordingly:

      Location: Results section 2.2

      Original manuscript excerpt: “The high MOON scores indicate that treating cell lines with IFNA1 will consistently lead to the activation of IRF9.”

      Updated manuscript excerpt: “The high MOON scores indicate that treating cell lines with IFNA1 will consistently lead to the activation of IRF9. An analysis of the impact of a GENIE3-inferred regulatory network on IFNA1 scoring is detailed in Supplementary Text S7, alongside the failure-mode analysis in Supplementary Text S6.”

      R1 minor comments

      A transcriptional consistency check can be performed that removes any

      Interaction

      number of downstream TF(s) participating

      Check for contracted forms , typically not accepted in written text

      Figure 3 these should be D and E, there are two Cs

      1. C) MOON Network connecting the top deregulated TFs and LR interactions of factor 4 based on a signed directed prior knowledge network. D) Heatmap of the top results of the Pathway control analysis. We represent pathways that are significantly over-represented downstream of given nodes of the MOON thresholded network and merely contextualize(s) a part of it. Check MOON caps

      The precision is again high among the top PC1

      Loading (s)

      the complementarity of MOON scores with

      expression value(s)

      Comma missing

      However, there are many more types of domain knowledge that can potentially be used to interpret feature weights of factors beyond pathway ontologies, such as prior knowledge in the form of footprints and signed-directed networks can help to provide interpretable insights from factor weights.

      Unclear:

      We saw that it was able to recover expected regulation mechanisms, as some of the top gene-pathway interactions were found to be e.g. JAK regulates the JAK-STAT pathway.

      Methods:

      The ULM method of decoupleR was used with CollecTRI to estimate patient specific transcription factor activity signatures (XXX min target per TF).

      Response:

      We thank the reviewer for raising these points. We have corrected the manuscript accordingly.

      Reviewer #1: Significance

      General significance comment

      Reviewer comment: This paper addresses two main issues in the field: 1) going beyond correlation and towards mechanistic and causal explanations in biomedically relevant regulation processes and 2) using prior knowledge while also checking its consistency with data. The approach proposed is likely to provide extremely useful insight, as shown by applications both in-vitro and in patient cohorts.

      The only limitation, which cannot easily be addressed but it is generally an issue in the field, is our lack of certainty about the ground truth which makes it very difficult to assess the relevance of the hypotheses provided by the computational approach.

      The paper will be of broad interest to the computational biology community, especially in oncology and drug discovery.

      We are researchers in the same field, we have tested several other approaches to perform similar tasks.

      Response:

      We thank the reviewer for this positive assessment and agree that uncertainty about biological ground truth is a central limitation of computational-mechanistic inference. COSMOS+ uses signed prior knowledge and multi-omic observations to prioritize mechanistically coherent explanations, but these explanations remain hypotheses whose biological relevance and causality require context-matched validation.

      With the revisions listed above, we aimed to make these limitations more explicit and, where possible, to quantify their impact. These complementary analyses support selected aspects of the framework, but none establishes every inferred regulator or network edge as a causal driver, therapeutic target, or clinically validated biomarker. The scope of the functional and clinical evidence is further detailed under R2.1 and R2.2. We therefore narrowed the corresponding claims and identified targeted perturbation experiments and independent clinical validation as necessary next steps.

      Reviewer #2: Evidence, Reproducibility And Clarity

      R2.1: Computational validation and functional-validation scope

      Reviewer comment: This manuscript presents COSMOS+, an extension of the COSMOS framework that integrates multi-omics factor analysis with prior-knowledge signaling and metabolic networks through the newly developed MOON algorithm.

      The authors demonstrate the approach using the NCI60 dataset, breast cancer resistance models, and a breast cancer patient cohort. The computational framework is technically sophisticated and addresses an important challenge in systems biology, the principal novelty resides in the development of the MOON/COSMOS+ computational framework itself. The biological applications presented throughout the manuscript function largely as case studies illustrating the algorithm rather than generating fundamentally new biological insights. The analyses of breast cancer resistance and the NCI60 dataset are interesting examples, but most conclusions remain computationally inferred and are not experimentally demonstrated.

      The manuscript proposes multiple signaling regulators, resistance-associated pathways, and mechanistic hypotheses, yet none of these predictions are directly tested. Given the emphasis on identifying resistance drivers and actionable biological mechanisms, additional experimental evidence would substantially strengthen the work. Validation through CRISPR-mediated perturbation, knockdown experiments, or other functional assays would help establish whether the inferred network regulators truly contribute to the phenotypes described.

      Response:

      We thank the reviewer for this comment and we agree that the primary contribution of this study is methodological and that COSMOS+/MOON outputs should be interpreted as mechanistically informed, testable hypotheses rather than direct evidence of causality. We have therefore revised the manuscript throughout to avoid describing inferred regulators or pathways as established resistance drivers, causal mechanisms, or clinically actionable targets.

      We also clarify the scope of the existing evaluations. The CytoSig analysis benchmarks recovery of known upstream perturbations, whereas comparison with an orthogonal CRISPR screen in the breast-cancer models provides limited, context-specific support for the prioritised genes. Neither analysis constitutes functional validation of individual network regulators or inferred mechanisms.

      We now explicitly acknowledge that targeted perturbation and, where appropriate, rescue experiments in the relevant model systems will be needed to establish causal roles for specific candidates. We have revised the Abstract, Introduction, Results, Methods, and Discussion accordingly.

      We have modified the following sections of the manuscript accordingly:

      Location: Abstract

      Original manuscript excerpt: “We apply this approach on a novel multi-omics dataset of cell line models of breast cancer resistance to evaluate the ability of such mechanistic hypotheses to identify resistance drivers, as well as a breast cancer patient cohort. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets.”

      Updated manuscript excerpt: “We apply this approach on a novel multi-omics dataset of cell line models of breast cancer resistance to explore resistance-associated mechanistic hypotheses, as well as a breast cancer patient cohort. Our approach offers an interpretable framework to generate mechanistically informed hypotheses from multi-omic data particularly suited for high dimensional datasets.”

      Location: Introduction

      Original manuscript excerpt: “We show that mechanistic hypotheses of signaling deregulation in resistant and sensitive cell lines treated with CDK inhibitors are correlated with resistance markers identified through knock-out screening.”

      Updated manuscript excerpt: “We show that mechanistic hypotheses of signaling deregulation in resistant and sensitive cell lines treated with CDK inhibitors are modestly correlated with gene sensitization profiles obtained through knock-out screening.”

      Location: Results, Section 2.3

      Original manuscript excerpt: “We can interpret such scores as TFs that are responsible for the transcriptional programs that are captured by given factors.”

      Updated manuscript excerpt: “We can interpret such scores as TF activities that are consistent with the transcriptional programs captured by given factors.”

      Location: Results, Section 2.4

      Original manuscript excerpt: “Therefore, the network associated with MCF7SYL recapitulates well the CDK2 mediated acquired resistance to treatment while highlighting a potential positive feedback loop through MYC and the NOTCH pathway.”

      Updated manuscript excerpt: “Therefore, the network associated with MCF7SYL is consistent with the hypothesis that CDK2 contributes to acquired resistance to treatment while highlighting a potential positive feedback loop through MYC and the NOTCH pathway.”

      Location: Results, Section 2.5

      Original manuscript excerpt: “We sought to evaluate their accuracy and functional relevance with respect to resistance to treatment by investigating whether feature weights of the PCA on MOON scores were associated with mechanisms of resistance to the CDK inhibitors CDK4/6i (CDK4/6) and CDK2/4/6i (CDK2/4/6).”

      Updated manuscript excerpt: “We assessed the concordance between feature weights of the PCA on MOON scores and gene sensitization profiles obtained in the presence of the CDK inhibitors CDK4/6i (CDK4/6) and CDK2/4/6i (CDK2/4/6).”

      Location: Results, Section 2.5

      Original manuscript excerpt: “Thus, while small, the correlation patterns are significantly consistent across every factor for the resistant cell line but not for the sensitive one. This agrees with the expectation that PCA factors that are specifically associated with resistant and sensitive cell separation would potentially be associated with resistance mechanisms that can be sensitization targets, but only in resistant cells (HCC1806) and not cells that are already sensitive (MCF7).”

      Updated manuscript excerpt: “Thus, while small, the relationship between the factor-specific MOON–crispR correlations and the separation between resistant and sensitive cells was significant for HCC1806 but not for MCF7. This indicates concordance between MOON-based prioritization and crispR sensitization profiles in this comparison, but does not establish that individual prioritized genes or inferred network edges mediate resistance.”

      Location: Results, Section 2.5

      Original manuscript excerpt: “...we first considered genes that had a KO sensitization score of -2 at least as true positives, that is genes that are driving resistance to CDK4/6i or CDK2/4/6i. We could then compute the area under the precision curve (AUPRC) for genes that have negative weights in PC1 (that is, genes that are more active in the resistant cell line after treatment with CDK4/6i and CDK2/4/6i). The AUPRC is favored in this case to the area under the receiving operator curve (AUROC) due to 1) the imbalance of the ratio of true positive and true negative (only 7% of true positive) and 2) precision is a usual metric for a model that we assume does not inherently capture the full complexity of the underlying biological mechanism, and merely contextualize a part of it.[...] Therefore, while the AUPRC is higher than a random baseline, this shows that the precision of the PC1 weight is better than the recall to capture sensitization drivers.”

      Updated manuscript excerpt: “...we considered genes with a KO sensitization score of -2 or lower as positive instances for the precision-recall analysis. We then computed the area under the precision-recall curve (AUPRC) for genes that have negative weights in PC1 (that is, genes that are more active in the resistant cell line after treatment with CDK4/6i and CDK2/4/6i). The AUPRC is favored over the area under the receiver operating characteristic curve (AUROC) because the positive instances are imbalanced (7% of genes) and because the MOON score PCA is not expected to capture the full complexity of the underlying biological mechanisms.[...] Therefore, while the AUPRC is higher than a random baseline, the PC1 weights had higher precision than recall for identifying genes with strong crispR sensitization scores.”

      Location: Methods, Section 4.10

      Original manuscript excerpt: “4.10 Analysis of crispR KO data and validation of resistance mechanisms”

      Updated manuscript excerpt: “4.10 Analysis of crispR KO data and comparison with resistance-associated MOON features”

      Location: Methods, Section 4.10

      Original manuscript excerpt: “It can then be interpreted as e.g. an indication of how much does a gene that is more active in a resistant cell line treated with a CDK inhibitor such as CDK4/6i actually is directly responsible for the resistance to the treatment.”

      Updated manuscript excerpt: “It can then be interpreted as an indication of the concordance between resistance-associated MOON features and crispR sensitization scores, rather than as evidence that the corresponding genes are directly responsible for resistance to treatment.”

      Location: Discussion

      Original manuscript excerpt: “We show how MOON can identify biological mechanisms underlying cancer treatment resistance.”

      Updated manuscript excerpt: “We show how MOON can be used to explore biological mechanisms associated with cancer treatment resistance.”

      Location: Discussion

      Original manuscript excerpt: “Therefore, we also assessed the ability of mechanistic hypotheses generated by COSMOS+ to support the identification of drivers of treatment resistance by applying it on a novel combined transcriptomic and phospho-proteomic dataset of breast cancer cell lines with different resistance profiles exposed to CDK inhibitor drugs measured at early (2-4 hours) and late time points (72-96 hours).”

      Updated manuscript excerpt: “Therefore, we applied COSMOS+ to a novel combined transcriptomic and phospho-proteomic dataset of breast cancer cell lines with different resistance profiles exposed to CDK inhibitor drugs measured at early (2-4 hours) and late time points (72-96 hours), and compared the resulting resistance-associated MOON prioritizations with crispR sensitization profiles.”

      Location: Discussion,

      Original manuscript excerpt: “In this context, we sought to use COSMOS+ to examine both known and potentially novel mechanistic hypotheses mediating response and resistance to CDK4/6i. COSMOS+ recapitulated known signaling and transcriptional regulation components following CDK inhibition such as the CDK2,4,6, RB1 and E2Fs crosstalk, while also highlighting a wide set of other potentially important mechanisms that were differentially regulated between the sensitive and resistant cell line, such as a YWHAQ/E2F1/FOXO3 and a CDK2/NOTCH crosstalk.”

      Updated manuscript excerpt: “In this context, we sought to use COSMOS+ to examine both known and potentially novel mechanistic hypotheses associated with response and resistance to CDK4/6i. COSMOS+ highlighted expected signaling and transcriptional regulation components following CDK inhibition such as the CDK2,4,6, RB1 and E2Fs crosstalk, while also highlighting a wide set of other candidate mechanisms that were differentially regulated between the sensitive and resistant cell line, such as a YWHAQ/E2F1/FOXO3 and a CDK2/NOTCH crosstalk.”

      Location: Discussion

      Original manuscript excerpt: “We then compared the mechanistic hypothesis identified [...]. The poor recall suggests that there is a large gap between the ability to identify deregulated signaling and gene regulation between different cell lines treated with a drug and the actual identification of direct drivers of drug resistance. [...] We also showed how pathway control analysis could correctly recapitulate the control of E2Fs transcription factors over cell cycle processes in a breast cancer cell line dataset and how it is differentially regulated between sensitive and resistant cell lines treated with CDK inhibitors.”

      Updated manuscript excerpt: “We then compared the mechanistic hypotheses generated [...]. The poor recall highlights the gap between identifying deregulated signaling and gene regulation between different cell lines treated with a drug and identifying genes with drug-specific sensitization in this assay. [...] We also showed how pathway control analysis highlighted the expected control of E2F transcription factors over cell cycle processes in a breast cancer cell line dataset and how it is differentially regulated between sensitive and resistant cell lines treated with CDK inhibitors.”

      Location: Discussion

      Original manuscript excerpt: “... this hypothesis can be validated and translated into actionable insights. A common example of such actionable insight in pharmacological research is the identification of new drug targets, especially for cells and tissues that are resistant to existing treatments. [...] In this study, we show that there is partial consistency between differentially regulated molecular drug responses and molecular drivers of treatment sensitization. Furthermore, the good precision of COSMOS+ to capture sensitization targets but poor recall is consistent with the idea that such a model, while being able to accurately capture important biological mechanisms, is not yet able to generate predictive mechanistic models of cell signaling.”

      Updated manuscript excerpt: “... it requires targeted experimental validation in the relevant biological context before it can be considered a causal or actionable mechanism. A common potential downstream application in pharmacological research is the identification of new drug targets, especially for cells and tissues that are resistant to existing treatments. [...] In this study, we show that there is partial consistency between differentially regulated molecular drug responses and genes with crispR sensitization scores. Furthermore, the higher precision than recall of COSMOS+ for genes with crispR sensitization scores is consistent with the idea that such a model can prioritize candidate mechanisms for experimental testing, but is not yet able to generate predictive mechanistic models of cell signaling.”

      R2.2: Clinical and translational scope

      Reviewer comment: I also have reservations regarding the translational implications proposed by the authors. The manuscript suggests potential utility for patient outcome prediction and resistance mechanism discovery. However, the patient cohort analyzed is relatively limited in size and, importantly, no independent external validation cohort is provided. As a result, it remains unclear how robust and generalizable these predictive signatures would be in broader clinical settings. This aspect should be discussed more cautiously.

      Response:

      We thank the reviewer for this important comment and agree that the PALOMA3 analysis requires a more cautious clinical interpretation. The analysed PALOMA3 RNA cohort is relatively limited in size (302 patients) and no independent external validation cohort was available. This analysis was not intended to develop or validate a clinically deployable progression-free-survival predictor. Its purpose was methodological: to examine, within one cohort, whether MOON-derived activity estimates capture outcome associations that are complementary to those obtained from RNA abundance alone.

      In particular, the Cox coefficients used to construct the RNA, MOON, and hybrid scores, and the correlations of those scores with observed PFS, were derived and assessed in the same PALOMA3 cohort. The higher correlation of the hybrid score is therefore an in-cohort illustration of potential complementarity between RNA and MOON representations. It does not quantify out-of-sample predictive performance, incremental clinical value, or generalisability to other breast-cancer cohorts.

      As detailed under R1.4, no individual MOON score remained significant after Benjamini-Hochberg correction in the separately evaluated treatment-arm node-wise Cox scan. The RB1 observation is accordingly presented as a nominal, hypothesis-generating illustration rather than an individually validated prognostic or predictive biomarker. Independent validation in larger, clinically comparable cohorts would be required before any RNA, MOON, or hybrid signature could be considered for outcome prediction, treatment selection, or clinical biomarker use.

      We therefore amended the Introduction, Results, and Discussion. They retain the PALOMA3 analysis as an exploratory within-cohort comparison of molecular representations, while removing language that implies validated clinical prediction, clinical biomarker identification, or generalisable treatment-efficacy prediction. The Figure 6 legend is retained unchanged.

      We have modified the following sections of the manuscript accordingly:

      Location: Introduction

      Original manuscript excerpt: “Finally, we apply the COSMOS+ framework to a breast cancer patient cohort, and we demonstrate the complementarity of the resulting features with omic data to predict patient outcomes.”

      Updated manuscript excerpt: “Finally, we apply the COSMOS+ framework to a breast cancer patient cohort to examine the complementarity of the resulting features with omic data in associations with patient outcomes.”

      Location: Results, Section 2.6

      Original manuscript excerpt: “We also evaluated if the PC1 loadings from the cell line data could be used as a signature to predict poor patient response.”

      Updated manuscript excerpt: “We also explored whether the PC1 loadings from the cell line data were associated with patient response in the PALOMA3 treatment arm.”

      Original manuscript excerpt: “This showed that there is a significant amount of information that can be extracted from a sensitive/resistance treatment response cell line model to inform patient response.”

      Updated manuscript excerpt: “Because the signature size and its association with PFS were explored in the same cohort, this result is descriptive and does not establish a clinically generalisable prediction signature.”

      Location: Results, Section 2.6

      Original manuscript excerpt: “Finally, to further explore the complementarity of MOON scores with expression value, we created a hybrid Cox coefficient signature by combining the most extreme cox coefficients computed from the RNA data on the one hand and from MOON scores on the other hand (Figure 6G). That is, for each gene, the cox coefficient that was the most extreme between MOON and RNA was included. We then evaluated the ability of such a signature to estimate patient outcome by multiplying the hybrid Cox coefficient signature with the corresponding hybrid patient cohort of MOON scores and RNA measurements. The resulting vector essentially corresponds to scaled PFS predictions. The scaled PFS prediction was compared with the actual PFS for the hybrid signature, as well as with a signature based on RNA or MOON scores alone. The correlation for the hybrid signature PFS prediction was 0.45, while the RNA alone and MOON scores alone were 0.4 and 0.35, respectively (Figure 6H). This further supports that both MOON score and RNA values can bring complementary information to inform us about gene regulation events and processes that may be associated with better or worse outcomes in patients.”

      Updated manuscript excerpt: “Finally, to further explore the complementarity of MOON scores with expression values, we created a hybrid Cox coefficient signature by combining the most extreme Cox coefficients computed from RNA data and MOON scores (Figure 6G). For each gene, the most extreme coefficient between the MOON and RNA analyses was included. We then compared the association of this hybrid score with observed patient outcomes, using the same PALOMA3 cohort to derive the Cox coefficients and assess the resulting scores. The correlation with observed PFS was 0.45 for the hybrid score, compared with 0.40 and 0.35 for RNA-only and MOON-only scores, respectively (Figure 6H). This within-cohort comparison is intended to illustrate the potential complementarity of RNA and MOON features; it does not assess out-of-sample predictive performance or establish a generalisable model for PFS prediction.”

      Location: Discussion

      Original manuscript excerpt: “These results also illustrate how mechanistic hypotheses generated in the context of pre-clinical cell-line models can translate into a clinical setting and potentially help predict treatment efficacy as well as identify underlying molecular causes of treatment resistance.”

      Updated manuscript excerpt: “These results also illustrate how mechanistic hypotheses generated in the context of pre-clinical cell-line models can be compared with outcome associations in a clinical cohort, but do not establish generalisable prediction of treatment efficacy or the underlying molecular causes of treatment resistance.”

      Location: Discussion

      Original manuscript excerpt:* “Finally, we demonstrated how COSMOS+ can be used with clinical baseline patient cohort data to complement biomarker predictions based on transcriptomics data alone. We showed how a Cox survival analysis performed with MOON scores instead of transcriptomic data allowed for the identification of expected markers that were missed by transcriptomic data alone. We then showed that the Cox coefficients based on MOON scores were correlated with MOON scores estimated from a cell line model of resistant and sensitive cell lines treated with similar drug regimens, suggesting that mechanisms identified in such a cell line model could support the identification of clinical biomarkers. While small (Pearson correlation coefficients = -0.12), the correlation was highly significant (p-value *

      Updated manuscript excerpt:* “Finally, we used baseline data from the PALOMA3 patient cohort to explore whether MOON-derived activity estimates captured outcome associations complementary to transcriptomic data alone. The correlation between the cell-line PC1 loadings and MOON-score Cox coefficients (Pearson correlation coefficient = -0.12, p-value *

      Location: Discussion

      Original manuscript excerpt: “Nonetheless, the mechanisms hypothesized by COSMOS+ seem to complement the information in the omic dataset alone. Indeed, when COSMOS+ was applied at the level of the transcriptome measurements of the breast cancer cohort Paloma3 patients, it revealed markers of treatment outcomes that could not have been captured at the level of their expression alone. For example, RB1 activity estimated from its expected downstream regulated expression target was significantly associated with the worst outcome in the treatment arm of the Paloma3 cohort, while its own expression was marginally associated with the outcome in the wrong direction (that is, high expression of the RB1 tumor suppressor associated with worst outcome).”

      Updated manuscript excerpt: “Nonetheless, the analysis of the PALOMA3 cohort suggests that MOON scores may capture outcome associations complementary to RNA expression. For example, the RB1 MOON score was nominally associated with worse outcome in the treatment arm, whereas its expression was not associated with outcome in the same direction (see Results); this is presented as a hypothesis-generating illustration rather than an individual prognostic biomarker.”

      Location: Discussion

      Original manuscript excerpt: “This complementarity is further illustrated by the increased correlation between a PFS prediction performed with RNA or MOON scores alone compared to a hybrid signature. Therefore, it will be interesting in the future to assess if COSMOS+ could be used in combination with omic features to improve the performance of predictive approaches.”

      Updated manuscript excerpt: “This complementarity is further illustrated by the higher within-cohort correlation with observed PFS of the hybrid RNA/MOON score than of the RNA- or MOON-only scores. Because the Cox coefficients and scores were derived and assessed in the same relatively limited PALOMA3 cohort, this comparison does not establish the performance or generalisability of a PFS prediction model; independent validation in larger, clinically comparable cohorts will be required.”

      R2.3: Transcriptomic, prior-knowledge, and interpretation biases

      Reviewer comment: Suggest to have a look at paper such as "Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research" which discuss database bias.

      The authors themselves provide a compelling example in which an erroneous IL6ST annotation leads to incorrect MOON predictions. This example highlights a broader issue: the quality of the inferred mechanistic networks is inherently constrained by the accuracy and completeness of resources such as OmniPath, STITCH, and related databases. Consequently, false or incomplete annotations may propagate through the analytical pipeline and influence biological interpretation.

      Response:

      We thank the reviewer for highlighting this important limitation. We agree that the accuracy and completeness of the prior-knowledge resources constrain the mechanistic hypotheses generated by COSMOS+. The OSM–IL6ST example is a clear illustration that an incorrect interaction can propagate through the network and affect the resulting scores and interpretation.

      As detailed under R1.9, we explored the four ligands with consistently negative MOON scores. This distinguishes the localized OSM–IL6ST annotation error from broader sensitivity of the IL6 result to network representation, while no comparably specific annotation error was identified for FGF10 or IGF1. The diagnostic comparison is not presented as a replacement benchmark and does not exclude biological-context or other experimental explanations.

      Consistent with the claim-scope revisions under R2.1 and R2.2, we therefore interpret MOON scores and subnetworks as prior-knowledge-dependent, mechanistically informed hypotheses rather than definitive mechanisms, causal drivers, or clinical biomarkers. Furthermore, we believe such an approach can in fact help pin-pointing problems in prior knowledge and help fix them, as in the OSM case. No additional manuscript-text amendment is proposed for R2.3: the detailed ligand-level analysis is addressed under R1.9, and the applied R2.1/R2.2 amendments already make this interpretation boundary explicit.

      R2.4: Positioning relative to integrative oncology studies

      Reviewer comment: The authors may wish to discuss how COSMOS+ compares conceptually with recent efforts aimed at identifying clinically relevant oncogenic determinants through integrative multi-omics analyses. The article "Comprehensive analysis of oncogenic determinants across tumor types via multi-omics integration" would be a valuable addition and would help position the present framework within the broader landscape of cancer systems biology and biomarker discovery.

      Response:

      We thank the reviewer for this helpful suggestion. We added a Discussion paragraph that positions COSMOS+ relative to complementary integrative oncology approaches. Pan-cancer studies combine multiple molecular layers to characterize recurrent oncogenic alterations and molecular subtypes, whereas COSMOS+ combines activity estimates with a signed prior knowledge network to formulate context-specific, testable mechanistic hypotheses. We make explicit that COSMOS+ is not intended to establish recurrent genomic drivers or validated clinical biomarkers.

      We cite the suggested article as a recent broad review of this landscape, together with two primary TCGA studies that directly support the respective recurrent-alteration and molecular-subtype statements. The functional-validation and clinical-interpretation boundaries are already specified in the accepted R2.1 and R2.2 amendments.

      Citation additions: Sanchez-Vega et al (2018), Cell, doi:10.1016/j.cell.2018.03.035; and Ubaid et al (2025), Cancer Genetics, doi:10.1016/j.cancergen.2025.08.010.

      We have modified the following sections of the manuscript accordingly:

      Location: Discussion

      Additional text in manuscript: “These analyses demonstrate the flexibility and efficiency of COSMOS+ to generate mechanistic hypotheses across multi-omic layers and in diverse contexts. These results also illustrate how mechanistic hypotheses generated in the context of pre-clinical cell-line models can be compared with outcome associations in a clinical cohort, but do not establish generalisable prediction of treatment efficacy or the underlying molecular causes of treatment resistance. ** Pan-cancer studies have integrated multiple molecular layers to characterize recurrent oncogenic alterations and molecular subtypes (Sanchez-Vega et al, 2018; Hoadley et al, 2018) Ubaid et al (2025). COSMOS+ is orthogonal to these approaches, as it combines activity estimates with a signed prior knowledge network to formulate context-specific, testable mechanistic hypotheses rather than to establish recurrent genomic drivers or validated clinical biomarkers. This opens the possibility to combine both approaches, using functional mutations as inputs for COSMOS+.”

      Reviewer #2: Significance

      R2.S1: Validation scope and significance

      Reviewer comment: I find the methodological contribution potentially valuable, but the current manuscript remains largely computational and exploratory. Additional functional validation and stronger clinical validation would considerably enhance the biological significance and translational impact of the study.

      Response:

      We agree that additional targeted, context-matched perturbation or rescue experiments and independent clinical validation would strengthen any individual mechanistic or translational claim. The primary contribution of this study is indeed computational, and in particular methodological. The relevant clarifications of scope are detailed above (R2.1 and R2.2). These amendments make the functional and clinical validation boundaries explicit. We did not add further wet-lab validations or an independent clinical cohort in this revision; such validation would need to be designed separately for each prioritized mechanism and biological context and we estimated that they would fall outside of the clarified scope of this manuscript.

      Reviewer #3: Evidence, Reproducibility And Clarity

      General comment

      Reviewer comment: The model and datasets are clearly presented for the reproducibility and clarity.

      Response:

      We thank Reviewer 3 for recognizing the clear presentation of the model and datasets.

      Reviewer #3: Significance

      R3.1: Sparse biologically meaningful subnetworks

      Reviewer comment: It is a good idea to integrate multi-omics data to mine the potential signaling pathways. One challenge is that the network is complex and it is hard to identify a small scale, sparse but biologically meaningful signaling pathways/networks. It might be helpful to provide solid results or discussion about this challenge.

      Response:

      We thank the reviewer for highlighting this important challenge. We agree that reducing complex prior-knowledge networks to small, sparse, and biologically interpretable subnetworks remains difficult, particularly because alternative network paths can explain the same multi-omic observations.

      COSMOS+ already provides a set of complementary, user-accessible functions intended to improve interpretation at different scales. For a selected upstream node, get_moon_scoring_network extracts a focused scoring network containing the downstream footprint used to compute its MOON score, allowing the local evidence underlying an individual prioritization to be examined. For a compact solution-level view, reduce_solution_network applies a user-chosen absolute-score cutoff, retains only sign-consistent interactions, restricts the network to nodes reachable from the specified upstream inputs, and prunes unsupported sources and sinks. Pathway-control analysis provides a complementary pathway-level summary: it tests which pathways are over-represented among the downstream neighbourhoods of high-scoring nodes and represents the results in a node--pathway matrix. In the manuscript, these approaches are illustrated through score-specific ligand networks and through the thresholded factor-4 MOON network together with its pathway-control analysis.

      We further provide additional functions in the COSMOS NCI60 tutorial; here a dual-threshold network-extraction procedure. A stringent primary threshold defines a high-scoring core, while a lower secondary threshold retains score-supported neighbouring nodes of that core; sign-consistency filtering is then applied. Although this procedure is not used for the networks shown in the main manuscript, it provides a practical analysis option for obtaining compact, interpretable subnetworks while preserving locally supported context.

      These functions improve the interpretability of complex network results but do not provide a general solution to sparse-network inference. Developing and evaluating more robust strategies to produce sparse, biologically meaningful network summaries remains an active area of our ongoing work.

      We have added the following sections to the manuscript accordingly:

      Location: Discussion

      Additional text in manuscript: “In order to pin-pointing such errors more easily, we have developed a range of functions to help extract and interpret sparse subnetworks for the contextualised prior knowledge. For example, some functions help extract sub-networks based on minimum activity scores of nodes, other focus on connecting a specific node to downstream measurements that it relies on for its score, and a function to explore over-representation of biological terms and pathways downstream of a given node.”

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      Referee #3

      Evidence, reproducibility and clarity

      The model and datasets are clearly presented for the reproducibility and clarity.

      Significance

      It is a good idea to integrate multi-omics data to mine the potential signaling pathways. One challenge is that the network is complex and it is hard to identify a small scale, sparse but biologically meaningful signaling pathways/networks. It might be helpful to provide solid results or discussion about this challenge.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript presents COSMOS+, an extension of the COSMOS framework that integrates multi-omics factor analysis with prior-knowledge signaling and metabolic networks through the newly developed MOON algorithm.

      The authors demonstrate the approach using the NCI60 dataset, breast cancer resistance models, and a breast cancer patient cohort. The computational framework is technically sophisticated and addresses an important challenge in systems biology,the principal novelty resides in the development of the MOON/COSMOS+ computational framework itself. The biological applications presented throughout the manuscript function largely as case studies illustrating the algorithm rather than generating fundamentally new biological insights. The analyses of breast cancer resistance and the NCI60 dataset are interesting examples, but most conclusions remain computationally inferred and are not experimentally demonstrated.

      The manuscript proposes multiple signaling regulators, resistance-associated pathways, and mechanistic hypotheses, yet none of these predictions are directly tested. Given the emphasis on identifying resistance drivers and actionable biological mechanisms, additional experimental evidence would substantially strengthen the work. Validation through CRISPR-mediated perturbation, knockdown experiments, or other functional assays would help establish whether the inferred network regulators truly contribute to the phenotypes described.

      I also have reservations regarding the translational implications proposed by the authors. The manuscript suggests potential utility for patient outcome prediction and resistance mechanism discovery. However, the patient cohort analyzed is relatively limited in size and, importantly, no independent external validation cohort is provided. As a result, it remains unclear how robust and generalizable these predictive signatures would be in broader clinical settings. This aspect should be discussed more cautiously. Suggest to have a look at paper such as ""Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research" which discuss database bias.

      The authors themselves provide a compelling example in which an erroneous IL6ST annotation leads to incorrect MOON predictions. This example highlights a broader issue: the quality of the inferred mechanistic networks is inherently constrained by the accuracy and completeness of resources such as OmniPath, STITCH, and related databases. Consequently, false or incomplete annotations may propagate through the analytical pipeline and influence biological interpretation.the authors may wish to discuss how COSMOS+ compares conceptually with recent efforts aimed at identifying clinically relevant oncogenic determinants through integrative multi-omics analyses. The article "Comprehensive analysis of oncogenic determinants across tumor types via multi-omics integration" would be a valuable addition and would help position the present framework within the broader landscape of cancer systems biology and biomarker discovery.

      Significance

      I find the methodological contribution potentially valuable, but the current manuscript remains largely computational and exploratory. Additional functional validation and stronger clinical validation would considerably enhance the biological significance and translational impact of the study.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Dugourd et al. present COSMOS+, a computational framework that integrates multi-omics data with prior knowledge networks to generate mechanistic hypotheses connecting signaling, transcriptional regulation, and metabolism. The study addresses a relevant challenge in the field: the difficulty of moving beyond purely data-driven factor analysis toward biologically interpretable and causally grounded insights, introducing MOON, a scalable iterative network scoring algorithm as a practical alternative to computationally expensive optimization-based approaches such as CARNIVAL. Importantly, the method has the potential to highlight errors in prior knowledge networks. This will not palliate the incompleteness of the existing prior knowledge but, at least, it can identify inconsistencies and help 'correct' the databases. It is not entirely clear whether inconsistencies stem from specificities of cell lines/samples that are not reflected in the general databases, or simply mistakes in the databases. In any case, reconstructing mechanistic hypotheses based on (multi)omics datasets remains an important endeavour, necessary to understand biological processes and also with clear applications in medicine. The code is available and clearly documented ensuring reproducibility. The literature survey is comprehensive and useful to understand the need for developments in this field.

      While the framework is flexible and the applications span a diverse range of contexts from cell line collections to clinical cohorts, some aspects of the work require stronger justification and additional evaluation tests might increase the usefulness of the methods for the community.

      1. MOON scores are ULM t-values at layer 1, but at deeper layers they are t-values computed from previous t-values. It is unclear whether the propagated scores follow the same distribution across layers, which is a requirement to apply the same threshold of |score| > 1.5 uniformly. The authors should either provide a justification for why the threshold remains meaningful at deeper layers, or characterize through simulation how score distributions change across layers and propose a layer-specific thresholding strategy.
      2. The transcriptional consistency check removes TF -> target edges where TF score is incoherent with target expression, but no analogous check is applied to kinase -> TF or receptor -> kinase edges. The upstream signaling layer is therefore only constrained by the upstream anchor comparison while the transcriptional layer (TF -> target gene) undergoes aggressive pruning. The authors should justify this asymmetry explicitly or extend the consistency check to upstream layers where phosphoproteomic data is available. We are aware that prior knowledge on signs of activation might also be lacking, often it will be hard to know what is the real ground truth without doing specific experiments in the right cell types/conditions. Some more discussion about this would clarify the difficulty of this problem.
      3. For the MOFA section, the authors state they used the parameter viewsscale_views = False. This means the three omics layers were not variance-normalized before input to MOFA. Since transcriptomics, proteomics, and metabolomics have different features ranges, the omic layer with higher absolute variance will dominate the factor structure. The authors should either justify this choice explicitly or show that the factor structure is not dominated by a single omic layer.
      4. There is no multiple testing correction in the clinical association analysis. The ULM-based clinical metadata association tests each factor against each clinical category independently. With 9 factors and the number of clinical categories available in NCI60 (tissue of origin alone has ~10 categories, plus age, pathology, and other variables), the number of simultaneous tests is large. The same issue applies to the Cox survival analysis in the Paloma3 section, where a separate Cox model is fitted for every node in the MOON network and results are reported without any correction for multiple comparisons. The authors should apply FDR correction at both stages to confirm that reported associations are not false positives arising from the large number of simultaneous tests performed.
      5. The threshold of |t| > 2 is applied uniformly across TF activity scoring, kinase scoring, LR scoring, and clinical associations without justification. A t-value of 2 corresponds approximately to p < 0.05 only for specific sample sizes, and the implied p-value therefore differs across the three datasets analyzed in this paper which have substantially different sample sizes. The authors should justify this threshold, perform a sensitivity analysis, or demonstrate that conclusions are robust to alternative thresholds.
      6. The methods section does not report what minimum regulon size was used for the Cytosig analysis, and no justification is provided for this parameter. It is not clear if it is still 10 or a different one. Cytosig signatures vary widely in how many genes were measured across experiments, meaning a uniform minimum threshold will exclude TFs not because they are inactive but simply because their targets were not measured in a given experiment. The authors should explicitly report the threshold used, assess whether using a lower threshold such as 5 recovers additional scorable signatures without substantially degrading TF activity reliability, and report the distribution of gene coverage across Cytosig signatures to contextualize how many signatures are affected by this limitation.
      7. The maximum number of propagation steps for the reachability filtering is not reported or justified. In the case of the Cytosig analysis, the paper states that 31 ligands could not be scored because they were not reachable upstream of TFs within the allowed number of steps, but never states what that number was. This parameter directly determines which ligands can be benchmarked, but no sensitivity analysis is provided showing whether increasing the step limit recovers additional ligands or changes the benchmark results. The tool might be enhanced with a report of alternative values, discussion of optimal values and discussion of the tradeoff between reachability and the MOON score reliability at greater network distances.
      8. The benchmark scores only 549 out of 1359 available signatures (40% of the data) due to PKN reachability and TF coverage limitations. Since both limiting parameters are neither reported nor optimized, it is unclear whether the excluded 60% represents a fundamental limitation of the method or an artifact of conservative parameter choices. The authors should explore alternative parameter values and report their effect on both benchmark coverage and performance jointly.
      9. Four ligands received consistently negative MOON scores across experiments where they were applied (the opposite of the expected direction). The paper identifies and corrects the OSM annotation error but does not investigate or explain the remaining consistently negative ligands. The authors should identify the source of the systematic sign inversion for each of these ligands whether it reflects additional PKN annotation errors, missing interactions, or biological context specificity, and report whether correcting those errors changes the overall benchmark performance.
      10. The paper reports only 16 out of 63 ligands (25%) show statistically significant positive MOON scores across experiments. The authors should investigate and discuss what drives this low reliability. Is the variability in scores across experiments for the same ligand explained by cell type specificity, experimental protocol differences (if there are any), PKN incompleteness, or limitations of the MOON scoring procedure itself? The use of a generic PKN like OmniPath, which contains interactions derived from many different cell types and conditions, may introduce context-irrelevant edges that add noise or produce sign inversions when scoring ligands in specific experimental contexts. Context-specific network inference approaches such as ARACNE, GENIE3, SCENIC applied directly to the transcriptomic data of each experiment or to other datasets in similar cell types/conditions, could potentially improve MOON's reliability by restricting propagation to interactions that are actually active in the biological context being analyzed. Without understanding the sources of failure it is unclear whether performance can be improved through such strategies or whether the low reliability reflects a more fundamental limitation of the prior knowledge network approach in diverse biological contexts.

      Minor comments:

      A transcriptional consistency check can be performed that removes any Interaction number of downstream TF(s) participating

      Check for contracted forms , typically not accepted in written text

      Figure 3 these should be D and E, there are two Cs C) MOON Network connecting the top deregulated TFs and LR interactions of factor 4 based on a signed directed prior knowledge network. D) Heatmap of the top results of the Pathway control analysis. We represent pathways that are significantly over-represented downstream of given nodes of the MOON thresholded network

      and merely contextualize(s) a part of it.

      Check MOON caps The precision is again high among the top PC1 Loading (s)

      the complementarity of MOON scores with expression value(s)

      Comma missing However, there are many more types of domain knowledge that can potentially be used to interpret feature weights of factors beyond pathway ontologies, such as prior knowledge in the form of footprints and signed-directed networks can help to provide interpretable insights from factor weights.

      Unclear: We saw that it was able to recover expected regulation mechanisms, as some of the top gene-pathway interactions were found to be e.g. JAK regulates the JAK-STAT pathway.

      Methods: The ULM method of decoupleR was used with CollecTRI to estimate patient specific transcription factor activity signatures (XXX min target per TF).

      Significance

      This paper addresses two main issues in the field: 1) going beyond correlation and towards mechanistic and causal explanations in biomedically relevant regulation processes and 2) using prior knowledge while also checking its consistency with data. The approach proposed is likely to provide extremely useful insight, as shown by applications both in-vitro and in patient cohorts. The only limitation, which cannot easily be addressed but it is generally an issue in the field, is our lack of certainty about the ground truth which makes it very difficult to assess the relevance of the hypotheses provided by the computational approach. The paper will be of broad interest to the computational biology community, especially in oncology and drug discovery.

      We are researchers in the same field, we have tested several other approaches to perform similar tasks.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Response to the points raised by the reviewers.

      Once again, we would like to thank the reviewers for their comments. We have systematically addressed their concerns, as detailed below.

      Reviewer #1

      Evidence, reproducibility and clarity

      This study demonstrates that BICD2, previously known as an adaptor protein for dynein, is involved in regulating centriole engagement during mitosis. First, using different antibodies, it was shown that BICD2 localizes near the mother centriole, as observed by super-resolution microscopy. During G1 and S phases, BICD2 localizes slightly outside the Cep152 ring, while in G2 to mitosis, it localizes near the cartwheel component SAS-6. Moreover, analysis of deletion mutants revealed that BICD2 localizes to the centrosome in a CC domain-dependent manner at the C-terminal end. The localization pattern resembling a ring in the cytoplasm was also observed through the CC3 domain. Next, BICD2 knockout (KO) cells were generated to investigate centriole dynamics. In BICD2 KO cells, the distance between the mother and daughter centrioles was observed to increase from G2 to mitosis compared to controls. Along with this, early centriole disengagement and centriole amplification phenotypes were observed. The increased distance phenotype between centrioles was rescued in BICD2 wild-type (WT) and mutant forms lacking the CC1 domain at the N-terminus, suggesting that this function of BICD2 is independent of dynein. Additionally, BICD2 mutants mimicking phosphorylation at the C-terminus showed reduced centrosome localization and were unable to rescue the phenotypes seen in BICD2 KO cells.

      While the study clearly demonstrates BICD2's contribution to centriole engagement, the underlying mechanisms of how BICD2 is involved in centrosome localization and centriole engagement remain unclear. As it is anticipated that the function of BICD2 is independent of dynein, further exploration of this unknown mechanism would enhance the value of the paper. Below are the concerns that should be addressed, including new experiments.

      Main Points:

      1. __ Fig. 1-3: Regarding the localization of BICD2 to centrioles, during the G1-S phase, its localization appears to overlap with PCM. Experimental investigation should be performed to examine whether BICD2's centrosomal localization is influenced by knockdown of PCM components like PCNT, Cep192, or Cep152.__ We now show that BICD2 localization does not depend on pericentrin (Supplementary Figure S4B). We also show that the two proteins do not colocalize (Supplementary Figure S4A and S4D) and are functionally independent (Figure 5).

      We also show that BICD2 localization does depend on the torus protein CEP152 (Figure 8B). Importantly, our data indicate that BICD2 interacts with the N-terminal region of CEP152, suggesting that this interaction places BICD2 at the outer region of the torus (Figures 8C and 8D). We propose that this provides a mechanistic basis for BICD2 function in maintaining mother-daughter centriole engagement.

      __ Fig. 7: The experiments using BICD2 mutants suggest that the function of BICD2 here is independent of dynein. To further investigate whether BICD2's role in centriole engagement is independent of dynein, experiments should be conducted to examine the effect of dynein knockdown on BICD2 localization to the centrosome and centriole engagement.__

      Using Dynapyrazole-A, a fast-acting and potent dynein inhibitor, we demonstrate that acute inhibition of dynein motor activity affects neither the centrosomal localization of BICD2 during G2 and M phases (Supplementary Figure S3A) nor centriole engagement (Supplementary Figure S3B). Together with experiments using BICD2 mutants deficient in dynein interaction (Figure 7B), these data compellingly demonstrate that the recruitment and function of BICD2 at the centriole is dynein-independent.

      __ Fig. 4: The CC4 domain at the C-terminus of BICD2 is important for its centrosomal localization, but identifying the binder/recruiter responsible for BICD2's centrosome localization would be desirable.__

      We thank the reviewer for prompting us to investigate this further. We are delighted that we now identify the torus protein CEP152 as the BICD2 binder/recruiter at the centriole (Figure 8). As we discuss in the manuscript, our observation that the outward-facing N-terminus of CEP152 interacts with the C-terminal region of BICD2 provides a mechanistic basis for understanding BICD2's role in maintaining mother–daughter centriole engagement.

      __ Fig. 7: Rescue experiments using BICD2 mutants suggest that BICD2's functional domains are critical. Further experiments by creating mutants missing parts of CC2 or CC3 could identify functionally important domains of BICD2 by observing any loss-of-function phenotypes at the centrosome.__

      We fully appreciate the reviewer’s suggestion to examine the roles of the CC2 and CC3 regions. We believe that these domains, and particularly the unstructured loop within CC3, are important for both BICD2 localization, function and regulation at the centrosome. However, given that our current data already establish a clear mechanism for BICD2 centriolar recruitment via CEP152 and the CC4 region, we feel these additional structural studies fall outside the core scope of the present manuscript. We hope the reviewer agrees that the current evidence provides a robust foundation for our conclusions, and we look forward to addressing the roles of CC2 and CC3 in a dedicated future study.

      __ Fig. 6: Regarding the BICD2 KO cell phenotype, is there experimental evidence showing an increase in centriole number during mitosis? For instance, while no abnormality in centriole number may occur during G2, a trend of increase in mitosis should be experimentally demonstrated. Also, how should the slight differences in phenotypes between Ndelta4 and Ndelta5 BICD2 KO cells be interpreted?__

      We thank the reviewer for highlighting this point, but we would like to clarify that we do indeed observe a significant increase in centriole number during both mitosis and G2 phase across multiple cell lines in our BICD2 KO models and RNAi experiments (Figure 4E, RPE-1 KO cells, and 4G, U2OS cells, RNAi) and G2 (Figure 5C, both RPE-1 and U2OS, RNAi). As the main text was not explicit enough on this point, we have revised the manuscript to describe these observations more clearly.

      Regarding the phenotypic differences between BICD2 KO lines, we assign them to the clone-to-clone functional heterogeneity often seen in CRISPR/Cas9-generated cell lines. Importantly both clones show a consistent, statistically significant phenotype (e.g., impaired engagement and increased centriole numbers) compared to wild-type controls, confirming that the overall defect is robust and specific to BICD2 loss. We have added a clarifying note on this in the revised manuscript: “Figure 4F; we assign the differences between BICD2-/- cell lines to standard clone-to-clone phenotypic heterogeneity often seen in CRISPR/Cas9-generated cell lines.

      __ Fig. 8: Regarding the phosphorylation of BICD2 at the C-terminus: The phenotypes of mutants where these two phosphorylation sites are changed to alanine should be experimentally observed. It is expected that the removal of BICD2 from the centrosome during mitosis could be rescued. Additionally, the effect of PLK1 or CDK1 inhibitors on the removal of BICD2 from the centrosome should be investigated.__

      We agree with the reviewer that phosphonull mutants should be added to these experiments. As mentioned above we have decided to remove the preliminary data regarding BICD2 phosphorylation from the manuscript data to present a more comprehensive, dedicated study on BICD2 phosphorylation in the near future. In fact, we have already performed the suggested experiments, including the phosphonull mutants and kinase inhibitor treatments, and would be glad to share these additional results if the reviewers would find them helpful. Interestingly, our experiments show that BICD2 centrosomal amounts are not affected by PLK1 inhibition (using BI 2536); CDK1 inhibition (RO-3306), although not significatively changing the amount of BICD2 at the centrosome, slightly diminishes it. We currently favor a model in which BICD2 is predominantly regulated by CDK1, and we are actively defining the precise molecular mechanism governing this regulation.

      Minor Points:

      __ Fig. 1-3: During G1 and S phases, BICD2 localizes near the mother centriole, and from G2 onward, it colocalizes with SAS-6. How can this be explained?__

      We currently do not have a clear explanation for this transition, as our focus has been in understanding BICD2 recruitment to the centriole (and its role in centriole engagement). We view this as a very interesting question that could be studied together with BICD2 regulation through phosphorylation. Our current hypothesis is that most of BICD2 is removed through phosphorylation in late G2 and M, with a pool remaining at the mother-daughter interface, possibly protected by a yet to be understood mechanism. We have added a sentence in the discussion addressing this (“ A pool of protein could be protected and correspond to the observed remnant of BICD2 at the mother-daughter interface.“). This last pool, as we discuss in the manuscript, could be further phosphorylated at the M/G1 transition or cleaved by separase (although this last point is of course highly speculative).

      __ Fig. 4: The GFP-BICD2 488-820 fragment forms cytoplasmic rings, which is interesting. This domain contains the CC4 domain, so it can localize near the centriole, but why does it not form a perfect ring there? Also, which other centriole/centrosome markers were used for colocalization studies? Does knockdown of PCM1 affect BICD2's centrosomal localization?__

      We show in Figures 6G and 6H that BICD2 488-820 can form a ring around the centriole. Indeed this polypeptide contains the CC4 region, which our results indicate it will guide it to the centriole (through an interaction with CEP152). Once the available CEP152 is occupied with BICD2 we assume that BICD2 488-820 forms oligomers that assemble ring-like structures outside the centriole.

      Other centrosomal markers used are SAS-6.

      We now show that PCM1 knockdown does not affect BICD2's centrosomal localization (Supplementary Figure S4C). Although our results indicate that partial forms of BICD2 such as BICD2 488-820 can colocalize with PCM-1 (Figure 6D), full length endogenous BICD2 (or GST-BICD2) does not seem to colocalize with this protein and thus the centriole satellites (Supplementary Figure S4A). We note this discrepancy in the text: “The presence of BICD2 at the centriolar satellites has been suggested previously (Quarantotti et al, 2019); we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 but not the full-length protein”. The relationship between satellites and BICD2 grants further studies. Our data suggests that centriolar localization of the protein may be regulated, possibly by its intramolecular structure, and that regulated binding of BICD2 to a yet to be identified partner may recruit the protein to satellites either for its transport to the centrosome or in order to perform a specific function at the satellites. We have added a sentence to the text to note this: “This suggests that BICD2 satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein.”.

      __ Fig. 4A: What are the aggregates observed in the cytoplasm under the GFP-BICD2 + ice condition? Also, does the 1-575 mutant fail to localize to the centrosome upon ice treatment?__

      We currently do not know the nature of the GFP-BICD2 full length aggregates observed upon microtubule depolymerization. We also observe GFP-BICD2 aggregates in cells that express high amounts of the polypeptide, leading us to hypothesize that it may be insoluble and the disappearance of microtubules may liberate it from motor complexes resulting in its aggregation -although of course more work would be needed to clarify this.

      We now show new data (Supplementary Figure S5), showing that the localization of not only GFP-BICD2 1-575 but also the C-terminal fragments 272-820 and 488-820 are not significantly affected by cold-induced microtubule depolymerization. These last results strongly suggest that BICD2 localization at the centriole is microtubule independent and are compatible with our data showing that BICD2 can directly interact with the centriolar protein CEP152.

      __ Can similar phenotypes be observed in other cell types when BICD2 is knocked down? This should be experimentally validated.__

      Our current manuscript now shows that similar phenotypes regarding centriole separation and amplification are observed upon BICD2 depletion in RPE-1 cells (non-transformed, p53-wildtype) and U2OS cells (transformed). These are shown in Figure 4 (RPE-1 knockout, U2OS RNAi knockdown) and Figure 5 (RPE-1 and U2OS RNAi knockdown).

      __ Are there previous studies suggesting that this function of BICD2 is evolutionarily conserved? This should be addressed.__

      To our knowledge there are no previous studies describing BICD2 function at the centrosome, excepting the recent article by Kuang et al., (Kuang W et al. 2025. BICD2 promotes ciliogenesis by facilitating CP110 removal from the mother centriole. EMBO reports 26:5567–5588. DOI: https://doi.org/10.1038/s44319-025-00597-0), that describes a role for BICD2 during ciliogenesis in non-cycling cells. As we mention in our discussion this new role may be related to the distal pool of protein that we observe using ExM, and we don’t think is related to the function of the proximal pool of BICD2 at the torus in cycling cells that we describe in our manuscript.

      Regarding functional conservation, BICD2 orthologs are widely distributed across metazoans (as reflected in OrthoDB, which lists ~5,000 ortholog genes across ~2,500 species). They share a remarkably conserved C-terminal domain that acts as a docking interface mediating subcellular targeting independently of dynein motor activity (i.e. through binding to Rab6, RanBP2 and, as shown here, CEP152). Cross-species analyses show that this C-terminal domain is preserved in most eukaryotic orthologs, including Drosophila melanogaster BICD (UniProt P16568) and Caenorhabditis elegans BICD-1 (UniProt V6CJ04). Interestingly, several predicted orthologous sequences in public databases retain high C-terminal similarity while completely lacking the N-terminal regions containing the CC1 box motif (residues 29–57 in human BICD2) required for dynein interaction (e.g., predicted isoforms in mouse or camels). Thus, dynein-independent scaffolding functions may represent an ancient, foundational role of the BICD protein family, or alternatively (and perhaps most probably, given that basal metazoans like sponges or Cnidaria do show a conserved N-terminus), these truncated forms may have evolved to fulfill distinct cellular roles operating independently of motor-adaptor activity. We have added a passage at the end of the discussion to reflect this.

      Significance

      In this paper, the identification of BICD2 as a novel factor regulating centriole engagement is of significant importance. However, the mechanisms through which BICD2 controls its localization to the centrosome and regulates centriole engagement remain largely undefined. Further exploration of these mechanisms would likely enhance the value of the paper.

      The findings are likely to be of great interest to researchers in the field of cell biology, particularly those focusing on centrosome biology.

      The above feedback comes from a researcher specializing in centrosome studies.

      __ __

      Reviewer #2

      Evidence, reproducibility and clarity

      Montez-Ruiz and colleagues explore the role of a dynein adaptor BICD2 in the engagement of mother and daughter centrioles. Cells need to maintain centriole engagement in interphase to prevent centriole reduplication and in early mitosis to prevent the formation of aberrant mitosis spindles. The authors demonstrate that BICD2 is a centriolar protein that surrounds the mother centriole adjacent to the daughter centriole. It is removed from centrosomes in mitosis, which, in turn, is responsible for centriole disengagement. Further, they suggest that in BICD2 knock-out G2 and early mitotic cells, centrioles disengage prematurely. By conducting rescue experiments, the authors conclude that BICD2 regions CC2, CC3, and CC4, which are dynein-independent, are essential for their function at the centrosome. Finally, they show that the phosphorylation of S817 and S819 of BICD1 controls its centrosome localization.

      Major comments:

      1. __ Based on F1 and SF1, BICD2 is reduced from centrosomes already in early G2. So, it is hard to square how removing a factor that is not present at the centrosomes at the time of disengagement would dysregulate disengagement. The study at this stage does not explain how BICD2 contributes to centriole engagement only in mitosis, while it does not affect centrioles in S.__ We now present new data obtained using expansion microscopy (ExM) that, together with our super-resolution observations, clarifies this point. As shown in the new Figure 3 and Figure EV2 (and supported by Figures EV3 and EV4), although the total amount of BICD2 at centrosomes is significantly reduced from G2 to M, a pool of BICD2 persists at the mother centriole until late mitosis. Importantly, this pool tends to localize close to the daughter centriole. We note this in the text (“BICD2 remained visible in both diplosomes, associated with the SAS-6 foci (Figure 3, Figures EV2-3). Around anaphase, BICD2 was not detectable in some diplosomes, while others retained some protein (again, close to the SAS-6 foci, which at this point were disappearing from the centrioles as the result of the disassembly of the cartwheel).”). Supported by our BICD2 depletion experiments, we propose that this centriolar pool enables BICD2 to contribute to engagement until late mitosis, when the remaining protein at the centrosome is ultimately removed. We highlight this model in the Discussion section (“In mitosis, when the protein progressively disappears from the centrosomes, BICD2 remains functionally relevant -likely via the small pool that persists at the mother–daughter centriole interface.”).

      Based on our new data demonstrating an interaction with CEP152, we propose that BICD2 forms an outer component of the centriolar torus. Centriole engagement is known to be maintained during S phase by the cartwheel (Huang F et al. 2022. Cartwheel disassembly regulated by CDK1-Cyclin B kinase allows human centriole disengagement and licensing. The Journal of Biological Chemistry 298:102658. DOI: https://doi.org/10.1016/j.jbc.2022.102658; Ito KK et al. 2025. Multimodal mechanisms of human centriole engagement and disengagement. The EMBO journal 44:1294–1321. DOI: https://doi.org/10.1038/s44318-024-00350-8), with the torus playing a role in cohesion later in the cell cycle. We note in our manuscript that this is consistent with our observations and supports a model in which BICD2 functions as part of the torus: “During S phase, mother–daughter centriole cohesion is maintained by the cartwheel (Huang et al, 2022; Ito et al, 2025) and, consistently, does not depend on BICD2.

      __ The interpretation that the longitudinal localization of the BICD2 signal coincides with SAS-6 and procentrioles requires further evidence. BICD2 seems largely localized to the other regions around the mother centriole, and in some examples, it does not colocalize with the site of the daughter centriole or SAS-6 (for instance: F2B second row; SF4B, second row; SF5, fourth row; SF6 upper row).__

      We have added an ExM characterization of BICD2 centrosomal localization in the revised manuscript (Figures 2 and 3), that we think further clarify this point, showing that BICD2 longitudinally coincides with the torus and the daughter centriole. This is supported by new additional superresolution images (Figure 2, Figure EV2).

      Note that ExM revealed an additional stable pool of BICD2 at the distal end of the centrioles that is not detected using standard methanol fixation combined with 3D-SIM. As discussed in the text this distal pool may reflect additional centriolar functions of BICD2.

      __ BICD2 is important for centrosome-nucleus tethering during centrosome separation in G2, and its global removal likely affects the dynamics of the spindle assembly. Is G2 and mitotic progression affected in knockouts? Do the knockout cells show issues with chromosome alignment? Such analyses are critically missing from the manuscript.__

      BICD2 knockout cells indeed show a slightly higher mitotic index than their wild type counterparts, and a higher frequency of lagging chromosomes in anaphase and telophase as well (new data, shown in Figure EV5C). We agree with the reviewer that these might result from the role of BICD2 tethering centrosomes to the nuclear envelope to facilitate their separation during the initial steps of spindle formation. We have added a sentence in the text noting this: “As expected from cells with supernumerary centrioles, BICD2-/- cells showed a slightly higher mitotic index and a higher frequency of lagging chromosomes in anaphase and telophase (Figure EV5C), although these mitotic defects might also be partially attributed to the role of BICD2 in centrosome separation (Splinter et al, 2010; Gallisà-Suñé et al, 2023).

      __ In general, SCLT experiments are ambiguous. Centriole disengagement spontaneously occurs during prolonged prometaphase induced by SCLT. Accordingly, F6D shows that many centriole pairs in the control sample are disengaged after 16h of SCLT treatment. Although the distance between centrioles in knockout cells is, on average, larger, without knowing how BICD2 perturbations affect the dynamics of the mitotic spindles and mitosis progression, SCLT experiments do not provide enough insight.__

      After 16h of SCLT treatment, the authors regularly measure centriole distances in mitosis smaller than 500 nm in all samples. This suggests that the used method (which also needs to be described) cannot reliably assess centriole engagement status. Centrioles can be disengaged but adjacent. The authors reference Shukla et al. 2015 to compare the centriole-to-centriole distances here with those from that publication. However, in Shukla 2015, centriole-to-centriole distances increase from S to M. But here, in F6, the control centriole distances in S, G2, and early M are almost identical and less than 500 nm. This discrepancy needs to be addressed.

      We thank the reviewer for these constructive comments. We appreciate the opportunity to further clarify our methodology and experimental rationale.

      Validity and necessity of STLC treatment (Figures 4D and 7)

      We fully agree with the reviewer that prolonged STLC treatment (16 hours) carries inherent limitations and should not serve as the sole experimental system for studying centriole engagement. As the reviewer notes, 16 hours of STLC treatment results in a baseline population of control cells displaying disengaged centrioles. This population likely represents cells that entered mitosis early during the treatment and remained arrested for the longest duration, or cells with inherently less robust engagement machinery.

      However, we would like to highlight two key observations that validate STLC as a useful comparative tool in our study:

      • The significative increase in the number of cells with higher intercentriolar distances that indicate disengagement in particular experimental conditions. We show that depletion of BICD2 consistently leads to a statistically significant increase in mean intercentriolar distances compared to controls under identical STLC conditions, indicating a distinct weakening of centriole engagement in a substantial number of cells (and thus suggesting that BICD2 is part of the engagement mechanism).

      • Validation in unarrested cells: Crucially, this effect is not an artifact of mitotic arrest. Unarrested, normally cycling mitotic cells also display significantly increased intercentriolar distances in the absence of BICD2 (Figure 4C).

      Following the initial characterization in Figure 4D, we restricted the use of STLC exclusively to experiments requiring cell transfection and recombinant protein expression (Figure 7). Human RPE-1 cells offer the key advantage of being an untransformed, p53-wild-type model. However, they also present technical challenges, including lower transfection efficiencies and sensitivity to experimental manipulation. Capturing a statistically robust sample of transfected, unarrested mitotic cells proved technically challenging. STLC treatment provided a necessary tool to enrich for mitotic cells while allowing clear observation of rescue effects.

      We have explicitly clarified this technical rationale in the manuscript text:

      "Although this treatment inherently increased mean intercentriolar distances, it nevertheless enabled clear observation of the effects of BICD2 ablation, while yielding a sufficient number of mitotic cells expressing the recombinant proteins."

      Assessment of centriole engagement

      We agree that centrioles can occasionally be disengaged while still remaining adjacent. To avoid oversimplifying the observed phenotypes, we chose to report raw intercentriolar distances rather than applying an arbitrary binary classification of "engaged" versus "disengaged." Furthermore, we do not rely solely on distance measurements to assess engagement status. We complemented these data by quantifying c-NAP1-positive centrioles in unarrested, cycling mitotic cells (Figure 4E, F). Because c-NAP1 loading marks centriole-to-centrosome conversion (and thus licensing), this functional readout independently confirms that BICD2 loss promotes premature centriole disengagement.

      Intercentriolar distances across the cell cycle and cell-type variation

      Regarding the comparison with Shukla et al. (2015), we note that their study was conducted in HeLa cells, whereas our primary model is RPE-1 (alongside U2OS cells). Variations in centriole engagement dynamics and distance kinetics can likely be attributed to intrinsic differences among these cell types:

      RPE-1 cells: baseline intercentriolar distances in S-phase control RPE-1 cells (0.4–0.5 µm, measured using centrin) match those reported for HeLa cells in S-phase by Shukla et al. However, in RPE-1 control cells, these distances remain relatively constant from S phase through early M phase (Figure 4).

      U2OS cells: U2OS cells exhibit a slight increase from 0.43±0.01 µm in G2 to 0.50±0.01 µm in M (Figure 5), illustrating that slight variations occur between cell lines.

      Other studies similarly report persistent baseline distances around 0.5 µm through early cell cycle stages. For example, Yaguchi et al. (Yaguchi K et al. 2018. Uncoordinated centrosome cycle underlies the instability of non-diploid somatic cells in mammals. The Journal of Cell Biology 217:2463–2483. DOI: https://doi.org/10.1083/jcb.201701151) observed intercentriolar distances close to 0.5 µm in diploid HAP1 cells throughout mitosis and into early G1 phase, with substantial disengagement (>0.8 µm) occurring only well after cytokinesis onset.

      To address this discrepancy, we have added the following sentence to the manuscript text: "Note that in wild-type S-phase RPE-1 cells, intercentriolar distances measured using centrin as a marker were similar to those described in S-phase HeLa cells (Shukla et al., 2015), namely 0.4–0.5 µm; however, in contrast to HeLa cells, these distances remained fairly constant from S to early M phase in RPE-1 cells." We have also updated the Materials and Methods section to provide a precise description of how intercentriolar distances were measured: “Intercentriolar distances were assessed as the distance between centrin foci of the same diplosome in maximum projections of z-stacks

      __ The authors suggest that BICD2's functions at the centrosome are independent of its dynein functions. They show that GFP-BICD2 1-820 DD rescues centriole engagement among several other mutants. However, it is still possible that the expression of the mutants affects some yet uncovered BICD2 function outside of centrosomes. At least, T821A and S823A should be mutated to Ala. From what I gathered, such mutant should remain associated with mitotic centrosomes. The authors should analyze whether mitotic progression remains unperturbed, and centriole engagement status should be analyzed without SCLT treatment in G2, M, and in ensuing G1.__

      We agree with the reviewer that phosphonull mutants should be added to these experiments. In fact, and as mentioned in the responses to Reviewer 1, we have already performed experiments with the phosphonull mutants, observing that they are more retained at centrosomes than the phosphomimetic counterparts. We would be happy to share these results with the reviewers upon request if helpful. Nevertheless, and as mentioned above, we have decided to remove the preliminary data regarding BICD2 phosphorylation from the manuscript data in order to present a separate and more comprehensive study on BICD2 phosphorylation in the near future.

      Significance

      The question explored is relevant to the centrosome field and beyond since the processes leading to premature centriole disengagement and amplification are not fully understood. The study provides some novel insights. However, at the current stage, the study is preliminary. Additional experiments would be needed to strengthen the conclusion that BICD2 directly regulates centriole disengagement.

      My expertise is in centriole and centrosome assembly and the mechanisms that regulate centrosome homeostasis in human cells.

      __ __

      __Reviewer #3 __

      Evidence, reproducibility and clarity (Required):

      Centrosome duplication is tightly control during cell cycle to prevent loss or amplification of centrosome numbers, which are detrimental for cell proliferation. In preparation for centriole duplication in S-phase, mother and daughter centrioles disengaged late mitosis, a process that functions as a licensing factor for duplication. While several mechanism have been proposed to be important for centriole disengagement, differences between systems and organisms exist, suggesting alternative pathways may play a role.

      In this manuscript, Montes-Ruiz and colleagues investigate the role of the dynein adaptor protein BICD2 during centriole disengagement. They found that BICD2 localises to the centrioles, with a peak in S-Phase. Super resolution microscopy suggests that BICD2 localises to the mother centrioles and is mostly absent in mitosis cells after anaphase, when centrioles are disengaging. KO of BICD2 in REP-1 cells does not some t have strong phenotypes, but the authors found that centriole separation is increased, suggesting a role in centriole cohesion. While there is limited mechanist insight about the regulation of BICD2 and its function at the centrosomes, the data presented suggests a role for BICD2 in centriole cohesion that is independent of dynein interaction. There are however several issues with data presentation, image analyses and data interpretation the authors could improve.

      Major comments

      - On page 5, the authors state that figure 1 and supplementary figure1 data strongly suggest that BICD2 associates with mother centrioles and not the PCM. This is not very clear from the images on these figures. In fact, PCM is often associated with mother centriole as well, thus I am not sure they can make these conclusions based on the data presented in these 2 figures. Also, the fact that PCM is more abundant in G2/M, when BICD2 is not, does not mean it does not localize to the PCM. Higher resolution of expansion will be needed.

      The data presented in supplementary figure 3 does not help the conclusion above as it seems form the images that there is co-localization between BICD2 and pericentrin. It is impossible to conclude also that there is co-localization with the satellite marker PCM-1. In fact, they seem to have no overlap from the images provided. Higher resolution of expansion will be needed.

      Following the reviewer’s suggestion we embarked in a full characterization of BICD2 localization using expansion microscopy (ExM). We think that our new data further clarifies this together with new superresolution data.

      Additionally we now have a figure (Supplementary Figure S4) addressing the relation between pericentrin and the localization of BICD2. We show that pericentrin downregulation does not affect centrosomal BICD2 levels. And that both proteins do not colocalize as observed using 3D-SIM.

      Also regarding pericentrin, the revised version of the manuscript now includes a figure that functionally compares the results of its depletion to those of BICD2 (Figure 5).

      We also provide data showing that BICD2 localization does not significatively change upon PCM-1 depletion (Supplementary Figure S4C). As we mention in the text, previous reports have suggested that BICD2 is indeed in the satellites (Quarantotti V et al. 2019. Centriolar satellites are acentriolar assemblies of centrosomal proteins. The EMBO Journal e101082. DOI: https://doi.org/10.15252/embj.2018101082) . But we only observed clear colocalization of PCM-1 with C-terminal fragments of BICD2. Thus, while GFP-BICD2 488–820 strongly colocalizes with satellites, endogenous BICD2 and full-length GFP-BICD2 do not (Figure 6D). We ignore the reason for this, but the data suggests that satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein. To address this we have added a sentence to the text that now reads: “The presence of BICD2 at the centriolar satellites has been suggested previously (Quarantotti et al, 2019); we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 (but not the full-length protein, see Supplementary Figure S4) colocalize with satellites. This suggests that BICD2 satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein.”.

      - In figure 2, to confirm localization to the mother centrioles, could the authors use a mother centriole marker? Such as a distal appendage protein of ninein? CEP152 localizes to both centrioles in the images provided.

      I was surprised that BICD2 localizes to both distal appendages and linker? These are not close to each other. Can the authors comment on this? In supplementary figure 4C orthogonal view it seems like BICD2 is in between distal appendages and linker?

      We believe that the new ExM data (Figures 2 and 3) directly address the reviewer's concerns.

      Regarding the original supplementary figure S4C, indeed in the orthogonal projections of 3D-SIM images the signal corresponding to BICD2 was observed between distal appendages and linker and was quite broadly distributed. We recognize that this could lead to confusion. We have now removed part of this figure (original Figures 4B and 4C) from the manuscript, as we think that the data is made redundant with our new ExM data. Our new data, with a much higher resolution shows that BICD2 localization corresponds to that of the proximal torus (see new Figures 2D and 2E, and Figure 3). Note that in our new ExM images we use a daughter centriole marker (SAS-6) that (in addition to CEP152) we think helps confirm that BICD2 localizes around the mother centriole.

      -The IF data suggests that BICD2 localization to the centrosome is dynamically regulated during cell cycle. Did the authors consider that this protein could be degraded? Is it a matter of recruitment or total protein levels?

      We agree that protein degradation has to be considered when analyzing cell cycle-dependent localization. However, our data suggest that the dynamic behaviour of BICD2 at the centrosomes does not reflect changes in its total protein amount. We have previously shown that total BICD2 levels are not reduced in mitosis, as assessed by western blot (Gallisà-Suñé N et al. 2023. BICD2 phosphorylation regulates dynein function and centrosome separation in G2 and M. Nature Communications 14:2434. DOI: https://doi.org/10.1038/s41467-023-38116-1). To make this clear in the current manuscript, we have additionally added Figure EV1B depicting BICD2 levels in S, G2 and M phase, and the following note to the text : “Total levels of BICD2 remained constant during the different phases of the cell cycle (Figure EV1B and (Gallisà-Suñé et al, 2023))“.

      - The authors propose that the dynamic localization of BICD2 is associated with licensing. However, it is rather surprising that the phenotype of centriole separation they describe is only observe in mitosis when BICD2 in knockdown and not in S-phase when the levels of BICD2 are higher? If the role of BICD2 is to prevent premature centiole disengagement, shouldn't that be observed in S-phase as well? Why only in mitosis when in control cells BICD2 levels are already very low?

      Recent data supports the notion that in S phase centriole cohesion is maintained by the cartwheel (Huang F et al. 2022. Cartwheel disassembly regulated by CDK1-Cyclin B kinase allows human centriole disengagement and licensing. The Journal of Biological Chemistry 298:102658. DOI: https://doi.org/10.1016/j.jbc.2022.102658; Ito et al. 2025. Multimodal mechanisms of human centriole engagement and disengagement. The EMBO journal 44:1294–1321. DOI: https://doi.org/10.1038/s44318-024-00350-8). Our data, including the new results showing that BICD2 interacts with CEP152, suggests that BICD2 is a dynamic part of the mother centriole torus, a structure that does not seem to be implicated in maintaining cohesion in S. We now note this in the manuscript’s text: “During S phase, mother-daughter centriole cohesion is maintained by the cartwheel (Huang et al, 2022; Ito et al, 2025), and, consistently, does not depend on BICD2.”. Of note, after Ito etl al. BICD2 (and the torus) may have a role in late S if the cartwheel is compromised, something that could be tested in future studies by downregulating cartwheel components and BICD2 simultaneously.

      - The images of C-Nap1 localization in figure 6E are not very convincing to illustrate the pint the authors are making in the main text (additional C-Nap1 foci are visible in the KO cells)

      We would like to note that visualizing C-NAP1 in mitosis is technically challenging, as a significant pool of the protein is displaced from the centrioles after phosphorylation in G2. However, the protein has been widely used as a marker of centriole disengagement (e.g. in the seminal Tsou M-FB et al. 2006. Mechanism limiting centrosome duplication to once per cell cycle. Nature 442:947–951. DOI: https://doi.org/10.1038/nature04985). We therefore consider it a valuable tool to support our conclusions regarding centriole engagement. Regarding extra c-NAP1 foci in BICD2 knockout cells, these may reflect additional centrioles that appear in these cells, as a result of abnormal disengagement and early licensing. To have this into account our data quantifies both c-NAP-1 positive centrioles (increased in KO cells, Figure 4E) and number of c-NAP-1 positive centrioles /total centriole number (with an increase in the abnormal >2:4 configuration in BICD2 KO cells, Figure 4F).

      - The authors propose that PLK1 and CDK1 phosphorylation sites regulate the association of BICD2 with the centrioles. Could this be tested with a PLK1 inhibitor?

      As noted above, we have removed the phosphorylation data from the manuscript, as we aim to report these findings in a dedicated upcoming study. Nevertheless, to address the reviewer's query, we now consider BICD2 to be predominantly regulated by CDK1, supported by data using BI 2536 showing that BICD2 centrosomal levels are unaffected by PLK1 inhibition. In contrast, CDK1 inhibition slightly reduces these levels, though this effect does not reach statistical significance under the tested conditions. We would be glad to share these additional results with the reviewers upon request.

      - On page 13, the authors state that their results do not agree with previous literature showing that pericentrin cleavage can result in disengagement. However, it was unclear from this manuscript what is the evidence to demonstrate that this is the case? The data presented in figure 5B for example only demonstrates that pericentrin levels do not change in the absence of BICD2 in what looks like S-phase cells. Did the authors look at pericentrin levels when they observe centriole disengagement in the ko cells? In G2 or early M-phase?

      We recognize that pericentrin is widely considered a crucial factor in centriole engagement, and have added new data in the manuscript studying the relationship between it and BICD2 (the partially new Supplementary Figure S4), and their relative importances for engagement both in G2 and M (the new Figure 5). Our data suggests that both proteins act independently in a partially redundant manner, BICD2 as part of the torus (key for engagement in G2) and pericentrin of the PCM (more important in M).

      We have updated the Discussion to present this and our view on pericentrin importance for engagement more clearly, specially our concerns that its importance may have been overestimated. Specifically we write that “BICD2 depletion reduces its centrosomal levels to a degree that mirrors those naturally observed during late M and early G1 in unperturbed cells. In contrast, experimental depletion of pericentrin reduces its levels far below physiological baselines across any phase of the cell cycle. This severe reduction produces marked centriole separation in mitosis that is likely amplified by spindle-derived forces. Consequently, the individual contribution of pericentrin to regulating physiological centriole cohesion may be somewhat overestimated under standard experimental knockdowns, and this regulation may rely more heavily on torus components, such as BICD2, than previously appreciated.

      Regarding the phases of the cell cycle in which we quantify pericentrin levels in the original Figure 5B (now Figure EV5B), we realize that the figure could lead to confusion as it was, as they were measured in M (when its amount is maximal, as specified in the figure legend) but the figure did not show examples in this cell cycle phase. We have added new examples of mitotic cells to the figure, and modified the figure labels and wording of the figure legend to clarify this.

      Minor comments

      - A more general reference (review) missing in the second paragraph of the introduction that describe the centrosomes.

      We have added a recent general reference when introducing centrioles (Gönczy P. 2025. Critical constituents and assembly principles of centriole biogenesis in human cells. Nature Reviews Molecular Cell Biology 1–18. DOI: https://doi.org/10.1038/s41580-025-00921-5). Later in the paragraph, when centriole duplication is introduced, we now use this reference plus the also recent Fernandes-Mariano C et al. 2025. Centrosome biogenesis and maintenance in homeostasis and disease. Current Opinion in Cell Biology 94:102485. DOI: https://doi.org/10.1016/j.ceb.2025.102485.

      - Some figures are not well organized, difficult to see which panel they correspond to? The authors could consider labelling panels better to make this clear. For example, figure 4 and 6 could benefit from additional panel labels.

      We have added additional panel labels to Figure 4 (now Figure 6) and Figure 6 (now Figure 4), that we have also slightly reorganized with the aim of making it clearer).

      - On page 7, what the authors mean by: "... we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 but not the fill-length protein co-localize with these pericentriolar structures"?

      By "pericentriolar structures” we were referring to the centriolar satellites. We realize that that was not clear and updated the wording of the sentence that now reads “we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 (but not the full-length protein, see Supplementary Figure S4) colocalize with satellites.”. We subsequently propose a possible a possible explanation for this: "This suggests that BICD2 satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein.".

      Reviewer #3 (Significance (Required)):

      In general this work has limited mechanistic insight and BICD2 localization to the centrosomes was known. However, the authors do go into more detail description of the centriole localization of BICD2 . In addition, their established KO cell lines provide some insights into the role of BICD2 in centriole disengagement, which is of interest to the field. But the limited scope of the conclusions does not advance the field significantly as it is.

      this work will interest a specialized audience.

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      Referee #3

      Evidence, reproducibility and clarity

      Centrosome duplication is tightly control during cell cycle to prevent loss or amplification of centrosome numbers, which are detrimental for cell proliferation. In preparation for centriole duplication in S-phase, mother and daughter centrioles disengaged late mitosis, a process that functions as a licensing factor for duplication. While several mechanism have been proposed to be important for centriole disengagement, differences between systems and organisms exist, suggesting alternative pathways may play a role. In this manuscript, Montes-Ruiz and colleagues investigate the role of the dynein adaptor protein BICD2 during centriole disengagement. They found that BICD2 localises to the centrioles, with a peak in S-Phase. Super resolution microscopy suggests that BICD2 localises to the mother centrioles and is mostly absent in mitosis cells after anaphase, when centrioles are disengaging. KO of BICD2 in REP-1 cells does not some t have strong phenotypes, but the authors found that centriole separation is increased, suggesting a role in centriole cohesion. While there is limited mechanist insight about the regulation of BICD2 and its function at the centrosomes, the data presented suggests a role for BICD2 in centriole cohesion that is independent of dynein interaction. There are however several issues with data presentation, image analyses and data interpretation the authors could improve.

      Major comments

      On page 5, the authors state that figure 1 and supplementary figure1 data strongly suggest that BICD2 associates with mother centrioles and not the PCM. This is not very clear from the images on these figures. In fact, PCM is often associated with mother centriole as well, thus I am not sure they can make these conclusions based on the data presented in these 2 figures. Also, the fact that PCM is more abundant in G2/M, when BICD2 is not, does not mean it does not localize to the PCM. Higher resolution of expansion will be needed. The data presented in supplementary figure 3 does not help the conclusion above as it seems form the images that there is co-localization between BICD2 and pericentrin. It is impossible to conclude also that there is co-localization with the satellite marker PCM-1. In fact, they seem to have no overlap from the images provided. Higher resolution of expansion will be needed. In figure 2, to confirm localization to the mother centrioles, could the authors use a mother centriole marker? Such as a distal appendage protein of ninein? CEP152 localizes to both centrioles in the images provided. I was surprised that BICD2 localizes to both distal appendages and linker? These are not close to each other. Can the authors comment on this? In supplementary figure 4C orthogonal view it seems like BICD2 is in between distal appendages and linker? The IF data suggests that BICD2 localization to the centrosome is dynamically regulated during cell cycle. Did the authors consider that this protein could be degraded? Is it a matter of recruitment or total protein levels? The authors propose that the dynamic localization of BICD2 is associated with licensing. However, it is rather surprising that the phenotype of centriole separation they describe is only observe in mitosis when BICD2 in knockdown and not in S-phase when the levels of BICD2 are higher? If the role of BICD2 is to prevent premature centiole disengagement, shouldn't that be observed in S-phase as well? Why only in mitosis when in control cells BICD2 levels are already very low? The images of C-Nap1 localization in figure 6E are not very convincing to illustrate the pint the authors are making in the main text (additional C-Nap1 foci are visible in the KO cells) The authors propose that PLK1 and CDK1 phosphorylation sites regulate the association of BICD2 with the centrioles. Could this be tested with a PLK1 inhibitor? On page 13, the authors state that their results do not agree with previous literature showing that pericentrin cleavage can result in disengagement. However, it was unclear from this manuscript what is the evidence to demonstrate that this is the case? The data presented in figure 5B for example only demonstrates that pericentrin levels do not change in the absence of BICD2 in what looks like S-phase cells. Did the authors look at pericentrin levels when they observe centriole disengagement in the ko cells? In G2 or early M-phase?

      Minor comments

      A more general reference (review) missing in the second paragraph of the introduction that describe the centrosomes. Some figures are not well organized, difficult to see which panel they correspond to? The authors could consider labelling panels better to make this clear. For example, figure 4 and 6 could benefit from additional panel labels. On page 7, what the authors mean by: "... we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 but not the fill-length protein co-localize with these pericentriolar structures"?

      Significance

      In general this work has limited mechanistic insight and BICD2 localization to the centrosomes was known. However, the authors do go into more detail description of the centriole localization of BICD2 . In addition, their established KO cell lines provide some insights into the role of BICD2 in centriole disengagement, which is of interest to the field. But the limited scope of the conclusions does not advance the field significantly as it is.

      this work will interest a specialized audience.

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      Referee #2

      Evidence, reproducibility and clarity

      Montez-Ruiz and colleagues explore the role of a dynein adaptor BICD2 in the engagement of mother and daughter centrioles. Cells need to maintain centriole engagement in interphase to prevent centriole reduplication and in early mitosis to prevent the formation of aberrant mitosis spindles. The authors demonstrate that BICD2 is a centriolar protein that surrounds the mother centriole adjacent to the daughter centriole. It is removed from centrosomes in mitosis, which, in turn, is responsible for centriole disengagement. Further, they suggest that in BICD2 knock-out G2 and early mitotic cells, centrioles disengage prematurely. By conducting rescue experiments, the authors conclude that BICD2 regions CC2, CC3, and CC4, which are dynein-independent, are essential for their function at the centrosome. Finally, they show that the phosphorylation of S817 and S819 of BICD1 controls its centrosome localization.

      Major comments:

      1. Based on F1 and SF1, BICD2 is reduced from centrosomes already in early G2. So, it is hard to square how removing a factor that is not present at the centrosomes at the time of disengagement would dysregulate disengagement. The study at this stage does not explain how BICD2 contributes to centriole engagement only in mitosis, while it does not affect centrioles in S.
      2. The interpretation that the longitudinal localization of the BICD2 signal coincides with SAS-6 and procentrioles requires further evidence. BICD2 seems largely localized to the other regions around the mother centriole, and in some examples, it does not colocalize with the site of the daughter centriole or SAS-6 (for instance: F2B second row; SF4B, second row; SF5, fourth row; SF6 upper row).
      3. BICD2 is important for centrosome-nucleus tethering during centrosome separation in G2, and its global removal likely affects the dynamics of the spindle assembly. Is G2 and mitotic progression affected in knockouts? Do the knockout cells show issues with chromosome alignment? Such analyses are critically missing from the manuscript.
      4. In general, SCLT experiments are ambiguous. Centriole disengagement spontaneously occurs during prolonged prometaphase induced by SCLT. Accordingly, F6D shows that many centriole pairs in the control sample are disengaged after 16h of SCLT treatment. Although the distance between centrioles in knockout cells is, on average, larger, without knowing how BICD2 perturbations affect the dynamics of the mitotic spindles and mitosis progression, SCLT experiments do not provide enough insight. After 16h of SCLT treatment, the authors regularly measure centriole distances in mitosis smaller than 500 nm in all samples. This suggests that the used method (which also needs to be described) cannot reliably assess centriole engagement status. Centrioles can be disengaged but adjacent. The authors reference Shukla et al. 2015 to compare the centriole-to-centriole distances here with those from that publication. However, in Shukla 2015, centriole-to-centriole distances increase from S to M. But here, in F6, the control centriole distances in S, G2, and early M are almost identical and less than 500 nm. This discrepancy needs to be addressed.
      5. The authors suggest that BICD2's functions at the centrosome are independent of its dynein functions. They show that GFP-BICD2 1-820 DD rescues centriole engagement among several other mutants. However, it is still possible that the expression of the mutants affects some yet uncovered BICD2 function outside of centrosomes. At least, T821A and S823A should be mutated to Ala. From what I gathered, such mutant should remain associated with mitotic centrosomes. The authors should analyze whether mitotic progression remains unperturbed, and centriole engagement status should be analyzed without SCLT treatment in G2, M, and in ensuing G1.

      Significance

      The question explored is relevant to the centrosome field and beyond since the processes leading to premature centriole disengagement and amplification are not fully understood. The study provides some novel insights. However, at the current stage, the study is preliminary. Additional experiments would be needed to strengthen the conclusion that BICD2 directly regulates centriole disengagement.

      My expertise is in centriole and centrosome assembly and the mechanisms that regulate centrosome homeostasis in human cells.

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      Referee #1

      Evidence, reproducibility and clarity

      This study demonstrates that BICD2, previously known as an adaptor protein for dynein, is involved in regulating centriole engagement during mitosis. First, using different antibodies, it was shown that BICD2 localizes near the mother centriole, as observed by super-resolution microscopy. During G1 and S phases, BICD2 localizes slightly outside the Cep152 ring, while in G2 to mitosis, it localizes near the cartwheel component SAS-6. Moreover, analysis of deletion mutants revealed that BICD2 localizes to the centrosome in a CC domain-dependent manner at the C-terminal end. The localization pattern resembling a ring in the cytoplasm was also observed through the CC3 domain. Next, BICD2 knockout (KO) cells were generated to investigate centriole dynamics. In BICD2 KO cells, the distance between the mother and daughter centrioles was observed to increase from G2 to mitosis compared to controls. Along with this, early centriole disengagement and centriole amplification phenotypes were observed. The increased distance phenotype between centrioles was rescued in BICD2 wild-type (WT) and mutant forms lacking the CC1 domain at the N-terminus, suggesting that this function of BICD2 is independent of dynein. Additionally, BICD2 mutants mimicking phosphorylation at the C-terminus showed reduced centrosome localization and were unable to rescue the phenotypes seen in BICD2 KO cells. While the study clearly demonstrates BICD2's contribution to centriole engagement, the underlying mechanisms of how BICD2 is involved in centrosome localization and centriole engagement remain unclear. As it is anticipated that the function of BICD2 is independent of dynein, further exploration of this unknown mechanism would enhance the value of the paper. Below are the concerns that should be addressed, including new experiments.

      Main Points:

      1. Fig. 1-3: Regarding the localization of BICD2 to centrioles, during the G1-S phase, its localization appears to overlap with PCM. Experimental investigation should be performed to examine whether BICD2's centrosomal localization is influenced by knockdown of PCM components like PCNT, Cep192, or Cep152.
      2. Fig. 7: The experiments using BICD2 mutants suggest that the function of BICD2 here is independent of dynein. To further investigate whether BICD2's role in centriole engagement is independent of dynein, experiments should be conducted to examine the effect of dynein knockdown on BICD2 localization to the centrosome and centriole engagement.
      3. Fig. 4: The CC4 domain at the C-terminus of BICD2 is important for its centrosomal localization, but identifying the binder/recruiter responsible for BICD2's centrosome localization would be desirable.
      4. Fig. 7: Rescue experiments using BICD2 mutants suggest that BICD2's functional domains are critical. Further experiments by creating mutants missing parts of CC2 or CC3 could identify functionally important domains of BICD2 by observing any loss-of-function phenotypes at the centrosome.
      5. Fig. 6: Regarding the BICD2 KO cell phenotype, is there experimental evidence showing an increase in centriole number during mitosis? For instance, while no abnormality in centriole number may occur during G2, a trend of increase in mitosis should be experimentally demonstrated. Also, how should the slight differences in phenotypes between Ndelta4 and Ndelta5 BICD2 KO cells be interpreted?
      6. Fig. 8: Regarding the phosphorylation of BICD2 at the C-terminus: The phenotypes of mutants where these two phosphorylation sites are changed to alanine should be experimentally observed. It is expected that the removal of BICD2 from the centrosome during mitosis could be rescued. Additionally, the effect of PLK1 or CDK1 inhibitors on the removal of BICD2 from the centrosome should be investigated.

      Minor Points:

      1. Fig. 1-3: During G1 and S phases, BICD2 localizes near the mother centriole, and from G2 onward, it colocalizes with SAS-6. How can this be explained?
      2. Fig. 4: The GFP-BICD2 488-820 fragment forms cytoplasmic rings, which is interesting. This domain contains the CC4 domain, so it can localize near the centriole, but why does it not form a perfect ring there? Also, which other centriole/centrosome markers were used for colocalization studies? Does knockdown of PCM1 affect BICD2's centrosomal localization?
      3. Fig. 4A: What are the aggregates observed in the cytoplasm under the GFP-BICD2 + ice condition? Also, does the 1-575 mutant fail to localize to the centrosome upon ice treatment?
      4. Can similar phenotypes be observed in other cell types when BICD2 is knocked down? This should be experimentally validated.
      5. Are there previous studies suggesting that this function of BICD2 is evolutionarily conserved? This should be addressed.

      Significance

      In this paper, the identification of BICD2 as a novel factor regulating centriole engagement is of significant importance. However, the mechanisms through which BICD2 controls its localization to the centrosome and regulates centriole engagement remain largely undefined. Further exploration of these mechanisms would likely enhance the value of the paper.

      The findings are likely to be of great interest to researchers in the field of cell biology, particularly those focusing on centrosome biology.

      The above feedback comes from a researcher specializing in centrosome studies.

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      Reply to the reviewers

      Reviewer #1

      Comment: A limitation of this study is that it is mainly descriptive. It lacks a clear indication of the most promising targets that should help to advance our understanding of PMP22 biology in Schwann cells.

      Response: We thank the reviewer for this important comment. We agree that the original version did not adequately prioritize the identified PMP22 interaction candidates. To address this concern, we performed additional cross-cell type enrichment and protein interaction network analyses to identify the most biologically relevant pathways emerging from the dataset. Moreover, we performed new orthogonal validation experiments using NanoBRET and in situ PLA, thereby moving beyond purely descriptive proteomic observations. Changes in the manuscript: • We added a new paragraph to the Results section entitled “Cross-cell type enrichment and network analysis highlight (sphingo-)lipid metabolism as a prominent cluster among PMP22-ALFA PPIs” (p.9, l.23) corresponding to new Figure 5: “Cross-cell type enrichment, PPI network analysis and initial orthogonal validation”. Previous Figure 5B-B’’ was moved to the supplementary material as new supplementary Figure 4. • We introduced a cross-cell type pathway enrichment analysis integrating GO, KEGG and curated pathway databases (new Figure 5B-D). • We added a PPI network analysis highlighting lipid metabolism and sphingolipid biosynthesis pathways as recurring biological themes (new Figure 5C,D). • We added BRET ratios of PMP22-HaloTag and NanoLuc-tagged PPI candidates, demonstrating close proximity in living cells (new Figure 5E). • We added images showing in situ PLA of PMP22-ALFA and endogenous PPI candidates in MDCKII cells (new supplementary Figure 5). • The methodology describing these analyses was added to the Methods section under “PPI and functional annotation enrichment analysis” (p.15, l.31), NanoBRET assay” (p.15, l.48) and “In situ PLA” (p.16, l.8). • We have adapted the part of the previous discussion highlighting enzymes of the de novo sphingolipid synthesis pathway as PMP22 PPI candidates and moved it to the Results section (new sub-heading “PPIs of PMP22 are in line with specialized functions of adhesion and lipid metabolism in epithelial cells”, p.6, l.38), as enzymes of sphingolipid metabolism, in addition to other potentially important lipid-associated proteins (CAV1, PLLP), appear as enriched PPI candidates for the first time in the MDCKII data. • In the Discussion section we additionally highlighted a possible connection between PMP22’s role in regulation of calcium signaling and lipid metabolism through ORMDL3 (p.11, l.1).

      Comment: The Schwann cell experiments are the most important. However, considering the low transfection efficacy (primary SC) or the low differentiation (MSC80) these are very limiting in the present form. A suggestion could be to improve transfection efficiency by nucleofection or by using lentiviral vectors to transduce primary rat Schwann cells.

      Response: We agree that the limited transfection efficacy of primary Schwann cells and the incomplete differentiation status of MSC80 cells represent important limitations of the present study, which we acknowledge in the Results section (p.8, l.9; p.9, l.14) and in the revised Discussion (p.12., l.22). By discussing DNA-editing in more complex cellular systems as a potential approach for future studies, we are also proposing a method that can address these shortcomings all at once (p.12, l.25). While nucleofection or viral transduction may indeed improve transgene delivery, the establishment and validation of these approaches go beyond the scope of the present study, particularly in light of the alternative discussed, which we have in fact already begun to implement as part of a new project. To strengthen confidence in the identified candidates–also in the experiments using Schwann cells–despite the limitations of the present study, we performed new orthogonal validation experiments and expanded the presentation of the top candidates in the volcano plots. Changes in the manuscript: • We added NanoBRET and in situ PLA data described in the previous response (Figure 5E, supplementary Figure 5). • To better illustrate that we have identified multiple myelin proteins in Schwann cells as PMP22 PPI candidates despite the limitations, we have modified the annotations of the hits in the volcano plots so that the 25 top enriched proteins are now displayed in an enlarged inset instead of 15. This revealed several canonical myelin proteins such as MPZ, PLP1, MCAM, and CMTM5 in these plots (Figure 4A, D).

      Reviewer #2

      Comment: This study across different cell types is the most comprehensive to date to document PPIs of PMP22. Disappointingly the PPIs differ enormously between cells.

      Response: We agree with the reviewer that PPI candidates vary depending on cell type and conditions. However, we do not share the view that this is disappointing. Rather, we interpret this finding as a reflection of the different functional roles of PMP22 in the various cell types. The significance of this result is why we state in the abstract: “We confirm known interactors, and uncover distinct, cell type-specific enrichment patterns following functional annotation analysis.” In the revised manuscript, we also discuss as to how our results reflect the multifunctional role of PMP22 (p.11, l.13).

      Comment: The study appears to have been performed well and the results are clearly presented on the whole. This reviewer was expecting to see some sort of illustration in figure 5 (or separately in the Discussion) that distils the principal linked candidates according to biological processes or pathways with a scoring or ranking system to communicate how strong a case we are looking at.

      Response: We thank the reviewer for this excellent suggestion. In response to this criticism, as well as the concerns raised by Reviewer #1 that the study was merely descriptive, we added a new integrative analysis that summarizes the major biological themes emerging from the dataset and highlights (sphingo-)lipid metabolism as an example of particular interest to PMP22 biology (new Figure 5B-D). Changes in manuscript: • Figure 5A summarizing overlap between datasets was adopted after minor calculation errors were corrected. • The original overlap-based presentation was moved to the supplemental material and in the main replaced by a new integrative analysis framework (Figure 5B–D) that prioritizes biological pathways and interaction clusters, as also described in our response to the first comment by Reviewer #1. • Figure 5B presents pathway enrichment analysis integrating GO, KEGG and curated pathway databases across cell types and conditions. Resulting clusters are sorted by significance and P values of representative terms displayed as a heatmap. Additionally, the top 3 enriched PPI candidates per term across all cell types and conditions are indicated along with their highest log2-fold enrichment in the PMP22-ALFA eluate. • Figure 5C presents a PPI network of all identified interaction candidates. • Figure 5D highlights proteins associated with lipid metabolism and sphingolipid biosynthesis, as these analyses identify these pathways as one of the most prominent recurring biological themes across datasets.

      Comment: In the version downloaded from the website the discussion appears as a single paragraph, which affects clarity and masks the limited depth of conclusions. The text needs to be structured better to be clear where discussion of each topic begins and ends, and the repetition from other sections should be removed. ¨ In the Schwann cell line MSC80 we found the term myelin sheath enriched (Fig. 4B), with several known myelin proteins like PLP1, MPZ, MCAM and ANXA1 enriched in the PMP22-ALFA eluates of MSC80 and primary Schwann cells. At the onset of myelination, Schwann cells mount a transcriptional program enabling coordinated synthesis of both myelin proteins and lipids that are required in large quantities for myelin sheath formation (LeBlanc et al, 2005; Pertusa et al, 2007; Fledrich et al, 2018; Kim et al, 2018; Poitelon et al, 2020).¨ The link to sphingolipid synthesis (that follows) could be improved, this reviewer missed entirely the fact that there is a link until the third time of reading.

      Response: We fully agree and have revised the Results and Discussion section. Changes in manuscript: • As already stated in our response to the first comment by Reviewer #1, we adapted the part of the previous Discussion in which we highlighted enzymes of the de novo sphingolipid synthesis pathway as PMP22 PPI candidates and moved it to the Results (p.6, l.38) in order to reflect the central theme of our findings already at this point, which is continued in new Figure 5 (“Cross-cell type enrichment, PPI network analysis and initial orthogonal validation”) and in the newly added paragraph “Cross-cell type enrichment and network analysis highlight (sphingo-)lipid metabolism as a prominent cluster among PMP22-ALFA PPIs” (p.9, l.23). • We have organized the Discussion section thematically into four paragraphs, discussing (1) methodological advances of the ALFA-tag interactomics approach, and how our results can be interpreted with regard to PMP22’s multifunctional role across cell types and subcellular compartments, (2) a novel mechanistic link between PMP22 and lipid metabolism as the central theme of our findings, as well as a possible connection to the described role of PMP22 in calcium signaling, (3) limitations of Co-IP-based interactomics and our study in particular, along with future directions, and (4) summarizing, the significance of our study despite the limitations. • In addition, we have provided the individual parts of the Results section with subheadings so that important findings can be grasped at a glance.

      Comment: Re sphingolipid synthesis, SPTLC1 and 2 are quite far down the list in figure 5 and ORMDLs that are mentioned in the text don´t appear at all. On further reading we come to 2 stronger candidates CERS and KDSR, so the section needs reordering. The fact that SPTLC, CERS and KDSR are not known to interact comes at the end of this section not mixed up in the middle.

      In the same section is interfere the right word? In any case likely seems too strong given the lack of validation. ¨It seems likely that PMP22 can directly interfere with sphingolipid synthesis in the ER.

      Response: We thank the reviewer for this insightful observation. In the revised Results section, we now list the candidates ordered by enrichment (p.7, l.8). ORMDL2, which was previously easy to overlook, is listed along with other candidates in Fig. 5B and D. We also agree that “interfere” is too strong with regard to our results, and now state: “Our results thus indicate that PMP22 associates with multiple ER-resident enzymes of the de novo sphingolipid synthesis, with a possible functional role of contributing to physiological regulation of sphingolipid supply to the myelin sheath and to its dysregulation in CMT1A.” (p11, l.31)

      Comment: The limitations are substantial and largely acknowledged by the authors, above all a complete lack of further investigation of any of the candidates; and with these in mind the study amounts to a list of PPIs of PMP22 that will be of value to the narrow field of those specializing in CMT1A, and might also be given a cursory glance by those with an interest in sphingolipid synthesis. Overall without further validation the work is somewhat preliminary. Generally omic studies are used to build hypotheses, which are then further investigated, whereas this study is limited to proteomic analyses.

      This reviewer is not an expert in CMT or PMP22, but has carried out a number of protoemic studies and has extensive experience of cell and molecular biology. This reviewer is not an expert in CMT or PMP22, but has carried out a number of protoemic studies and has extensive experience of cell biology.

      Response: We thank the reviewer for their critical assessment. Acknowledging the limitations, we argue that our study represents a significant step forward in uncovering the molecular role of the still poorly understood PMP22. We not only reveal a molecular link to (sphingo-)lipid biosynthesis, but also provide starting points for structural and functional investigations in various directions.

      Reviewer #3

      Comment: This is a beautiful proteomics study to identify proteins that interact with peripheral myelin protein 22 (a tetraspan membrane protein) in four different cell types: HEK293T (a generic model mammalian cell line), MDCKII epithelial cells, a Schwann cell line, and primary rat Schwann cells. An impressively-optimized protocol was used based on fusing the recently-developed alpha tag to the C-terminus of PMP22 employed. This work also presents an optimized protocol for solubilizing the PMP22, regardless of which intracellular compartment it is in. While there have been previous proteomic studies of PMP22, this study VERY significantly extends previous results. The manuscript is clearly written and the figures are clear. One suggestion for improvement is that I did not find clear descriptions of the contents of supporting Tables 1 and 2. This is needed and also the columns of those excel tables could be improved to make them easier to grasp.

      Response: We thank the reviewer for the positive assessment of our study, and for their suggestion to improve the supplementary tables. Changes in manuscript: • We revised the legends and layouts of Supplementary Tables 1 and 2. • In Supplementary Table 2, additional rows containing differential hit counts together with explanations of table contents were added to facilitate readability as well as traceability of Figures 2A, 3A, 4A, 5B, Supplementary Figure 4 and the candidate numbers we state in the text.

      Comment:

      **Referee cross-commenting**

      The other two authors have raised concerns and offered suggestions that are excellent and worthy of address. However, my enthusiasm for this work remains high and I think that, even in the present version of this manuscript, this work advances of our understanding of PMP22 biology and pathobiology.

      Response: In response to the concerns raised by the Reviewers #1 and #2, we substantially strengthened the manuscript by: • Adding a new integrative pathway and interaction network analysis (Figure 5A-D). • Performing new NanoBRET validation experiments (Figure 5E). • Performing new in situ PLA validation experiments (Supplementary Figure 5). • Expanding and restructuring the Discussion. • Improving prioritization of biologically relevant PMP22-associated pathways and PPI candidates. • Revising the shown volcano plots to make more PPI candidates visible at a glance (Figures 2B, 3B, 4B) • Revising the supplementary information. We believe that these additions have further strengthened the biological interpretation of the dataset and improved the overall significance of the study.

      Comment: Gene variations that alter the expression levels or sequence of the tetraspan membrane protein PMP22 cause about 2/3 of all cases of Charcot-Marie-Tooth disease (CMT), a peripheral neuropathy that impacts 1:2500 humans, making it a top-10 genetic disorder. There is no treatment or cure for CMT beyond orthotics and physical therapy. The normal function of PMP22 in promoting myelination of peripheral nerve axons by Schwann cells is poorly understood, reflecting understudy. This paper presents results that represent a huge step forward relative to previous proteomics studies of PMP22 both in terms of the approach used and in terms of the novel insights derived. Of particular significance are the results present herein that PMP22 very likely plays a central role in regulating sphingolipid biosynthesis in Schwann cells. This extends previous results that PMP22 is involved in cholesterol homeostasis. Given that Schwann cells expand their membrane areas by a factor of several thousand during myelination and given the lipid-raft like composition of the myelin membranes, recognition that PMP22 is centrally involved in BOTH cholesterol and sphingolipid homeostasis illuminates its native function and suggests possible pathological roles for PMP22 under conditions of CMT. I think this paper will be of interest to the growing CMT research community, as well as to scientists interested the basic biology of the peripheral nervous system. While I would not describe this paper as having any significant weaknesses, as a "pulldown"-based proteomics study, the results are subject to the key limitation that observation that a given protein associates with PMP22 cannot be taken to indicate DIRECT interaction without additional studies. The observed proteomic interaction could be mediated by other proteins. Thus, the results of this work are most important both for supporting results from previous studies and for suggesting relationships of PMP22 to other proteins and their associated pathways that are worthy of future studies.

      Response: We thank the reviewer for placing our study within the context of ongoing research on PMP22 and related neuropathies, as well as within the PNS field as a whole. In the Discussion, we acknowledge the limitation that the approaches used cannot distinguish between direct and indirect interactions, and we suggest alternative techniques to elucidate structural and functional details of the revealed PPIs in future investigations (p.12, l.16).

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      Referee #3

      Evidence, reproducibility and clarity

      This is a beautiful proteomics study to identify proteins that interact with peripheral myelin protein 22 (a tetraspan membrane protein) in four different cell types: HEK293T (a generic model mammalian cell line), MDCKII epithelial cells, a Schwann cell line, and primary rat Schwann cells. An impressively-optimized protocol was used based on fusing the recently-developed alpha tag to the C-terminus of PMP22 employed. This work also presents an optimized protocol for solubilizing the PMP22, regardless of which intracellular compartment it is in. While there have been previous proteomic studies of PMP22, this study VERY significantly extends previous results. The manuscript is clearly written and the figures are clear. One suggestion for improvement is that I did not find clear descriptions of the contents of supporting Tables 1 and 2. This is needed and also the columns of those excel tables could be improved to make them easier to grasp.

      Referee cross-commenting

      The other two authors have raised concerns and offered suggestions that are excellent and worthy of address. However, my enthusiasm for this work remains high and I think that, even in the present version of this manuscript, this work advances of our understanding of PMP22 biology and pathobiology.

      Significance

      Gene variations that alter the expression levels or sequence of the tetraspan membrane protein PMP22 cause about 2/3 of all cases of Charcot-Marie-Tooth disease (CMT), a peripheral neuropathy that impacts 1:2500 humans, making it a top-10 genetic disorder. There is no treatment or cure for CMT beyond orthotics and physical therapy. The normal function of PMP22 in promoting myelination of peripheral nerve axons by Schwann cells is poorly understood, reflecting understudy. This paper presents results that represent a huge step forward relative to previous proteomics studies of PMP22 both in terms of the approach used and in terms of the novel insights derived. Of particular significance are the results present herein that PMP22 very likely plays a central role in regulating sphingolipid biosynthesis in Schwann cells. This extends previous results that PMP22 is involved in cholesterol homeostasis. Given that Schwann cells expand their membrane areas by a factor of several thousand during myelination and given the lipid-raft like composition of the myelin membranes, recognition that PMP22 is centrally involved in BOTH cholesterol and sphingolipid homeostasis illuminates its native function and suggests possible pathological roles for PMP22 under conditions of CMT. I think this paper will be of interest to the growing CMT research community, as well as to scientists interested the basic biology of the peripheral nervous system.

      While I would not describe this paper as having any significant weaknesses, as a "pulldown"-based proteomics study, the results are subject to the key limitation that observation that a given protein associates with PMP22 cannot be taken to indicate DIRECT interaction without additional studies. The observed proteomic interaction could be mediated by other proteins. Thus, the results of this work are most important both for supporting results from previous studies and for suggesting relationships of PMP22 to other proteins and their associated pathways that are worthy of future studies.

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      Referee #2

      Evidence, reproducibility and clarity

      PMP22 is primarily located in the compact myelin of the peripheral nervous system which explains why mutant forms cause CMT. Its partners and function in cells are still not well established although several previous studies have suggested 54 partners.

      This study across different cell types is the most comprehensive to date to document PPIs of PMP22. Disappointingly the PPIs differ enormously between cells. The study appears to have been performed well and the results are clearly presented on the whole. This reviewer was expecting to see some sort of illustration in figure 5 (or separately in the Discussion) that distils the principal linked candidates according to biological processes or pathways with a scoring or ranking system to communicate how strong a case we are looking at.

      In the version downloaded from the website the discussion appears as a single paragraph, which affects clarity and masks the limited depth of conclusions. The text needs to be structured better to be clear where discussion of each topic begins and ends, and the repetition from other sections should be removed.

      ¨ In the Schwann cell line MSC80 we found the term myelin sheath enriched (Fig. 4B), with several known myelin proteins like PLP1, MPZ, MCAM and ANXA1 enriched in the PMP22-ALFA eluates of MSC80 and primary Schwann cells. At the onset of myelination, Schwann cells mount a transcriptional program enabling coordinated synthesis of both myelin proteins and lipids that are required in large quantities for myelin sheath formation (LeBlanc et al, 2005; Pertusa et al, 2007; Fledrich et al, 2018; Kim et al, 2018; Poitelon et al, 2020).¨

      The link to sphingolipid synthesis (that follows) could be improved, this reviewer missed entirely the fact that there is a link until the third time of reading.

      Re sphingolipid synthesis, SPTLC1 and 2 are quite far down the list in figure 5 and ORMDLs that are mentioned in the text don´t appear at all. On further reading we come to 2 stronger candidates CERS and KDSR, so the section needs reordering. The fact that SPTLC, CERS and KDSR are not known to interact comes at the end of this section not mixed up in the middle.

      In the same section is interfere the right word? In any case likely seems too strong given the lack of validation. ¨It seems likely that PMP22 can directly interfere with sphingolipid synthesis in the ER.

      Significance

      The limitations are substantial and largely acknowledged by the authors, above all a complete lack of further investigation of any of the candidates; and with these in mind the study amounts to a list of PPIs of PMP22 that will be of value to the narrow field of those specializing in CMT1A, and might also be given a cursory glance by those with an interest in sphingolipid synthesis.

      Overall without further validation the work is somewhat preliminary. Generally omic studies are used to build hypotheses, which are then further investigated, whereas this study is limited to proteomic analyses.

      This reviewer is not an expert in CMT or PMP22, but has carried out a number of protoemic studies and has extensive experience of cell and molecular biology.

      This reviewer is not an expert in CMT or PMP22, but has carried out a number of protoemic studies and has extensive experience of cell biology.

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      Referee #1

      Evidence, reproducibility and clarity

      This paper reports the identification of candidate interactors of PMP22, a myelin protein mutated in the most frequent form of Charcot-Marie-Tooth disease, the demyelinating CMT1A. The authors performed co-IP experiments using overexpressed PMP22-ALFA tagged at the C-terminus followed by LFQ-MS analysis to identify candidate interactors. Different cell types were used such as HEK, MDCK, MSC80, and primary rat Schwann cells. The authors also tested several lysate buffers and detergent to optimize PMP22 solubility without affecting binding to interacting partners.

      The authors present a list of the most significant hits that have been identified in at least two different cell types. This result should represent a starting basis for functional analysis and further evaluation.

      Significance

      A limitation of this study is that it is mainly descriptive. It lacks a clear indication of most promising targets that should help to advance our understanding of the PMP22 biology in Schwann cells.

      The Schwann cell experiments are the most important. However, considering the low transfection efficacy (primary SC) or the low differentiation (MSC80) these are very limiting in the present form. A suggestion could be to improve transfection efficiency by nucleofection or by using lentiviral vectors to transduce primary rat Schwann cells.

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      Reply to the reviewers

      Response to Reviewers # # Reviewer #1

      We thank Reviewer #1 for the positive assessment, in particular for recognizing that the sex-independent role of PAGE4 is clearly supported by multiple layers of protein evidence, that the proteomic, phosphoproteomic and interactomic techniques are conducted at an expert level, and that the reporting of results is appropriate. We have addressed the constructive points on outlier testing and methods completeness in full.

      Reviewer comment: Figure 1B: the first PCA component is dominated by a single sample far to the right (PC1 33.9%). Was an outlier test (Mahalanobis distance, Z-score on PC1) performed? If so it should be reported. No PCA or outlier test is reported for the phosphoproteomics. The full-proteome and phosphoproteomics data need more analysis detail to ensure reproducibility.

      __Response: __We agree, and we have addressed this in two ways. First, we have replaced the Figure 1B proteome PCA with multidimensional scaling (MDS) computed from the top 500 most variable proteins, which displays the paired tumor-myometrium structure directly and is not dominated by a single component; the inputs are stated in the Methods and the Figure 1B legend: MDS was computed from the top 500 most variable proteins using log2-transformed intensities, with no imputation of missing values. Second, to address the reviewer's underlying concern about undetected technical outliers, we inspected the per-sample distribution of log2 protein intensities for every sample in the DIA cohort. One HMGA2-subtype tumor measurement was clearly aberrant at the technical level: its distribution is bimodal and its median is collapsed (~2.9 versus a cohort median of 7.8), the signature of a sample-specific quantification failure rather than biological variation (rebuttal figure below, panel A), whereas the same sample is unremarkable in the phosphoproteome (panel B), showing that the defect is specific to that one proteome measurement and not a property of the tumor. That measurement is not part of the dataset reported in the manuscript: the DIA cohort as analyzed comprises 28 matched tumor–myometrium pairs (56 samples), which is what the Methods, the figure legends and Supplementary Dataset 2 describe throughout. Every remaining sample passes this inspection, so no result reported here rests on a sample with a failed proteome measurement. We provide the per-sample distributions here for the reviewer's inspection rather than in the manuscript, since they document the composition of the analyzed cohort rather than adding to its findings.

      Rebuttal figure1 for reviewer inspection only. Per-sample intensity distributions across the DIA cohort. (A) Proteome; (B) phosphoproteome. Each box is one sample's distribution of log2 intensities; the blue dashed line marks the cohort median (7.8). The aberrant sample (red) is a clear technical outlier in the proteome but unremarkable in the phosphoproteome; it is not part of the 28-pair dataset reported in the manuscript.

      Reviewer comment: Figure 3A: perform an outlier test for HMGA2 (proteome, left) and for the FH_UL and COL4A5-COL4A6_UL points (phosphosites) that are visually separated. As shown, the PCAs are hard to interpret because points get squeezed onto one axis. Outliers may affect statistics and the conclusions about within-group variation; this should be tested if discussed.

      Response: We have addressed both points. The same per-sample inspection described above covers every sample contributing to the Figure 3A proteome, phosphoproteome and phosphosite analyses, and all 28 pairs shown there pass it. The COL4A5-COL4A6 UL and FH UL samples highlighted by the reviewer are technically sound on these metrics and are retained; the text now attributes their separation to inter-tumor heterogeneity in phosphorylation state on the basis of these metrics rather than by assertion, which resolves the biological-versus-technical ambiguity the reviewer identified. Regarding axis compression, we agree that the phosphosite panel is stretched by a small number of samples with genuinely divergent phosphorylation profiles. Because these samples are technically sound, we have retained them rather than rescaling or trimming the axes, which would misrepresent the spread of the data; the proportion of variance explained by each component is stated on the axes so that the scale of the separation can be judged directly. The conclusions drawn from Figure 3 concern the tumor-myometrium contrast within each subtype, which is unaffected by the position of these individual samples.

      Reviewer comment: SNRNP70_S226 is described as one of the most significantly hyperphosphorylated sites, but Table S1 lists log2FC 5.92, p 0.02, classified non-significant; MARCKSL1 (log2FC 2.14, p 0.016) and NDRG2 are likewise non-significant. The volcano/caption indicate a 0.05 cutoff but Table S1 apparently uses 0.01. Please clarify.

      Response: We thank the reviewer for catching this, and we apologize for the inconsistency. On re-checking the source data we confirmed that the significance-classification column in the earlier version of the supplementary table had been generated with a stricter cutoff than the one used for the volcano plots and stated in the captions. All of the sites raised by the reviewer meet the stated criteria (|log2 fold-change| > 2 and p ##

      Reviewer comment: Add citations for Protein Atlas and ProteomicsDB.

      __Response: __Added: Human Protein Atlas (Uhlén et al., Science 2015; proteinatlas.org) and ProteomicsDB (Schmidt et al., Nucleic Acids Res 2018).

      __Reviewer comment: __PAGE4 expression is selectively enriched in MED12-mutant UL (page 14): "PAGE4 expression indeed was significantly elevated in MED12-mutant ULs ... ". The figure caption, methods and text do not provide any information of the statistical test used regarding the statement of significant upregulation. Please provide the data or change wording as a statistical significance is implied.

      Response: The panel reproduces published RNA-seq data (Berta et al., 2021) descriptively, and the source data do not provide a formal cross-subtype test that we can report. We have therefore softened the wording from “significantly elevated” to “consistently higher” in the Results, and the caption now states that the panel is descriptive and that no statistical test was applied.

      Reviewer comment: Method section "Liquid chromatography-mass spectrometry (LC-MS)" report the UniProtKB database download date to ensure reproducibility. Does the database contain isoforms and TrEMBL sequences or uses a filtered version (e.g.: status reviewed, canonical?) (report it like on p.36 for the AP-MS/BioID samples). Further add a statement if missing values were imputed on which level (peptide, protein), which normalization strategy was employed, and was there filtering of incomplete values performed. All these analysis steps are common practice for DDA measurements of clinical samples and may affect subsequent statistical finding and thus should be reported in the method section if they were or were not performed (for the PPI data this was reported on p.37, but for the full proteome and phosphoproteome the information provided is not sufficient to judge the validity of data processing). Method section "GO enrichment and statistical analysis" (p.37): Please add citations for the used tools SAINT, CRAPome and Enrichr.

      __Response: __We have expanded the discovery DDA proteome and phosphoproteome Methods to the same level of detail as the AP-MS/BioID section: the UniProtKB human database version, download date and entry count; whether isoforms/TrEMBL were included or a reviewed-canonical subset was used; the valid-value filtering rule per group; the normalization strategy; and the imputation method and level. The corresponding DIA workflow is now also described (see Reviewer 2-J). Also added references for SAINTexpress (Teo et al., J Proteomics 2014), CRAPome (Mellacheruvu et al., Nat Methods 2013) and Enrichr (Kuleshov et al., Nucleic Acids Res 2016).

      Reviewer comment: The Figure 1c: The scale of the (Gene) count does not correspond with the circle sizes of the plot. It is thus not possible to judge the number of genes per category. This should be sized equally as else it is not possible for a reader to judge the number of genes per enriched terms.

      __Response: __We have corrected the GO enrichment panel (Figure 1D) so that dot area is drawn to a single consistent scale with an accurate, labeled size legend, allowing the gene count per term to be read directly.

      Reviewer comment: Figure 1G: Expression levels for ProteomicsDB. As the log2 of the expression level is used, it is not clear if the white squares correspond to missing values in the ProteomicsDB, or if the value was very low. Maybe indicating absent values in a grey color scheme makes this more explicit.

      __Response: __We have clarified this in the caption rather than by recolouring, because in this figure white already carries a defined meaning in both panels: in the Human Protein Atlas panel it is the explicit “not detected” category of the key, and in the ProteomicsDB panel the log2 intensity scale begins at zero, so a white cell corresponds to a value of zero, that is, no detected expression, rather than to an absent measurement. The caption now states this for both panels, so a white square can no longer be confused with a very low value.

      Reviewer comment: Figure 1C: the caption indicates a "gray box" but it seems that PAGE4 was highlighted as a red dot, and a white box was added for indicating the gene name.

      __Response: __The caption has been corrected to match the figure (PAGE4 shown as a highlighted point with a labeled callout), and color terminology has been harmonized across all captions.

      Reviewer comment: Figure 3D is described as a volcano plot but is a horizontal scatter with a significance threshold rather than a classic volcano (which would have adjusted p-value on the axis).

      Response: The reviewer is correct. These panels display log2 fold-change per subtype with significance indicated by color and do not plot a p-value axis, so “volcano plot” was the wrong term. We have relabeled them as grouped dot plots in the figure legend and at the two places they are referred to in the Results. Figure 2C and Figure 5A, which are conventional volcano plots with a -log10 p-value axis, retain that description.

      Reviewer comment: The PPI network has too many edges and nodes to read. It could help to collapse categories/proteins or provide a subnetwork, with the full network in the supplement.

      __Response: __We have moved the dense, full node-level PAGE4-centered network out of the main figure and into the supplement, where it now appears as Figure S3A. The main figure retains a complex-level view of these interactions as a focused interaction matrix (revised Figure 4E), in which preys are grouped by annotated functional complex with confidence-weighted encodings. This is the same change requested by Reviewer 3 (point G).

      Reviewer #2

      We thank Reviewer #2 for the positive overall assessment, that we convincingly identified a novel role for PAGE4 in uterine leiomyoma and Mediator transcriptional processes, that this represents an important contribution to understanding UL pathobiology, and that the dataset is very comprehensive. We are grateful for the detailed, panel-by-panel reading and the constructive suggestions on figure design and methods organization, all of which we have adopted.

      Reviewer comment: The validation cohort is not technically a validation cohort, it uses the same 9 MED12-mutant UL samples and so cannot validate PAGE4 upregulation/phosphorylation in an independent cohort. A second MED12-mutant UL cohort should be used.

      __Response: __We thank the reviewer for raising this, and we apologize that our original Methods wording was ambiguous. The discovery DDA, and validation DIA analyses were in fact performed on independent sets of MED12-mutant UL/myometrium samples (n = 9 each), with no patient shared between the sets; we originally sized (MED12-mutant) the DDA/IHC and DIA sets to match the nine discovery pairs, and then also had the opportunity to analyse also the other mutations causing UL. The DIA experiment therefore does provide independent validation of PAGE4 upregulation and Thr51/Thr85 phosphorylation in a separate cohort of MED12-mutant tumors, while additionally establishing subtype specificity against the HMGA2, FH and COL4A5-COL4A6 subclasses. We have revised the Methods to state the cohort composition explicitly so that the independence of the discovery (DDA) and validation (DIA) MED12 sample sets is unambiguous, and independent confirmation is further supported by the external RNA-seq dataset (Berta et al. 2021) and the IHC validation.

      Reviewer comment: No meaningful change in peak width or height is readily observable. Statistical tests would be required to make the claims believable.

      __Response: __We agree, and we have replaced visual assertions with quantification and statistics, reporting effect sizes alongside p-values. We now distinguish signal amplitude (which changes) from peak shape (which does not).

      Amplitude is reduced (supported). Across a common set of 19,942 GENCODE v38 protein-coding TSSs, median promoter-proximal RNAP II signal (TSS ±1 kb) decreased by 10.4% in MED12 G44D, 16.5% in PAGE4 S9D/T51E/T85E and 21.2% in PAGE4 S9A/T51E/T85A relative to the matched wild type; the area under the curve decreased by the same margins, and median peak height (TSS ±250 bp) decreased by 4.4%, 20.4% and 19.3% respectively. All reductions are statistically significant (two-sided Wilcoxon rank-sum test, Benjamini–Hochberg-adjusted p Shape is unchanged (we concede this). Median TSS-derived FWHM was 201 bp in all five conditions (Δ = 0); the very small FWHM p-values arise from the large number of regions tested (n ≈ 17,000) and do not indicate a biologically meaningful shape change. MACS2 peak-width medians shifted inconsistently (+23 bp for G44D, +1 bp for S9D, −49 bp for S9A), i.e. no consistent broadening or narrowing.

      We removed the statements that all four metrics concord and that peaks are narrower/broader. The revised Results now state that promoter-proximal RNAP II signal amplitude is modestly reduced, whereas peak shape (FWHM and MACS2 peak width) shows no consistent change, and we report effect sizes (median % change) for every metric. The ChIP-seq analysis pipeline (alignment, MACS2 peak calling, normalization, quantification) has been added to Methods, which previously lacked it.

      Reviewer comment: HIPK2 was proposed as the candidate kinase (Fig 3E) but there is no HIPK2 on the heatmap; instead CLK2 is in red with an increased score. HIPK2 and CLK2 are different, unrelated kinases. Please rectify.

      __Response: __We apologize for the inconsistency, which our cross-audit confirmed. We have replaced the kinase-family enrichment heatmap (original Figure 3E) with a per-site kinase-prediction map for PAGE4 S9, T51 and T85, now shown as the lower panel of Figure 4A, and revised the text so that figure and text agree. Both sites are proline-directed (S/T-P) CMGC substrates; the CLK and HIPK families score highest, with CLK2 and HIPK1 the most probable kinases, as both have been experimentally validated as PAGE4 kinases at these sites (Kulkarni et al., 2017; HIPK1 also reported at T51). We no longer single out HIPK2: motif scoring cannot resolve closely related paralogs, so HIPK2/HIPK3 and other CLKs remain possible but are not named individually, while the prior experimental literature points to HIPK1. The prediction is now explicitly framed as in silico and requiring direct validation (in vitro kinase assays with recombinant CLK2/HIPK1, and inhibitor or genetic perturbation).

      Reviewer comment: The text states Mut1 retained 73% of PAGE4-WT prey proteins, but Fig 4B shows Mut1 with 25 preys vs 149 for WT, 25/149 = 17% retained, an 83% decrease. Please rectify.

      __Response: __The reviewer is correct, and the original 73% was an error. We recomputed AP-only interactor retention directly from the final filtered interactor table (Supplementary Dataset 3). Defining retention as the fraction of PAGE4-WT AP high-confidence interactors (gene-level, n = 149) that are also recovered for Mut1, only 20 are shared, a retention of 13% and a net loss of 87% of stable associations; Mut1 captured 25 AP high-confidence interactors in total (bar plot, revised Figure 4C). The text and the Figure 4C legend now report these values consistently, and the statement about which categories are preferentially lost versus retained is tied to Supplementary Dataset 3 rather than asserted.

      Reviewer comment: The text states the MED12-G44D bait is strongly reduced vs MED12 WT (Fig 5A), but the Fig 5A right panel does not support this. Please rectify.

      __Response: __We thank the reviewer, on checking the data, the original statement was in fact wrong in the opposite direction, and we have corrected the text. Quantifying MED12 bait recovery directly from Table S3, the MED12-G44D bait is recovered at levels equal to or higher than MED12-WT (average spectral counts: AP-MS 519 vs 339; BioID 481 vs 191), not reduced. Despite this comparable-to-higher bait abundance, MED12-G44D captures markedly fewer high-confidence interactors (AP-MS 38 vs 115; BioID 297 vs 362). The Results now state this explicitly: the G44D mutation reduces MED12's high-confidence interaction repertoire while the bait itself is well expressed, so the loss of interactors is a genuine effect rather than a bait-recovery artifact. Figure 5A is a differential-interactor volcano (mutant vs WT) in which the bait's own abundance is not directly legible, which is why it appeared not to support the now-removed reduction claim. Bait spectral counts for every bait and both acquisition modes are reported in Supplementary Dataset 3, so bait recovery can be checked directly for each construct. In the interest of full transparency we note that the same check gives a different answer for two of the PAGE4 constructs: Mut1 and Mut6 are recovered at roughly ten-fold lower spectral counts than PAGE4 WT in both modes. We have therefore added an explicit limitation to the Discussion stating that reduced construct abundance or stability may contribute to the loss of stable interactors and to the compartmental shift observed for these two variants, and we have tempered the corresponding claims.

      Reviewer comment: The text states MUT1 has upregulated base-excision repair and SUMOylation, but the Fig S3 heatmap supports this for MUT6, not MUT1. Please rectify.

      __Response: __Thank you. We re-examined the pathway enrichment heatmap, which is Figure S4 in the revised manuscript, and confirm the reviewer is correct: base-excision repair, translesion synthesis and SUMOylation of DNA damage response proteins are enriched for the S9A/T51E/T85A variant (Mut6), not for Mut1. The Results text has been corrected so that text and figure agree.

      Reviewer comment: Figure 4E shows the full bait-prey graph and becomes a hairball; bait names are illegible, edge widths/colors and AP/PL/Both cannot be distinguished. Suggest aggregating preys by annotated complex with a single summarized weighted edge per bait-complex, and showing only differentially associated preys; provide the full network in the supplement.

      __Response: __We agree. The dense, full node-level network that produced the hairball has been moved out of the main figure and is now provided as a supplementary figure (Figure S3A) with a clearly keyed AP/PL/Both encoding. In the main figure, the corresponding interactions are presented at the level of annotated complexes as a focused interaction matrix (revised Figure 4E), which avoids the unreadable node-level graph while preserving the key bait-complex relationships.

      Reviewer comment: The figure presents PAGE4 phosphosites but is labeled “GAGE,” a different (unused) name; importantly the figure appears copied directly from PhosphoSitePlus without permission. Permission should be obtained.

      __Response: __We thank the reviewer and have resolved both issues. First, we replaced the PhosphoSitePlus-derived schematic with an original figure generated by us from the underlying site annotations, so no third-party copyrighted image is reproduced, removing the permissions concern entirely. Second, the “GAGE” label is showing the GAGE domain (PAGE4 belongs to the GAGE/PAGE family); all panel labels now read PAGE4 consistently, with the alias noted once in the text.

      Reviewer comment: Inferring phosphorylation involvement from phosphomimetic changes is not formally valid and should at least be stated as a hypothesis.

      __Response: __We agree and have reframed accordingly. We now state explicitly that phosphomimetic (S/T→D/E) and phosphodead (S/T→A) substitutions approximate the charge state of (de)phosphorylated residues but do not reproduce native, dynamic phosphorylation; the phosphorylation-dependence conclusions are therefore presented as a hypothesis supported by, but not proven by, these surrogates.

      Reviewer comment: The MS methods are insufficient to reproduce the experiments. DIA methods are missing entirely; with both DDA and DIA used across total, phospho, AP and PL experiments, the methods should be reorganized by experiment type, specifying what was done in each mode. State how total and phospho data were normalized and integrated; how PL data were analyzed and whether identically to AP; whether SAINT used spectral counts or intensities; and, since the baits are nuclear, whether NLS-containing controls were used.

      __Response: __We have substantially expanded and reorganized the MS Methods by experiment type and acquisition mode. (1) DIA acquisition and analysis for the validation proteome and phosphoproteome are now fully described, instrument, gradient and window scheme, search/quantification software and settings, library strategy, FDR and normalization. (2) For each dataset we state the acquisition mode: discovery proteome and phosphoproteome = DDA (Q-Exactive); validation proteome and phosphoproteome = DIA-PASEF (timsTOF Pro); AP-MS and PL-MS = DDA-PASEF (timsTOF Pro/Pro 2); host-cell-line proteome = DIA on an Orbitrap Astral, for which the full acquisition and DIA-NN parameters, including database version, entry count and the absence of isoforms, TrEMBL entries and imputation, are now given in Methods. (3) Total- and phospho-proteome integration: phosphosite intensities were normalized to matched parent-protein abundance for the protein-adjusted, site-level testing, and we describe the normalization, batch handling and integration of the two layers. (4) AP vs PL: we state explicitly that AP and PL data were processed through the identical pipeline (QRILC imputation → median normalization → SAINTexpress → CRAPome filtering), noting any differences. (5) SAINT input: we clarify that SAINTexpress was run on spectral counts (and CRAPome filtering used average spectral-count fold-change ≥ 3); the QRILC/normalization step applies to the quantitative interactor comparisons, and the previous ambiguity between counts and intensities is removed. (6) Nuclear-bait controls: GFP-MAC3 served as the negative control, and compartment-matched contaminants (nucleolar/chromatin) are additionally controlled by CRAPome frequency filtering; we discuss this explicitly and note its limitation for nuclear-bait specificity.

      __Reviewer comment: __Overall the manuscript has many issues with data presentation. To illustrate just a few: - Fig. 3B and 3D are described as volcano plots. They are not volcano plots. - Fig. 3B legend mentions a lower panel. There is no lower panel for Fig. 3B. - Fig. 3A, please use different shape markers for the UL subtypes and suggest connecting with edges to thus support their text conclusions of separate groupings. - For Fig. 3E the colors for the families and groups are not distinguishable (and the font is too small). Please rectify. - Please label the units and scale for Fig. 6A. - Please mention the MAC3 tag uses Ultra-ID to thus justify the 10 min labeling time. - In the relevant protein and phosphoproteomic datasets and figures please mention the number of proteins and phosphosites obtained to thus allow an interpretation of the data quality. -Table 4 is in a format that is uninterpretable. - The text and figures interchangeably use the mutant number (e.g. MUT1) or mutant composition (e.g. S9D_T51E_T85E - for MUT1), this makes reading and interpreting the manuscript very challenging. Suggest using one format.

      __Response: __We have addressed each item:

      • Fig 3B and 3D “volcano” plots: corrected. Both panels are now labeled grouped dot plots in the figure legend and in the Results, matching our response to Reviewer 1; Figure 2C and Figure 5A, which do plot a -log10 p-value axis, retain the term volcano plot.
      • Fig 3B legend “lower panel” with no lower panel: the legend conflated the protein panel (3B) with the phosphosite panel (3D); the caption now matches the actual panels.
      • Fig 3A subtype markers: we have retained the colour-coded points. Each of the eight groups already has its own colour and the legend states the group and n for each, and the eight-level palette was chosen so that each subtype's tumour and myometrium share a colour family, which is the comparison the panel is meant to support. We considered adding shape markers and 95% confidence ellipses, but at these group sizes (n = 5 for two subtypes) an ellipse conveys more confidence in the grouping than the data warrant, and connecting edges between unpaired samples would imply a trajectory that does not exist. The panel is therefore presented as a descriptive overview of sample structure; the subtype and tumour-versus-myometrium differences that the manuscript actually concludes on are tested statistically and reported in Figure 3B and Supplementary Dataset 2, not inferred from visual separation in the PCA.
      • Fig 3E colors/fonts: the original Figure 3E no longer exists; it has been replaced by the site-level kinase-prediction panel that is now the lower panel of Figure 4A, which uses a larger font and a distinguishable family/group palette.
      • Fig 6A units/scale: the Figure 6A legend now specifies that the heatmap shows MACS2 fold-enrichment (FE) signal over local background (macs2 bdgcmp, FE mode), in 50-bp bins across ±5 kb from GENCODE v38 protein-coding TSSs, with the display color scale capped at 20 FE units (values above 20 capped only for visualization; quantitative analyses used uncapped values).
      • MAC3 labeling time: we now state that the MAC3 tag incorporates the UltraID biotin ligase, justifying the 10-minute labeling window.
      • Dataset sizes: the number of proteins and phosphosites identified in the discovery datasets is stated in the Results, and the complete per-feature quantifications for both the discovery and validation datasets, from which the totals can be read directly, are provided in Supplementary Datasets 1 and 2.
      • Table S4 formatting: reformatted into an interpretable layout.
      • Mutant nomenclature: standardized to “Mut1 (S9D/T51E/T85E)” on first mention and “Mut1” thereafter. The full Mut1-Mut8 mapping is now stated explicitly in the Results at the point the panel is introduced, and the same convention is applied throughout.

      Reviewer #3

      We thank Reviewer #3 for the positive and thorough assessment, in particular for recognizing that we convincingly demonstrated the specific elevation of PAGE4 mRNA, protein and T51/T85 phosphorylation in MED12-mutant UL, that this points to a broader, sex-independent function for a traditionally male-specific marker, and that the work will be of significant interest to the UL research community. We address the major and minor points in turn.

      Reviewer comment: The interactome, reporter and ChIP-seq studies relied exclusively on tagged, exogenously expressed proteins. Endogenous PAGE4/MED12 could substantially affect the observed networks. Measure endogenous expression in the Flp-In 293 T-Rex cells and discuss its influence. Did phosphovariants affect PAGE4 nuclear localization? Comparing T51/T85 phosphorylation between WT and G44D MED12 cells would be informative.

      __Response: __We have characterized endogenous PAGE4 and MED12 in the parental Flp-In T-REx 293 line. By single-shot DIA proteomics of the parental cells on an Orbitrap Astral, PAGE4 was below detection, indicating that the tagged PAGE4 constructs are expressed against an effectively null endogenous background, consistent with PAGE4's restricted cancer-testis expression; the reported PAGE4 interactions, localization and reporter effects are therefore not confounded by an endogenous PAGE4 pool. MED12, an essential Mediator subunit, is endogenously expressed, so the tagged MED12-WT and MED12-G44D constructs are present alongside the endogenous protein, as we now note in the Discussion. We have also assessed whether PAGE4 phosphovariants alter nuclear/cytoplasmic distribution using our developed MS microscopy analysis (Liu et al, 2018, Nature Communications), We also agree that comparing PAGE4 T51/T85 phosphorylation between WT and G44D MED12 backgrounds would be informative; this requires phosphosite-specific reagents that we do not currently have, and we have flagged it explicitly in the Discussion as a priority for follow-up rather than claiming it here. The revised Discussion frames the heterologous overexpression system and its bearing on interpretation accordingly.

      Reviewer comment: Turunen et al. (2014, PMID 24746821) reported mutant MED12 interactomes using a similar strategy. Comparing with this dataset would assess reproducibility and robustness and add confidence.

      __Response: __We thank the reviewer for this excellent suggestion, and we note that a co-author of the present study also co-authored Turunen et al. (2014). We have compared our MED12 WT and G44D affinity-purification (AP-MS) interactors with the normalized spectral-count data of Turunen et al. (2014, Table S1), normalizing both datasets to the MED12 bait (see the rebuttal figure below). The two studies are concordant on the central finding: the G44D mutation specifically reduces association of the Mediator kinase (CDK) module while core Mediator is retained. In our data the bait-normalized G44D/WT ratio for CDK8 is 0.45 and for Cyclin C (CCNC) is 0.56 (Turunen et al.: 0.33 and 0.36, respectively), and CDK19 — the module member lost most completely in Turunen et al. (ratio 0.00) — was not detected as a high-confidence interactor of G44D in our AP-MS data. By contrast, core Mediator subunits are retained in both studies (median G44D/WT ratio 0.87 across 23 shared subunits in our data, versus 0.92 in Turunen et al.). Thus our MED12 G44D interactome independently reproduces the selective CDK8/Cyclin C uncoupling reported by Turunen et al. (2014), while our combined AP-MS/PL approach extends this to the PAGE4 variants and to transient/proximal associations. We have added a sentence to the Discussion noting this concordance; the comparison figure is provided here for the reviewer's inspection.

      Rebuttal figure 2. Concordance of the MED12 G44D interactome with Turunen et al. (2014). Both datasets are normalized to the MED12 bait and expressed as the G44D/WT ratio. (A) Mediator kinase (CDK) module: CDK8 and Cyclin C (CCNC) are reduced in both studies; CDK19, the module member lost most completely in Turunen et al. (ratio 0.00), was not detected (n.d.) as a high-confidence G44D interactor in our AP-MS data. (B) Core Mediator subunits are retained in both studies (median G44D/WT 0.92 in Turunen et al., 0.87 in the present study). The selective reduction of the CDK module with retention of core Mediator reproduces the central finding of Turunen et al. (2014).

      Reviewer comment: It is unclear whether the system is appropriate for ER-dependent transcription. Endogenous ERα/ERβ levels are not given, and estrogen signaling is ligand-dependent, yet no estrogen treatment appears to have been included. Without receptor expression and ligand stimulation, the estrogen-reporter results are hard to interpret.

      __Response: __This is a valid concern. The estrogen reporter is a defined dual-luciferase construct in which tandem estrogen response elements (ERE) drive firefly luciferase, and it does not include a co-expressed estrogen receptor. Because HEK293 cells express very low to negligible endogenous ERα and the assays were performed without ER co-expression or 17β-estradiol stimulation, the readout reflects ERE-responsive promoter activity in a near-ER-null context by design, rather than bona fide ligand-dependent ER signaling. We now (i) report the endogenous receptor status directly: neither ESR1 nor ESR2 is detected among the 8,708 protein groups quantified in the parental Flp-In T-REx 293 line by Orbitrap Astral DIA (Supplementary Data 5), confirming an effectively ER-null background; and (ii) explicitly qualify the estrogen-reporter interpretation in both the Results and the Discussion. Repeating the assay with ERα co-expression and 17β-estradiol stimulation would be required for a ligand-dependent readout, and we agree this is the appropriate next experiment, but it falls outside the scope of the present revision and we have therefore limited our claims accordingly. The phosphorylation-dependent PELP1 association (PL-specific to Mut1) is presented as motivating, not demonstrating, an ER-signaling link.

      Reviewer comment: Page 15, the authors described "Among individual kinases, HIPK2 was the top candidate for PAGE4-T51/T85, both sites fitting their known serine/threonine consensus motifs.". HIPK2 did not appear in the list of kinases in Figure 3E. Please clarify the data source and the criteria for the ranking.

      __Response: __Reconciled as described for Reviewer 2-C: the kinase-family enrichment heatmap has been replaced by a site-level kinase-prediction panel for S9, T51 and T85 (lower panel of Figure 4A), figure and text name the same kinases, and the scoring method, ranking criterion and data source are stated in the caption and Methods.

      Reviewer comment: Supplementary Figure 4, significance marks were missing.

      __Response: __Significance annotations have been added to all panels of the luciferase figure, which is Figure S5 in the revised manuscript (Figure S4 is the pathway-enrichment heatmap). Each panel now shows the individual replicate values with the mean ± SD, and the legend states the test, the number of replicates, the comparator (all comparisons against PAGE4 WT) and the meaning of each asterisk level.

      Reviewer comment: The biological significance of the enriched motifs is somewhat overinterpreted, since motif enrichment alone does not establish functional involvement. The presentation could be improved with a figure of top motifs, enrichment statistics and/or logos; e.g. “PAGE4 WT maintained AP-1-driven transcription, whereas phosphorylation induced an expanded motif repertoire” is hard to follow.

      __Response: __We agree, and we have reworked the motif analysis to lead with effect size rather than statistical significance, correcting several over-statements in the original text.

      __The p-values overstate the enrichment. __Across the five conditions, 129–172 of 440 known motifs pass q ≤ 0.05, but ~80% of these have fold-enrichment __The individually named transcription factors were over-called. __Most factors named in the original text reach statistical significance yet have fold-enrichment close to 1.0 (e.g. AP-1 1.03–1.11, E2F3 1.03, ETS1 1.07, FOXA1 1.03–1.09), i.e. no meaningful enrichment. We have removed these factor-specific claims and the “AP-1-driven transcription” / “expanded motif repertoire” framing.

      CTCF is the one robust result. CTCF is the only motif with fold-enrichment ≥ 1.3 in all five conditions (range 1.38–2.29, peaking at 2.29 in PAGE4 S9D); its paralog BORIS is second-strongest (1.18–1.58). No other known motif exceeds 1.5-fold enrichment in more than one condition. We therefore name CTCF as the principal candidate regulatory motif, reported with effect sizes, and provide a summary figure of the top enriched motifs by fold-enrichment (Rebuttal Figure 3, below).

      Rebuttal Figure 3. Top enriched known motifs (HOMER) by fold-enrichment across the five conditions. Colour intensity = fold-enrichment (% target / % background); grey dash = motif not detected in that condition; n.s. = not significant (HOMER Benjamini q > 0.05). CTCF is the only motif with fold-enrichment >= 1.3 in all five conditions.

      Venn filter and counts (corrected). Consistent with the effect-size-first approach described above, the Venn diagrams in Figure 6E now show robustly enriched known motifs (HOMER Benjamini q ≤ 0.05 AND fold-enrichment ≥ 1.2); we recomputed the overlaps directly from the five HOMER outputs using this filter (union denominator). Corrected values — MED12: WT = 10, G44D = 18, union = 21, G44D-unique = 11 (52.4%), shared = 7 (33.3%); PAGE4: WT = 19, S9D = 12, S9A = 11, union = 25, shared by all three = 3 (12.0%), S9D-unique = 4 (16.0%), S9A-unique = 1 (4.0%). This supersedes our earlier q ≤ 0.05-only statement, which counted many weakly enriched motifs (fold-enrichment Reviewer comment: The Figure 6D data do not sufficiently support the statement that MED12 WT enriched for chromatin remodeling/transcriptional regulation, G44D for stress-associated functions, and phospho-PAGE4 for nucleosomal DNA binding and ribosomal interaction terms. Please clarify.

      __Response: __We agree — the original panel over-interpreted small per-construct differences by assigning a distinct function to each construct. We have replaced the panel (now Figure 6G) with a corrected analysis that no longer makes per-construct functional assignments.

      The new Figure 6G is a term-by-condition dot plot of Gene Ontology Molecular Function enrichment of the peak-associated genes (clusterProfiler, redundant terms reduced by rrvgo at similarity 0.9, Benjamini–Hochberg-adjusted p-values). It shows that all five constructs enrich for the same core terms: the most strongly enriched term in every condition is structural constituent of ribosome, and the other enriched terms (cadherin binding, rRNA binding, ribonucleoprotein-complex binding, ubiquitin-ligase-related terms) are likewise shared across conditions rather than unique to any one. No construct-specific functional classes were detected, consistent with RNAP II promoter occupancy at highly transcribed genes. The Results text and Figure 6G legend have been rewritten to describe this shared-core finding, and the earlier “differentially enriched across constructs” wording and the stale “Figure 6D” reference have been removed.

      Reviewer comment: The legend says AP (light) vs PL (dark), but the figure shows AP green and PL yellow. And on the 73% statement, if the labels are correct, AP HCIs are 25 for Mut1 and 149 for WT, i.e. only 17% retained.

      __Response: __The legend has been corrected to the actual colors used in the figure, and the retention value has been recomputed and corrected as described for Reviewer 2-D.

      Reviewer comment: The data supporting “interactions with basal transcription factors and cell-cycle regulators were preferentially lost, while Mediator subunits and RNA-processing factors were selectively retained” are not readily apparent. Indicate the relevant figure/table.

      __Response: __We now cite the specific evidence, the focused interaction matrix (revised Figure 4E) and Supplementary Dataset 3, and have added a categorized lost-versus-retained summary so the statement is directly traceable to the data.

      Reviewer comment: Page 19, it is unclear how the statement that "In the MED12 p.G44D dataset, the bait (MED12-G44D) itself is strongly reduced compared with MED12 WT, consistent with decreased recovery of the mutant complex." is supported by the data presented in Figure 5A. Please clarify.

      __Response: __Addressed as for Reviewer 2-E: the data show the MED12-G44D bait is recovered at levels equal to or higher than WT, so the original “bait reduced” statement was corrected; the reduced number of G44D interactors is retained as a genuine, bait-independent effect.

      Reviewer comment: Page 19, reference is needed for "PELP1 is a well-established scaffolding protein that couples estrogen receptor signaling to chromatin remodeling and has been implicated in hormone-dependent tumor progression in breast and ovarian cancers."

      __Response: __A reference for PELP1 as an estrogen-receptor coactivator/scaffold implicated in hormone-dependent tumors has been added.

      Reviewer comment: For luciferase reporter assay: In the materials and methods section, the authors wrote "Data are presented as mean {plus minus} standard deviation (SD)", but in the figure legend, the authors wrote "Data represent mean {plus minus} SEM". Please clarify this discrepancy. In addition, please provide information regarding the promoter used in the luciferase assay plasmids, and replace "Firefly luciferase reporter plasmid containing the promoter of interest" with the specific promoter name.

      __Response: __We have harmonized the error-bar reporting: the Methods and the figure legend now both state mean ± SD, with the same number of replicates given in each. We have also replaced “promoter of interest” with the specific response element for each pathway (cell cycle/pRb-E2F → E2F; p53/DNA-damage → p53; MAPK/ERK → serum response element/SRE; MAPK/JNK → AP-1; Wnt → TCF/LEF; estrogen → estrogen response element/ERE).

      Reviewer comment: “Buyukcelebi K et al, 2023” is cited as both 2023a and 2023b; the same issue affects “Lin et al, 2018” and “Turunen et al, 2014.”

      __Response: __We have consolidated the duplicated entries and made the year-suffix usage consistent for these and all other multiply-cited references. We also carried out a complete audit of the bibliography against the in-text citations: every citation now resolves to exactly one entry, no entry is duplicated, and no entry is left uncited. Several references that were missing from the list have been added, and two citations that could not be traced to a specific source were removed from a sentence that is adequately supported by the remaining reference. Prompted by this comment we also checked each citation against the statement it supports rather than only against the bibliography, and corrected several placements: two extracellular-matrix references have been moved onto the matrix sentence they actually support, a reference on the co-occurrence of MED12 and HMGA2 alterations has been moved to the sentence reporting concurrent MED12 mutations in the COL4A5-COL4A6 tumors, a PAGE4-specific reference has been added alongside the general androgen-receptor citation, and one claim in the Discussion has been narrowed to the extracellular-matrix and cytoskeletal remodeling that the cited work actually addresses.

      Reviewer comment: “PAGE4 emerged … at protein, transcript, and IHC levels …”, IHC characterizes protein expression; did the authors mean phosphorylation levels?

      __Response: __Corrected. IHC measured PAGE4 protein abundance and (cytoplasmic-predominant) localization; the sentence has been rewritten so the three independent layers are stated accurately (protein by MS, transcript by RNA-seq, and protein by IHC), without implying IHC measured phosphorylation.

      Reviewer comment: “COL4A5-COL4A6 subclass showed upregulation of several extracellular matrix markers (WNT4, COL3A1, COL4A1, MMP2)”, it is unclear whether WNT4 should be an ECM marker.

      __Response: __We agree and have reclassified WNT4 as a hormone/Wnt-signaling factor rather than an ECM component, and corrected the sentence so only bona fide ECM markers are grouped together.

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      Referee #3

      Evidence, reproducibility and clarity

      Uterine leiomyomas (UL) occur in more than 70% reproductive age women and mutations in the RNA Polymerase II (RNAP II) transcriptional Mediator subunit MED12 account for ~70% of these tumors. Despite this genetic insight, the molecular mechanisms linking MED12 mutations to UL pathogenesis remain incompletely understood, hindering the development of effective therapeutics to the disease. Through unbiased multi-omics analyses, including proteomics, phosphoproteomics, and interrogation of previously published RNA-seq datasets, followed by IHC validation, the authors identified a specific upregulation of PAGE4 protein expression, phosphorylation at residues T51 and T85, and transcript abundance in MED12 mutated UL. Using Flp-In{trade mark, serif} 293 T-Rex cell model system, the authors performed affinity purification and proximity labeling assays to characterize the interactomes of different phosphorylation variants of PAGE4 and G44D mutant MED12. They also conducted ChIP-seq analyses to assess whether these mutations alter RNAP II occupancy on chromatin and performed luciferase reporter assays to determine their effects on the activities of major signaling pathways, including pRb, p53, MAPK/ERK, MAPK/JNK, Wnt, and estrogen. They concluded that PAGE4 phosphorylation acts as a molecular switch, dramatically remodeling its protein interaction landscape to favor associations with the Mediator complex and core transcriptional machinery. Their findings establish phospho-PAGE4 as a central mechanistic node in MED12-mutant leiomyoma pathogenesis, expand the biological role of PAGE4 beyond prostate cancer to encompass female reproductive tract neoplasia, and identify a promising biomarker and potential therapeutic target for the most common molecular subtype of UL. The presented findings are supported by the experimental data. The manuscript is well written and provides sufficiently detailed methods to ensure experimental reproducibility. Overall, the study is interesting and important and warrants publication. However, I do have several specific concerns that need to be resolved.

      Major comments:

      1. For assays of interactome, luciferase reporter, and RNAP II occupancy on chromatin of PAGE4 phosphorylation variants and MED12 G44D mutants, one critical limitation is that the studies exclusively relied on tagged, exogenously expressed proteins without evaluation of the levels of endogenous proteins. The endogenous protein could substantially affect the observed interaction networks, particularly if PAGE4 and MED12 expressed at appreciable levels in the Flp-In{trade mark, serif} 293 T-Rex cells. I recommend that the authors measure endogenous protein expression levels in these cells and discuss how endogenous protein levels may influence interpretation of the data. Did different phosphorylation variants of PAGE4 affect their nuclear localization? It would also be interesting to compare the T51 and T85 PAGE4 phosphorylation status between wild-type MED12 and G44D MED12 cells.
      2. Turunen et al (2014, PMID: 24746821) reported mutant MED12 interactomes using the similar strategy. It would be valuable for the authors to compare their findings with this published dataset. Such a comparison could help assess the reproducibility and robustness of the identified protein-protein interactions and provide additional confidence of the current results.
      3. The authors used luciferase reporter assays to assess the effects of PAGE4 phosphorylation variants and G44D MED12 on multiple signaling pathways, including estrogen signaling. However, it is unclear whether the experimental system is appropriate for evaluating estrogen receptor (ER)-dependent transcriptional activity. Specifically, the manuscript did not provide information regarding the endogenous expression levels of ERα and/or ERβ in the cells. In addition, estrogen signaling is typically ligand-dependent, yet no estrogen treatment appears to have been included in the experimental design. Without demonstrating receptor expression and appropriate ligand stimulation, it is difficult to interpret the negative or positive effects of mutant proteins on estrogen-responsive reporter activity. Inclusion of information of estrogen receptor expression levels and ligand stimulation experiments would substantially strengthen the conclusions regarding the role of these variants in estrogen signaling.

      Minor comments:

      1. Page 15, the authors described "Among individual kinases, HIPK2 was the top candidate for PAGE4-T51/T85, both sites fitting their known serine/threonine consensus motifs.". HIPK2 did not appear in the list of kinases in Figure 3E. Please clarify the data source and the criteria for the ranking.
      2. Supplementary Figure 4, significance marks were missing.
      3. For the motif enrichment section, while the findings are potentially interesting, the manuscript devotes substantial discussion to the presumed roles of these motifs in UL pathogenesis based solely on motif enrichment analysis. In my opinion, the biological significance of these motifs is somewhat overinterpreted, as motif enrichment alone does not establish functional involvement of the corresponding transcription factors in tumor development or progression. In addition, the presentation of the motif analysis could be improved. I recommend including a figure summarizing the top enriched motifs, along with enrichment statistics and/or motif logos. A visual representation would make the results more accessible and help readers better appreciate the key findings without relying exclusively on lengthy textual descriptions. For example, it is difficult to understand the statement that "PAGE4 WT maintained AP-1-driven transcription, whereas phosphorylation induced an expanded motif repertoire."
      4. The data presented in Figure 6D did not sufficiently support the statement that "MED12 WT enriched for chromatin remodeling and transcriptional regulatory functions, G44D enriched for stress-associated functions, and phosphorylated PAGE4 mutants enriched for nucleosomal DNA binding and ribosomal interaction terms". Please clarify.
      5. Figure 4B: In Figure legend, the authors wrote "Stacked bars distinguish interactions captured by AP (light) versus PL (dark).". But AP was labeled as green and PL was yellow on Figure 4B. Page 76, the authors described "Among the AP-derived HCIs, the phosphomimic triple mutant (Mut1: S9D/T51E/T85E) retained only 73% of the interactors captured by PAGE4 WT, representing a net loss of approximately 27% of stable associations". If labels on Figure 4B were correct, this reviewer's understanding is that "In AP, HCIs for MUT1 is 25, and HCIs for WT is 149. Therefore, only 17% (25/149) stable interaction was retained". Please clarify.
      6. Page 16, The data supporting the statement that "This reduction was not uniform: while interactions with basal transcription factors and cell-cycle regulators were preferentially lost, associations with Mediator subunits and RNA-processing factors were selectively retained." are not readily apparent. Please indicate the relevant figure, table, or dataset.
      7. Page 19, it is unclear how the statement that "In the MED12 p.G44D dataset, the bait (MED12-G44D) itself is strongly reduced compared with MED12 WT, consistent with decreased recovery of the mutant complex." is supported by the data presented in Figure 5A. Please clarify.
      8. Page 19, reference is needed for "PELP1 is a well-established scaffolding protein that couples estrogen receptor signaling to chromatin remodeling and has been implicated in hormone-dependent tumor progression in breast and ovarian cancers."
      9. For luciferase reporter assay: In the materials and methods section, the authors wrote "Data are presented as mean {plus minus} standard deviation (SD)", but in the figure legend, the authors wrote "Data represent mean {plus minus} SEM". Please clarify this discrepancy. In addition, please provide information regarding the promoter used in the luciferase assay plasmids, and replace "Firefly luciferase reporter plasmid containing the promoter of interest" with the specific promoter name.
      10. The same ref "Buyukcelebi K et al, 2023" was cited differently as 2023a and 2023b. The same mistakes for refs. "Lin et al, 2018" and "Turunen et al, 2014".
      11. Page 25, the authors wrote "PAGE4 emerged as a consistently induced marker in MED12-mutant ULs at protein, transcript, and IHC levels, with cytoplasmic-predominant staining that cleanly distinguishes UL from matched myometrium". The IHC level characterized protein expression. Did the authors mean "phosphorylation levels" rather than "IHC levels"?
      12. Page 13, the authors wrote "Besides PAGE4, COL4A5-COL4A6 subclass showed upregulation of several extracellular matrix markers (WNT4, COL3A1, COL4A1, and MMP2)". It is unclear whether WNT4 should be categorized as an ECM marker. The authors should provide a rationale for the classification or consider using a more appropriate designation.

      Significance

      The authors convincingly demonstrated that PAGE4 mRNA and protein levels and the levels phosphorylation at T51 and T85 are specifically elevated in MED12 mutant UL. Although PAGE4 is traditionally regarded as a male-specific marker of prostate cancer and other urogenital diseases, the authors found it to be markedly upregulated and hyperphosphorylated in these female-specific tumors. This unexpected observation suggests that PAGE4 may have a broader, sex-independent function in cellular signaling pathways and UL progression. One limitation of this study is the absence of functional investigations examining the effect of PAGE4 on cell proliferation, apoptosis, or other UL-associated phenotypes, which would be important for establishing its biological relevance. In contrast, much of the manuscript focuses on interactome (affinity purification and proximity labeling approaches) or transcriptional activity (ChIP-seq analysis of RNAP II chromatin occupancy and luciferase-based transcriptional reporter assays) analyses of mutation variants in a non-disease-relevant cell model, making it difficult to assess the significance of these findings for UL pathogenesis. Consequently, the mechanistic conclusions remain preliminary without functional validation in biologically relevant UL models. Nevertheless, this study will be of significant interest to UL researcher community, stimulating further investigation into the role of PAGE4 in UL cell survival and growth, and supporting translational efforts to evaluate its utility as a biomarker or and potential therapeutic target.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Bong et al. describes a comprehensive proteogenomic analysis of uterine leiomyomas tumors (ULs). The goal was to identify protein level effectors that drive MED12 mutant UL tumors. They employed discovery DDA global and phosphoproteomic analyses of 9 MED12-mutated UL and matched normal tissues. They identified that PAGE4 was upregulated at both the protein and phosphosite level. They further characterized MED12 ULs against other subtypes of UL (HMG2A, FH, COL4A) and confirmed PAGE4 expression and phosphorylation is confined to the MED12-mutant subtype. They constructed isogenic cell lines (HEK293) of PAGE4 and MED12 variants to characterize the interactomes of PAGE4 and MED12. These results showed PAGE4 is involved in the mediator complex functions of MED12.

      Luciferase reporter assays were used to characterize how PAGE4 and MED12 mutants affect the transcription of key genes.

      They performed ChIP-seq experiments for RNA Pol2 to characterize how PAGE4 and MED12 mutants affect Pol2 transcription.

      Overall, Bong et al. convincingly identified a novel role of PAGE4 in UL and mediator transcriptional processes. Thus, this work will represent an important novel contribution to understanding the pathobiology of UL.

      There are several critiques for this manuscript. Major critiques:

      A) The validation cohort is not technically a validation cohort as it uses the same 9 MED12 mutant UL samples. Thus, it cannot validate PAGE4 upregulation and phosphorylation in an independent cohort. A second MED12-mutant UL cohort should be used.

      B) The ChIP-seq data are over interpreted. No meaningful changes in peak width or height is readily observable. To be believable statistical tests would be required.

      C) HIP2K was proposed as a candidate kinase that phosphorylates PAGE4. This is based on data presented in Fig. 3E. However, there is no HIP2K on this heatmap. Instead CLK2 is highlighted in red font and has an increased score, indicating it could be responsible kinase. HIP2K and CLK2 are different unrelated kinases. Please rectify.

      D) For Fig. 4B the text states MUT1 retained 73% of prey proteins contained in the PAGE4 WT interactome. However, in Fig. 4B, MUT1 had 25 preys versus 149 preys for WT; this is 25/149 = 17% retained for a decrease of 83%. Please rectify.

      E) The text states the MED12-G44D bait protein is strongly reduced compared to MED12 WT, referring to Fig. 5A. Fig. 5A right panel does not support this conclusion. Please rectify.

      F) In Fig. S3 the text states MUT1 has upregulated processes for base-excision repair and SUMOylation. However, the Fig. S3 heatmap does not support this for Mut1. Instead the data support MUT6 not MUT1. Please rectify.

      G) Figure 4E currently shows the full bait-prey graph and becomes a "hairball" where edge density prevents the reader from seeing the differences claimed in the result section. This figure is uninterpretable. The bait protein names are in miniscule point font and are illegible. There are too many elements to see the difference between AP, PL, or Both or the name of the prey proteins. The edge lines widths and colors are uninterpretable. Suggest simplifying the visualization or split it into focused panels so the figure evidence matches the claims. Specifically, recommend aggregating preys by annotated complex (or functional group) and showing a single summarized edge between each bait and complex (with edge weight or thickness reflecting the number or strength of prey links), and then displaying only the individual preys that are differentially associated between baits to support the key statements. H) Fig. 5A presents the known phosphorylation sites for PAGE4. However, the figure shows the data for 'GAGE', which is a different (unused) name for PAGE4. Please rectify. Importantly, this figure is directly copied from PhosphoSitePlus®.org without apparent permission. Permission should be obtained.

      I) The discussion and conclusions suggest making phosphomimetic changes at key sites in PAGE4 and MED12 are conclusive for phosphorylation being involved. This is not formally true, and should at least be mentioned as a hypothesis.

      J) In general, the methods describing the mass spectrometry experiments need to be expanded and clarified. The current description is insufficient for reproducing the experiments or fully understanding how the different datasets were generated and analyzed. DIA MS methods are missing entirely, and given that the manuscript uses both DDA and DIA acquisition modes across multiple MS experiments (total proteomics, phosphoproteomics, AP, and PL), the methods should be reorganized to clearly explain how acquisition and subsequent analyses were performed for each experiment type, specifying what was done in DDA and what in DIA modes. In the downstream analysis, how were the total and phosphoproteomics normalized and integrated? It is not clear how the PL data were analyzed or whether they were treated the same way as AP data; please explicitly state whether PL and AP data were processed identically or with different pipelines and describe any differences in preprocessing, normalization, or statistical treatment. Was SAINT used on spectral counts or intensities? Since the baits are nuclear proteins, was anything with Nuclear Localization Signals used as controls?

      K) Overall the manuscript has many issues with data presentation. To illustrate just a few:

      • Fig. 3B and 3D are described as volcano plots. They are not volcano plots.
      • Fig. 3B legend mentions a lower panel. There is no lower panel for Fig. 3B.
      • Fig. 3A, please use different shape markers for the UL subtypes and suggest connecting with edges to thus support their text conclusions of separate groupings.
      • For Fig. 3E the colors for the families and groups are not distinguishable (and the font is too small). Please rectify.
      • Please label the units and scale for Fig. 6A.
      • Please mention the MAC3 tag uses Ultra-ID to thus justify the 10 min labeling time.
      • In the relevant protein and phosphoproteomic datasets and figures please mention the number of proteins and phosphosites obtained to thus allow an interpretation of the data quality.
      • Table 4 is in a format that is uninterpretable.
      • The text and figures interchangeably use the mutant number (e.g. MUT1) or mutant composition (e.g. S9D_T51E_T85E - for MUT1), this makes reading and interpreting the manuscript very challenging. Suggest using one format.

      Significance

      Overall, Bong et al. convincingly identified a novel role of PAGE4 in UL and mediator transcriptional processes. Thus, this work will represent an important novel contribution to understanding the pathobiology of UL.

      This is a very comprehensive dataset. However, there are multiple issues on data interpretation and presentation.

      The results would be of interest to cancer biologists, primarily at the basic science level. Clinical translation would require additional research.

      The expertise of the reviewer is in cancer proteomics (interactomes, DIA and DDA mass spectrometry, proteomic data analysis), and general cancer biology across most common cancer types.

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      Referee #1

      Evidence, reproducibility and clarity

      Bong Yih Tyng and Xiaonan Liu employed a multi-omics proteomics approach (full proteome profiling, phosphoproteomics), mining of external datasets (RNAseq, ProteomeDB, Protein Atlas and an experimental validation strategy interactome mapping, proximity interactome mapping) to establish PAGE4 phosphorylation status as driver in MED12-mutant uterine leiomyomas. They used state-of-the-art DIA proteomics approaches to investigate in addition to MED12-variant tumors the PAGE4 expression in other tumors (COL4A5-COL4A6, HMGA2, and FH subclasses) substantiating their initial findings in MED12-variant tumors. The analysis strategies employed are suitable and mostly well-described (see major and minor comments regarding some points which might can be improved). The interaction proteomics and proximity labelling interactomics capture of PAGE4 WT and phosphomimics PAGE4 mutants (Mut1 triple phospho, Mut6 T51E mutant) and the MED12-G44D variant reveal extensive network rewiring towards RNA processing, ribosome biogenesis, translation and protein folding, with less interactions linking to its core transcriptional function. Together the study offers insights into the functional consequences of Med12-mutants uterine leiomyomas and establishes PAGE4 phosphorylation as a key factor for the network rewiring leading to adaptions in cell cycle progression, proliferation, and cellular stress responses.

      Major comments:

      Figure 1B: The first PCA component is mostly dominated by a single sample far to the right dominating the first principal component (33.9%). Was there an outlier test (such as Mahalanobis Distance or Z-score on PC1) performed to ensure that subsequent statistical analysis is not affected (e.g. skew group means, inflation of variance) by this single measurement? If an outlier test was conducted it should be reported in the method section. Also for the phosphoproteomics no PCA or outlier test is reported to judge data and analysis strategy this would be necessary. The full proteome and phosphoproteomics data need more details regarding the analysis to ensure reproducibility. Figure 3A: Full proteome PCA: please perform an outlier test for the HMGA2 on the left side. Phosphosites PCA: please perform an outlier test for the FH_UL and COL4A5-COL4A6_UL data points which are visually separated from the other groups. Like this the PCA are difficult to interpret as the data points for example in the full proteome PCA get squeezed together on an axis (PC1 mainly is driven by one measurement). In the manuscript the outliers are discussed showing inter-tumor heterogeneity, but it could be as well origin from variability in tissue collection or MS-performance. Outliers might affect statistics and conclusions regarding the statement about variation within the group. This should be tested if discussed in the manuscript.

      Minor comments:

      Phosphoproteomic analysis identifies aberrant kinase networks and PAGE4 hyperphosphorylation (page 8). Why is SNRNP70_S226 mentioned as being one of the most significantly hyperphosphorylated sites in UL. According to table S1 it has a log2FC of 5.92 and a p-value of 0.02 and was classified as non-significant. The data in table S1 contradicts the statement in the manuscript. The other example MCM2_S139 is significantly dysregulated. Also, MARCKSL1 (log2FC of 2.14 and p-value of 0.016, assessed as "Non-significant") also for NDRG2. The volcano plot and figure caption indicate a p-value cutoff of 0.05 instead of 0.01 which is apparently shown in table S1. Please clarify.

      Phosphoproteomic analysis identifies aberrant kinase networks and PAGE4 hyperphosphorylation (page 8). Add citations for Protein Atlas and ProteomicsDB.

      PAGE4 expression is selectively enriched in MED12-mutant UL (page 14): "PAGE4 expression indeed was significantly elevated in MED12-mutant ULs ... ". The figure caption, methods and text do not provide any information of the statistical test used regarding the statement of significant upregulation. Please provide the data or change wording as a statistical significance is implied.

      Method section "Liquid chromatography-mass spectrometry (LC-MS)" report the UniProtKB database download date to ensure reproducibility. Does the database contain isoforms and TrEMBL sequences or uses a filtered version (e.g.: status reviewed, canonical?) (report it like on p.36 for the AP-MS/BioID samples). Further add a statement if missing values were imputed on which level (peptide, protein), which normalization strategy was employed, and was there filtering of incomplete values performed. All these analysis steps are common practice for DDA measurements of clinical samples and may affect subsequent statistical finding and thus should be reported in the method section if they were or were not performed (for the PPI data this was reported on p.37, but for the full proteome and phosphoproteome the information provided is not sufficient to judge the validity of data processing). Method section "GO enrichment and statistical analysis" (p.37): Please add citations for the used tools SAINT, CRAPome and Enrichr.

      Figure 1c: The scale of the (Gene) count does not correspond with the circle sizes of the plot. It is thus not possible to judge the number of genes per category. This should be sized equally as else it is not possible for a reader to judge the number of genes per enriched terms. Figure 1G: Expression levels for ProteomicsDB. As the log2 of the expression level is used, it is not clear if the white squares correspond to missing values in the ProteomicsDB, or if the value was very low. Maybe indicating absent values in a grey color scheme makes this more explicit. Figure 1C: the caption indicates a "gray box" but it seems that PAGE4 was highlighted as a red dot, and a white box was added for indicating the gene name.

      Figure 3D: in the manuscript (p.15) the plot is described as volcano plot. This looks like a scatterplot with horizontal scattering and indication of the significant threshold than a volcano plot (in principle it shows the data of the volcano plot, but one expects the adjusted p-value as axis in a classic volcano).

      Figure 5E: The PPI network is difficult to read as there are too many edges and nodes. Optional: It could make sense to provide a more refined analysis (collapsing the categories/ proteins) as like this it is impossible to catch differences or provide a subnetwork and provide the full network in the supplementary.

      Significance

      The identification of PAGE4 in MED12 mutant ULs, which is typically expressed (see Figure S1) in male as an upregulated and hyperphosphorylated protein in leiomyomas is a novel finding. The sex-independent role was clearly supported by various data layers (proteome and phosphoproteome screen, IHC and secondary DIA validation dataset). The strength of the study is that they have multiple layers of protein evidence for this specific finding. In addition, they performed additional functional genomics and interactomics experiments to elucidate mechanistic understanding and consequences of PAGE4 hyperphosphorylation and identified a phosphorylation switch and were able to link HIPK2 as likely kinase. These findings are appropriately highlighted and discussed in the study. The study does provide a deeper understanding of uterine leiomyomas tumors.

      The study links PAGE4 expression and phosphorylation to MED12-mutant ULs. It provides levels of evidence for this on the mRNA, protein and phosphoproteome. It establishes that PAGE4 is MED12-mutant specific using a second cohort with different cancer markers. The techniques (proteomics, phosphoproteomics, interactomes) are conducted on an expert level. The data analysis is conducted at sufficient level, only outlier tests for samples which do not follow the trends are lacking as a major criticism. The reporting of the results is appropriate. A strength is that the phosphoproteome data was of high quality to provide side-specific information and employ normalization using the unphosphorylated abundance. I recommend providing outlier tests and providing more detail on the method section for the initial DDA measurements.

      Audience:

      The study is of broad interest, due to the prevalence of the uterine leiomyomas. Further, the combined proteomics/ interactomics approach combined with functional validation will be appealing for several fields including systems biology, clinical/translational, and molecular biology orientated researchers. The research might serve as starting point for larger clinical proteomics or biomarker studies. Further studies might focus on strengthening the validation of HIPK2 or investigating the link to PELP1 upon phosphorylation of PAGE4.

      My expertise:

      My field of expertise are molecular systems biology, full proteome profiling, and MS-based interaction proteomics analysis.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Ozga, et al. develop a more reproducible method for cranial neural crest cell (CNCC) differentiation from human pluripotent stem cells, which is compatible with arrayed screening techniques such as automated microscopy. The method is based on a prior neurosphere aggregation protocol, but optimized here for cell number and attachment conditions.

      Major comments:

      The key conclusions are overall well-supported for CNCC specification and migration, but I have three major comments that could be reasonably addressed without much additional work:

      1. The gene expression analysis lacks statistical power, and does not enable a comprehensive transcriptome-wide comparison with previously published differentiation methods. A minimum of 3-4 technical replicates are necessary and standard for bulk RNA-seq analyses with DESeq2. This explains why the number of differentially expressed genes and enriched GSEA terms is so low. While single-cell analysis is likely beyond the scope of this methods paper, I would have liked to see a comprehensive expression analysis that enables readers to understand how this method compares to prior methods (not only the standard method included here).
      2. H9 hESCs are biased towards neural differentiation. Although the authors include an unrelated hiPSC line (SV20) to demonstrate CNCC differentiation by immunofluorescence (IF) and qRT-PCR, these cells were not included in the bulk RNA-seq study, and IF only included passage1 (Fig. S6). The homogeneity of H9-derived CNCCs at passage 4 is intriguing (Fig. 5BC), and I would like to see the same staining for SV20-derived CNCCs at passage 4, to learn whether this method is broadly applicable to other human pluripotent stem cells.
      3. It is unclear whether the different substrates bias CNCC toward specific terminal cell fates. Whether true CNCC multipotency is maintained is not addressed. I would have liked to see stainings for terminal CNCC-derived cell types to rule out that some matrices impact terminal differentiation. The DESeq2 analysis does not address this, since it's underpowered with only 2 replicates per condition. Also in the IF analysis there were more SOX9-TWIST+ cells with FN; do these cells differentiate more quickly in the presence of FN?

      Minor comments:

      When CNCCs are passaged, is the core left behind during accutase treatment or removed prior to accutase? If it's not removed, how is passage 4 so homogenous? Do SOX2+SOX9- cells become SOX9+ upon accutase passage, or are they lost from culture? Fig. 5C: the legend is clever but not that helpful to the reader - the supplementary figure has a simpler legend. Fig. 5D: This should be a boxplot, as it is difficult for the reader to discern color gradients on a log-scale. But the authors need at least 1-2 more RNA-seq replicates for a boxplot.

      Significance

      The authors framed the significance of the work appropriately, provided the authors can address the major comments that describe what caveats currently limit the impact of this study.

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      Referee #2

      Evidence, reproducibility and clarity

      A study by Ozga et al. presents a new protocol and methodology for neurosphere-based differentiation of cranial neural crest cells (CNCCs) from human embryonic stem cells (hESCs), designed to enable robust quantification of early developmental processes, including specification, delamination, and migration. The method utilizes single-cell aggregation combined with arrayed plating to standardize neurosphere size, adherence, growth, and CNCC formation. The authors demonstrate that this approach yields a more robust and experimentally tractable in vitro CNCC model for quantitative phenotyping and screening compared with standard protocols.

      The study further shows that this in vitro system can be used to evaluate the effects of distinct extracellular matrix (ECM) components on CNCC behavior. The authors also propose that the model could be applied to investigate CNCC migratory patterns, gene expression changes, drug responses, and genetic perturbations. Overall, the manuscript is well written and logically organized, and it introduces a promising in vitro platform that may facilitate investigations into human birth defects associated with abnormalities in CNCC development and differentiation, which contribute to the formation of orofacial structures and the peripheral nervous system.

      Major Comments

      1. The authors provide transcriptional profiling and immunofluorescence staining of key transcription factors to support their claim that the differentiated cells derived from hESCs possess CNCC characteristics. However, a defining property of CNCCs is their multipotency and their ability to differentiate into multiple lineages, including osteoblasts, chondrocytes, neurons, and smooth muscle cells. It would be important to clarify whether the differentiation potential of the derived CNCCs was experimentally evaluated. If such assays were not performed, the authors should discuss how their current data support the multipotent identity of the cells and their capacity to generate these distinct lineages.
      2. The neural crest differentiation medium and the neural crest long-term maintenance medium contain BMP2 and the Wnt agonist CHIR-99021. The manuscript does not provide sufficient justification for the inclusion of these factors. Additional explanation should be provided regarding why these components were selected and which downstream signaling pathways or transcriptional programs they are expected to activate during CNCC differentiation and maintenance.
      3. The authors state that the large number of differentially expressed genes observed across different ECM conditions likely reflects signaling events downstream of ECM binding. However, no direct evidence is presented to support this claim. To strengthen this conclusion, the authors should identify enriched or upregulated signaling ligands and receptors (signaling pathways) associated with ECM-cell interactions in the ECM comparison datasets and in the passage-4 CNCCs maintained in the long-term maintenance medium.
      4. In Figure 7H, additional explanation is needed for why genes associated with early neural tube identity and patterning (OTX2, PAX6, WNT8A/B, ZIC1, and MEIS1) are enriched in cells cultured on vitronectin-coated plates. The authors should clarify whether this observation reflects a shift in cell identity or whether it could be due to contamination by non-CNCC populations originating from the neurospheres.
      5. The authors report that 10,000-cell aggregates frequently developed necrotic centers and occasionally dissociated into smaller clusters, whereas 8,000-cell aggregates did not display these features. Additional clarification would be helpful to explain what distinguishes the 10,000-cell aggregates from the smaller aggregates. For example, differences in oxygen or nutrient diffusion, mechanical stability, or signaling gradients could potentially account for this observation.
      6. In Figure 5B (ARRAY-FN condition), the level of TWIST1 protein appears reduced in CNCCs compared with other culturing conditions. However, the transcriptomic data presented in the heatmap (Figure 5D) do not indicate a corresponding difference at the mRNA level. The authors should comment on this apparent discrepancy and discuss whether post-transcriptional regulation, protein stability, or methodological differences between assays might explain the observation.

      Minor Comments.

      -None

      Referee cross-commenting

      In line with the reviewer's comment, I suggest performing statistical analysis and significance testing for all quantitative measurements.

      Significance

      The authors demonstrate that this neurosphere-based Array-CNCC yields a more robust and experimentally tractable in vitro CNCC model for quantitative phenotyping and screening compared with standard protocols. The study introduces a promising in vitro platform that may facilitate investigations into human birth defects associated with abnormalities in CNCC development and differentiation, which contribute to the formation of orofacial structures and the peripheral nervous system.

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      Referee #1

      Evidence, reproducibility and clarity

      Ozga et al present an adapted method of generating neurospheres derived from human cell lines in order to standardize CNCC generation. They utilize multi-well plates and single cell aggregation to standardize and synchronize neurosphere generation and maintenance, namely growth, patterning, attachment and migration. They also show examples of possible downstream experiments. Their technique allows for comparisons between environments, conditions, genetic disturbances and drug responses, to better our understanding of neural crest cell processes and associated diseases, while reducing sample variability. Minor suggestions are outlined below.

      Major Comments

      There needs to be some discussion between the array CNCC protocol under consideration and other differentiation protocols. In the Okuno et al protocol, (2017) hiPSC is cultured for 3d in ES Media to generate embryo bodies before switching to NC media to generate NCC. How would this new protocol be amenable to alternative methods of 3D neural crest cultures such as the one by Okuno et al, and others? Comparison of this work to other methods of NCC 3D culturing would be beneficial. In addition, Ozga et al., mention that the most standard method for NCC culturing is either 2D culturing or 3D culturing. The manuscript would be improved if the authors discuss how this proposed method compares to commonly used 2D culturing. Overall, additional discussion about how the array CNCC protocol improves other commonly used NCC differentiation techniques is necessary to better place the impact of this work.

      Minor comments:

      1. FIG 1: It is not clear which of the in vivo steps listed are aligned to stages of the in vitro culture system proposed. Could the authors add this comparison either in the figure or in the text that describes the technique?
      2. Pg7 LINE 175: Neurospheres generated from a larger number of cells (specifically 8000-10000) are described as having a necrotic core and figure 2 is referenced. Could this be pointed out on the image or a different image used to show this necrotic core?
      3. FIG S3b: It is not clear whether the n=2 is referring to 2 plates generated from one experiment or 2 separate experiments. Please be more explicit about what the n's mean throughout
      4. FIG S7A: Could the same picture style and quantification be done for the ones expressing mcherry, then perhaps a graph of distribution of cells (y axis 100%) expressing mcherry vs gfp (based on your principle both should be close to 50%)
      5. I suggest that the section describing the effects of different ECM (Pg14 LINE 438-523 and associated Figure) be moved to immediately after the section outlining fibronectin's role on morphology and gene expression (Pg 10 LINE 313-347) and before the section describing long-term passaging effects of fibronectin use (Pg 12 LINE 349-379). The wording of the introductory paragraph of this section can be modified and moved towards the end of the paragraph, stressing that this experiment was to determine the best ECM protein to use and to depict an example of a downstream application of this technique.
      6. Pg 17 LINE 539-40 seems to be referring to the wrong figure
      7. Pg 17 LINE 544-545, it was not presented in the results that there was an analysis of neurospheres generated from fewer than 1000 cells, though this is a logical conclusion. If this analysis was not done, please make it clear that this is a presumption.

      Referee cross-commenting

      In line with comments made by reviewers 2 and 3, an exploration into if the proposed method of culturing affects the multipotency of cNCCs would be beneficial. cNCCs are capable of differentiating into chondrocytes, osteoblasts, neurons etc. The impact of the different ECM proteins on capability to conduct terminal differentiation should be addressed, either experimentally or with logical conclusions from the available RNA-seq data and literatures.

      Significance

      This work highlights an advancement in techniques used to study neural crest specific processes. It allows for the standardization of samples that can be used for environmental, genetic and drug-based studies. It adapts and expands on techniques previously used in mouse embryonic stem cells and shows its use in human embryonic stem cells and IPSCs. Readers might be interested in downstream analyses made possible by this technique, such as mosaic co-culturing and array plating. Better contextualization of this work in the context of currently used differentiation techniques will add power to the overall message.

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      Reply to the reviewers

      POINT-BY-POINT response letter

      We thank both reviewers for useful suggestions. Our responses are indicated in blue in the text below.


      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Alves et al investigate the mechanisms of anterograde IFT in Euglenozoa that lack the canonical heterotrimeric kinesin-2 motor and the kinesin-associated protein KAP. Through a combination of comparative genomics, in vitro analyses, live-cell imaging, and genetic analyses in Trypanosoma brucei and Leishmania mexicana, the authors show that the kinesin-2 proteins KIN2A and KIN2B form homodimeric motors in vitro with distinct in vivo ciliary localization and functions. The authors report that KIN2B is essential for flagellum assembly despite contributing to only a minority of long-range anterograde transport events that colocalize with IFT trains. By contrast, KIN2A is dispensable for flagellum assembly despite being proposed to mediate most anterograde IFT. Based on these observations, the authors propose a division-of-labour model in which KIN2B mediates entry of IFT proteins into the flagellum while KIN2A performs the majority of anterograde transport.

      The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

      We agree that there are limitations to our study and have discussed them below and in the revised manuscript. We make it clearer that it is a working model, which we believe is currently the best one to explain the available data. We also note that Reviewer 2 considers the model “compelling”.

      Major comments:

      1- The authors should revise the statement in the Introduction that in bloodstream-form T. brucei "only knockdown of both KIN2A and KIN2B impacted flagellum length". However, Douglas et al. (2020) reported that individual depletion of either kinesin reduced flagellum length, with a stronger additive phenotype upon combined depletion. The authors should correct this point and clarify more explicitly how the present study extends the earlier work, both conceptually and technically.

      Thanks for the comment, the statement has been corrected (see below). Compared to the Douglas study, there are major advances both conceptually and technically. Below, we are talking only about function, the studies on kinesin motility (Figs 2,3,5,6) being entirely new since this was not looked at in the 2020 publication where localisation studies were limited to fixed cells.

      In technical terms, our study relies on gene deletion or inactivation using potent Cas9 approaches in both T. brucei (procyclic stage) and L. mexicana (Beneke et al. 2017, ref [58]; Asencio et al., 2024 [72]) while the Douglas study used RNAi, with strong efficiency against KIN2A (~10% mRNA still detected) and more limited impact on KIN2B (~30% mRNA left). This could explain the stronger phenotype reported for KIN2A depletion. Moreover, the published study was done with bloodstream stage trypanosomes, which are much more sensitive to flagellar pertubation than procyclic ones (Broadhead et al., 2006; Ralston & Hill, 2006 [34, 35]) used here. A reminder has been added in the text for non-trypanosome readers. The introduction has been edited as follows (p. 5):

      “In the trypanosome bloodstream stage (which develops in mammals and can also be manipulated), knockdown of KIN2A or KIN2B individually reduced flagellum length by 4-5 µm, and an additional reduction was observed with combined silencing [30]. These results suggest that the two kinesins may be redundant. Nevertheless, silencing of KIN2A alone, but not KIN2B, severely impacted the growth rate of bloodstream trypanosomes and led to spectacular cytokinesis defects [30], hinting at potentially some distinct functions. However, knockdown efficiency was less potent for KIN2B (~30% residual mRNA vs ~10% for KIN2A), hindering firm conclusions. It should also be reminded that bloodstream trypanosomes are more sensitive to flagellum perturbation compared to procyclic cells [34, 35].”

      In conceptual terms, there are major advances. The 2020 study demonstrated the importance of KIN2A and KIN2B for proper flagellum assembly but did not provide information about the contribution of each kinesin, neither on their trafficking. First, we formally demonstrate that KIN2A and KIN2B are homodimeric and that heterodimers cannot be formed. Second, this study is revealing not only distinct localisation and motility profiles, but also different contributions to flagellum construction: KIN2B is mostly found in the proximal portion of the flagellum and governs access of IFTs to the flagellar compartment while ensuring limited protein transport while KIN2A is found all along the flagellum and is responsible for the majority of IFT transport, although it cannot import IFT proteins. Third, KIN2B can substitute to KIN2A (albeit a bit less efficiently), while KIN2A cannot substitute for KIN2B. These results are the basis of the original division-of-labour model presented at Figure 9.

      2- The localization of KIN2A in the current manuscript appears somewhat different from that reported previously, where KIN2A was described as more enriched near the basal body region. This discrepancy should be discussed. In particular, the relatively strong cytoplasmic signal in the current study may obscure a weak basal enrichment, and this possibility should be addressed

      The Douglas et al. study used a rabbit antiserum raised against aa 391-696 of KIN2A. Following aldehyde fixation, the signal was found in the cytoplasm, in the basal body area and along the flagellum. The region selected contains two coiled-coil domains (aa 406-464 and aa 604-636), and antibodies recognising coiled-coil domains are well known in the community for producing false positive on centrosomes and basal bodies, due to over-representation of these domains in centrosomal proteins (see for example Nido et al. Mol. Biosyst. 2012). In the 2020 study, western blot analysis detected a reduction in the amount of KIN2A upon triggering RNAi confirming specificity by this assay. However, no immunofluorescence (IFA) images of the knockdown cells were presented. Many years ago (actually well before the 2020 paper was published), Dr Welch kindly shared with us an aliquot of their anti-KIN2A that we used in both western blot and IFA in a cell line expressing double-stranded RNA of KIN2A in a tetracycline-inducible manner. Western blot demonstrated an at least 8-fold reduction of the signal (see below, so reproducing data in the Douglas publication) but the signal obtained by IFA was not much modified, especially the spot at the flagellar base (see images below). It is therefore likely that this signal is not KIN2A-specific.

      Figure response 1. Cells from the KIN2ARNAi cell line were grown without (non-induced) or with tetracycline for 3 days to trigger RNAi against KIN2A. A. Western blot with the published anti-KIN2A antibody demonstrates RNAi efficiency and antibody specificity by this assay (Bertiaux et al. Curr. Biol. 2018 [33]). B-C. IFA with the published anti-KIN2A antiserum produces signal at the flagellar base, along the flagellum and in the cytoplasm in KIN2ARNAi cells in both non-induced (NI) and induced conditions (3 days)(B. Morga, unpublished data). Since the IFA signal is not reduced upon induction, it is likely unspecific.

      Therefore, the discussion has been updated as follows (p. 20):

      “The localisation of KIN2A had been previously reported in the T. brucei bloodstream stage using a rabbit antiserum raised against aa 391-696 of KIN2A, which labeled the flagellum, the basal body area and the cytoplasm [30]. Using the same antiserum provided by Dr. Welch, we confiremd this localisation in procyclic trypanosomes. However, while expression of KIN2B double-stranded RNA produced an 8-fold reduction of the signal obtained with this antibody by western blot, it did not impact the IFA signal at the flagellar base (our unpublished data), questioning antibody specificity in this assay.”

      Here, we used endogenous N-terminal tagging with mNG to ensure expression via the 3’UTR of each kinesin, which is the major element controlling expression level (Clayton MBP2014). This allowed direct live imaging, avoiding potential biases due to fixation or antibody specificity. The mNG::KIN2A signal was detected as moving particles along the flagellum (in both anterograde and retrograde directions) without visible enrichment in the basal body area. As requested below (point 3), quantification has been performed on individual images from videos where the full flagellum appears in focus. Again, no concentration at the base could be detected, in contrast to IFT81::mNG or mNG::KIN2B.

      Similar results were obtained upon N-terminal tagging by the TrypTag consortium who detected signal in the flagellum and the cytoplasm, but no specific enrichment at the flagellar base (Billington et al. 2023 [49], see image below).

      Tagging KIN2A at its N-terminal end with mNG labels the flagellum without visible enrichment at its base and with some cytoplasmic signal (image from TrypTag.org)

      Finally, tagging KIN2A and KIN2B in L. mexicana produced the same location as observed in T. brucei (Fig. S4, with improvements as requested below, see point 5).

      3- Temporal projections are useful for interpreting particle movement, but they can be misleading when used to assess protein distribution along the flagellum. Representative single-frame images would be more appropriate for localization analyses, and quantitative fluorescence intensity profiles would strengthen the conclusions, particularly for Figures 4B-D and 7D/F.

      Individual images are actually visible in the videos showing the full image series (Video S3 for IFT81::mNG, S4 for mNG::KIN2A, S5 for mNG::KIN2B, S6 for mNG::KIN2B with tdT::IFT140, S7 for mNG::IFT81 in KIN2A KO and S8 for mNG::KIN2B in the KIN2A KO). Kymograph analyses monitoring the movement of individual particles (Fig 5B,D,F) provide a global representation along the length of the flagellum. As requested, we extracted temporal projections from five cells where the full flagellum length is in focus and made graphs with their fluorescence intensity profile. This is redundant with the videos but these images can be presented as supplementary material if considered useful by the editor. This further confirms the conclusions: KIN2A is found along the length of the flagellum without obvious concentration at its base while KIN2B is present at the base and mostly at the proximal portion of the flagellum.

      Fluorescence intensity profiles of 5 cells where the flagellum base is in focus. A clear enrichment is detected at the base for mNG::KIN2B (right) but not for mNG::KIN2A (left). (“series” correspond to cells)

      4- The authors compare the localization and dynamics of KIN2A and KIN2B with those of IFT81. However, only one allele of IFT81 is tagged, meaning that a proportion of IFT81 molecules within trains are presumably unlabelled. This could influence measurements of train frequency, intensity, and colocalization, and should be discussed when interpreting the data. This limitation should be explicitly discussed.

      We haven’t done double tagging for IFT genes since the signal is very bright for all IFT-B proteins that were looked at by us or others (Absalon et al. MBoC2008; Adhiambo et al. JCS2009; Franklin et al., MolMic2010; Bhogaraju et al., Science2013; Huet et al., eLife2014, JCS2019; Edwards et al., PNAS2018). Nevertheless, we have compared IFT trafficking in a cell line where one allele of IFT172 (gene encoding another IFT-B protein present with same stoechiometry, Subota et al. 2014, [46]) was tagged with tdTomato and the other one was not (like here with mNG::IFT81) with a derived cell line where the wild-type IFT172 allele had been deleted. Frequency of tdTomato::IFT172 trafficking was similar in both conditions, showing that tagging one allele is sufficient to detect all IFT trains (Jung et al. unpublished data).

      This was expected knowing the size of IFT trains and the relatively large number of copies of the IFT-B complex. Briefly, volumetric electron microscopy data show that train length varies from 200 to 900 nm (Bertiaux et al., 2018 [38]). CryoEM data revealed a periodicity of 8 nm for each complex B present in trypanosome IFT trains (Staggers et al. 2025 [61]), so each train should contain at least 25 copies. Therefore, tagging one allele out of two should provide at least 12 copies of the fluorescent protein per train. Therefore, it is reasonably likely that virtually every train contains the fusion protein.

      We have added this sentence in the text (p.11):

      “Since only one allele of IFT81 was labelled, the fusion protein is competing with products of the untagged allele. However, IFT trains are composed of multiple copies of IFT-B complexes with an 8 nm periodicity [61] and their average length of ~200 nm [38] means that each train should contain at least 25 copies, making it highly likely that the vast majority of trains is labelled.”

      5- The localization analysis of KIN2A and KIN2B in L. mexicana provides important validation of the observations made in T. brucei. However, no marker of the basal body or transition zone is included. Therefore, the statement that "KIN2A was found throughout the flagellum without clear enrichment at the base, whereas KIN2B is highly concentrated at the flagellum base" is not fully supported. Co-labelling with a basal body or transition zone marker would strengthen this conclusion.

      When talking about L. mexicana, we are not making a statement about a specific area (basal body, transition zone or transition fibres) but are always using the term “flagellum base”. This is easily visible thanks to its proximity with the mitochondrial genome (kinetoplast), that is tightly connected to the proximal part of the basal body (Robinson & Gull, 1991 [113]). This proximity is obvious on the transmission electron microscopy image shown at Fig. 8A or on the double staining of live cells with DAPI at Fig. 8B-C. For more clarity, we have added images of cells expressing mNG::KIN2A or mNG::KIN2B costained with DAPI, showing the close proximity of the kinetoplast signal with both KIN2A and KIN2B (Fig. S4A-B); further confirming the absence of enrichment for KIN2A (Fig. S4A) and a clear concentration for KIN2B (Fig. S4B).

      6- The distinction between proximal and full-length KIN2B particles is central to the proposed model. However, alternative explanations should be considered and discussed. For example, differences in particle intensity, signal-to-noise ratio, focal plane position, train convergence near the base, photobleaching, or effects of fluorescent tagging on train stability could potentially contribute to the observed behaviour. The authors should also to clarify whether tdT::IFT140 is expressed from the endogenous locus (whether all cellular IFT140 is tagged). If untagged IFT140 remains present, this could influence the interpretation of the colocalization analyses.

      IFT140 is endogenously tagged, here with the plasmid tagging strategy that has been validated previously [54]. This is now clearly stated at page 13:

      “However, the combination of mNG::KIN2B with endogenously tagged tdT::IFT140 was the only one to result in a viable cell line where both signals were positive and exhibited the same profile as in single tagging.”

      as well as in Material and Methods (page 29)

      For the double-tagged cell line, cells expressing mNG::KIN2B were nucleofected with the plasmid p2845TdTomatoIFT140 [54] linearised with MfeI, allowing the expression of IFT140 (Tb927.10.14470) fused to tdTomato (tdT) at the N-terminus from its endogenous locus.”

      For the proposed alternative explanations:

      -particle intensity/SNR: it is correct that fluorescent particles display variable intensities, something that we previously reported (Buisson et al. 2013 [39]). Nevertheless, kymograph analyses did not detect particular correlations. For example, brighter traces do not display faster (or slower) movements. The signal-to-noise ratio is indeed more complex in the proximal part of the flagellum for mNG::KIN2B due to the higher abundance of anterograde and retrograde particles in this area. Nevertheless, full-length particles can usually be monitored from the base on kymographs (see Fig. 5F). These differences therefore cannot explain the results.

      -focal plane position: analyses are exclusively performed with cells where the flagellum is in full focus or with a segment of the flagellum that is in full focus.

      -train convergence near the base: what the reviewer means by “convergence” is not clear to us. Anterograde trains are assembled in this area while retrograde trains complete their trip at the base of the flagellum. Previous quantification performed on cells expressing GFP::IFT52 showed that the frequency of both anterograde and retrograde was not higher at the base compared to the tip (Buisson et al. 2013 [39]). Therefore, they do not “converge” (like trains coming from different lines and ending at the same train station for example).

      -photobleaching: kymograph observations show that the vast majority of anterograde traces convert to several retrograde ones (this was quantified in details in Buisson et al. 2013 [39]), including KIN2B proximal particles, so bleaching could not explain the results.

      -effects of fluorescent tagging on train stability: adding a fluorescent reporter could indeed impact train behaviour, as observed by our difficulty to achieve double tagging while single tagging worked for almost all IFT proteins and motors tested. Since flagella are essential for trypanosomes, disruption of IFT train assembly would mimick IFT knockdowns (Kohl et al. 2003 [71]) and explain why cells either did not grow or retained only one tagged IFT/motor. In terms of stability, once a fluorescent particle is detected, kymographs show that it usually runs throughout flagellum length and is converted to retrograde trains (see above). Fig 7J shows that less than 5% of trains labelled with IFT81::mNG arrest during their trip in control cells. The only exception is of course mNG::KIN2B where the majority of fluorescent particles arrest and convert to retrograde ones towards the end of the proximal portion of the flagellum. This could have reflected an impact on motor trafficking. However, IFA performed with an anti-KIN2B antibody on a wild-type cell line demonstrates that most KIN2B signal is found in this portion (Fig. S3B), showing that untagged protein displays a similar location, ruling out the possibility of an artefact due to mNG tagging of KIN2B. In contrast to the published anti-KIN2A antibody, IFA performed with anti-KIN2B antibody on knockdown cells showed a drastic signal reduction, confirming signal specificity (Fig. S3D-E).

      Only one copy of IFT140 was tagged, but as discussed above (point 4), it is unlikely that many trains would be missed given the high number of IFT complexes present per train.

      In summary, none of these alternative explanations are likely. Describing each of them individually would take quite a lot of space, therefore we do not wish to increase excessively the length of the manuscript to avoid confusing the reader.

      7- The velocity comparison between proximal and full-length KIN2B particles may be confounded by positional effects along the flagellum. Since transport is expected to be slower near the transition zone, it would be more appropriate to compare proximal particles with the proximal segment of full-length particle trajectories only.

      IFT rates have not been quantified in the trypanosome transition zone since this portion is rather challenging for live imaging both because of its short length (350 nm; Trépout et al, JSB 2018) and of its positioning in the flagellar pocket. Since acquisition time is 100 ms and that trains run at a speed of around 2 µm/s, this means that only 2 time points would be available, preventing reliable measurements. Assuming the IFT velocity was slower in this area (as shown in C. elegans), the impact would be minimal since the proximal portion of the flagellum where most KIN2B particles are detected is 10-15 µm. 8- The authors state that IFT81 distribution in KIN2A knockout flagella resembles that observed in wild-type cells. However, comparison of the images and movies suggests potential differences, including reduced proximal signal and increased distal accumulation in the shorter mutant flagella. Representative single-frame images, together with fluorescence intensity profiles along multiple flagella, would facilitate a more rigorous comparison between genotypes.

      As explained above, the full sequence of individual images is available in the videos. Nevertheless, we indeed noticed some variabilities in the IFT distribution profile, but these are also encountered in control cells (see figure below). For technical reasons, the deletion was done in pSMOX cells (Beneke et al. 2017, ref [58]) that turn out to be more difficult to immobilise for image acquisition, increasing variability from cell to cell compared to our usual 427 cells. We are showing below temporal projections of several control and KO cells. If the editor finds these useful, these panels can be provided as supplementary material. Beyond this distribution aspect, quantifications presented at Fig. 8H-I-J revealed parameters that are unchanged (frequency, 8H) and those that are moderately (speed, 8I) or drastically (frequency of arrested trains, 8J) modified.

      Temporal projections of cells expressing mNG::IFT81 where most of the flagellum is in focus. (A) pSMOX (control) cells, (B) kin2a-/- cells. Although the flagellum is shorter, the flagellar distribution profile looks fairly similar.

      9- The current data do not fully exclude a handover mechanism between KIN2B and KIN2A within the proximal flagellum. While the proposed model is attractive, alternative cooperation-based models remain plausible and should be acknowledged more explicitly.

      We indeed considered a handover mechanism between KIN2B and KIN2A at the exit of the transition zone and have now further expanded this section (p. 21):

      “At this stage, it is not clear if KIN2B progressively hands over IFT trains to KIN2A, in a situation equivalent to the transition that takes place in the intermediate portion of cilia between the heterotrimeric kinesin-2 and OSM-3 in C. elegans [21], or whether IFT trains are released once the transition zone is crossed and then associate again with any of the two kinesins. In the first situation, single molecule imaging revealed that the heterotrimeric kinesin is responsible for progression of IFT trains through the transition zone before being progressively replaced by the homodimeric kinesin OSM-3 in the proximal segment of the axoneme, OSM3 ensuring transport to the tip of the cilium [21]. A similar case could be considered here, but in a shorter portion of the axoneme and between two homodimeric kinesins. In the second situation, trains might “hang around” after they have crossed the transition zone and then be picked up by KIN2A for efficient transport.”

      10- The presented data do not yet fully support that KIN2A contributes to most anterograde transport and that KIN2B mainly regulates IFT entry into the cilium while mediating only a minority of long-range anterograde transport events. This interpretation is also difficult to reconcile with the genetic data, given that KIN2B is essential for flagellum assembly whereas KIN2A is not. More generally, the proposed division-of-labour model remains largely inferential and should be presented more cautiously.

      We agree that a model has always limitations and is bound to evolve. The model actually explains the genetic data since KIN2B can substitute to KIN2A, while the reverse is not possible, something observed in two different organisms (T. brucei and L. mexicana). Definitive evidence would have been the coexpression of KIN2A and IFT proteins with two different fluorescent reporters, but unfortunately, this turned out to be impossible. A third kinesin able to transport IFT particles was not identified, neither by genome mining (Wickstead et al. 2006; 2010 [36, 83], this study), nor during the TrypTag project (Billington et al. 2023 [49]). Therefore, these two kinesins must be responsible for all the transport of IFT proteins (in the anterograde direction).

      We have added these sentences to the discussion (p.22):

      “However, formal evidence that KIN2A transports IFT particles could not be obtained since co-expression of KIN2A and an IFT protein with two different fluorescent reporters turned out to be impossible. One therefore cannot rule out the possible contribution of other kinesins as observed in Tetrahymena [14, 82]. Nevertheless, a third kinesin able to transport IFT particles was not identified, neither by genome mining ([36, 83], this study), nor during the TrypTag project [49]. Therefore, KIN2A and KIN2B must be responsible for all the transport of IFT proteins (in the anterograde direction).”

      Other comments:

      1- For consistency, the authors should consider using the same plotting style for similar datasets (e.g. Figures 2C and 7C).

      Maybe there is a confusion in figure number but Figure 2C quantifies the in vitro movement of truncated KIN2B on brain microtubules while Figure 7C reports flagellum length in trypanosomes without KIN2A, so there are very different datasets. Fig. 2C relates to 2A and 2B, which are all in the same format and Fig. 7C is the only graph reporting flagellum length.

      2- Figures S2A and S2D are difficult to interpret due to the low signal-to-noise ratio of the staining. The authors should consider whether these data provide sufficient additional information to justify inclusion.

      IFT172 staining looks indeed “cleaner” on methanol-fixed cells where signals is present mostly on the flagellum and at its base. However, most cytoplasmic IFT material is lost in these conditions (Absalon et al. MBoC2008 [52]). Here, we wanted to show that there was not impact on the global distribution of IFT proteins in the various cell lines used for the study, hence the PFA fixation followed by methanol extraction, which looks perhaps less nice but shows all the IFT material present in the cell (Bertiaux et al. 2018 [38]). As a reminder, biochemical fractionation have shown that a lot of IFT proteins are found in the cytoplasm, not only in trypanosomes, but also in other organisms (see for example Ahmed et al. JCB2008).

      The same argument is valid for Figure S2D to probe for a possible pool of KIN2A at the base of the flagellum. We therefore consider important to maintain these two series of figures.

      3- Figures 4 and S4 would benefit from schematics of T. brucei and L. mexicana highlighting cell morphology, flagellum, and the position of the basal body/transition zone. Such schematics would greatly aid readers less familiar with these systems. Larger panels and higher-magnification insets at the flagellar base would also improve readability. Given the overlap in content, Figures 4 and 5 could potentially be combined.

      Such cartoons have been published in multiple articles but if the editor finds them useful, we can add them to the figures. Higher magnification panels reach the resoltion limit and do not add more information to the manuscript.

      4- The statement that KIN2A and KIN2B exhibit velocities "compatible with IFT" and are "a bit slower than IFT81" should be corrected, since Figure 5G indicates significant differences among populations.

      The sentence has been rewritten as :

      “This showed that KIN2A and KIN2B particles have a speed compatible with IFT, although they are both a bit slower than IFT81, a difference that is statistically significant (Fig. ____5G).”

      5- Figure 6A would benefit from higher-magnification insets highlighting representative proximal and full-length KIN2B particles.

      As said above, increasing the magnification hits the resolution limit and is not very useful. Video S7 provides annotations highlighting individual examples of KIN2B associated to IFT140 navigating till the tip of the flagellum and of KIN2B particles trafficking without IFT140 and limited to the proximal portion of the flagellum.

      6- The manuscript is somewhat descriptive in places and would benefit from tigher editing. A more focused presentation of the key findings and their implications would improve readability and sharpen the paper's central message.

      We have rewritten some parts of the text and added sub-headings in the discussion as requested by Reviewer 2.

      Reviewer #1 (Significance (Required)):

      The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY: In this manuscript, the authors characterize kinesin-2, which is responsible for flagellum formation and function in Trypanosoma brucei and Leishmania mexicana. They show that T. brucei kinesin-2 comprises KIN2A and KIN2B proteins, each of which forms a homodimer and moves processively along microtubules in vitro. In cells, KIN2A and KIN2B exhibit distinct behaviors: KIN2A moves faster and traverses the full length of the flagellum, whereas KIN2B is slower and enriched near the flagella base. KIN2A KO mutants are cilia assembly competent and display only mild effects on IFT transport and flagellum assembly, while KIN2B KO mutants fail to assemble a normal flagellum. KIN2B-depleted cells are largely non-flagellated, with a clearly shortened flagellum remnant. Notably, KIN2A trafficking appears largely normal within these short flagella, indicating that KIN2A can still access the flagellum in the absence of KIN2B. Based on these findings, the authors propose a division-of-labor model in which KIN2B is primarily responsible for importing IFT components, whereas KIN2A performs most anterograde transport within the flagellum.

      COMMENTS: Overall, the authors use a broad range of biochemical and cell-biological approaches to define the properties of Trypanosome kinesin-2 and conclude with a compelling working model. While the experiments appear carefully executed, the results are generally convincing, several points should be addressed before publication: • Figure 2: The in vitro reconstitution assays show two velocity populations for KIN2A (slow and fast), and a slow-moving population is also observed for GCN4-fused KIN2B. The authors interpret the slow-moving populations as "autoinhibited" conformations and the fast populations as "active". Consistently, in live-imaging (Figure 6E), proximal KIN2B particles move slowly when not co-localizing with IFT140 but move faster when associated with IFT trains. This suggests multiple motile states may reflect regulation by tail conformation and/or cargo loading rather than canonical autoinhibition. By definition, many autoinhibited kinesins are characterized by reduced microtubule engagement (or failure to bind) rather than simply reduced velocity. Therefore, it is not yet clear that the slow populations observed here should be described as autoinhibited. It rather seems more akin to a 'gear shifting' mechanism described for kinesin-1, -2, and -3 (Coppin et al, 1997; Gicking et al., 2022). Otherwise, if the authors retain this terminology, they should provide additional evidence, such as microtubule-binding affinity measurements for the slow vs. fast populations. It would also be informative to test whether kinesin-2 velocities shift in the presence of defined cargo/IFT components in the in-vitro assay.

      We thank the referee for this important comment and apologize for our overly specific interpretation of the two velocity populations. We agree that a reduced velocity alone is insufficient to identify an autoinhibited state. The principal purpose of the experiments in Figure 2 was to determine the maximum in vitro velocities attainable by the individual motor proteins and to assess whether these were compatible with the transport velocities measured in vivo. For this reason, we removed the distal C-terminal stalk and tail regions and replaced the native dimerization regions with a GCN4 leucine zipper, thereby minimizing potential regulatory effects arising from the native stalk and tail.

      We have therefore revised the manuscript to remove the terms “autoinhibited” and “active” when referring to these populations. We now describe them operationally as “slow-moving” and “fast-moving” populations.

      We agree that measurements of microtubule-binding or landing rates, together with reconstitution using defined IFT components, would be valuable for resolving the mechanism underlying the different motile states. However, such experiments would require a systematic analysis of motor–IFT interactions and stoichiometrically defined complexes and are beyond the principal scope of the present study, which is focused on the composition, function and evolutionary diversification of kinesin-2 complexes.

      • Figure 3A: In the SDS-PAGE, the apparent sizes of KIN2A and KIN2B proteins appear larger than expected. The authors should clarify whether this is due to tags, unusual amino acid composition, gel conditions, or known anomalous migration of these constructs. Both proteins migrate at the expected position (124 kDa for KIN2A and 126 kDa for KIN2B). To make this clearer, we have added the position of the 130kDa molecular marker on Figure 3A-D. Images of the whole gels (Fig. 3A-D) are shown below.

      • Figure 3D: The co-immunoprecipitation experiments require additional controls to support the conclusions. Specifically, the authors should include input (total lysates) lanes prior to immunoprecipitation and compare His-tagged KIN2B levels between co-immunoprecipitated and flow-through fractions. Reciprocal co-immunoprecipitation (e.g., pull-down via His-tag followed by anti-Flag Western blotting) would further strengthen the evidence. The experiment has been repeated to include input (total lysates, new Fig. 3D, lanes e,f) and with reciprocal co-immunoprecipitation, either with Flag-tagged KIN2A (new Fig. 3D, lanes a-b) or 6xHis tagged KIN2B (new Fig. 3D, lane c-d). It further confirms that KIN2A cannot pull down KIN2B and vice-versa.

      • Figure 5H: The observation that KIN2A (0.88 {plus minus} 0.19 trains/s) and KIN2B (1.18 {plus minus} 0.18 trains/s) are lower than IFT81 (1.31 {plus minus} 0.21 trains/s) does not by itself prove that "neither motor alone can perform with the whole anterograde transport". These frequency differences could arise if KIN2A and KIN2B are not independent transport populations. The key missing test is whether KIN2A and KIN2B bind the same IFT trains. If such an experiment is technically infeasible, the language should be toned down. We agree and have modified the text accordingly:

      “This suggests that neither KIN2A nor KIN2B alone could perform the whole anterograde transport of IFT complexes. If KIN2A and KIN2B are binding independently to IFT trains, the sum would be too high to explain the frequency of IFT81 trafficking. However, two other options could be considered: either some kinesins do not associate to IFTs or some KIN2A and KIN2B associate together to the same IFT train.”

      The ideal experiment would be to follow KIN2A and KIN2B simultaneously with reporters of different colours. We tried tagging KIN2A and KIN2B with various reporters (GFP, YFP, mCherry, tdTomato, mNG, mScarlet), but so far only mNG worked, so it’s not been possible to do two-colour imaging.

      Another option was to monitor mNG::KIN2A simultaneously with a fluorescent IFT marker (as for KIN2B and IFT140, Figure 6 & Video S7), but unfortunately all the attempted combinations failed, either because cell lines did not grow or because the signal for one of the two markers was lost.

      • Figure 7B: Douglas et al. (JCS, 2020) reported suppressed cell proliferation, cytokinesis, and motility in KIN2A-depleted T. brucei cells, but not in KIN2B-depleted cells. The authors should discuss how their findings differ from the previous report. As discussed above and now modified in the text (see response to point 1 of reviewer 1), this is explained by two reasons. First, there is a clear difference in RNAi efficiency in the published study with only 10% mRNA left for KIN2A but still 30% for KIN2B. Second, the 2020 work was performed in the bloodstream stage of T. brucei, which is more sensitive to flagellar pertubations than the procyclic stage (Broadhead et al. 2006; Ralston & Hill, 2006 [34, 35]). Here, we used complete gene deletion in L. mexicana or Cas9-guide disruption with interruption of all three reading frames in T. brucei. In these conditions, the KIN2B gene product is absent, leading to the strong phenotype observed for both organisms. This is now mentioned in the discussion (p. 21):

      “These results differ from the RNAi knockdown results published on the bloodstream stage of the parasite where knockdown of KIN2A, but not KIN2B, turned out to be lethal. This could be explained by the less potent efficiency of RNAi against KIN2B [30]. Since bloodstream cells are more sensitive to flagellar perturbations [34, 35], the reduced flagellar length observed here upon deletion of KIN2A might be sufficient to interfere with cell division.”

      • Formatting: Some paragraphs are very long and would benefit from division into shorter paragraphs. Similarly, substructuring the long discussions with subheadings aligned with the results would improve readability. We have improved the presentation of the manuscript by splitting some paragraphs and have added subheadings in the discussion.

      Reviewer #2 (Significance (Required)):

      While the existence of KIN2A and KIN2B has been reported previously, their ability to form a homodimer and their distinct contributions to flagella assembly and IFT have not been clearly established. The manuscript further discusses the evolutionary origin of IFT-transporting motors, expands on the separability of import into cilia and of transport along the axoneme, and, for the first time, demonstrates that homodimeric motors can build cilia. Thus, it is expected that the manuscript will appeal to a broad readership.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: In this manuscript, the authors characterize kinesin-2, which is responsible for flagellum formation and function in Trypanosoma brucei and Leishmania mexicana. They show that T. brucei kinesin-2 comprises KIN2A and KIN2B proteins, each of which forms a homodimer and moves processively along microtubules in vitro. In cells, KIN2A and KIN2B exhibit distinct behaviors: KIN2A moves faster and traverses the full length of the flagellum, whereas KIN2B is slower and enriched near the flagella base. KIN2A KO mutants are cilia assembly competent and display only mild effects on IFT transport and flagellum assembly, while KIN2B KO mutants fail to assemble a normal flagellum. KIN2B-depleted cells are largely non-flagellated, with a clearly shortened flagellum remnant. Notably, KIN2A trafficking appears largely normal within these short flagella, indicating that KIN2A can still access the flagellum in the absence of KIN2B. Based on these findings, the authors propose a division-of-labor model in which KIN2B is primarily responsible for importing IFT components, whereas KIN2A performs most anterograde transport within the flagellum.

      Comments: Overall, the authors use a broad range of biochemical and cell-biological approaches to define the properties of Trypanosome kinesin-2 and conclude with a compelling working model. While the experiments appear carefully executed, the results are generally convincing, several points should be addressed before publication:

      • Figure 2: The in vitro reconstitution assays show two velocity populations for KIN2A (slow and fast), and a slow-moving population is also observed for GCN4-fused KIN2B. The authors interpret the slow-moving populations as "autoinhibited" conformations and the fast populations as "active". Consistently, in live-imaging (Figure 6E), proximal KIN2B particles move slowly when not co-localizing with IFT140 but move faster when associated with IFT trains. This suggests multiple motile states may reflect regulation by tail conformation and/or cargo loading rather than canonical autoinhibition. By definition, many autoinhibited kinesins are characterized by reduced microtubule engagement (or failure to bind) rather than simply reduced velocity. Therefore, it is not yet clear that the slow populations observed here should be described as autoinhibited. It rather seems more akin to a 'gear shifting' mechanism described for kinesin-1, -2, and -3 (Coppin et al, 1997; Gicking et al., 2022). Otherwise, if the authors retain this terminology, they should provide additional evidence, such as microtubule-binding affinity measurements for the slow vs. fast populations. It would also be informative to test whether kinesin-2 velocities shift in the presence of defined cargo/IFT components in the in-vitro assay.
      • Figure 3A: In the SDS-PAGE, the apparent sizes of KIN2A and KIN2B proteins appear larger than expected. The authors should clarify whether this is due to tags, unusual amino acid composition, gel conditions, or known anomalous migration of these constructs.
      • Figure 3D: The co-immunoprecipitation experiments require additional controls to support the conclusions. Specifically, the authors should include input (total lysates) lanes prior to immunoprecipitation and compare His-tagged KIN2B levels between co-immunoprecipitated and flow-through fractions. Reciprocal co-immunoprecipitation (e.g., pull-down via His-tag followed by anti-Flag Western blotting) would further strengthen the evidence.
      • Figure 5H: The observation that KIN2A (0.88 {plus minus} 0.19 trains/s) and KIN2B (1.18 {plus minus} 0.18 trains/s) are lower than IFT81 (1.31 {plus minus} 0.21 trains/s) does not by itself prove that "neither motor alone can perform with the whole anterograde transport". These frequency differences could arise if KIN2A and KIN2B are not independent transport populations. The key missing test is whether KIN2A and KIN2B bind the same IFT trains. If such an experiment is technically infeasible, the language should be toned down.
      • Figure 7B: Douglas et al. (JCS, 2020) reported suppressed cell proliferation, cytokinesis, and motility in KIN2A-depleted T. brucei cells, but not in KIN2B-depleted cells. The authors should discuss how their findings differ from the previous report.
      • Formatting: Some paragraphs are very long and would benefit from division into shorter paragraphs. Similarly, substructuring the long discussions with subheadings aligned with the results would improve readability.

      Significance

      While the existence of KIN2A and KIN2B has been reported previously, their ability to form a homodimer and their distinct contributions to flagella assembly and IFT have not been clearly established. The manuscript further discusses the evolutionary origin of IFT-transporting motors, expands on the separability of import into cilia and of transport along the axoneme, and, for the first time, demonstrates that homodimeric motors can build cilia. Thus, it is expected that the manuscript will appeal to a broad readership.

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      Referee #1

      Evidence, reproducibility and clarity

      Alves et al investigate the mechanisms of anterograde IFT in Euglenozoa that lack the canonical heterotrimeric kinesin-2 motor and the kinesin-associated protein KAP. Through a combination of comparative genomics, in vitro analyses, live-cell imaging, and genetic analyses in Trypanosoma brucei and Leishmania mexicana, the authors show that the kinesin-2 proteins KIN2A and KIN2B form homodimeric motors in vitro with distinct in vivo ciliary localization and functions. The authors report that KIN2B is essential for flagellum assembly despite contributing to only a minority of long-range anterograde transport events that colocalize with IFT trains. By contrast, KIN2A is dispensable for flagellum assembly despite being proposed to mediate most anterograde IFT. Based on these observations, the authors propose a division-of-labour model in which KIN2B mediates entry of IFT proteins into the flagellum while KIN2A performs the majority of anterograde transport.

      The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

      Major comments:

      1. The authors should revise the statement in the Introduction that in bloodstream-form T. brucei "only knockdown of both KIN2A and KIN2B impacted flagellum length". However, Douglas et al. (2020) reported that individual depletion of either kinesin reduced flagellum length, with a stronger additive phenotype upon combined depletion. The authors should correct this point and clarify more explicitly how the present study extends the earlier work, both conceptually and technically.
      2. The localization of KIN2A in the current manuscript appears somewhat different from that reported previously, where KIN2A was described as more enriched near the basal body region. This discrepancy should be discussed. In particular, the relatively strong cytoplasmic signal in the current study may obscure a weak basal enrichment, and this possibility should be addressed
      3. Temporal projections are useful for interpreting particle movement, but they can be misleading when used to assess protein distribution along the flagellum. Representative single-frame images would be more appropriate for localization analyses, and quantitative fluorescence intensity profiles would strengthen the conclusions, particularly for Figures 4B-D and 7D/F.
      4. The authors compare the localization and dynamics of KIN2A and KIN2B with those of IFT81. However, only one allele of IFT81 is tagged, meaning that a proportion of IFT81 molecules within trains are presumably unlabelled. This could influence measurements of train frequency, intensity, and colocalization, and should be discussed when interpreting the data. This limitation should be explicitly discussed.
      5. The localization analysis of KIN2A and KIN2B in L. mexicana provides important validation of the observations made in T. brucei. However, no marker of the basal body or transition zone is included. Therefore, the statement that "KIN2A was found throughout the flagellum without clear enrichment at the base, whereas KIN2B is highly concentrated at the flagellum base" is not fully supported. Co-labelling with a basal body or transition zone marker would strengthen this conclusion.
      6. The distinction between proximal and full-length KIN2B particles is central to the proposed model. However, alternative explanations should be considered and discussed. For example, differences in particle intensity, signal-to-noise ratio, focal plane position, train convergence near the base, photobleaching, or effects of fluorescent tagging on train stability could potentially contribute to the observed behaviour. The authors should also to clarify whether tdT::IFT140 is expressed from the endogenous locus (whether all cellular IFT140 is tagged). If untagged IFT140 remains present, this could influence the interpretation of the colocalization analyses.
      7. The velocity comparison between proximal and full-length KIN2B particles may be confounded by positional effects along the flagellum. Since transport is expected to be slower near the transition zone, it would be more appropriate to compare proximal particles with the proximal segment of full-length particle trajectories only.
      8. The authors state that IFT81 distribution in KIN2A knockout flagella resembles that observed in wild-type cells. However, comparison of the images and movies suggests potential differences, including reduced proximal signal and increased distal accumulation in the shorter mutant flagella. Representative single-frame images, together with fluorescence intensity profiles along multiple flagella, would facilitate a more rigorous comparison between genotypes.
      9. The current data do not fully exclude a handover mechanism between KIN2B and KIN2A within the proximal flagellum. While the proposed model is attractive, alternative cooperation-based models remain plausible and should be acknowledged more explicitly.
      10. The presented data do not yet fully support that KIN2A contributes to most anterograde transport and that KIN2B mainly regulates IFT entry into the cilium while mediating only a minority of long-range anterograde transport events. This interpretation is also difficult to reconcile with the genetic data, given that KIN2B is essential for flagellum assembly whereas KIN2A is not. More generally, the proposed division-of-labour model remains largely inferential and should be presented more cautiously.

      Other comments:

      1. For consistency, the authors should consider using the same plotting style for similar datasets (e.g. Figures 2C and 7C).
      2. Figures S2A and S2D are difficult to interpret due to the low signal-to-noise ratio of the staining. The authors should consider whether these data provide sufficient additional information to justify inclusion.
      3. Figures 4 and S4 would benefit from schematics of T. brucei and L. mexicana highlighting cell morphology, flagellum, and the position of the basal body/transition zone. Such schematics would greatly aid readers less familiar with these systems. Larger panels and higher-magnification insets at the flagellar base would also improve readability. Given the overlap in content, Figures 4 and 5 could potentially be combined.
      4. The statement that KIN2A and KIN2B exhibit velocities "compatible with IFT" and are "a bit slower than IFT81" should be corrected, since Figure 5G indicates significant differences among populations.
      5. Figure 6A would benefit from higher-magnification insets highlighting representative proximal and full-length KIN2B particles.
      6. The manuscript is somewhat descriptive in places and would benefit from tigher editing. A more focused presentation of the key findings and their implications would improve readability and sharpen the paper's central message.

      Significance

      The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

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      Reply to the reviewers

      We thank you and the reviewers for the thoughtful and constructive evaluation of our manuscript, “Metabolic Rewiring by α-Synuclein Enables Mitohormetic Protection.” We appreciate the reviewers’ recognition that the study addresses an important and relatively underexplored aspect of αSyn biology, namely the physiological contribution of αSyn to cellular metabolism and adaptation to mitochondrial stress.

      In response to the reviewers’ comments, we have substantially revised the manuscript, added new experimental data, and clarified several points of interpretation. Importantly, we now include: (i) comparison of αSyn expression in the HEK293T αSyn-expressing clones with endogenous αSyn levels in mouse brain lysates; (ii) additional analysis of αSyn-immunoreactive high-molecular-weight species following chronic mild-dose rotenone treatment; (iii) a non-crosslinked co-immunoprecipitation control; (iv) PRX6-Flag as a negative control for the LDHA-Flag pull-down; (v) proximity ligation assay data in HEK293T cells; (vi) mitochondrial DNA and mitochondrial membrane potential measurements; (vii) Seahorse analysis of ECAR reserve capacity and OCR following acute and chronic mild-dose rotenone treatment.

      We also revised the wording throughout the manuscript to avoid overinterpretation. In particular, we now refer to LDHA as an αSyn-associated protein and to the PLA data as evidence for close proximity, rather than as definitive proof of direct binding. We also clarify that the growth advantage observed under chronic rotenone represents adaptation to chronic mild mitochondrial stress, not protection from acute severe toxicity.

      In addition, we made a substantial effort to address the relevance of our findings for PD and could successfully demonstrate, using proximity ligation assay, that endogenous αSyn associates with endogenous LDHA in cultured mouse primary hippocampal neurons. The fact that endogenous aSyn and LDHA associate with each other also in neurons argues that this association is likely to be relevant to PD. On the other hand, showing that overexpression of aSyn protects neurons from mitochondrial stress was more challenging, and so far, we could not find an experimental setting to demonstrate this. None-the-less, we laid the foundation for a longer-term approach, and we are in the process of expanding C57BL6 SNCA+/+ and SNCA−/− mouse colonies. These mice, and cultured primary neurons prepared from them, will be the ultimate approach to address the relevance of our findings to PD.

      We were also asked to contextualize our findings against contradictory literature, and thus added the following paragraph to the Discussion: “How do our findings contextualize against contradictory literature? For example, Lee et al reported that αSyn overexpression exacerbates rotenone-induced ATP loss, suggesting that αSyn indirectly sensitizes mitochondria to complex I inhibition by endogenous/exogenous stressors in cells [41]. Our findings challenge this view that αSyn is intrinsically toxic. We demonstrate that αSyn promotes mitohormesis: mild energetic stress induces adaptive bioenergetic remodeling. Based on our findings we propose that αSyn acts as a stress-response protein recruited during increased energetic demand to maintain bioenergetic homeostasis. This response preserves cellular function, whereas repeated demand may exhaust the program, leading to mitochondrial dysfunction, ATP loss, pathological αSyn conversion and cell death.”

      Point-by-point description of the revisions

      Reviewer 1

      Comment 1. Overexpression of α-synuclein does not seem to affect cell growth, but is its level in the range that might occur in living organisms?

      Response: We thank the reviewer for raising this important point. To address it, we added a new Western blot comparison between αSyn expression in HEK293T αSyn-expressing clones and endogenous αSyn levels in mouse brain lysates. This analysis is now shown in Supplementary Fig. 1B. As expected, αSyn expression in the HEK293T stable clones is higher than in the endogenous tissue reference. We now explicitly state this in the Results section and have adjusted the interpretation accordingly. Importantly, despite this elevated expression, αSyn stable overexpression did not affect basal cell growth or transgene stability under standard culture conditions. Thus, while this model is not intended to reproduce endogenous neuronal αSyn levels, it provides a controlled gain-of-function system to uncover αSyn-dependent metabolic effects.

      Changes made:

      We added Supplementary Fig. 1B and revised the Results section to state that αSyn expression in HEK293T clones is higher than in mouse brain lysates. We also added the corresponding Methods section describing preparation of whole-brain lysates.

      Comment 2. Protein-protein interactions of α-synuclein are shown under treatment with supramaximal complex I inhibition. To show relevance of the interaction in resistance to prolonged and mild complex I inhibition, it would be important to show the protein complexes under those conditions.

      Response: We agree with the reviewer that it is important to distinguish between the effects of acute high-dose rotenone and chronic mild-dose rotenone. We therefore analyzed αSyn-immunoreactive high-molecular-weight species after chronic mild-dose (40nM) rotenone treatment. These data are now included in Supplementary Fig. 4. Interestingly, chronic mild-dose rotenone did not increase the intensity of the αSyn-immunoreactive HMW species. This suggests that increased formation of these HMW species is not required for the adaptive growth resilience observed after chronic mild-dose rotenone treatment. We have revised the Results section to discuss this point more explicitly. We also note that chronic treatment may alter the composition, localization, or functional state of αSyn-associated complexes, even if the overall abundance of DSG-captured HMW species is not increased.

      Changes made:

      We added Supplementary Fig. 4 and revised the Results section to clarify that acute and chronic high-/low-dose rotenone has distinct effects on αSyn-immunoreactive HMW species.

      Comment 3. Would it be possible to test the physiological relevance of α-synuclein by silencing/knockout strategy?

      Response: We agree that loss-of-function or endogenous models are important to support physiological relevance. We made a large effort to identify a cell line in which we could detect endogenous aSyn, to knock it out, and were not successful. We could detect endogenous aSyn only in mouse brain lysates (shown in Supplementary Fig. 1B), and therefore we are in the process of expanding C57BL6 SNCA+/+ and SNCA−/− mouse colonies. These mice, and cultured primary neurons prepared from them, will be the ultimate approach to test the physiological relevance of αSyn.

      Comment 4. Table 1 is difficult to understand and is not explained well by the legend.

      Response: We thank the reviewer for pointing this out. We have rewritten the legend to Table 1 to explain the LC-MS/MS analysis, the meaning of the columns, and the interpretation of peptide-spectrum matches, unique peptides, and protein coverage.

      Changes made:

      We revised the Table 1 legend to improve clarity.

      Comment 5. Fig. 2A scale bar 20uM has to be changed to 20µm.

      Response: We thank the reviewer for noting this error. The scale bar label has been corrected to 20 µm.

      Changes made:

      We corrected the scale bar label in Fig. 2A and checked additional figure panels for similar unit-formatting issues.

      Reviewer 2

      Comment 1. For one, the proposed interaction of LDHA and αSyn was only studied by coimmunoprecipitation of tagged and crosslinked proteins after massive transient overexpression in HEK cells. As HEK cells only express very low and negligible amounts of endogenous αSyn this overexpression probably results in vast amounts of mislocalized αSyn. No attempts are described to verify this interaction in a more relevant cellular model with endogenous proteins. I would ask for native co-immunoprecipitation with and without crosslinking and proximity ligation assays from at least something like SH-SY5Y cells which express endogenous αSyn.

      Response: We agree with the reviewer, and below is a description of the new experiments we performed and data added to the revised manuscript:

      • We used PRX6-Flag as a FLAG-tagged negative control protein in the co-immunoprecipitation experiment, and showed that αSyn-HA was detected in the LDHA-Flag pull-down but not in the PRX6-Flag control pull-down, supporting specificity of the LDHA-associated αSyn signal (Fig 1C).
      • we performed co-immunoprecipitation without crosslinkers in HEK293T cells co-expressing LDHA-Flag and αSyn-HA. In the absence of DSG, αSyn was not detected in LDHA-Flag immunoprecipitates )supp Fig. 2B), suggesting that the LDHA-αSyn association is not efficiently preserved under conventional lysis and immunoprecipitation conditions. We envision that the αSyn-LDHA complex requires the intact cellular setting to remain a complex, and DSG preserves this setting.
      • We performed a proximity ligation assay (PLA; [31]) in intact HEK293T cells and in intact mouse primary hippocampal neurons.
      • HEK293T cells: Using antibodies against LDHA and aSyn, we detected a high PLA signal in aSyn-expressing cells, and a very low signal in vector control cells (Supp Fig. 2C).
      • Mouse primary hippocampal neurons: Using antibodies against LDHA and aSyn, we detected endogenous LDHA and endogenous αSyn, without tagged protein overexpression, in neurons prepared from ICR-SNCA+/+ and C57BL6JHUK-SNCA−/− mice. In this experiment we observed a robust PLA signal in ICR-SNCA+/+ neurons and a very low signal in C57BL6JHUK-SNCA−/− neurons (Supp Fig. 3B, C). These new results add important support that aSyn associates with LDHA, but additional data are needed to confirm direct binding, e.g., AlphaFold-based structural predictions to model potential binding interfaces. Therefore, we revised the wording throughout the manuscript, and we now use “associates with”, “αSyn-associated” and “close proximity” where appropriate, and we avoid using “direct interaction”.

      Changes made:

      We added PRX6-Flag control data in Fig. 1C, non-crosslinked co-IP in Supplementary Fig. 2B, PLA in Supplementary Fig. 2C and Supplementary Fig. 3, and revised the wording throughout the manuscript.

      Comment 2. αSyn was expressed with an IRES-GFP but the control contained only GFP which is not ideal. It is unclear why rotenone results in increased levels of αSyn. Less degradation, increased expression? At least a qPCR could help.

      Response: We agree that the vector design should be considered when interpreting the data. The αSyn construct expresses αSyn-IRES-GFP, whereas the control expresses GFP from the corresponding empty IRES-GFP vector. We emphasize that the major metabolic comparisons were performed across multiple independent αSyn-expressing and vector-control clones, reducing the likelihood that the observed effects reflect a single clonal artifact.

      Regarding the 2nd part: “It is unclear why rotenone results in increased…

      Response: We thank the reviewer for this important point. We have revised the text to clarify that we do not interpret the increased αSyn signal after chronic mild-dose rotenone as evidence for transcriptional induction or altered degradation. Because the αSyn construct is linked to IRES-GFP, the shift toward higher GFP intensity after chronic low-dose rotenone strongly suggests enrichment or selective expansion of cells with higher transgene expression, rather than necessarily increased expression within each individual cell. This interpretation is supported by both fluorescence microscopy and flow cytometry showing enrichment of high-GFP cells specifically in the αSyn-expressing population.

      We agree that qPCR could distinguish between transcriptional upregulation and selection of high-expressing cells. However, because the FACS data already show a population shift in GFP intensity, and because the main conclusion is selection or enrichment of high αSyn-expressing cells under chronic mild mitochondrial stress, we have revised the wording to avoid claiming a specific mechanism of increased αSyn expression.

      Changes made:

      We revised the Results and the Fig. 2 legend to describe enrichment of high-GFP and high-αSyn-expressing cells, rather than implying transcriptional induction.

      Comment 3. For the claim that αSyn overexpression shifts metabolism toward increased glycolysis and OXPHOS, important controls are missing. What about cell numbers, mitochondrial mass, mitochondrial membrane potential, etc. Were these experiments done with or without rotenone preconditioning?

      Response: We agree that these controls are essential. ECAR and OCR measurements were normalized to cell number per well, as described in the Methods. In addition, we added new analyses of mitochondrial DNA content and mitochondrial membrane potential. These data are presented in the Supplementary Fig. 5C and 5D and show no differences between vector and αSyn-expressing clones in mitochondrial DNA content or mitochondrial membrane potential, either under basal conditions or after chronic 40nM rotenone treatment. These data support the conclusion that the observed metabolic differences are not simply explained by increased mitochondrial number or altered mitochondrial membrane potential.

      In the revised manuscript, we show that αSyn-expressing clones exhibit higher LDHA activity and increased lactate secretion both under basal conditions and after chronic low/mild-dose (40nM) rotenone treatment. We also added Seahorse analysis under acute and chronic mild-dose rotenone conditions. These data show that chronic mild-dose rotenone treatment abolished mitochondrial respiration in both aSyn- and vector clones (Supp Fig 6). In glycolysis, aSyn clones showed a larger increase in glycolytic capacity as compared to vector clones following chronic mild-dose rotenone treatment (Fig 4A, B).

      Importantly, we also detected differences between the aSyn- and vector clones in glycolytic reserve, which is the value obtained by the subtraction of the glycolysis capacity (max glycolysis) from the basal rate of glycolysis [34]. aSyn–expressing clones preserve high glycolytic reserve following either acute or chronic mild-dose rotenone treatment, whereas similar treatments abolish the glycolytic reserve in vector clones (Fig 4C). These results are consistent with the idea that the presence of aSyn enables cells to continue to proliferate by maintaining the glycolytic energy reserve when mitochondria energy production is abolished.

      Changes made:

      We added Supplementary Fig. 5C, Supplementary Fig. 5D, Fig. 4A and Supplementary Fig. 6, and revised the Seahorse Methods section.

      Comment 4. MitoSOX measures mitochondrial hydrogen peroxide and not mitoROS.

      Response: We agree that the readout should be described with greater precision. According to Thermo Fisher/Invitrogen, MitoSOX-Red is a mitochondrial superoxide indicator. They describe it as a mitochondria-targeted dye whose oxidation is by mitochondrial superoxide, O₂•⁻. MitoSOX-Red fluorescence is commonly used as a mitochondrial superoxide-sensitive signal, but it should not be interpreted as a comprehensive measurement of all mitochondrial reactive oxygen species. To avoid overinterpretation, we revised the relevant text to describe the signal more cautiously as MitoSOX-Red fluorescence or mitochondrial superoxide-sensitive ROS signal, where appropriate, rather than as a broad measure of total mitoROS.

      Changes made:

      We revised the Results, Methods, and figure legend terminology to define the MitoSOX-Red assay more precisely and to avoid overgeneralization.

      Reviewer 3

      Major comment 1. Figure 1C: The co-IP lacks important controls, such as cells expressing an empty vector or a FLAG-tagged non-interacting control protein (e.g., cytosolic GFP-FLAG). Also, IP alone does not sufficiently support a direct interaction between αSyn and LDHA. While co-association in a complex is evident, additional data are needed to confirm direct binding, e.g., AlphaFold-based structural predictions to model potential binding interfaces.

      Response: We agree with the reviewer, and below is a description of the new experiments we performed and data added to the revised manuscript:

      • We used PRX6-Flag as a FLAG-tagged negative control protein in the co-immunoprecipitation experiment, and showed that αSyn-HA was detected in the LDHA-Flag pull-down but not in the PRX6-Flag control pull-down, supporting specificity of the LDHA-associated αSyn signal (Fig 1C).
      • We performed co-immunoprecipitation without crosslinkers in HEK293T cells co-expressing LDHA-Flag and αSyn-HA. In the absence of DSG, αSyn was not detected in LDHA-Flag immunoprecipitates )Supp Fig. 2B), suggesting that the LDHA-αSyn association is not efficiently preserved under conventional lysis and immunoprecipitation conditions. We envision that the αSyn-LDHA complex requires the intact cellular setting to remain a complex, and DSG preserves this setting.
      • We performed a proximity ligation assay (PLA; [31]) in intact HEK293T cells and in intact mouse primary hippocampal neurons.
      • HEK293T cells: Using antibodies against LDHA and aSyn, we detected a high PLA signal in aSyn-expressing cells, and a very low signal in vector control cells (Supp Fig. 2C).
      • Mouse primary hippocampal neurons: Using antibodies against LDHA and aSyn, we detected endogenous LDHA and endogenous αSyn, without tagged protein overexpression, in primary neurons prepared from ICR-SNCA+/+ and C57BL6JHUK-SNCA−/− mice. In this experiment we observed a robust PLA signal in ICR-SNCA+/+ neurons and a very low signal in C57BL6JHUK-SNCA−/− neurons (Supp Fig. 3B, C). Please find below further information about this experiment and our planned future experiments in our reply to Major comment 4.
      • We generated an AlphaFold model of the αSyn-LDHA complex but it did not reach a high enough confidence score, and thus did not include it in the revised manuscript. These new results add important support that aSyn associates with LDHA, but we agree with the reviewer that additional data is needed to confirm direct binding, e.g., AlphaFold-based structural predictions to model potential binding interfaces. Therefore, we revised the wording throughout the manuscript, and we now use “associates with”, “αSyn-associated” and “close proximity” where appropriate, and we avoid using “direct interaction”.

      Changes made:

      We added PRX6-Flag control data in Fig. 1C, non-crosslinked co-IP in Supplementary Fig. 2B, PLA in Supplementary Fig. 2C and Supplementary Fig. 3, and revised the wording throughout the manuscript.

      Major comment 2. Figure 2D shows that preconditioned αSyn-expressing cells exhibit a growth advantage under rotenone treatment, but broader metabolic consequences are absent. How does low-dose rotenone preconditioning impact glycolysis versus OXPHOS? What about lactate secretion or other metabolic readouts? Also, could the authors contextualize these findings against contradictory literature in their Discussion? For example, an old study (doi: 10.1074/jbc.M105326200) reports that both wild-type and mutant αSyn expression exacerbate rotenone-induced mitochondrial membrane potential loss, suggesting αSyn indirectly sensitizes mitochondria to complex I inhibition by endogenous/exogenous stressors in cells.

      Response: We agree with the reviewer that it is important to connect the growth phenotype to metabolic adaptation. In the revised manuscript, we show that αSyn-expressing clones exhibit higher LDHA activity and increased lactate secretion both under basal conditions and after chronic mild-dose (40nM) rotenone treatment. We also added Seahorse analysis under acute and chronic mild-dose rotenone conditions. These data show that chronic mild-dose rotenone treatment abolished mitochondrial respiration in both aSyn- and vector clones (Supp Fig 6). In glycolysis, aSyn clones showed a larger increase in glycolytic capacity as compared to vector clones following chronic mild-dose rotenone treatment (Fig 4A, B).

      Importantly, we also detected differences between the aSyn- and vector clones in glycolytic reserve, which is the value obtained by the subtraction of the glycolysis capacity (max glycolysis) from the basal rate of glycolysis [34]. aSyn–expressing clones preserve high glycolytic reserve following either acute or chronic mild-dose rotenone treatment, whereas similar treatments abolish the glycolytic reserve in vector clones (Fig 4C). These results are consistent with the idea that the presence of aSyn enables cells to continue to proliferate by maintaining the glycolytic energy reserve when mitochondria energy production is abolished.

      Changes made:

      We revised the Results to integrate LDHA activity, lactate secretion, ECAR, glycolytic reserve, and OCR after acute/chronic mild-dose rotenone treatment. Please see the revised Fig. 4 and Supplementary Fig. 6.

      Regarding the reviewer’s request to contextualize our findings against contradictory literature in the Discussion.

      Response: We thank the reviewer for this important suggestion. We have added this reference to the Discussion (Ref 41 in the revised manuscript) and wrote the following paragraph on p. 16: “How do our findings contextualize against contradictory literature? For example, Lee et al reported that aSyn overexpression exacerbates rotenone-induced ATP loss, suggesting that αSyn indirectly sensitizes mitochondria to complex I inhibition by endogenous/exogenous stressors in cells [41]. Our findings challenge this view that αSyn is intrinsically toxic. We demonstrate that aSyn promotes mitohormesis: mild energetic stress induces adaptive bioenergetic remodeling. Based on our findings we propose that αSyn acts as a stress-response protein recruited during increased energetic demand to maintain bioenergetic homeostasis. This response preserves cellular function, whereas repeated demand may exhaust the program, leading to mitochondrial dysfunction, ATP loss, pathological αSyn conversion and cell death.”

      Major comment 3. Figure 4. αSyn clones display elevated basal respiration and maximal respiratory capacity. This phenotype is underexplored and should be integrated earlier rather than sidelined until the Discussion.

      Response: We agree with the reviewer. We revised the Results to more explicitly discuss the observation that αSyn-expressing clones exhibit increased OCR in addition to increased ECAR. We now frame this as evidence that αSyn increases metabolic flexibility and energetic capacity, rather than acting only through a simple shift from OXPHOS to glycolysis. We also added mitochondrial DNA and membrane potential measurements to show that increased respiration is not explained by major changes in mitochondrial DNA content or mitochondria membrane potential. Finally, we added OCR analysis after acute and chronic rotenone exposure, showing that mitochondrial respiration is suppressed by rotenone in both vector and αSyn-expressing clones, supporting the idea that the adaptive advantage under chronic rotenone depends on preserved glycolytic reserve rather than maintained mitochondrial respiration.

      Changes made:

      We revised the Results and Discussion, added Supplementary Fig. 5C, Supplementary Fig. 5D, and Supplementary Fig. 6, and updated the Seahorse Methods section.

      Major comment 4. Considering the relevance of the proposed mechanism for PD, how and would the observed effects hold in neurons? And how certain readouts, e.g. the proliferative advantage observed upon mitohormesis, would apply to post-mitotic neurons? To this end, it would be critical to recapitulate some of the effects in more relevant systems, e.g. patient-derived iPSCs with SNCA multiplications/mutations, αSyn-silenced primary neurons, or through reanalysis of publicly available PD patient datasets.

      Response: We thank the reviewer for raising this important point. In the past six months, we have setup cultures of neurons to begin to address the relevance of our findings for PD.

      As mentioned above, we initially asked whether the association between endogenous LDHA and endogenous aSyn occurs in intact primary neurons. For this purpose, we cultured primary hippocampal neurons derived from ICR )SNCA+/+) mice and from C57BL6JHUK (SNCA−/−) mice (Ref 32 in revised manuscript). We first validated aSyn expression in ICR (SNCA+/+) brain lysates, and its absence from C57BL6JHUK (SNCA−/−) brain lysates, by Western blot analysis using anti-aSyn Abs (Supp Fig 3A). Mouse primary hippocampal neurons prepared from both mouse strains were cultured, and proximity ligation assay (PLA) was performed 12 days-post culture using anti-LDHA and anti-aSyn Abs. This analysis revealed a strong PLA signal in multiple ICR-SNCA+/+ primary neurons, which was largely absent from C57BL6JHUK-SNCA−/− primary neurons (Supp Fig 3B, C). The fact that endogenous aSyn and LDHA associate with each other in neurons argues that this association is likely to be relevant to PD.

      On the other hand, showing that overexpression of aSyn protects neurons from mitochondrial stress was more challenging, and so far, we could not find an experimental setting to demonstrate this. None-the-less, we laid the foundation for a longer-term approach, and we are in the process of expanding C57BL6 SNCA+/+ and SNCA−/− mouse colonies. We envision that these mice, and cultured primary neurons prepared from them, will be the ultimate approach to address the relevance of our findings to PD.

      Minor comment 1. loading control for the Western blot in Figure 1B is missing.

      Response: We thank the reviewer for noting this point. In the revised manuscript, we replaced the original panel with a new subcellular fractionation experiment that more directly addresses the point we intended to make. Specifically, our aim was to determine whether the DSG-cross-linked αSyn-immunoreactive high-molecular-weight (HMW) species are present only in the cytosolic fraction or also in the mitochondria-enriched heavy membrane fraction. To this end, cells from the aSyn-clone #2 were treated with DSG and then subcellularly-fractionated into the cytosolic S100 and the mitochondria-enriched heavy membrane fractions. The results show that both p17-aSyn and the HMW-cross-linked aSyn-immuno-reactive bands/complexes are detected only in the cytosolic S100 fractions (Fig 1B). These results are consistent with the idea that the HMW-cross-linked aSyn-immuno-reactive bands/complexes detected in whole cells are comprised of cytosolic protein(s).

      Changes made:

      We revised Fig. 1B and the corresponding figure legend.

      Minor comment 2. "P17-αSyn" abbreviation (intro, page 6) has never been introduced nor explained.

      Response: We thank the reviewer for pointing this out. We revised the text to introduce this notation clearly as the monomeric approximately 17-kDa αSyn band. Where possible, we also simplified the wording to “monomeric αSyn” to avoid unnecessary confusion.

      Changes made:

      We revised the Results to clarify the meaning of p17-αSyn.

      Minor comment 3. Labels in figures are sometimes not properly explained, e.g., shades of red for aSyn clones in Figures 3 and 4 are not keyed to specific clones.

      Response: We thank the reviewer for this helpful comment. We revised the figures to include a separate legend for each clone, better explain the clone labeling and experimental groups.

      Changes made:

      We revised Figures 3A, 3D, 4A, 5A and 6 to include each clone separately.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript the authors aim to further elucidate the physiological role of aSyn, a protein highly enriched at synaptic terminals that supports key neuronal functions and contributes prominently to Parkinson's disease (PD) pathology when SNCA gene mutations alter/ increase its expression. Based on previous accounts that a) monomeric aSyn alone is not sufficient to promote PD pathology in vivo and b) α-syn can exert cytoprotective effects during oxidative stress or provide neuroprotection in hypoxia-challenged mice when overexpressed, the authors seek to uncover α-syn's metabolic functions.

      Using HEK293T stable clones expressing aSyn and exposed to the complex I inhibitor rotenone, the authors provide evidence that aSyn expression enhances LDHA activity, boosts mitochondrial respiration, and lowers mitochondrial ROS levels. Interestingly, pre-treatment of HEK293T with rotenone discloses a mito-hormetic effect of aSyn which confers a proliferative advantage over control cells. Based on these data the authors conclude that aSyn functions as a metabolic rheostat enabling cells adaptation during stress. While these findings suggest a novel role for aSyn in metabolism with potential implications for PD pathology, stronger mechanistic evidence is needed. Critically, despite aSyn's central role in PD, the study provides no data demonstrating relevance in neuronal cells.

      Major points:

      • Figure 1C: The co- IP lacks important controls, such as cells expressing an empty vector or a FLAG-tagged non-interacting control protein (e.g., cytosolic GFP-FLAG). Also, IP alone does not sufficiently support a direct interaction between aSyn and LDHA. While co-association in a complex is evident, additional data are needed to confirm direct binding, e.g., AlphaFold-based structural predictions to model potential binding interfaces.

      • Figure 2D shows that pre-conditioned aSyn-expressing cells exhibit a growth advantage under rotenone treatment but broader metabolic consequences are absent. How does low-dose rotenone preconditioning impact glycolysis vs OXPHOS? What about lactate secretion or other metabolic readouts? Also, could the authors contextualize these findings against contradictory literature in their Discussion? For example, an old study (doi: 10.1074/jbc.M105326200) reports that both wild-type and mutant aSyn expression exacerbate rotenone-induced mitochondrial membrane potential loss, suggesting aSyn indirectly sensitizes mitochondria to complex I inhibition by endogenous/exogenous stressors in cells.

      • Figure 4. All aSyn clones display elevated basal respiration and maximal respiratory capacity, adding complexity to the narrative of aSyn as direct interactor of LDHA, and leaving also this phenotype rather underexplored: what does it imply for mitochondrial function or bioenergetics? Along these lines, this aSyn-driven increased mitochondrial respiration is introduced but sidelined until the Discussion section, where it's framed as aSyn boosting "overall energetic capacity." This aligns better with the data than overemphasizing LDHA/glycolysis, and I would suggest the authors to integrate and discuss this earlier in the manuscript.

      • Considering the relevance of the proposed mechanism for PD, how and would the observed effects hold in neurons? And how certain readouts, e.g. the proliferative advantage observed upon mitohormesis, would apply to post-mitotic neurons? To this end, it would be critical to recapitulate some of the effects in more relevant systems, e.g. patient-derived iPSCs with SNCA multiplications/mutations, aSyn-silenced primary neurons, or through reanalysis of publicly available PD patient datasets.

      Minor points:

      • A loading control for the Western blot in Figure 1B is missing.

      • "P17-aSyn" abbreviation (intro, page 6) has never been introduced nor explained.

      • Labels in figures are sometimes not properly explained, e.g., shades of red for aSyn clones in Figures 3 and 4 are not keyed to specific clones.

      Significance

      The study proposes a novel metabolic role for aSyn via LDHA/glycolysis modulation which the authors suggest is relevant for OXPHOS-dependent neurons (as also stated at page 16 in the Discussion), but the study requires additional work to substantiate their proposed model, and the exclusive use of HEK293 cells limits the translational impact of the study.

      Audience: basic research.

      Expertise: mitochondrial metabolism, cellular neuroscience, neurodegeneration

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      Referee #2

      Evidence, reproducibility and clarity

      In their contribution, Geul et al. found that chronic overexpression of a-synuclein (aSyn) in human embryonic kidney (HEK293) cells shifts cellular metabolism towards increased glycolysis AND simultaneously increased oxidative phosphorylation (OxPhos). These changes correlated with a proposed interaction of aSyn and lactate dehydrogenase A (LDHA). aSyn is the key component of Lewy bodies, the histopathological hallmark of Parkinson's disease (PD) and aSyn mutation and increased gene dosage causes PD which makes the project scientifically interesting. Previous work has apparently found that shifting metabolism of dopaminergic neurons, which degenerate in PD, towards glycolysis has some protective effect. However, the work has several substantial flaws.

      • For one, the proposed interaction of LDHA and aSyn was only studied by coimmunoprecipitation of tagged and crosslinked proteins after massive transient overexpression in HEK cells. As HEK cells only express very low and negligible amounts of endogenous aSyn this overexpression probably results in vast amounts of mislocalized aSyn. No attempts are described to verify this interaction in a more relevant cellular model with endogenous proteins. I would ask for native co-immunoprecipitation with and without crosslinking and proximity ligation assays from at least something like SH-SY5Y cells which express endogenous aSyn.

      • aSyn was expressed with an IRES-GFP but the control contained only GFP which is not ideal It is unclear why rotenone results in increased levels of aSyn. Less degradation, increased expression? At least a qPCR could help.

      • For the claim that aSyn overexpression shifts metabolism towards increased glycolysis and OxPhos important controls are missing. What about cell numbers, mito mass, mitochondrial membrane potential etc. Were these experiments done with or without rotenone preconditioning?

      • MitoSox measures mitochondrial hydrogen peroxide and not mitoROS

      Significance

      I have my doubts whether the reported findings are relevant for the understanding of aSyn physiology and aSyn-caused PD.

      My expertise is in mitochondrial dysfunction and neurodegeneration. I feel well qualified in assessing this work.

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      Referee #1

      Evidence, reproducibility and clarity

      Geula et al seek insight into the physiological function of a-synuclein in the context of cell metabolism. Misfolded and aggregated form of a-synuclein in neurodegeneration has been a major focus of research but less is known about the function of the physiological monomeric form. The Authors created a stable a-synuclein overexpression in HEK and demonstrated interaction of a-synuclein with LDHA with increased lactate production and decreased ROS production. They also show that a-synuclein overexpressing cells have favorable response to mild and prolonged complex I inhibition in terms of metabolic adaptation and survival. The Authors conclude that a-synuclein has a role in metabolic adaptation during prolonged mitochondrial stress. It is an interesting study and a well written ms.

      The following points need consideration:

      • Overexpression of a-synuclein does not seem to affect cell growth but is its level in the range that might occur in living organisms? Protein protein interactions of a-synuclein are shown under treatment with supramaximal complex I inhibition. To show relevance of the interaction in resistance to prolonged and mild complex I inhibition (40nM rotenone), it would be important to show the protein complexes under those conditions.

      • Would it be possible to test the physiological relevance of a-synuclein by siliencing/knockout strategy?

      • Table 1 is difficult to understand and is not explained well by the legend. Fig2A scale bar 20uM has to be changed to 20um.

      Significance

      I am expert in mitochondrial biology

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      Reply to the reviewers

      We sincerely thank the reviewers for the time dedicated to providing us with feedback on our work. In response to their helpful remarks, we have generated new data and made several changes to the manuscript. We hope the reviewers agree that, together with the rebuttal points below, this improved version addresses their concerns.

      Reviewer #1

      Evidence, reproducibility and clarity

      In this report the authors have provided evidence for the involvement of transposable elements in the regulation of gene expression in murine trophoblast cells and placenta. They utilized data that they generated and also published data in their analyses. They concluded that the involvement of transposable elements in the regulation of genes in differentiated trophoblast cells was less than utilized in trophoblast cells in the stem state. They provide evidence for the utilization of intracisternal A particle elements in the modulation of gene transcription of the mouse placenta, which represents a more recent evolutionary adaptation. Overall the report is descriptive presenting correlations with limited testing of specific hypotheses. There is also the impression that the manuscript consists of the merging of two projects, which have not been fully developed. Some concerns with the experimental design and interpretation of the results are provided below.

      1. Some concerns with the model systems used in the analysis. First of all, there are methods for inducing the differentiation of mouse trophoblast stem cells, which usually involves the removal of factors that promote trophoblast stem cell proliferation. The authors do not describe their method for inducing trophoblast stem cell differentiation nor did they show evidence that they directly investigated differentiated trophoblast stem cells.

      We have added information in the Methods section to clarify that differentiation was performed by culturing cells in TS base medium (no conditioned media, FGF or heparin) for 4 days. We also provide RT-qPCR data confirming TSC differentiation (Figure S1A).

      1. There is a published report presenting data from single cell analysis of mouse trophoblast stem cells in the stem and differentiated states that was not acknowledged or used in the authors' analyses. Please see: Angelova et al. 2025 Nature Communications (PMID:39747179).

      We appreciate the reviewer’s suggestion, but the purpose of our single-cell analysis was to assess the expression of IAP elements and their associated chimeric transcripts in vivo. This has more significance than single-cell data from in vitro differentiated cells. We also found that at least some of the chimeric transcripts seen in vivo are not detected in vitro.

      1. Much of the analysis, compared mouse trophoblast stem cells and murine placentas. Interpretation of single nucleus sequencing data from mouse placentas can provide information regarding the behavior of trophoblast cells; however, bulk sequencing of placentas is limited. The placenta contains trophoblast cell and non-trophoblast cell components. More specifically the placenta contains fetal endothelial, immune, and mesenchymal cells and depending upon dissections and the gestational stage of dissections variable amounts of uterine decidua and yolk sac-derived tissues. The authors need to be clear in the comparisons that they are making. More specifically, the authors need to effectively communicate the cell types from the placenta contributing to the results they are describing. Attributing analyses of the placenta to trophoblast cells is problematic.

      We agree with this point. In our original submission we had included a cell type deconvolution analysis to infer the composition of our bulk placental tissue (Figure S1B of the revised submission). This clarifies the heterogeneity of the tissue and shows that most cells are trophoblast. We also used a genetic model to isolate trophoblast from placentas to address this point. Cell type deconvolution confirms that >90% of cells are trophoblast.

      1. How were the newly derived mouse trophoblast stem cells characterized? Do they behave like authentic mouse trophoblast stem cells? Were the newly derived trophoblast stem cells capable undergoing differentiation? What parameters were measured?

      We now include data on trophoblast stem cell and differentiation markers, comparing our newly derived line with the well-established GFP-TSC line (Figure S1A). While not included in this submission, the cells also presented with the expected morphologies when cultured under stem or differentiation conditions.

      1. Were analyses with the newly derived mouse trophoblast stem cells performed in the stem or differentiated states?

      Our initial analyses were only from cells cultured in stem conditions. However, we now also include an analysis of TE regulatory activity in differentiation conditions (updated Figures 1B, 1D, S1C).

      1. TSC derivation and culture section. The authors appear to be initially describing the generation of mouse embryonic fibroblast conditioned medium not trophoblast stem cell conditioned medium as stated. Some clarification will be helpful. As stated above, the authors do not provide any information on the characterization and validation of the newly derived mouse trophoblast stem cells, which is problematic.

      We appreciate the confusion with the nomenclature. To clarify we have changed the start of that section to: “Conditioned medium for the culture of TSCs (TS-CM) was prepared by…”. This conditioned medium is generated using MEFs and is then used to culture TSCs.

      1. Discussion. The authors state that there are fundamental differences between the mouse and human placenta regarding the co-option of transposable element subfamilies. Human trophoblast stem cells represent a highly tractable model and could be compared with mouse trophoblast stem cells to further explore this observation.

      We previously published a paper focused on TE co-option in human trophoblast (PMID: 37012406), and we made a brief comparison to those data in the current manuscript (Figure S1G). Interestingly, in contrast to mouse, many of the TEs with regulatory activity in human TSCs remain active in term placenta.

      Significance

      Efforts to understand roles for transposable elements in the regulation of trophoblast cell gene expression and placental evolution are very important. We recognize significant differences in placentation across various species but do not have a good understanding how this important developmental process evolved.

      Assessment: The authors have a potentially interesting story. However, it appears that they have merged two incomplete research efforts: i) effects of trophoblast cell differentiation on utilization transposable elements to regulate gene expression; ii) IAP involvement in regulating murine placental transcription.

      Whilst we appreciate this viewpoint, our investigation of IAPs as regulators of gene expression was triggered from the analysis in the first part of the manuscript and thus follows logically in our view. Moreover, the overarching theme remains consistent: the effects of TEs (whether IAPs or others) on gene expression/transcription.

      Advance: The scientific advance is somewhat fragmented. There is a reinforcement of our existing understanding of the involvement of transposable elements in trophoblast and placental gene regulation but other new insights are limited or not well developed.

      Both the TE and placental scientific communities largely assume that the placenta is a privileged organ for co-option of TEs as regulatory elements. Here we demonstrate that TE co-option in the placenta can be quite limited and species-specific. Additionally, the roles of IAPs as gene regulators in the placenta had not been previously described. We believe these two novel observations constitute significant advancements in the field.

      Audience: Evolutionary biologists and reproductive and developmental biologists.

      Reviewer #2

      Evidence, reproducibility and clarity

      Summary: This manuscript describes the characterization of transposable elements (TEs) in mouse trophoblast stem cells and in the mature mouse placenta. The authors find that overall, the trophoblast stem or progenitor state of TSCs harbours a greater abundance of active TE elements, while their activity levels decline as trophoblast differentiates. Instead, the dominant repetitive element that is active in the mature placenta are intracisternal A particle (IAP)-derived elements. Indeed, the authors show that these provide the initiation sites for differential isoforms of some 27 chimeric transcripts that are specific to differentiated trophoblast cell types. The authors attempt to epigenetically silence these IAPs in TSCs using CRISPRi methodolgy, and find reduced expression of 4 IAP-driven transcripts and many presumably secondary transcriptional changes. Finally, they also compare IAP activity in the placentas of different mouse species or sub-species, and conclude that IAP insertion sites close to genes can affect their expression in the placenta, with potential consequences for development and evolution.

      Major comments: This is a well-conducted study that brings significant novelty, albeit to a more specialized audience.

      There are several aspects that need clarification, addition and some experimental work: 1. Figure 1A shows carefully separated cell types, in particular extraembryonic mesoderm, that have also been assessed by the various cut&tag and ATAC-seq methods, but are not mentioned in the remainder of the manuscript. This should be added. I.e., is the same activity pattern of IAPs evident in the ExMes cells, or do they follow a more somatic pattern?

      Apologies if additional analyses of extraembryonic mesoderm were not obvious, but we did analyse IAP expression in these cells and show in Figure 2B that it is much lower when compared to trophoblast. We also used the comparison between trophoblast and extraembryonic mesoderm in Figures 2A, 3B, S2A-D and S4B.

      1. Page 4, top: The mention of a "custom pipeline" for cut&tag analysis is vague, and the modifications and what they stand for is hardly mentioned. These details need to be elaborated, so to be more accessible to a wider audience.

      We have tried to clarify the overall strategy of the analysis: “Using CUT&Tag and ATAC-seq data, we aimed to identify TE subfamilies that bear classic hallmarks of active promoters (open chromatin, H3K4me3, H3K27ac) and/or enhancers (open chromatin, H3K27ac, H3K4me1). We used a custom pipeline that selects TE subfamilies bearing more elements overlapping CUT&Tag/ATAC-seq peaks than expected by chance.”.

      1. For differentiated TSCs, only ATAC-seq data were analysed. How do they relate to the various cut&tag profiles, and do they result in a robust detection of putative active repeat elements at a detection limit similar to the chromatin marks? I would think that it might be prudent to include the same cut&tag for differentiated TSCs as well, so to be directly comparable to the other data. This is important to establish whether TE elements are really less active in differentiating trophoblast, or whether this feature is intrinsic to the placenta and not to pure trophoblast cells in culture, in which case it may be influenced by tissue context.

      We are thankful for this important suggestion. We have now carried out CUT&Tag on differentiated cells and include the findings in the revised Figures 1B, 1D and S1C. Consistent with our observations using ATAC-seq data, we find that TE regulatory activity is diminished upon in vitro differentiation.

      1. Are the IAP-initiated chimeric transcripts including new coding regions? If so, a Western Blot analysis of a few of the 27 candidates should be performed to prove this. Suv39h2 is a particularly interesting candidate where such protein analysis would be very informative.

      We performed a search for ORFs in IAP-driven transcripts and identified a putative protein isoform of SUV39H2 that includes a portion of the IAP and that is larger than the canonical form by 28 kDa. However, by Western blot we see no major size shift in the main band when comparing placenta (where the IAP isoform predominates) with TSCs (where only the canonical form is expressed). We now include this in a new Supplementary Figure S5. To note is that in our hands the main SUV39H2 band runs at a lower molecular weight than expected (54 kDa), which could be due to buffer/gel conditions and/or expression of a shorter isoform (ENSMUSG00000026646, 46 kDa). But we are reassured that the antibody used has been validated in multiple human KO lines, as well as in at least one mouse knockdown model (PMID: 32698678).

      1. A WB analysis should for sure be performed on the M. musculus and M. pahari placentas. The IHC staining is not interpretable as to whether or not SUV39H2 levels are reduced in M pahari.

      We appreciate the reviewer’s point, but the main hypothesis to be tested here was whether there was an obvious difference in the spatial distribution of SUV39H2, which we did not find. Any more subtle differences would be cell-type specific and would require complex cell sorting approaches before attempting a western blot. This would not affect our conclusion that, despite differences between species at the transcriptional level, this does not lead to an overt redistribution of SUV39H2 protein expression.

      1. Could the authors please also provide more global proof of the CRISPRi success. The display of two candidate gene tracks is not very telling.

      In the original submission we had included a subfamily-level analysis of IAP expression in the CRISPRi experiment (Figure S6A of the revised version). This shows a mild downregulation of IAP expression overall. Whilst an element-based analysis would be preferable due to potential caveats with subfamily-level analyses, very few TSC-expressed elements are sufficiently mappable to ensure a robust analysis, which is why we only showed two highly expressed loci where the effects of CRISPRi can be evaluated. Importantly, we observe effects on gene expression that, whilst mild, are non-random and support a role for IAPs in regulating the expression of nearby genes (Figure 4D).

      Significance

      General assessment: Collectively, this is a carefully conducted study that needs to be bolstered by some few additional experiments, as suggested above. The discovery of changing patterns of repetitive element activity in differentiating trophoblast cells is important and intriguing, as it has direct impact on the evolutionary divergence of gene expression and, as a consequence, cell type differentiation, through the insertion of IAP and L1 elements close to placenta-expressed genes. This will be a major contributor and even driver of the barrier to inter-species hybridization that the placenta represents.

      Advance: Currently, the main TE elements known to drive placenta-specific gene expression are retrovirally derived LTR elements. Here, however, the authors show that the relevance of these elements diminishes in the mature placenta, and instead is taken over by a different class, the IAP elements. This is important, as many these elements retain the capacity for retrotransposition, and thus actively contribute to ongoing evolutionary divergence of placental gene expression patterns that ultimately may drive speciation.

      Audience: The manuscript is not particularly easy to follow, even for the informed reader, and it appeals to a relatively specialized audience in the field of genome regulation coupled to evolutionary aspects of repetitive element insertion/transposition. The authors should be encouraged to spell out some aspects of their thought process throughout the study in some more detail, so not to "lose" the reader.

      We have made multiple changes throughout the manuscript that we hope improve readability.

      __Reviewer #3 __

      __Evidence, reproducibility and clarity __ The cis-regulatory roles of TEs in human/mouse TSCs have been extensively studied, yet in vivo studies on their roles in placenta tissue is largely absent. In this manuscript, Amante and colleagues compared the regulatory landscape of TEs across the trophoblast cell lines and placenta samples in human and mouse, and after revealing the shared and species-specific patterns (including some that are surprising), they further investigated the regulatory function of the murine-specific IAP retrotransposons in house mouse and other mouse strains. Specifically, it presents several findings regarding the shared and diverged function of TEs across: 1) in vivo vs. in vitro placental models, 2) human vs. mouse, 3) and different mouse strains. The writing is of good quality, the results are well visualized and interpreted, the conclusions are reasonable, and the novelty is high. It significantly extended previous studies from the same group as well as many other researchers. I think this manuscript should fit publication after a minor revision. Below I have a few comments:

      1. In Fig. 1B, it seems the differences of TE enrichment between the same groups of samples (e.g., B6 TSC vs. GFP TSC) is also remarkable. Is this expectable? I am curious if such difference is robust, or it is just due to the TE sub-families with too few copies, whose enrichment can be influenced by just a couple of overlapping counts. The authors may double-check if possible.

      This is an interesting hypothesis, but the main subfamilies that are H3K27ac-enriched in TSCs are quite abundant (e.g., 683 copies of RLTR13D5, 260 copies of RLTR13B3). We believe these are cell line-specific differences, possibly partly driven by genetics, since they were derived from different mouse strains. Nonetheless, there is good agreement between the two lines with respect to the TE subfamilies that are enriched.

      1. The authors demonstrate that the association of TEs to cis-regulatory elements is much weaker in the placenta of mouse relative to human, and in mouse the activation of TEs is indeed similar to most other tissues. And based on this observation, they propose that "Co-option of TEs as regulatory elements within the mature placenta may therefore not be as promiscuous across species as commonly thought" (page 4 paragraph 1). While this finding is quite interesting, how it is related to the popular hypothesis that "maternal-fetal conflict leads to the strong TE activation in placenta"? I am curious if the authors have any idea on this point.

      It is indeed a fascinating topic. We would dispute that the conflict hypothesis leads to TE activation in the placenta, but rather that it creates selective pressures that drive their co-option. But this still requires for TEs to be available for co-option. What we suggest here is that TE co-option opportunities can be tightly constrained by transcriptional silencing mechanisms, even in the placenta. We added the following text to that section of the discussion: “Whilst maternal-fetal conflicts may create selective pressures for TE co-option in the placenta, epigenetic mechanisms can still act as gatekeepers and dictate the frequency of co-option events in this organ.”

      1. For the highly active IAP subfamilies identified in mouse placenta, have the authors tried to identity the enriched motifs, which may be helpful for uncovering transcription factors responsible for their activation?

      This is an interesting question, given the specific expression of IAP elements in the spongiotrophoblast. We now performed transcription factor motif analysis on subfamilies that are highly expressed in the placenta (IALTR1/2). We then filtered this list for motifs that are absent/mutated in lowly expressed IAP subfamilies (IAPLTR3/4) and whose associated transcription factor is highly expressed in spongiotrophoblast. In the revised manuscript we highlight our top candidate, MITF, which is a spongiotrophoblast-specific marker (Figure S3C).

      1. In Fig. 3B, the IAPEY_LTR-adjacent Zfp229 gene is demonstrated, yet this gene is not mentioned at all in the main text. The authors may consider providing more details for this gene.

      Unfortunately, nearly nothing is currently known about this zinc finger protein gene, but we did not feel that should prevent us from using it as a strong example of placenta-specific usage of an IAP-derived promoter. Future work on this gene may indeed be triggered by highlighting this observation.

      1. A few errors for the citations should be corrected. For example, the journal names are missed for ref56 and ref58 at page 22.

      We have reviewed all our references and added missing information

      1. A few typos should be corrected. For example, at page 11 line 2, "of" is missed between "presence this IAP-driven.

      We have corrected this typo and made additional changes to the manuscript to improve readability.

      Significance

      Overall, this is an interesting and technically-sound study with substantial novelty, which significantly extends previous knowledge on TE function in placenta which largely relies on in vitro models.I believe this study will be attractive to the fields about TE function and placenta evolution.

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      Referee #3

      Evidence, reproducibility and clarity

      The cis-regulatory roles of TEs in human/mouse TSCs have been extensively studied, yet in vivo studies on their roles in placenta tissue is largely absent. In this manuscript, Amante and colleagues compared the regulatory landscape of TEs across the trophoblast cell lines and placenta samples in human and mouse, and after revealing the shared and species-specific patterns (including some that are surprising), they further investigated the regulatory function of the murine-specific IAP retrotransposons in house mouse and other mouse strains. Specifically, it presents several findings regarding the shared and diverged function of TEs across: 1) in vivo vs. in vitro placental models, 2) human vs. mouse, 3) and different mouse strains. The writing is of good quality, the results are well visualized and interpreted, the conclusions are reasonable, and the novelty is high. It significantly extended previous studies from the same group as well as many other researchers. I think this manuscript should fit publication after a minor revision. Below I have a few comments:

      1. In Fig. 1B, it seems the differences of TE enrichment between the same groups of samples (e.g., B6 TSC vs. GFP TSC) is also remarkable. Is this expectable? I am curious if such difference is robust, or it is just due to the TE sub-families with too few copies, whose enrichment can be influenced by just a couple of overlapping counts. The authors may double-check if possible.
      2. The authors demonstrate that the association of TEs to cis-regulatory elements is much weaker in the placenta of mouse relative to human, and in mouse the activation of TEs is indeed similar to most other tissues. And based on this observation, they propose that "Co-option of TEs as regulatory elements within the mature placenta may therefore not be as promiscuous across species as commonly thought" (page 4 paragraph 1). While this finding is quite interesting, how it is related to the popular hypothesis that "maternal-fetal conflict leads to the strong TE activation in placenta"? I am curious if the authors have any idea on this point.
      3. For the highly active IAP subfamilies identified in mouse placenta, have the authors tried to identity the enriched motifs, which may be helpful for uncovering transcription factors responsible for their activation?
      4. In Fig. 3B, the IAPEY_LTR-adjacent Zfp229 gene is demonstrated, yet this gene is not mentioned at all in the main text. The authors may consider providing more details for this gene.
      5. A few errors for the citations should be corrected. For example, the journal names are missed for ref56 and ref58 at page 22.
      6. A few typos should be corrected. For example, at page 11 line 2, "of" is missed between "presence this IAP-driven.

      Significance

      Overall, this is an interesting and technically-sound study with substantial novelty, which significantly extends previous knowledge on TE function in placenta which largely relies on in vitro models.I believe this study will be attractive to the fields about TE function and placenta evolution.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: This manuscript describes the characterization of transposable elements (TEs) in mouse trophoblast stem cells and in the mature mouse placenta. The authors find that overall, the trophoblast stem or progenitor state of TSCs harbours a greater abundance of active TE elements, while their activity levels decline as trophoblast differentiates. Instead, the dominant repetitive element that is active in the mature placenta are intracisternal A particle (IAP)-derived elements. Indeed, the authors show that these provide the initiation sites for differential isoforms of some 27 chimeric transcripts that are specific to differentiated trophoblast cell types. The authors attempt to epigenetically silence these IAPs in TSCs using CRISPRi methodolgy, and find reduced expression of 4 IAP-driven transcripts and many presumably secondary transcriptional changes. Finally, they also compare IAP activity in the placentas of different mouse species or sub-species, and conclude that IAP insertion sites close to genes can affect their expression in the placenta, with potential consequences for development and evolution.

      Major comments:

      This is a well-conducted study that brings significant novelty, albeit to a more specialized audience.

      There are several aspects that need clarification, addition and some experimental work:

      1. Figure 1A shows carefully separated cell types, in particular extraembryonic mesoderm, that have also been assessed by the various cut&tag and ATAC-seq methods, but are not mentioned in the remainder of the manuscript. This should be added. I.e., is the same activity pattern of IAPs evident in the ExMes cells, or do they follow a more somatic pattern?
      2. Page 4, top: The mention of a "custom pipeline" for cut&tag analysis is vague, and the modifications and what they stand for is hardly mentioned. These details need to be elaborated, so to be more accessible to a wider audience.
      3. For differentiated TSCs, only ATAC-seq data were analysed. How do they relate to the various cut&tag profiles, and do they result in a robust detection of putative active repeat elements at a detection limit similar to the chromatin marks? I would think that it might be prudent to include the same cut&tag for differentiated TSCs as well, so to be directly comparable to the other data. This is important to establish whether TE elements are really less active in differentiating trophoblast, or whether this feature is intrinsic to the placenta and not to pure trophoblast cells in culture, in which case it may be influenced by tissue context.
      4. Are the IAP-initiated chimeric transcripts including new coding regions? If so, a Western Blot analysis of a few of the 27 candidates should be performed to prove this. Suv39h2 is a particularly interesting candidate where such protein analysis would be very informative.
      5. A WB analysis should for sure be performed on the M. musculus and M. pahari placentas. The IHC staining is not interpretable as to whether or not SUV39H2 levels are reduced in M pahari.
      6. Could the authors please also provide more global proof of the CRISPRi success. The display of two candidate gene tracks is not very telling.

      Significance

      General assessment:

      Collectively, this is a carefully conducted study that needs to be bolstered by some few additional experiments, as suggested above. The discovery of changing patterns of repetitive element activity in differentiating trophoblast cells is important and intriguing, as it has direct impact on the evolutionary divergence of gene expression and, as a consequence, cell type differentiation, through the insertion of IAP and L1 elements close to placenta-expressed genes. This will be a major contributor and even driver of the barrier to inter-species hybridization that the placenta represents.

      Advance:

      Currently, the main TE elements known to drive placenta-specific gene expression are retrovirally derived LTR elements. Here, however, the authors show that the relevance of these elements diminishes in the mature placenta, and instead is taken over by a different class, the IAP elements. This is important, as many these elements retain the capacity for retrotransposition, and thus actively contribute to ongoing evolutionary divergence of placental gene expression patterns that ultimately may drive speciation.

      Audience:

      The manuscript is not particularly easy to follow, even for the informed reader, and it appeals to a relatively specialized audience in the field of genome regulation coupled to evolutionary aspects of repetitive element insertion/transposition. The authors should be encouraged to spell out some aspects of their thought process throughout the study in some more detail, so not to "lose" the reader. The study would sit well in journals that cover a wide spectrum of biology.

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      Referee #1

      Evidence, reproducibility and clarity

      In this report the authors have provided evidence for the involvement of transposable elements in the regulation of gene expression in murine trophoblast cells and placenta. They utilized data that they generated and also published data in their analyses. They concluded that the involvement of transposable elements in the regulation of genes in differentiated trophoblast cells was less than utilized in trophoblast cells in the stem state. They provide evidence for the utilization of intracisternal A particle elements in the modulation of gene transcription of the mouse placenta, which represents a more recent evolutionary adaptation. Overall the report is descriptive presenting correlations with limited testing of specific hypotheses. There is also the impression that the manuscript consists of the merging of two projects, which have not been fully developed. Some concerns with the experimental design and interpretation of the results are provided below.

      1. Some concerns with the model systems used in the analysis. First of all, there are methods for inducing the differentiation of mouse trophoblast stem cells, which usually involves the removal of factors that promote trophoblast stem cell proliferation. The authors do not describe their method for inducing trophoblast stem cell differentiation nor did they show evidence that they directly investigated differentiated trophoblast stem cells.
      2. There is a published report presenting data from single cell analysis of mouse trophoblast stem cells in the stem and differentiated states that was not acknowledged or used in the authors' analyses. Please see: Angelova et al. 2025 Nature Communications (PMID:39747179).
      3. Much of the analysis, compared mouse trophoblast stem cells and murine placentas. Interpretation of single nucleus sequencing data from mouse placentas can provide information regarding the behavior of trophoblast cells; however, bulk sequencing of placentas is limited. The placenta contains trophoblast cell and non-trophoblast cell components. More specifically the placenta contains fetal endothelial, immune, and mesenchymal cells and depending upon dissections and the gestational stage of dissections variable amounts of uterine decidua and yolk sac-derived tissues. The authors need to be clear in the comparisons that they are making. More specifically, the authors need to effectively communicate the cell types from the placenta contributing to the results they are describing. Attributing analyses of the placenta to trophoblast cells is problematic.
      4. How were the newly derived mouse trophoblast stem cells characterized? Do they behave like authentic mouse trophoblast stem cells? Were the newly derived trophoblast stem cells capable undergoing differentiation? What parameters were measured?
      5. Were analyses with the newly derived mouse trophoblast stem cells performed in the stem or differentiated states?
      6. TSC derivation and culture section. The authors appear to be initially describing the generation of mouse embryonic fibroblast conditioned medium not trophoblast stem cell conditioned medium as stated. Some clarification will be helpful. As stated above, the authors do not provide any information on the characterization and validation of the newly derived mouse trophoblast stem cells, which is problematic.
      7. Discussion. The authors state that there are fundamental differences between the mouse and human placenta regarding the co-option of transposable element subfamilies. Human trophoblast stem cells represent a highly tractable model and could be compared with mouse trophoblast stem cells to further explore this observation.

      Significance

      Efforts to understand roles for transposable elements in the regulation of trophoblast cell gene expression and placental evolution are very important. We recognize significant differences in placentation across various species but do not have a good understanding how this important developmental process evolved.

      Assessment: The authors have a potentially interesting story. However, it appears that they have merged two incomplete research efforts: i) effects of trophoblast cell differentiation on utilization transposable elements to regulate gene expression; ii) IAP involvement in regulating murine placental transcription.

      Advance: The scientific advance is somewhat fragmented. There is a reinforcement of our existing understanding of the involvement of transposable elements in trophoblast and placental gene regulation but other new insights are limited or not well developed.

      Audience: Evolutionary biologists and reproductive and developmental biologists.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary: The manuscript describes a modular, doxycycline-inducible lentiviral vector platform that enables conditional overexpression, RNAi-mediated knockdown, and CRISPR-based perturbations, combined with fluorescent and luminescent reporters for multiplexed tracking of cell populations. Using this system, the authors perform pooled, competitive in vitro and in vivo assays, focusing on hormone receptor dependencies (ER, PR, AR) in MCF7 breast cancer cells. The key biological conclusion is that AR depletion has minimal effects in vitro but significantly impairs growth in vivo, and that combined hormone receptor knockdown leads to synergistic growth suppression in a xenograft model.

      Major comments: 1. Strength of evidence supporting the key conclusions: The technical demonstration of the vector platform is generally convincing, particularly the modular design and the feasibility of multiplexed fluorescent tracking. However, the biological conclusions are only partially supported by the data. The claim that hormone receptor interdependence, and in particular AR dependence, is revealed specifically in vivo rests on a single cell line (MCF7), a limited number of animals, and a small set of shRNAs-some of which appear to have weak or no functional impact in vitro. As such, the conclusions should be clearly qualified as preliminary and context-specific, rather than presented as generalizable insights into hormone receptor biology. In particular, the strong concluding statements (final paragraph of the manuscript) should be toned down to reflect: the limited number of models tested, the absence of mechanistic insight, and the reliance on RNAi-based perturbations without rescue experiments.

      We thank the reviewer for this important point. We agree that reliance on a single cell line, limited animal numbers, and possible variability in shRNA performance warranted more cautious framing of the biological conclusions. We have added an explicit caveat paragraph before the closing summary stating that these findings should be regarded as proof of concept, generated in a single breast cancer cell line (MCF7) using a limited number of animals and a small panel of shRNAs whose individual potency was not exhaustively benchmarked. The AR-dependency finding, in particular, is based on RNA interference alone, without orthogonal rescue experiments or mechanistic follow-up, and should be interpreted as preliminary and context-specific rather than as a generalizable feature of AR biology. We have also softened the closing sentence of the manuscript from “accelerates the preclinical development...” to “may help inform future preclinical strategies for hormone-sensitive breast cancer and beyond, pending validation in additional models.”

      1. Claims that require qualification or revision: Several claims appear overstated relative to the data provided: a. The assertion that the system enables robust temporal control of perturbations is not supported by quantitative data on leakiness, induction kinetics, or stability of editing over time.

      Thank you for raising these points. To address the concerns, we repeated the experiments, including time-course analyses and DNA sequencing with the inducible CAS9 and qRNA, and now present the data in Figure 2. These results show the induction kinetics of the system and address the leakiness of the double-inducible CRISPR/CAS9 system. Because DNA editing is a permanent change to the DNA, we assume that the stability of the edit over time will also be permanent (see Figure 2 and Supplemental Table 2).

      The claim that pooled multiplexed perturbation "reveals" discrepancies between in vitro and in vivo AR function is based on a narrow experimental scope and should be reframed as an illustrative example rather than a definitive finding. Thank you for pointing this out. We have revised and softened the claims by removing “reveals” and replacing it with “suggests” throughout the manuscript.

      Statements implying reduced off-target effects due to temporal regulation are speculative and should be removed unless supported by data. Thank you for pointing this out. We have removed those parts from the manuscript and revised the relevant sentences to avoid speculation about off-target effects, which are largely determined by the design of gRNAs and shRNAs.

      Additional experiments essential to support the paper (limited and realistic): Only minimal additional experiments are required to support the manuscript as it stands: a. Quantification of inducibility and leakiness of the dual Tet CRISPR system (e.g., untreated vs. dox-treated control in Fig. 2C, and time-course of editing efficiency).

      Thank you for the suggestion. We have now performed this experiment and included the data (see the new Figure 2G).

      Quantification of FUCCI reporter outputs (cell-cycle phase distributions) rather than representative images alone. Thank you for the suggestion. We repeated the experiment and quantified the FUCCI reporter output (see Figure 3).

      Reproducibility and methodological clarity: Several aspects of the methodology require clarification to ensure reproducibility: a. Lentiviral titers and recombination rates are not reported. Given the complexity and size of the constructs, this information is essential. We have added more detailed descriptions of the experimental procedures to the Methods section and have repeated the Cas9 transduction experiments, including their corresponding results and viral titers (see Figure 2 and Supplementary Figure 6).

      It is unclear how background editing is prevented in the dual Tet CRISPR system, since both Cas9 and gRNA are present in the same cells and may exhibit basal expression. We use serum from South America, where standard antibiotic treatment is less common than in the United States, reducing the likelihood that doxycycline is present and minimizing basal expression. The rationale for using double-inducible systems is to minimize basal expression of both components and thereby reduce the chance that either one reaches levels sufficient for editing. We have also made the underlying design logic explicit in the manuscript: because unwanted editing requires both Cas9 and gRNA to be simultaneously present above a functional threshold, and each is independently repressed through a distinct tetracycline-responsive mechanism (Tet-On for Cas9, Tet-Off for gRNA), the probability of coincident leaky expression of both components is substantially lower than for either component alone. This is now linked to the empirically measured background editing rate (~4% over 11 days without doxycycline, by ONT sequencing; Fig. 2G).

      The manuscript does not adequately address integration-based leakiness of doxycycline-inducible systems in lentiviral backbones, especially compared to transposon-based approaches. Thanks for this point. While we have not directly compared our system with transposon-based approaches, we have added a brief section on this in the Discussion to address this point explicitly. Because our system relies on polyclonal, antibiotic- or FACS-selected populations rather than single-cell-validated clones, some degree of integration site–dependent leakiness in the noninduced state cannot be fully excluded and may contribute to the low background editing (~4%) we observe over extended culture. We contrast this with transposon-based delivery systems (piggyBac, Sleeping Beauty), which have distinct genomic integration profiles relative to lentivirus, and note that doxycycline-inducible piggyBac constructs have, in some contexts, achieved tighter regulation with undetectable basal leakiness in vivo. This may reflect the absence of viral LTR-associated regulatory elements and the lentiviral bias toward active chromatin. We discuss chromatin insulators, safe-harbor-targeted integration, and transposon-based delivery as potential strategies to further reduce leaky background expression in future iterations of the platform.

      The description of how MCF7-luciferase cells are used to generate lentiviral vectors is confusing and must be clarified. We have updated this in the revised manuscript and hope it is clearer now.

      Replication and statistical analysis: The statistical treatment of pooled competition data is insufficiently detailed. It is unclear how many animals, glands, or technical replicates contribute to each comparison. The manuscript does not clearly report the fraction of cells recovered for single, double, and triple knockdowns. Multiple comparisons and normalization strategies are not consistently explained. We apologize if these sections were difficult to follow and have substantially expanded them in the manuscript. Because a recurring concern with any pooled, barcoded competition assay is that the fluorescent tag, lentiviral integration site, or clonal origin of a population could itself influence engraftment or proliferation independently of the intended genetic perturbation, we now normalize each doxycycline-treated population to its own untreated (−Dox) baseline and then express it relative to the equivalently normalized NT population within the same tumor (Fig. 5C, F). We also report the numbers of animals and glands contributing to each comparison directly in the Fig. 5 legend, and we justify this normalization approach in more detail in a new paragraph in the Discussion.

      Minor comments: a. Comparison to existing Gateway-compatible systems (e.g. the John Doench system) would help contextualize the technical advance.

      Thank you. We have now added a paragraph to the Discussion that compares our platform with existing Gateway-based and CRISPR/Cas9 lentiviral resources, including the Broad Institute's Genetic Perturbation Platform co-developed by Doench and colleagues (Brunello, Dolcetto, Calabrese). This paragraph clarifies that our system is complementary to these genome-scale discovery tools rather than competing with them: it is designed for hypothesis-driven, combinatorial, multiplexed perturbation studies with tight temporal control and validated ex vivo and in vivo applications, rather than for large-scale single-modality screening.

      Page 5, line 6: "The ..." should be "It ...". Thank you for the comment and we have corrected this. c. P2A sequences are not self-cleaving; the manuscript should correctly state that the ribosome skips peptide bond formation between the last two amino acids.

      Thank you for the comment and we have corrected this.

      Fig. 2C lacks an untreated control. Thank you for this. Instead of using an untreated control, we used two different guides targeting USP14 and USP7 to validate the inducible gRNA with constitutively expressed Cas9. For the knockdown controls, each gene served as the control for the other in the corresponding experiments, demonstrating that knockdown could be achieved with the specific inducible gRNA.

      Fig. 2E (AkaLuc panel): the label "Reagent" is incorrect and overly broad; a clearer and more consistent nomenclature is needed. Thank you for bringing this to our attention; we have corrected it in the revised manuscript. The panel has also been moved and now appears as Fig. 3B, with the axis relabeled “Ctrl”/“+Substrate” in place of “Reagent.” The purpose of fluorescence-based tracking of cell populations is not clearly explained; the rationale should be explicitly stated. We have now added an explicit rationale at the start of this Results subsection: multiplexed fluorescence barcoding is used to pool several genetically distinct cell populations and track them side by side within the same well, culture, or animal. This allows different genotypes to be compared under identical experimental conditions rather than across separate parallel experiments. This internally controlled design reduces confounding variability arising from well-to-well, batch-to-batch, or animal-to-animal differences and increases the statistical power obtained per experiment. It is particularly valuable in vivo, where it substantially reduces the number of animals required, in keeping with the 3Rs principles.

      Claims that hormone supplementation rescues AR depletion are not supported, particularly given the lack of a clear AR-dependent phenotype in prior assays (e.g. Fig. S3A). Thank you for bringing this up. We have rewritten this section and clarified that it behaves more like the non-targeting control shRNA (NT).

      The large difference in proliferation between NT cells {plus minus} doxycycline in Fig. 4B is concerning and may reflect a normalization or analysis error; this should be addressed. Thank you for pointing this out. We have now included additional raw data for clarity and addressed the issue accordingly, so it should not be misinterpreted in the future. Figures would benefit from clearer legends specifying n, statistical tests, and normalization procedures. Thank you for pointing this out; we have addressed it in the revised manuscript. Reviewer #1 (Significance (Required)):

      Nature and significance of the advance: This work represents a technical advance, rather than a conceptual or biological one. The modular lentiviral platform for inducible perturbations and multiplexed fluorescent tracking is potentially useful, particularly for pooled in vivo competition assays where reducing animal use is desirable.

      Context within existing literature: Inducible lentiviral shRNA and CRISPR systems, as well as fluorescent barcoding strategies, are well established. The main contribution here is the integration of these elements into a single, modular framework. However, the manuscript would benefit from clearer comparison to existing systems (including Gateway-based and inducible CRISPR platforms) and discussion of known limitations of lentiviral Tet systems.

      Audience: The work will primarily interest I) cancer biologists performing functional genetic screens, ii) researchers developing or applying genetic perturbation tools, and iii) laboratories interested in pooled in vivo assays. The biological findings regarding hormone receptor interdependence are likely of more limited interest unless further validated.

      Reviewer expertise: My expertise includes functional cancer genomics, lentiviral and CRISPR-based perturbation systems, in vitro and in vivo genetic screening approaches, as well as bioinformatics. I have sufficient expertise to evaluate the technical platform and biological conclusions presented.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary This study presents a novel, modular lentiviral platform integrating inducible overexpression, shRNA knockdown, and CRISPR/Cas9 editing within a single system. Its core innovation lies in the versatile design, utilizing Gateway cloning to enable rapid exchange of 14 fluorescent proteins, 3 luminescent reporters, and selection markers, facilitating high-content phenotyping. A dual doxycycline-inducible architecture ensures precise temporal control, minimizing off-target effects. Methodologically, it introduces a robust fluorescence barcoding strategy, allowing multiplexed tracking of up to nine distinct genetic perturbations in pooled assays. When applied to breast cancer intraductal xenografts, this revealed critical in vivo receptor interdependencies-such as the essential roles of AR, ER and PR, in tumor growth. By enabling combinatorial genetic analysis within single animals, this technology significantly reduces experimental variability and animal usage by up to eightfold, advancing both the efficiency of functional genomics and adherence to ethical research standards.

      Major comments 1. Despite using an inducible system, shRNA and CRISPR components may still have off-target effects. In the F4 study, was whole-genome sequencing or transcriptomic analysis performed to assess unintended perturbations of non-target genes?

      Because we did not aim to conduct detailed mechanistic studies or present shRNA as a new technology, and because the hairpins used are not novel and have already been described in published studies, we did not attempt to identify or test potential off-target genes in this work.

      Barcode stability: Are the fluorescent barcodes stably expressed during long-term in vivo culture? Is there a risk of silencing or loss that could affect the reliability of long-term tracking?

      The in vivo studies we conducted lasted more than 30 days. The cells were first validated by qPCR and Western blot, generating transgenic cells that were then expanded for all replicates and validations, including the in vivo experiments. The barcodes are driven by the EF1α promoter, which is not expected to be susceptible to methylation and, in theory, should support expression in long-term in vivo studies even beyond the 30-day duration used here.

      Cell-cell interference: In mixed transplantation experiments, could different genotypes influence each other through paracrine signaling or competition for resources, leading to observed growth phenotypes that are not entirely cell-autonomous?

      Thank you for raising this important point. We observed similar ex vivo effects on growth reduction in single experiments (except for AR in vitro) as we did in vivo with the mixed population. In all in vivo studies, whether mixed or single, the cancer cells injected into a mouse are never cell-autonomous. That is also why one performs in vivo studies using genetic perturbations: to demonstrate that cellular dependencies on genes are not in vitro artifacts, to show that certain genes are important in an in vivo setting, and to reveal how a gene affects the microenvironment in vivo, not just ex vivo. Because tumors are inherently heterogeneous, one could also argue that a mixture of different genotypes allows us to study tumor evolution with greater insight into different gene losses.

      Applicability to non-coding genes or weak-effect genes: This platform relies on observable phenotypic changes for screening. Is it sensitive enough for genes with weak effects or functional redundancy in regulatory networks?

      Thank you for the interesting question. In theory, the approach should be sensitive enough to use with genes that do not cause a growth defect. The functional readouts should then be further tailored to this purpose, using reporter assays, biomarker assays, or alternative sequencing-based readouts to study different gene-specific consequences. The barcodes can still be used to separate cells by flow cytometry and then to analyze them with the assay of choice.

      Are there differences in the induction efficiency of different shRNA or CRISPR components? Could this lead to biases in certain barcode signals, thereby affecting the accuracy of competitive growth analysis?

      Thank you for the question. We did not detect any induction efficiency effects that would bias the barcoding, as we used hairpins and gRNA guides that have been validated or previously used in the literature. However, this does not mean such bias could not occur in some cases.

      Because we also use a “no doxycycline” control, one can not only study the intercell grafting efficacy of any transgenic cell but also estimate the theoretical growth based on NT and non-dox samples. This can normalize any calculations if such bias affects the dynamics of the hairpin or gRNA. Inclusion of the no-dox control accounts for this directly: each doxycycline-treated population is normalized to its own untreated (−Dox) baseline before being expressed relative to the similarly normalized NT population within the same tumor (Fig. 5C, F). We have now implemented and reported this normalization in the manuscript, with the rationale explained in a new Discussion paragraph, rather than treating it only as a theoretical mitigation.

      Are there variations in the packaging efficiency of different shRNA or CRISPR components into lentiviral vectors? Does this affect viral titer? How is copy number consistency achieved in cells after lentiviral transduction? Has the knockdown efficiency been compared between cells transduced with 3 shRNAs and those with single shRNA? Thank you for the questions. For the shRNA and gRNA transductions, the plasmids are approximately the same size, and we have not observed any differences in transduction efficiency between these vectors. Purity and other factors during vector isolation usually have a more pronounced effect on transduction efficiency.

      The vectors that are difficult to transduce are those encoding CAS9 itself; in particular, inducible CAS9 is more challenging to transduce, and achieving a good viral titer is important for efficient transduction. We repeated an experiment and transduced the cells with different viral titers and found that the number of cells surviving antibiotic selection correlated with titer, but after expansion they expressed similar levels of CAS9.

      Since we do not use monoclonal cells and instead work with polyclonal transgenic lines, we assume that random integration should occur in a similar way as in the NT control, and therefore we did not karyotype the cells or analyze copy numbers. The knockdown efficiency was similar when comparing single and triple knockdown. We have now incorporated this explanation directly into the Methods section of the manuscript, stating explicitly that all shRNA and gRNA expression plasmids are of comparable size with no consistent differences in packaging or transduction efficiency, that titer is more strongly influenced by plasmid purity and preparation than by insert identity, and that knockdown efficiency assessed by Western blot was comparable between single, double, and triple knockdown lines (Fig. 4E–G), indicating that combinatorial transduction with multiple shRNAs did not measurably compromise silencing efficiency per target.

      Minor comments 1. The labeling "NT" in Data F3 and Supplementary Data SF3 is unclear. Do they refer to the same condition? Is DOX added in the "NT" condition in SF3? Thank you for pointing this out. NT refers to non-targeting shRNA cells, and they are treated the same as the other cells in all panels. We have updated the figure legend to clarify this.

      Cost-effectiveness ratio: Although animal use is reduced, is the cost of constructing the multiplexed barcoded viral library significantly higher than traditional methods? Is its overall economic feasibility suitable for large-scale screening? The cost of creating any shRNA or gRNA is only negligibly higher when generating different guides or hairpins and producing them with multiple barcodes, especially compared with in vivo study and animal costs. That is why we based our calculations not on construct costs but on animal costs and, more importantly, on the reduction in the number of animals needed.

      For large-scale screening, the system can distinguish only among nine barcodes, gene targets, and all their combinations, so it is not currently designed for settings in which more than nine genes are targeted.

      Reviewer #2 (Significance (Required)):

      The most important significance of the study is integrating multiple technologies into one system enabling high-content phenotyping, which may facilitate the discovery of new biological pathways.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This study presents a novel, modular lentiviral platform integrating inducible overexpression, shRNA knockdown, and CRISPR/Cas9 editing within a single system. Its core innovation lies in the versatile design, utilizing Gateway cloning to enable rapid exchange of 14 fluorescent proteins, 3 luminescent reporters, and selection markers, facilitating high-content phenotyping. A dual doxycycline-inducible architecture ensures precise temporal control, minimizing off-target effects. Methodologically, it introduces a robust fluorescence barcoding strategy, allowing multiplexed tracking of up to nine distinct genetic perturbations in pooled assays. When applied to breast cancer intraductal xenografts, this revealed critical in vivo receptor interdependencies-such as the essential roles of AR, ER and PR, in tumor growth. By enabling combinatorial genetic analysis within single animals, this technology significantly reduces experimental variability and animal usage by up to eightfold, advancing both the efficiency of functional genomics and adherence to ethical research standards.

      Major comments

      1. Despite using an inducible system, shRNA and CRISPR components may still have off-target effects. In the F4 study, was whole-genome sequencing or transcriptomic analysis performed to assess unintended perturbations of non-target genes?
      2. Barcode stability: Are the fluorescent barcodes stably expressed during long-term in vivo culture? Is there a risk of silencing or loss that could affect the reliability of long-term tracking?
      3. Cell-cell interference: In mixed transplantation experiments, could different genotypes influence each other through paracrine signaling or competition for resources, leading to observed growth phenotypes that are not entirely cell-autonomous?
      4. Applicability to non-coding genes or weak-effect genes: This platform relies on observable phenotypic changes for screening. Is it sensitive enough for genes with weak effects or functional redundancy in regulatory networks?
      5. Are there differences in the induction efficiency of different shRNA or CRISPR components? Could this lead to biases in certain barcode signals, thereby affecting the accuracy of competitive growth analysis?
      6. Are there variations in the packaging efficiency of different shRNA or CRISPR components into lentiviral vectors? Does this affect viral titer? How is copy number consistency achieved in cells after lentiviral transduction? Has the knockdown efficiency been compared between cells transduced with 3 shRNAs and those with single shRNA?

      Minor comments

      1. The labeling "NT" in Data F3 and Supplementary Data SF3 is unclear. Do they refer to the same condition? Is DOX added in the "NT" condition in SF3?
      2. Cost-effectiveness ratio: Although animal use is reduced, is the cost of constructing the multiplexed barcoded viral library significantly higher than traditional methods? Is its overall economic feasibility suitable for large-scale screening?

      Significance

      The most important significance of the study is integrating multiple technologies into one system enabling high-content phenotyping, which may facilitate the discovery of new biological pathways.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The manuscript describes a modular, doxycycline-inducible lentiviral vector platform that enables conditional overexpression, RNAi-mediated knockdown, and CRISPR-based perturbations, combined with fluorescent and luminescent reporters for multiplexed tracking of cell populations. Using this system, the authors perform pooled, competitive in vitro and in vivo assays, focusing on hormone receptor dependencies (ER, PR, AR) in MCF7 breast cancer cells. The key biological conclusion is that AR depletion has minimal effects in vitro but significantly impairs growth in vivo, and that combined hormone receptor knockdown leads to synergistic growth suppression in a xenograft model.

      Major comments:

      1. Strength of evidence supporting the key conclusions: The technical demonstration of the vector platform is generally convincing, particularly the modular design and the feasibility of multiplexed fluorescent tracking. However, the biological conclusions are only partially supported by the data. The claim that hormone receptor interdependence, and in particular AR dependence, is revealed specifically in vivo rests on a single cell line (MCF7), a limited number of animals, and a small set of shRNAs-some of which appear to have weak or no functional impact in vitro. As such, the conclusions should be clearly qualified as preliminary and context-specific, rather than presented as generalizable insights into hormone receptor biology. In particular, the strong concluding statements (final paragraph of the manuscript) should be toned down to reflect: the limited number of models tested, the absence of mechanistic insight, and the reliance on RNAi-based perturbations without rescue experiments.
      2. Claims that require qualification or revision: Several claims appear overstated relative to the data provided:

      a. The assertion that the system enables robust temporal control of perturbations is not supported by quantitative data on leakiness, induction kinetics, or stability of editing over time.

      b. The claim that pooled multiplexed perturbation "reveals" discrepancies between in vitro and in vivo AR function is based on a narrow experimental scope and should be reframed as an illustrative example rather than a definitive finding.

      c. Statements implying reduced off-target effects due to temporal regulation are speculative and should be removed unless supported by data. 3. Additional experiments essential to support the paper (limited and realistic): Only minimal additional experiments are required to support the manuscript as it stands:

      a. Quantification of inducibility and leakiness of the dual Tet CRISPR system (e.g., untreated vs. dox-treated control in Fig. 2C, and time-course of editing efficiency).

      b. Quantification of FUCCI reporter outputs (cell-cycle phase distributions) rather than representative images alone. 4. Reproducibility and methodological clarity: Several aspects of the methodology require clarification to ensure reproducibility:

      a. Lentiviral titers and recombination rates are not reported. Given the complexity and size of the constructs, this information is essential.

      b. It is unclear how background editing is prevented in the dual Tet CRISPR system, since both Cas9 and gRNA are present in the same cells and may exhibit basal expression.

      c. The manuscript does not adequately address integration-based leakiness of doxycycline-inducible systems in lentiviral backbones, especially compared to transposon-based approaches.

      d. The description of how MCF7-luciferase cells are used to generate lentiviral vectors is confusing and must be clarified. 5. Replication and statistical analysis: The statistical treatment of pooled competition data is insufficiently detailed. It is unclear how many animals, glands, or technical replicates contribute to each comparison. The manuscript does not clearly report the fraction of cells recovered for single, double, and triple knockdowns. Multiple comparisons and normalization strategies are not consistently explained.

      Minor comments:

      a. Comparison to existing Gateway-compatible systems (e.g. the John Doench system) would help contextualize the technical advance.

      b. Page 5, line 6: "The ..." should be "It ...".

      c. P2A sequences are not self-cleaving; the manuscript should correctly state that the ribosome skips peptide bond formation between the last two amino acids.

      d. Fig. 2C lacks an untreated control.

      e. Fig. 2E (AkaLuc panel): the label "Reagent" is incorrect and overly broad; a clearer and more consistent nomenclature is needed.

      f. The purpose of fluorescence-based tracking of cell populations is not clearly explained; the rationale should be explicitly stated.

      g. Claims that hormone supplementation rescues AR depletion are not supported, particularly given the lack of a clear AR-dependent phenotype in prior assays (e.g. Fig. S3A).

      h. The large difference in proliferation between NT cells {plus minus} doxycycline in Fig. 4B is concerning and may reflect a normalization or analysis error; this should be addressed.

      i. Figures would benefit from clearer legends specifying n, statistical tests, and normalization procedures.

      Significance

      Nature and significance of the advance: This work represents a technical advance, rather than a conceptual or biological one. The modular lentiviral platform for inducible perturbations and multiplexed fluorescent tracking is potentially useful, particularly for pooled in vivo competition assays where reducing animal use is desirable.

      Context within existing literature: Inducible lentiviral shRNA and CRISPR systems, as well as fluorescent barcoding strategies, are well established. The main contribution here is the integration of these elements into a single, modular framework. However, the manuscript would benefit from clearer comparison to existing systems (including Gateway-based and inducible CRISPR platforms) and discussion of known limitations of lentiviral Tet systems.

      Audience: The work will primarily interest I) cancer biologists performing functional genetic screens, ii) researchers developing or applying genetic perturbation tools, and iii) laboratories interested in pooled in vivo assays. The biological findings regarding hormone receptor interdependence are likely of more limited interest unless further validated.

      Reviewer expertise: My expertise includes functional cancer genomics, lentiviral and CRISPR-based perturbation systems, in vitro and in vivo genetic screening approaches, as well as bioinformatics. I have sufficient expertise to evaluate the technical platform and biological conclusions presented.

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      Reply to the reviewers

      Reply to the reviewers:

      We thank the three reviewers for their thoughtful and constructive comments, which have helped us to better explain our arguments and strengthen the manuscript. In this revision, we have clarified several points raised in the initial review and added new evidence that we believe further supports our conclusions.

      Point-by-point responses:

      Reviewer #1:

      _Additional in vitro experiment using artificial R-loop structures as substrate for lambda-exonuclease could prove the efficacy of the nuclease to digest through R-loop structures._

      We agree this experiment would be the cleanest way to demonstrate λ-exo's behavior on an R-loop substrate under defined biochemical conditions, and we would have liked to include it. However, this work originated in a laboratory that has since been closed due retirement, and further wet-lab experimentation is not feasible for this revision.

      Nonetheless, the manuscript already provides strong convergent evidence for RNA:DNA hybrid-mediated obstruction, short of this direct reconstitution. First, in vivo colocalization analysis shows that tSNS-seq (but not iSNS-seq) signal is enriched at S1-DRIP-seq and S9.6 ChIP-seq sites in an RNase H-sensitive manner, directly implicating RNA:DNA hybrids at the genomic loci where tSNS-seq peaks form. The reciprocal is also true, RNA:DNA hybrid signal is enriched at tSNS-seq sites, but not iSNS-seq sites (nor at known origins), in an RNase H-sensitive matter. Second, this obstruction signature is not a yeast-specific artifact of our analysis. The same asymmetric, tRNA/snoRNA-anchored peak shape is present in independently generated tSNS-seq datasets from Drosophila and C. elegans. In fact, C. elegans shows this artifact is in the non-replicating control, and is eliminated in the RNase control. Furthermore, a recent publication using (strand-specific) tSNS-seq in Trypanosoma bruceireports that large majority (90%) of its peaks map to R-loops (Stanojcic et al. 2026), although they report it as mapping true replication origins, not as a demonstration of the artifact. Finally, we added an analysis comparing R-loops mapped by RIAN-seq in mouse (Li et al. 2025) and two independently generated mouse tSNS-seq datasets (Cayrou et al. 2015 and Pratto et al.2021) that show strong correlation. Taken together, this is not one experiment pointing to hybrid-mediated blockage, but a repeated, multiple-species in vivo-validated pattern observed in tSNS-seq by multiple independent laboratories.

      What our data cannot do is isolate the enzyme's behavior on a defined R-loop substrate in vitro, separate from all other cellular context. That is a distinct and narrower question from whether hybrid-mediated obstruction explains the peaks we observe. The latter is what the convergent evidence above addresses. Future work can nail down the exact enzymology and conditions needed to replicate this in vitro.

      In Fig. 1C, there seems to be a minor increase of lambda-exo activity on R10D30 substrate in the Tris-HCI buffer compared to Glycine-KOH buffer, maybe a quantification using the loading control (D20) would help.

      As suggested by the reviewer, we have quantified band intensities for the RNA-DNA chimera (R10D30), the G4 oligo (D10G427D23), and the all-DNA control (D25) shown in Fig. 1C, normalized to the D20 loading control at each timepoint (new Supplementary Figure S5). As this gel-based densitometry is semi-quantitative at best, the values below should be interpreted as indicative rather than precise measurements; nonetheless, they confirm the reviewer's observation: the RNA-DNA chimera was digested to a somewhat greater extent in Tris-HCl (35% signal remaining at 16 h) than in Glycine-KOH (65% remaining). We have revised the Results text to report these values explicitly and to clarify our claim. The relevant passage in the Results now reads: "However, the entire oligo (D10G427D23) was digested in Tris-HCl buffer very efficiently, with barely visible bands after as early as 1 h of λ-exo digestion (1% remaining at 16 h, comparable to the D25 control). The RNA-DNA chimera (R10D30) was digested slowly in both buffers over the 16 h time course, though to a somewhat greater extent in Tris-HCl (35% remaining) than in Glycine-KOH (65% remaining). This shows that RNA-primers offer partial, but not full, protection from λ-exo digestion in either buffer, distinct from the buffer-dependent protection conferred by G-rich DNA in glycine-KOH buffer. Thus, conditions for better digestion through G4-containing DNA in Tris-HCl buffer may be more suitable for mapping origins with SNS-seq."

      We further note that this observation does not bear on the final iSNS-seq protocol, since iSNS-seq relies on 5'-OH rather than 5' RNA to protect nascent strands from λ-exo digestion. The buffer-dependent difference in RNA-primer protection described here is therefore relevant only to the mechanistic characterization of λ-exo behavior in Fig. 1C, and does not affect the enrichment logic or performance of iSNS-seq itself.

      In Fig. 2D, lower panel, the digestion time of RecJf at different units were not indicated.

      We thank the reviewer for catching this point. We have revised the Figure 2D legend to remove this ambiguity, clarifying that the top panel shows a RecJf timecourse, while the bottom panel shows a titration of RecJf units (0–300 U) at a fixed 16 h digestion time.

      In Fig. 3B, since SNS-seq detect fired origins, an overlap of iSNS rep 1 and iSNS rep 2 peaks with confirmed origins from OriDB that are also early and efficient origins may yield a higher percentage of overlap.

      We thank the reviewer for this suggestion. Rather than stratifying discrete peak-overlap percentages, which are sensitive to peak-calling thresholds and tend to understate genuine concordance, we assessed this using continuous signal enrichment at OriDB-confirmed origins, that were stratified into quartiles by annotated origin efficiency (Hawkins et al., 2013) (see modified Fig. 3C). Both iSNS-seq replicates showed a clear trend of increasing median signal from the least to the most efficient origin quartile. This is consistent with the expected behavior of SNS-seq, which detects origins in proportion to their firing frequency in the asynchronous population, and we now state this explicitly in the manuscript.

      In Fig. 3D, in addition to showing the tSNS and iSNS data on ChrIV with indicated known origins, showing at the same time the corresponding FORK-seq, OK-seq, and ORC ChIP signal will give a more comprehensive view of the performance of different origin mapping methods.

      We agree with the reviewer that including complementary origin-mapping datasets alongside tSNS-seq and iSNS-seq would provide a more comprehensive view of method performance. We have added FORK-seq initiation site midpoints, ORC ChIP-seq signal, and OK-seq origin efficiency metric (OEM) signal to Fig. 3D, alongside the tSNS-seq and iSNS-seq tracks and annotated known origins on Chr IV. In addition, we have added close-up views of the tSNS-seq and iSNS-seq tracks around three selected origins in Fig. 3D, which highlight that the prominent tSNS-seq peaks do not align with known origins, in contrast to iSNS-seq.

      In Fig. 5F, in supporting the model, a comparison of tSNS-seq peak with known budding yeast DRIP-seq datasets would help to see if the tSNS method indeed enrich for RNA:DNA hybrid signals.

      We agree with the reviewer that a direct comparison to known R-loop maps would substantially strengthen the proposed model. We have added this analysis, described briefly below, in a new Results subsection and Figure 6.

      We obtained raw, publicly deposited budding yeast S1-DRIP-seq and S9.6 ChIP-seq data, including RNase H-treated and RNase H-deficient (RNase HΔ) conditions, and called peaks from these datasets ourselves using our own pipeline, so that all datasets were processed identically. A new heatmap colocalization figure (Fig. 6A) shows that tSNS-seq (but not iSNS-seq) signal is clearly elevated at these R-loop peaks, most strongly in the RNase HΔ condition and abolished in RNase-treated controls, consistent with genuine RNA:DNA hybrid dependence. The reciprocal analysis confirms this specificity: R-loop signal is concentrated at tSNS-seq peaks, but shows no enrichment at iSNS-seq, OK-seq, or FORK-seq peaks, or at confirmed origins. R-loop signal is also enriched at tRNA, snoRNA, and snRNA genes, mirroring the tSNS-seq enrichment pattern at these same loci. Together, these results directly demonstrate that tSNS-seq peaks colocalize with bona fide R-loops rather than replication origins, supporting the model in Fig. 5F.

      Finally, to test whether this observation generalizes beyond yeast, we correlated published mouse RIAN-seq signal — an antibody-free, nuclease-based R-loop mapping method developed independently of SNS-seq — with two independent mouse tSNS-seq-type datasets (Cayrou et al. 2015; Pratto et al. 2021). tSNS-seq enrichment correlated significantly with RIAN-seq R-loop signal across replicates and genomic scales, indicating that the same hybrid-mediated obstruction mechanism likely operates in a mammalian system as well. Moreover, analyses of tSNS-seq signal in Drosophila and C. elegans confirms that the enrichment bias at tRNA sites also exists in those systems. The C. elegans data shows it is not dependent on DNA replication, and is dependent on RNA, just like in yeast.

      _Reviewer #2:_

      The authors' sugestion that tSNS miscalling of origins may be due to the presence of RNA:DNA hybrids is largely persuasive in theoretical terms but is, surpisingly, untested. The first issue that is unexamined in whether or not RNA:DNA hybrids would survive the two rounds of 95 degree denaturation that are central to both froms of SNS; can the auhtors comment or provide evidence?

      Second, it is important that the authors test their proposal (Fig.5) of RNA:DNA hybrids being the cause of at least some non-origin tSNS signal by mapping the correspondance of their SNS-seq data with the locations of RNA:DNA hybrids, since there are several forms of such mapping available for S. cervisiae (eg using S9.6 antiserum DRIP-seq, or RNaseH1 ChIP-seq).

      We thank the reviewer for raising these two important points, both of which push us to be more precise about what our model does and does not claim.

      On denaturation and hybrid survival: We agree this is a critical point to clarify, and we think it reflects a distinction we had not made explicit enough in the original submission. We are not proposing that in vivo-formed R-loops survive the 95°C denaturation steps intact. That would indeed be difficult to reconcile with the protocol. Rather, our data suggest that the highly abundant RNA species themselves (tRNAs, snoRNAs) survive denaturation as free single-stranded RNA and re-anneal with their complementary genomic DNA strand at, or before, the λ-exo digestion step, which is carried out at 37°C over an extended overnight incubation. This reannealed RNA:DNA hybrid is what we propose obstructs λ-exo in tSNS-seq. Indeed, the preservation of this RNA throughout the protocol until the λ-exo step is a built-in feature of the traditional protocol, since the RNA is only hydrolyzed after the λ-exo digestion is complete. We have added the following sentence to the Discussion to make this explicit: “Importantly, we do not propose that in vivo R-loops survive the denaturation steps of the iSNS-seq protocol. Rather, we propose that RNA:DNA hybrids reform in vitro after denaturation at genomic loci that are prone to R-loop formation. The likelihood of hybrid reformation would be expected to increase with local RNA abundance. These reformed hybrids would then selectively block λ-exo digestion, producing the characteristic asymmetric enrichments observed in tSNS-seq.

      On testing correspondence with mapped RNA:DNA hybrids: We agree, and we have added a new Results subsection and Figure 6 to directly test this. We generated a new heatmap colocalization figure (Fig. 6A) comparing tSNS-seq and iSNS-seq signal against published budding yeast S1-DRIP-seq and S9.6 ChIP-seq maps, including RNase-treated controls. tSNS-seq signal is clearly elevated over R-loop sites identified by these orthogonal methods, but not peaks called from RNase-treated controls; iSNS-seq signal is flat over both R-loop and control peak sites. We also performed the reciprocal analysis and see that R-loop signal is highly concentrated at tSNS-seq peaks, but not iSNS-seq peaks (Fig. 6B). Importantly, this RNA:DNA hybrid enrichment signal at tSNS-seq peaks is abolished in the RNase H-treated controls and enhanced in RNase deficient mutants that accumulate R-loops, both consistent with genuine RNA:DNA hybrid dependence. Finally, we demonstrate that, like tSNS-seq, the R-loop signal is also highly enriched at tRNA, snoRNA, and snRNA genes (Fig 6C). Analyses of tSNS-seq signal in Drosophila and C. elegans shows this SNS enrichment bias at tRNA sites also exists in those systems. Importantly, the C. elegans data shows it is not dependent on DNA replication, and is dependent on RNA, and is thereby consistent with the RNA:DNA enrichment artifact we discovered in yeast.

      As a further, independent test of generalizability, we correlated tSNS-seq signal with RIAN-seq (which maps R-loops genome-wide) using two published mouse datasets: the original tSNS-seq data from Cayrou et al. 2015 and the strand-specific tSNS-seq data from Pratto et al. 2021. We found a significant positive correlation with RIAN-seq R-loop signal in both, indicating the same mechanism operates in a mammalian system (Figure 6D, E). We also note in the Discussion that a recent report (preprint at the time of this review, Stanojcic et al. 2026) similarly found that 90% of tSNS-seq peaks in Trypanosoma bruceioverlap with mapped R-loops, providing independent, multiple-species support for this interpretation.

      Fig.S7. Can the authors comments on the poor reproducibility between SNS-seq replicates? Though there isa four-fold increase in concordance between iSNS experments compared with tSNS, 80% of potetial origins are missed; can this be explained? In this regard, is the statment 'Both of the iSNS-seq samples showed signal enrichment around most of the confirmed origins' correct: while Fig.3C gives this impression, Fig.3B appears to diagree.____

      We agree the original Fig. 3B (Euler diagram) understated the reproducibility of iSNS-seq and, on reflection, we do not think it was the appropriate metric to lead with. Euler/peak-set overlap is a binary, threshold-dependent measure. Peaks that fall just above the calling threshold in one replicate and just below it in the other are counted as fully discordant even when the underlying signal is well correlated. We have moved the Euler diagram to the supplement (Fig. S9) and replaced Fig. 3B with a cross-replicate F1 curve, and cross-replicate signal heatmaps. Together these analyses show that concordance between replicates is graded and substantially above what the fixed-threshold Euler comparison implied. We have revised the text accordingly, and noted that the aggregate enrichment is not fully captured by peak-overlap statistics for the reasons above.

      The same failure mode of overlap analysis applies to overlap of iSNS (or tSNS) peaks with known origins. While the SNS signal may be fully correlated with origin positions and even origin efficiency, parameter choices in peak calling and using binary overlap statistics can mask the underlying concordance of the data with known replication origins. Moreover, the list of known replication origins, even confirmed origins, is not a list of origins most likely to be active. SNS methods can only detect active origins. Therefore, we assessed concordance of the SNS methods with origin efficiency using signal enrichment at OriDB-confirmed origins that were stratified into quartiles by annotated origin efficiency (Hawkins et al., 2013) (see modified Fig. 3C). Both iSNS-seq replicates showed a clear trend of increasing enrichment signal from the least to the most efficient origin quartile. This is consistent with the expected behavior of SNS-seq, which detect origins in proportion to their firing frequency in the asynchronous population, and we now state this explicitly in the manuscript.

      Given the very nice data in Figs.1 and 2 showing the confounding effect of G4s on tSNS, can the authors comment on why G4s show little overlap with either SNS-seq mapping in Fig.4, and why they saw no increase in tSNS-seq or iSNS-seq signal over G4 motifs in Supplementary Figure S9B? Might this indicate that the in vitro work does not translate well to in vivo mapping?

      We agree this warranted comment and have expanded the Discussion accordingly. We were ourselves somewhat surprised not to see a higher genome-wide G4 signal, though not entirely so. This is not evidence that the in vitro work fails to translate in vivo: the same λ-exo bias has been shown to manifest genome-wide in human cells (Foulk et al. 2015). Rather, we suggest the limited yeast signal likely reflects yeast-specific properties, which has a genome with more uniform local GC content than human (new Supplementary Figure S22), far fewer G4 motifs overall (Wu et al. 2021), and possibly less stable G4 folding in vivo (Tran et al. 2011). Moreover, the G4s in the human genome are non-randomly clustered in the GC-rich isochores, which compounds the G4 bias further with the known GC bias of Lambda exonuclease. These points are now made in the revised manuscript.

      The known G4 problem was the basis for searching for better buffer conditions in this study. While we found conditions that eliminate the G4 bias in vitro, the yeast genome did not provide ample opportunity to test this. However, the yeast genome ultimately allowed us to discover a possibly more dominant systematic bias, which is the correlation with tRNA and other high copy number RNA species that likely form RNA:DNA hybrids in vitro. Moreover, the lack of G4s allows a clean separation of G4s and R-loops, features that are highly correlated in the human genome.

      A requirement to validate the interesting suggestion that RNA:DNA hybrids are a cause of tSNS-seq miscalling of origins; this should be a combination of in vitro tests and colocalisation of tSNS-seq signal and RNA:DNA signal in vivo using available datasets (eg DRIP-seq).

      We agree that a direct in vitro biochemical demonstration that λ-exo digestion is specifically obstructed by an RNA:DNA hybrid substrate would further strengthen this model. However, as the laboratory where the wet-lab work for this study was performed has since been closed due to retirement, additional experiments of this kind are not feasible for this revision. We have instead addressed this request through a combination of new in vivo colocalization analyses, and an additional line of convergent evidence from the literature, which together we believe make a compelling case for the model.

      First, we have now added direct in vivo colocalization evidence: a new heatmap figure (Fig. 6A) comparing tSNS-seq and iSNS-seq signal to published budding yeast S1-DRIP-seq and S9.6 ChIP-seq maps, with RNase H-treated controls. tSNS-seq signal is clearly elevated over R-loop sites identified by these orthogonal methods, but not peaks called from RNase-treated controls; iSNS-seq signal is flat over both R-loop and control peak sites. Importantly, this RNA:DNA hybrid enrichment signal at tSNS-seq peaks is abolished in the RNase H-treated controls and enhanced in RNase deficient mutants that accumulate R-loops, both consistent with genuine RNA:DNA hybrid dependence. Finally, we demonstrate that, like tSNS-seq, the R-loop signal is also highly enriched at tRNA, snoRNA, and snRNA genes (Fig. 6C). Analyses of tSNS-seq signal in Drosophila and C. elegans shows this SNS enrichment bias at tRNA sites also exists in those systems. Importantly, the C. elegans data shows it is not dependent on DNA replication, and is dependent on RNA, and is thereby consistent with the RNA:DNA enrichment artifact we discovered in yeast.

      Second, we further demonstate that R-loop and tSNS-seq correlation extends to mouse as well. A novel R-loop mapping method RIAN-seq (Li et al. 2025) uses λ-exo, together with nuclease P1 and T5 exonuclease, to selectively degrade single-stranded RNA, single-stranded DNA, and double-stranded DNA from digested genomic DNA, while RNA:DNA hybrids resist this treatment and are selectively recovered. That λ-exo digestion is used as one of the core enzymatic steps to enrich for RNA:DNA hybrid-containing DNA is itself a genome-wide demonstration that λ-exo activity is impeded by RNA:DNA hybrid structures. Building on this, we correlated tSNS-seq signal against RIAN-seq signal using two independent published mouse tSNS-seq datasets (Cayrou et al. 2015 and Pratto et al. 2021) and found significant positive correlation in both, directly linking λ-exo obstruction by RNA:DNA hybrids to tSNS-seq peak formation in an independent system (Fig. 6D, E).

      Finally, a recent study (Stanojcic et al. 2026) using tSNS-seq on trypanosomes noted the majority (90%) of their peaks were associated with R-loop structures. Thus, we can defensibly conclude that this correlation is seen in yeast, C. elegans, Drosophila, mouse, and trypanosome datasets.

      An explanation and/or comment on the low reproducibility and low signal-to-noise ratio of iSNS-seq; specifically, although it improves on tSNS, does it provide accurate origin prediction?

      We do not believe reproducibility is in fact poor; the discrete peak-overlap metric we originally used was overly conservative (although still significantly above random). On signal-to-noise: we have added FRiP-vs-cumulative-ranked-peaks curve (Fig. 3E) showing that both iSNS-seq and tSNS-seq achieves higher than random raw FRiP. While tSNS-seq achieved higher FRiP, the iSNS-seq shows greater specific enrichment (SN/PPV against OriDB) at real origins. This is because the majority of tSNS-seq reads are in tRNA-associated non-origin peaks. In other words, tSNS-seq's apparently favorable S:N is driven in large part by non-origin enrichment (e.g., R-loop-forming loci), consistent with our RNA:DNA hybrid-mediated bias hypothesis. In contrast, iSNS-seq's lower overall S:N reflects the removal of much of that strong non-origin signal, leaving a smaller but more origin-specific signal. Whereas the non-origin biases are strongly present in a sample, nascent strands from a replication origin are very rare in the sample in comparison; present only in the fraction of S-phase cells in an asynchronous population where the nascent origin-proximal DNA is Comment on the lack of in vivo evidence, from available mapping data, for the confounding effect of G4s seen in the in vitro experiments.

      In vivo evidence for this λ-exo bias does exist in human cells (Foulk et al. 2015). Its absence in our yeast mapping data is better explained by yeast-specific genomic features than by the effect being artifactual, or in vitro-only. We have revised explicit comment on this in the Discussion:

      Given the clear G4-mediated λ-exo obstruction we observed in traditional conditions in vitro, as well as previously demonstrated genome-wide blockage at G4s in human cells and preference of λ-exo for AT-rich over GC-rich DNA (Foulk et al 2015), we were somewhat surprised not to observe a higher proportion of G4-overlapping fragments in tSNS-seq compared to iSNS-seq. However, this is not entirely unexpected for several reasons. First, the budding yeast genome is markedly more uniform than the human genome: local GC content (100 bp bins) spans a 10th–90th percentile range of 30–46% in yeast versus 26–55% in human (Supplementary Figure S22). In fact, the yeast genome has roughly half the variation of GC content found in the human genome as measured by standard deviation and median absolute deviation (MAD) (Supplementary Figure S18). Budding yeast also has substantially fewer G4 motifs than humans, both in absolute number and as a proportion of the genome (Wu et al. 2021), limiting the opportunity for a genome-wide G4 signal to emerge regardless of any per-motif blocking effect. Finally, we cannot rule out that budding yeast G4 motifs simply form less stable quadruplexes, as has been shown for yeast telomeric G4s specifically (Tran et al. 2011).”.

      Reviewer #3:

      While the method developed (iSNS-seq) appears to improve aspects of the previously used method (tSNS-seq), poor signal-to-noise ratios and reproducibility are major concerns. The signal-to-noise ratio of iSNS-seq, as shown in Figure 3D, appears low. The authors should discuss what practical consequences this has for the applicability of the method, especially in species with less well-defined/efficient origins, and how it affects the reliability of peak calling, and overlap with known origins. Consistently, the overlap of called peaks between biological replicates of iSNS-seq is relatively low (Fig3B). This raises a concern for the usability of the method, the interpretation of the genome-wide data and the quality metrics provided. The authors should discuss these issues.

      We have added a Discussion paragraph addressing this directly. Because the intrinsic signal-to-noise ceiling of SNS-seq-type methods scales with the size and firing efficiency/synchrony of the origin population (as detailed in our existing calculation based on Cadoret et al. (2008)), we now state explicitly that applying iSNS-seq to organisms or cell populations with less efficient, less synchronized, or less well-defined origins than budding yeast should be expected to yield correspondingly lower signal-to-noise and reduced peak-calling reliability, with a likely higher false-negative rate for genuine origins. We now acknowledge the need for further optimization to improve the underlying SNS:background ratio before extending the method to other organisms.

      On reproducibility: the original Fig. 3B (Euler diagram) understated the reproducibility of iSNS-seq and, on reflection, we do not think it was the appropriate metric to lead with. Euler/peak-set overlap is a binary, threshold-dependent measure. Peaks that fall just above the calling threshold in one replicate and just below it in the other are counted as fully discordant even when the underlying signal is well correlated. We have moved the Euler diagram to the supplement (Fig. S9) and replaced Fig. 3B with a cross-replicate F1 curve, and cross-replicate signal heatmaps. Together these analyses show that concordance between replicates is graded and substantially above what the fixed-threshold Euler comparison implied.

      We have replaced the Euler-diagram comparison with a graded, cross-replicate F1 analysis (new Fig. 3B) and supplementary correlation/Jaccard metrics, which show reproducibility is substantially above random for both methods and is in fact tighter for iSNS-seq than tSNS-seq at the level of peak-height correlation.

      Related to the above, please explain/discuss the rationale behind retaining all peaks, even the ones observed in only one of the biological replicates, when comparing called peaks to confirmed ORIs. In Figure 3B, clarify what are the percentages depicted.

      We agree with the reviewer that this point deserves fuller explanation, and we address the two parts in turn.

      Rationale for retaining single-replicate peaks. Origin firing is stochastic across an asynchronous population, and any single replicate is a depth-limited sample of the true origin population rather than an exhaustive one. Requiring a peak to be called in both replicates before comparing it to OriDB would systematically discard true origins that happen to be under-sampled in one replicate, artificially inflating apparent PPV at the direct cost of sensitivity. Since our goal in this analysis was to characterize each method's raw concordance with known origins, we compared each replicate's full peak set to OriDB independently.

      What the percentages in the diagram represent. We have moved this Euler diagram comparison from the main text to Supplementary Figure S9. We have clarified the legend of Supplementary Figure S9 (formerly Figure 3B) to read: "Percentages represent the fraction of the total combined peaks across all three sets being compared (i.e., the union of iSNS-seq rep 1, iSNS-seq rep 2, and confirmed ORIs for panel A; tSNS-seq rep 1, tSNS-seq rep 2, and confirmed ORIs for panel B) that fall into each region of the diagram."

      Why this analysis was moved to the supplement. Binary overlap statistics of this kind are informative but limited: they collapse a continuous relationship (signal strength versus origin identity) into a single threshold-dependent yes/no call, and are sensitive to peak-calling parameters. Moreover, the list of OriDB-confirmed origins is not itself a list of the origins most likely to be active in a given asynchronous population: most methods can only ever detect origins that actually fired, so a "true" origin that failed to overlap a peak may simply reflect biological non-firing rather than a false negative of the method.

      For these reasons, we chose to complement the overlap-based comparison with an analysis that queries origin correspondence directly against the experimental signal rather than against a binarized peak call. Specifically, we stratified OriDB-confirmed origins into quartiles by annotated origin efficiency (Hawkins et al., 2013) and examined SNS signal enrichment across quartiles (modified Fig. 3C). Both iSNS-seq replicates show a clear, monotonic increase in enrichment signal from the lowest to the highest efficiency quartile. This is the expected behavior for a method that detects origins in proportion to their firing frequency in an asynchronous population. We now state this rationale explicitly in the manuscript text: “To assess the reproducibility of iSNS-seq and tSNS-seq more rigorously than a fixed-threshold peak-overlap comparison allows […] Because this approach evaluates concordance at every possible rank cutoff rather than a single arbitrary threshold, it is not subject to the same sensitivity to near-threshold peaks that limits discrete overlap statistics such as Euler diagrams (Supplementary Figure S9).

      The title and abstract do not accurately convey the limitations of the improved method and must be rephrased.

      We thank the reviewer for this comment and have revised the abstract accordingly. Specifically, we have (1) clarified that our benchmarking was conducted in S. cerevisiae precisely because it offers a well-defined set of confirmed origins for direct comparison, (2) made explicit that our enrichment claims are relative to traditional SNS-seq rather than absolute, and (3) added language framing this work as a proof-of-concept benchmark, with extension to metazoan systems as a next step rather than a claim already established here. We believe these changes address the concern while accurately reflecting what our data show.

      We have retained the title, as we believe "enhances DNA replication origin detection and reduces non-origin biases" is a comparative claim relative to traditional SNS-seq, which is directly supported by our benchmarking data, and does not imply resolution of broader field-wide inconsistencies in origin mapping.

      One of the manuscript's central mechanistic claims is that residual cellular RNA reanneals to genomic DNA, creating RNA:DNA hybrids that obstruct λ-exonuclease digestion. While the presented data from cells are consistent with this model, the manuscript lacks a direct biochemical demonstration of hybrid-mediated obstruction in a controlled system. The manuscript would benefit from in vitro studies corroborating this conclusion, using the in vitro system established.

      We agree this would be a valuable addition, and in principle the in vitro system established in this study (Fig. 1, 2) is well suited to such a test. However, the laboratory where this wet-lab work was performed has since been closed due to retirement, and we are unable to carry out new biochemical experiments of this kind for this revision.

      We believe the manuscript already provides convergent evidence for RNA:DNA hybrid-mediated obstruction, short of this direct reconstitution. First, our in vivo colocalization analysis shows that tSNS-seq (but not iSNS-seq) signal is enriched at S1-DRIP-seq and S9.6 ChIP-seq sites in an RNase H-sensitive manner, directly implicating RNA:DNA hybrids at the genomic loci where tSNS-seq peaks form. Second, this obstruction signature is not a yeast-specific artifact of our analysis. The same asymmetric, tRNA/snoRNA-anchored peak shape is present in independently generated tSNS-seq datasets from Drosophilaand C. elegans. Furthermore, a recent publication using stranded tSNS-seq in Trypanosoma brucei reports that a large majority (90%) of its peaks map to R-loops (Stanojcic et al. 2026). Finally, we added an analysis comparing R-loops mapped by RIAN-seq in mouse (Li et al. 2025) and two independently generated mouse tSNS-seq datasets (Cayrou et al. 2015 and Pratto et al. 2021) that show strong correlation. Taken together, this is not one experiment pointing to hybrid-mediated blockage, but a repeated, cross-species in vivo-validated pattern observed in tSNS-seq by multiple independent laboratories. The distinct contribution of our yeast experiments is that a well-defined set of confirmed origins allowed us, for the first time, to directly distinguish genuine replication signal from this R-loop-associated background.

      What our data cannot do is isolate the enzyme's behavior on a defined R-loop substrate in vitro, separate from all other cellular context. That is a distinct and narrower question from whether hybrid-mediated obstruction explains the peaks we observe. The latter is what the convergent evidence above addresses. Future work can nail down the exact enzymology and conditions needed to replicate this in vitro.

      Currently, the RNA-dependence of the tRNA/snoRNA-associated obstruction signal is inferred from indirect evidence (UMAP clustering, feature overlap, peak shape) plus a re-analysis of an RNase-treated control from a previously published C. elegans dataset, not from a control generated within the authors' own yeast system, where the paper's primary genome-wide claims are made. Including such controls would be required to provide direct evidence for this central mechanistic claim.

      We appreciate the opportunity to clarify this, as we believe we already have exactly this control within our own yeast dataset. iSNS-seq itself functions as this RNA-dependence control for tSNS-seq: unlike tSNS-seq, the iSNS-seq protocol includes an RNA hydrolysis step (NaOH) performed before λ-exo digestion, removing the RNA that would otherwise be available to reanneal and form obstructing RNA:DNA hybrids. Both tSNS-seq and iSNS-seq are generated from the same size-selected input material from the same yeast cultures, differing specifically in whether RNA is retained (tSNS-seq) or hydrolyzed (iSNS-seq) prior to the λ-exo step (see Fig 3A, which is now improved to clarify this point). The result is a matched, within-system, RNA-present versus RNA-removed comparison, generated entirely in our own hands in budding yeast: tSNS-seq (RNA intact) shows strong signal and peak enrichment at tRNA/snoRNA loci, while iSNS-seq (RNA hydrolyzed) shows essentially none (Fig. 5). This is, in effect, an RNase-equivalent control built directly into our primary experimental design, rather than one requiring a separate treatment arm.

      We have clarified this point explicitly in the Discussion by noting that the tSNS-seq vs. iSNS-seq comparison itself constitutes the within-system RNA-dependence control, complementing the new yeast S1-DRIP-seq/S9.6 ChIP-seq RNase H colocalization analysis (Fig. 6A-C), which independently corroborates this at the level of orthogonal R-loop mapping methods.

      A substantial portion of the in vitro biochemical work (Figure 1B-C, E, Figure 2B-C with BKGmimic) is dedicated to establishing that G4 motifs cause significant λ-exo obstruction in glycine-KOH buffer, and that the Tris-HCl buffer switch resolves this. However, the genome-wide finding that G4 motif overlap is nearly identical between tSNS-seq and iSNS-seq (4-5% in both), with no significant differences in signal over G4 motifs in either dataset, requires clarification/discussion.

      We have clarified this in the expanded Discussion. We note it was somewhat surprising to us as well not to observe more obstruction in tSNS-seq samples. However, our data is consistent with prior human work showing the same λ-exo bias (Foulk et al. 2015), combined with the yeast genome's comparative GC uniformity (new Supplementary Figure S22) and its markedly lower G4 motif density (Wu et al. 2021), and possibly less stable yeast G4 folding (Tran et al. 2011). We believe these factors sufficiently explain why a robust in vitro effect does not produce a differential genome-wide signal in this organism.

      The documented G4 bias was a motivation to find new buffer conditions in the first place, and our further study of this problem highlighted something important for the field even if it doesn’t matter for the yeast genome: RNA primers are less effective at protecting downstream DNA than G4 motifs. Moreover, that observation led us to inventing a way where nascent strands were more strongly protected than G4s by using the 5’-OH after purposeful RNA hydrolysis. Finally, RNA hydrolysis led to the discovery that there is a major RNA-dependent bias in tSNS-seq. Thus, the paper documents the journey and key insights that led to the iSNS-seq protocol, and major findings in this study regarding the tSNS-seq protocol.

      None of the gel-based panels in Figure 1 (B, C, E) or Figure 2D state whether the result shown is from a single experiment or is representative of multiple independent experiments. Please state explicitly, for each gel-based panel, whether it represents a single experiment or is representative of repeated independent experiments (and if the latter, how many).

      We agree with the reviewer that this information was missing and should be stated explicitly. The gel panels in Figures 1B, 1C, 1E, and 2D arise from an extensive experimental search (for buffer conditions and additives) that would increase digestion through G4 motifs while preserving the RNA-DNA chimera, during which these conditions were tested many times over the course of the study. Across this large number of experiments testing different buffers and additives, the same core observations consistently emerged: (1) -exo digests through the RNA-DNA chimera, (2) -exo has difficulty digesting through G4 motifs in Glycine-KOH buffer, but not in Tris-HCl, (3) a 5'-OH end protects DNA from -exo digestion much more effectively than an RNA primer, and (4) RecJf does not digest through a 5' RNA end (or through G4 motifs), but can reduce background DNA levels. The panels shown are representative of these consistently observed outcomes, which are also consistent with the overall findings presented throughout the manuscript. We have added a statement to this effect in the Methods and to each relevant figure legend.

      In the Introduction section, the authors state: "...traditional SNS-seq enriches non-origin DNA that appears to originate from RNA:DNA hybrids due to contaminating RNA, while failing to enrich known replication origins above random expectation." Replace the term "contaminating RNA" with the term "residual cellular RNA", accurately conveying that this is endogenous RNA persisting through the protocol.

      We thank the reviewer raising this point. We corrected the sentence accordingly, which now reads: “[…]traditional SNS-seq enriches non-origin DNA that appears to originate from RNA:DNA hybrids due to residual cellular RNA, while failing to enrich known replication origins above random expectation.”

      The sentence describing that "the RNA-DNA chimera was not digested when 5' phosphorylation by PNK was performed prior to RNA degradation, rather than after the removal of the RNA primer by NaOH" introduces the PNK/NaOH order-of-operations logic before this concept has been explained to the reader, the significance of treatment order is not established until two sections later. A minimal fix would be adding a forward-referencing clause or relocating this result to a later paragraph.

      We agree that the result appeared to be out of place. We have relocated a slightly modified version of this sentence to a later paragraph, just before explaining the Figure 1C experimental logic. The relocated part now reads: “When characterizing λ-exo properties in vitro, we found that the RNA-DNA chimera was not digested when 5' phosphorylation by PNK was performed prior to RNA degradation, rather than after the removal of the RNA primer by NaOH (Supplementary Figure S4D); also confirming that a 5' phosphate is required for λ-exo activity. This result prompted us to further assess how hydrolyzing RNA primers to obtain 5'-OH DNA protects short nascent strand (SNS) DNA from λ-exo digestion compared to 5'-phosphorylated DNA, and to devise an experiment that varied the order of RNA hydrolysis (NaOH) and phosphorylation (T4 polynucleotide kinase, PNK) (Figure 1D).

      In Figure 2D. (top panel), please state the amount of λ-exo used to perform this experiment in the corresponding figure legend.

      We believe there may be a small mix-up here: Figure 2D shows RecJf digestion throughout (top and bottom panels) and λ-exo was not used in either. However, we agree with the underlying point: the amount of RecJf used in the top panel (timecourse) was not stated in the legend. We have corrected this omission and, as also noted in a related minor comment from Reviewer 1, revised the Figure 2D legend overall for clarity, now stating explicitly that the top panel shows a timecourse of RecJf digestion (with hours indicated) and the bottom panel shows a titration of RecJf units (0–300 U) over a fixed 16 h digestion.

      In Figure 2D. where sonicated, heat-denatured yeast genomic DNA is used as a substrate, please explain how this DNA is prepared and if residual cellular RNA reannealing to complementary genomic sequences would/would not be expected in this prep. Discuss with respect to the RecJf experiments shown.

      The sonicated genomic DNA substrate in Figure 2D (bottom panel) was RNase-treated prior to RecJf digestion, which removes cellular RNA and precludes RNA reannealing to complementary genomic sequences in this specific preparation. We acknowledge that RNase treatment means this experiment does not fully replicate the RNA content of genomic DNA in an actual iSNS-seq reaction, where residual cellular RNA is present. However, we note that in the iSNS-seq workflow, RecJf digestion is used solely as an additional bulk genomic DNA clean-up step prior to λ-exonuclease digestion. Any genomic DNA fragments that resist RecJf digestion due to reannealed residual RNA would still be subject to the downstream PNK, NaOH, and λ-exonuclease steps that carry out the primary enrichment for origin-proximal SNS molecules. We have modifed the Results section to clarify this point: “[…] RecJf significantly reduced the levels of yeast genomic DNA that had been sonicated to the average SNS size range (0.5--2.0 kb), RNase-treated, and denatured by heat (Figure 2D bottom panel). Note that in the iSNS-seq workflow, RecJf digestion serves as an additional bulk gDNA clean-up step upstream of λ-exo digestion. Any genomic DNA species that escape RecJf digestion, including those potentially protected by reannealed residual RNA, remain subject to the subsequent PNK, NaOH (that hydrolyzes residual RNA), and λ-exo steps that establish specificity for SNS molecules.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors of the paper "Improved short nascent strand sequencing (iSNS-seq) enhances DNA replication origin detection and reduces non-origin biases" investigate the biochemical basis of short nascent strand sequencing (SNS-seq), a widely used method for mapping DNA replication origins genome-wide. SNS-seq relies on λ-exonuclease digestion of genomic DNA, while retaining short nascent strands, which are protected by their 5' RNA primer. Despite its widespread use, SNS-seq has not been rigorously validated in a system with well-characterized replication origins and has often shown limited concordance with alternative origin-mapping approaches. Using a series of in vitro biochemical assays, the authors demonstrate that the key assumptions underlying SNS-seq are not well supported, G4 quadruplexes show strong protection against λ-exo digestion, while 5' RNA primers provide only partial protection. Based on these findings, the authors have developed an improved protocol (iSNS-seq) that achieves noticeably higher in vitro enrichment of nascent DNA than the traditional protocol. The authors then benchmark traditional SNS-seq (tSNS-seq) against iSNS-seq genome-wide in budding yeast (S. cerevisiae), a system with well-curated origin annotations. iSNS-seq appears to outperform tSNS-seq in sensitivity and precision against confirmed origins. The study also shows that tSNS-seq has a very poor performance for origin mapping in budding yeast and identifies a major source of false-positive signals in tSNS-seq, λ-exonuclease obstruction by RNA: DNA hybrids formed between residual cellular RNA and complementary genomic DNA during denaturation. The manuscript convincingly shows that tSNS-seq has important limitations for origin mapping and that iSNS-seq represents a meaningful improvement over the traditional protocol and resolves several of its biases. None-the-less, the signal-to-noise ratio of the improved method appears low and reproducible calling of peaks problematic. Furthermore, the data provided show that iSNS-seq under-performs relative to available alternative methods for nascent strand mapping.

      Major comments

      1. While the method developed (iSNS-seq) appears to improve aspects of the previously used method (tSNS-seq), poor signal-to-noise ratios and reproducibility are major concerns. The signal-to-noise ratio of iSNS-seq, as shown in Figure 3D, appears low. The authors should discuss what practical consequences this has for the applicability of the method, especially in species with less well-defined/efficient origins, and how it affects the reliability of peak calling, and overlap with known origins. Consistently, the overlap of called peaks between biological replicates of iSNS-seq is relatively low (Fig3B). This raises a concern for the usability of the method, the interpretation of the genome-wide data and the quality metrics provided. The authors should discuss these issues.
      2. Related to the above, please explain/discuss the rationale behind retaining all peaks, even the ones observed in only one of the biological replicates, when comparing called peaks to confirmed ORIs. In Figure 3B, clarify what are the percentages depicted.
      3. The title and abstract do not accurately convey the limitations of the improved method and must be rephrased.
      4. One of the manuscript's central mechanistic claims is that residual cellular RNA reanneals to genomic DNA, creating RNA:DNA hybrids that obstruct λ-exonuclease digestion. While the presented data from cells are consistent with this model, the manuscript lacks a direct biochemical demonstration of hybrid-mediated obstruction in a controlled system. The manuscript would benefit from in vitro studies corroborating this conclusion, using the in vitro system established.
      5. Currently, the RNA-dependence of the tRNA/snoRNA-associated obstruction signal is inferred from indirect evidence (UMAP clustering, feature overlap, peak shape) plus a re-analysis of an RNase-treated control from a previously published C. elegans dataset, not from a control generated within the authors' own yeast system, where the paper's primary genome-wide claims are made. Including such controls would be required to provide direct evidence for this central mechanistic claim.
      6. A substantial portion of the in vitro biochemical work (Figure 1B-C, E, Figure 2B-C with BKGmimic) is dedicated to establishing that G4 motifs cause significant λ-exo obstruction in glycine-KOH buffer, and that the Tris-HCl buffer switch resolves this. However, the genome-wide finding that G4 motif overlap is nearly identical between tSNS-seq and iSNS-seq (4-5% in both), with no significant differences in signal over G4 motifs in either dataset, requires clarification/discussion.
      7. None of the gel-based panels in Figure 1 (B, C, E) or Figure 2D state whether the result shown is from a single experiment or is representative of multiple independent experiments. Please state explicitly, for each gel-based panel, whether it represents a single experiment or is representative of repeated independent experiments (and if the latter, how many).

      Minor comments

      1. In the Introduction section, the authors state: "...traditional SNS-seq enriches non-origin DNA that appears to originate from RNA:DNA hybrids due to contaminating RNA, while failing to enrich known replication origins above random expectation." Replace the term "contaminating RNA" with the term "residual cellular RNA", accurately conveying that this is endogenous RNA persisting through the protocol.
      2. The sentence describing that "the RNA-DNA chimera was not digested when 5' phosphorylation by PNK was performed prior to RNA degradation, rather than after the removal of the RNA primer by NaOH" introduces the PNK/NaOH order-of-operations logic before this concept has been explained to the reader, the significance of treatment order is not established until two sections later. A minimal fix would be adding a forward-referencing clause or relocating this result to a later paragraph.
      3. In Figure 2D. (top panel), please state the amount of λ-exo used to perform this experiment in the corresponding figure legend.
      4. In Figure 2D. where sonicated, heat-denatured yeast genomic DNA is used as a substrate, please explain how this DNA is prepared and if residual cellular RNA reannealing to complementary genomic sequences would/would not be expected in this prep. Discuss with respect to the RecJf experiments shown.

      Significance

      This is a carefully executed and methodologically interesting study. Its principal strength is the use of budding yeast as a bench marking system for direct validation of SNS-seq peaks, the demonstration that there is a very small overlap between origins mapped by the traditional method and known origins in this well characterized system and the discovery of a specific, mechanistically coherent, and cross-species-reproducible artifact (RNA:DNA hybrid-mediated λ-exo obstruction) that provides an explanation for the observed low overlap of replication origin mapping by tSNSseq and alternative techniques. These findings are important for interpreting origin mapping studies in different organisms, including metazoa. In addition, the manuscript uses a well-controlled, quantitative in vitro biochemical assays to improve SNS-seq, and analysis in budding yeast shows that indeed the ability of this method to identify origins is improved over the traditional protocol. However, the low signal to noise ratio of the new method and rather low reproducibility between biological replicates, in a system (budding yeast) with well-defined and efficient origins, limits the applicability of the improved method for origin mapping. The improved method appears to be considerably underperforming against other available methods for mapping origins. Overall, this is a carefully executed study that would be of interest to an audience interested in origin mapping techniques in different species.

      Advance

      To our knowledge, this is the first study to benchmark SNS-seq against the well-characterized set of replication origins of yeast, despite the method being used for many years in different species. This is an important and overdue technical advance. Beyond the benchmarking itself, the identification of RNA: DNA hybrid-mediated obstruction as a systematic bias is a mechanistic finding that is notable. The identification of such biases is a conceptual contribution with implications reaching beyond this paper's own experiments, since it calls into question a standard control used throughout the published literature. The advance is primarily methodological and mechanistic, with secondary conceptual implications for ongoing debates over metazoan replication origins, since the paper raises legitimate doubt on whether SNS-seq-derived signals represent genuine origins.

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      Referee #2

      Evidence, reproducibility and clarity

      This is very nice paper, which combines meticulous in vitro and in vivo experiments to provide two new insights into a widely used approach - SNS-seq - to map DNA replication origins. The first insight is to detail a key limitation in traditional SNS-seq (tSNS), which leads to very substantial off-target mapping, resulting in predictions of origins that are not origins and, very likely, explaining the poor correspodance of this approach with orthogonal means of origin mapping in metazoans. Second, through carefully executed in vitro analysis, the authors provide a updated form of SNS-seq (iSNS) that more accurately predicts origins in yeast, where mutiple lines of evidence have provided a carefully curated list of origins; as result, the authors provide a new approach to origin mapping that should be widely applicable. I have one major issue and two minor issues with the work.

      Major issue.

      The authors' sugestion that tSNS miscalling of origins may be due to the presence of RNA:DNA hybrids is largely persuasive in theoretical terms but is, surpisingly, untested. The first issue that is unexamined in whether or not RNA:DNA hybrids would survive the two rounds of 95 degree denaturation that are central to both froms of SNS; can the auhtors comment or provide evidence? Second, it is important that the authors test their proposal (Fig.5) of RNA:DNA hybrids being the cause of at least some non-origin tSNS signal by mapping the correspondance of their SNS-seq data with the locations of RNA:DNA hybrids, since there are several forms of such mapping available for S. cervisiae (eg using S9.6 antiserum DRIP-seq, or RNaseH1 ChIP-seq).

      Minor comments.

      1. Fig.S7. Can the authors comments on the poor reproducibility between SNS-seq replicates? Though there isa four-fold increase in concordance between iSNS experments compared with tSNS, 80% of potetial origins are missed; can this be explained? In this regard, is the statment 'Both of the iSNS-seq samples showed signal enrichment around most of the confirmed origins' correct: while Fig.3C gives this impression, Fig.3B appears to diagree.
      2. Given the very nice data in Figs.1 and 2 showing the confounding effect of G4s on tSNS, can the authors comment on why G4s show little overlap with either SNS-seq mapping in Fig.4, and why they saw no increase in tSNS-seq or iSNS-seq signal over G4 motifs in Supplementary Figure S9B? Might this indicate that the in vitro work does not translate well to in vivo mapping?

      Referees cross-commenting

      From my reading of the three reviews, there are three overlapping issues that are brought up and should be addressed:

      1. A requirement to validate the interesting suggestion that RNA:DNA hybrids are a cause of tSNS-seq miscalling of origins; this should be a combination of in vitro tests and colocalisation of tSNS-seq signal and RNA:DNA signal in vivo using available datasets (eg DRIP-seq).
      2. An explanation and/or comment on the low reproducibility and low signal-to-noise ratio of iSNS-seq; specifically, although it improves on tSNS, does it provide accurate origin prediction?
      3. Comment on the lack of in vivo evidence, from available mapping data, for the confounding effect of G4s seen in the in vitro experiments.

      Significance

      This is a meticulously performed study with only some relatively minor issues needing resolved before publication, merely requiring new analysis and not new experiments. The work provides an important clarification on the shortcomings of a widely used strategy to map sites of DNA replication and, moreover, provides an updated and improved approach that should be widely adopted. This work will be of interest to researchers in the broad and fundamental field of DNA replication.

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      Referee #1

      Evidence, reproducibility and clarity

      Previous studies have shown that different origin mapping techniques could produce diverging results, introducing debate over whether metazoan origins are discrete or dispersed. Methods like SNS-seq and INI-seq suggested discrete initiation sites, whereas OK-seq, bubble-seq, and EdU/BrdU incorporated origin mapping suggested broad initiation zones with more dispersed initiation sites. Attempts to reconcile the diverged results presuppose that the discrete signals detected by SNS-seq represent bona fide replication intermediates. In this study, the authors set out to investigate the sensitivity and precision of traditional SNS-seq method, using budding yeast as the model organism, given that its replication origins are well known and defined with confidence. They addressed major known issues of lambda-exonuclease digestion in the traditional SNS-seq protocol, such as decreased efficiency of digesting through G4 DNA structure, and limited protection of RNA-primed DNA sequence, and showed that traditional SNS-seq failed to detect known origins. In addition, the authors test improved conditions (iSNS) suggested by the in vitro experiments and demonstrated an enhanced detection rate of known origins in yeast. Finally, the data show that the false positive signal derived from tSNS-Seq are enriched for tRNAs, snRNAs and highly transcribed regions forming RNA:DNA hybrids that overall likely represent regions with secondary structure formation potential that are protected from traditional lambda exonuclease digestion conditions.<br /> Overall, the study is timely and the data presented are convincing and should be published given that traditional SNS-Seq has been used extensively in the past to map the origin landscape of multiple organisms. Therefore, it will be important to reconcile these results in the light of the findings of this paper.

      Major comments:

      • Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

      The claims and the conclusion are supported by the data. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

      There are no additional experiments requested. - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".

      There are a few further reaching suggestions listed in the minor comments section. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments. - Are the data and the methods presented in such a way that they can be reproduced?

      Yes. They are. - Are the experiments adequately replicated and statistical analysis adequate?

      Yes. They are.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      Additional in vitro experiment using artificial R-loop structures as substrate for lambda-exonuclease could prove the efficacy of the nuclease to digest through R-loop structures. - Are prior studies referenced appropriately?

      Yes. - Are the text and figures clear and accurate?

      Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      Yes. They are the followings:

      In Fig. 1C, there seems to be a minor increase of lambda-exo activity on R10D30 substrate in the Tris-HCI buffer compared to Glycine-KOH buffer, maybe a quantification using the loading control (D20) would help. In Fig. 2D, lower panel, the digestion time of RecJf at different units were not indicated. In Fig. 3B, since SNS-seq detect fired origins, an overlap of iSNS rep 1 and iSNS rep 2 peaks with confirmed origins from OriDB that are also early and efficient origins may yield a higher percentage of overlap. In Fig. 3D, in addition to showing the tSNS and iSNS data on ChrIV with indicated known origins, showing at the same time the corresponding FORK-seq, OK-seq, and ORC ChIP signal will give a more comprehensive view of the performance of different origin mapping methods. In Fig. 5F, in supporting the model, a comparison of tSNS-seq peak with known budding yeast DRIP-seq datasets would help to see if the tSNS method indeed enrich for RNA:DNA hybrid signals.

      Significance

      This study investigates the sensitivity and precision of traditional SNS-seq methods, questioning the interpretation of existing SNS-seq datasets, which help to reconcile discrepancies among different origin mapping datasets. It also provides a first improved SNS-seq methodology that is tested against bona fide replication origins identified in budding yeast, allowing subsequent improvement of the SNS-seq methodology.

      The following aspects are important:

      • General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? The strengths in this study are the followings: It provided the first, and much improved SNS-seq dataset in budding yeast. It provided the first-ever assessment of traditional SNS-seq method using bona fide replication origins identified in budding yeast as quality control check, while at the same time established an improved SNS-seq method, again verified by quality control check using budding yeast origins. It demonstrated that traditional SNS-seq dataset may enrich a much lower percentage of replication origins, while having more false positive origins which are potential RNA:DNA hybrids. The aspect that could be improved or developed: More secondary DNA structures could be tested for lambda-exonuclease activity on them.
      • Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...). The study provides important insight on how to interpret the existing SNS-seq datasets and reconcile discrepancies among them. It provides immediate technical advance in SNS-seq sample preparation which could translate further to conceptual and mechanistic advance.
      • Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field? The audience will be basic researchers who work on DNA replication, particularly origin mapping.
      • Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Replication origin regulation, RNA:DNA hybrids and Transcription-Replication Conflicts. I feel confident in evaluating all aspects of the paper.
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      Reply to the reviewers

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Reviewer 2:

      1. Figure 7 - Panels A, B, H, F - please annotate the segmentations. Panel F it is not easy to identify the actin filaments within the microvilli, perhaps add some transparency to aid in visualization. Panel G - the intermediate filaments could be marked better. Panel J - the STA of the intermediate filament is not very convincing, the hollow tube could be a result of misalignment, reflected by the resolution, perhaps remove this. We plan to add annotations to the segmentation, we will also include additional references to intermediate filament structure to show the hollow tube structure consistent with our average,

      Figure S1 - The micrograph in panel A (bottom right) should have its contrast improved, at the current state it is very difficult to observe the details in the image.

      We will replace the tomogram with an image with improved contrast.

      Figure S2 - very nice gallery, perhaps some annotations or pointing to key features could aid in navigating the images.

      We plan to add annotations to the figure labeling doublet microtubules, filaments, and other features decribed in the main text.

      Figure S3 - In panels B and C it's not clear if the A-C linker fits the STA map density. Could you either show a fit into the model, generate an AlphaFold prediction (although I expect this would be quite challenging) or remove altogether since it's very hard to comment on whether this is the correct fit. Panel D - I'm not very convinced by the docked structure, since the resolution just shows a globular density. I would suggest removing this.

      We will remove the model from figure S3 and in panel D we will provide a different view of the docked density that better illustrates the fit.

      Reviewer 3:

      1. The transition-zone MIP helix is left molecularly unassigned, and the DNAJB6 narrative should not blur that. The MIP helix is one of the headline structural features, yet its identity is unresolved. The manuscript notes the density resembles SPACA9, then shows by U-ExM that SPACA9 is not enriched at the transition zone, then pivots to DNAJB6. This is handled carefully in the text (DNAJB6 is called a "transition-zone-associated microtubule-binding protein," not the MIP helix), but the section structure invites readers to equate the two. Two points need to be explicit: (a) U-ExM cannot place a protein inside versus outside the tubule, so DNAJB6 cannot be called a MIP on this basis, and the MIP helix identity remains unknown; (b) an HSP40 co-chaperone forming an ordered lumenal helix would be structurally unprecedented and should not be implied without direct density-level evidence. Separately, the resemblance-to-SPACA9-but-absence-of-SPACA9 tension is interesting and worth addressing directly: is a SPACA9/lamin-tail family paralog a candidate for the helix? Recommendation: state plainly that the MIP helix is unidentified, and reframe DNAJB6 as a transition-zone MAP candidate of undetermined topology. (Text revision, no new experiments essential.) We plan to modify the text to make it explicit that DNAJB6 is not the MIP helix.

      Calling MLF1 a MIP is not supported. MIP status rests on a single TUBA cross-link mapped to a lumenal position plus U-ExM, with no corresponding density (the authors themselves note STA fails for sub-stoichiometric binders). One cross-link and expansion microscopy cannot establish lumenal localization. The consistent signal is central-apparatus association. Recommendation: describe MLF1 as an axonemal/central-apparatus-associated microtubule-binding protein and drop or heavily qualify "MIP" unless additional evidence (e.g., a focused average showing lumenal density) can be provided.

      We will adjust the text and rather than referring to the MLF1 as a MIP, we will call it microtubule associated. We will also mention that the position of the XL to tubulin suggest that it binds the interior of the microtubules, but is not definitive.

      Cross-species integration needs explicit justification and caveats. The integrative logic combines a mouse density, a bovine cross-link, and a human localization to make single-protein identity claims. Core ciliary machinery is well conserved and the practical reasons for each choice are defensible (culture, biochemical yield, MucilAir availability), but this is currently unaddressed. Recommendation: add a short paragraph justifying cross-species integration, and flag any identification where conservation cannot be assumed. This is important precisely because the paper's value is the integration.

      The reviewer bring up a good point, we will add a paragraph expanding on the cross-species integration.

      Ciliary necklace: stoichiometry reasoning and coupling language. The claim of "approximately 6 necklace particles per doublet" does not follow from the 17 nm spacing alone without the circumference and doublet count, and it sits awkwardly with the measured 7 {plus minus} 0.68 rows (the "6" appears to derive from the older freeze-fracture rat value rather than from this dataset). Please show the geometric derivation and reconcile the 6-versus-7 numbers. Separately, Y-links are not resolved, so "strongly suggests functionally coupled" and "may tether the transition zone to the ciliary membrane" overstate what the mapback supports. Recommendation: soften to spatial coordination/alignment and present tethering as a model.

      We will soften the language and include additional references on the possibility of tethering to the membrane.

      For a resource paper, the XL/MS FDR and consensus definition must be specified. Two engines were used at different thresholds (Scout at 1% CSM FDR, xiSearch at 5%), and "consensus" is undefined (union or intersection). Since the deposited cross-link list is a primary deliverable, please define the consensus operation, report the effective FDR of the final set, and give the inter- versus intra-protein breakdown and decoy statistics.

      We will add all of this information into the methods.

      The EMDB accession is a placeholder ("XXXXX"); this must be resolved before the interactome and averages can be independently assessed.

      We will complete the depositions of the EMDB structures.

      Reference 38 (McCafferty et al., Cell) is "In Press" without volume; update at proofs.

      We will fix this.

      Please report STA particle numbers for the actin and intermediate-filament averages in the main text, as done for the necklace and IFT.

      We will include the numbers for each of these averages.

      The low GT335 signal at the transition zone is consistent with Chlamydomonas and nicely used; a one-line note on whether this reflects genuinely reduced glutamylation versus epitope masking would help.

      We plan to add a comment on this.

      IFT: tighten "anterograde" and the undocked-centriole interpretation, and report template-matching controls. At 38 Å the trains are identified by overall morphology, yet they are called anterograde "based on their extended morphology"; the morphological criterion should be stated explicitly, since polarity is not resolved at this resolution. The undocked-centriole-plus-vesicle observation (n = 2) is a genuinely nice rare-event capture but should stay framed as anecdotal and hypothesis-generating (the "primed for immediate transport" language is a reasonable interpretation, not a demonstration). Because template matching was initialized with the Chlamydomonas IFT-B reference before re-templating with the native average, please report the false-positive controls (e.g., matching against decoy/rotated references) so readers can judge that the trains are not reference artifacts.

      We ran template matching in pytom-match-pick using the phase-randomization option, which compensates for the effect of spurious correlations in the score maps. Furthermore, our IFT average from MTEC cells contains densities not present in the Chlamydomonas IFT-B average used as a template, which is strong indication that what we see is not reference bias. In Figure 6J-K, we provide an image of the bound anterograde train on the undocked centriole from the tomogram (direct visualization, not averaged), as well as the strong scoring template-matching peaks for the anterograde IFT-B template. This is often used in current cryo-ET papers as evidence of specific protein complex localization (see the NPC and microtubule work of the Beck & Turoňová groups: https://doi.org/10.1038/s41467-024-47839-8 ; https://doi.org/10.1016/j.cell.2024.12.008 ; https://doi.org/10.1016/j.cell.2025.07.025). Furthermore, it is important to clarify that the structures of antrograde and retrograde trains are dramatically different (see the work of the the Pigino group: https://doi.org/10.1016/j.cell.2024.06.041), and unmistakable even at our modest resolution. While believe that the data presented in Fig. 6 already passes the “eye test”, in the revision, we will perform the control experiment of comparing the template matching peaks for an anterograde IFT template vs. a retrograde IFT template.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      Reviewer 1:

      1. In the intro: "Defects in cilium structure and function in MCCs impair fluid movement and cause ciliopathies including primary ciliary dyskinesia15, chronic obstructive pulmonary disease16, and hydrocephalus." The sentence could be improved, by stating first that primary ciliary dyskinesia is a ciliopathy causing impaired MCCs/fluid flows, then stating how it impacts the diseases. PCD is certainly very much distinct in cause and associated genes from COPD (genetic vs. chronic/environmental). We thank the reviewer for this comment and have now changed the text to the following: “Defects in cilium structure and function in MCCs impair fluid movement resulting in chronic obstructive pulmonary disease15 and hydrocephalus16, while genetic perturbations in MCCs can cause ciliopathies such as primary ciliary dyskinesia17.”

      In figures 1 and 1 (and associated supplements), I would point towards the structures and name them (C tubules, but also MIPs/MAPs), experts understand, but non-experts likely will have trouble.

      Thank you for pointing this out, we now include labels for the A, B, C tubules.

      In S4B, C, the same images are shown as in Fig2J. Perhaps showing another example in the supplements would be good?

      We have removed the redundant panel S4C from the supplement.

      "To further test NME7 localization, we stained for the protein in hTERT RPE-1 cells using U-ExM and found that it localizes to the centriole exclusively, confirming this protein as both a motile and primary cilia centriolar MIP (Figure S5)." Is this expected that NME7 is in the axoneme of motile cilia only? Mind to comment on that?

      We thank the reviewer for inquiring on this observation, and we now include a brief comment on NME7 localization:

      “Previous work localizes NME7 to the gTuRC ring complex47 and suggest its significance in primary cilia signaling48. To further test NME7 localization, we stained for the protein in hTERT RPE-1 cells using U-ExM and found that it localizes to the centriole exclusively, confirming this protein as both a motile and primary cilia centriolar MIP (Figure S5).”

      "Our in situ MCC interactome was generated by adding a membrane permeable, MS cleavable cross-linker to detached cilia and basal bodies." This is now emphasized the second time in the main text. Seems like it would be good to mention its name in the main text.

      Thank you for the comment, we now explicitly state that we used DSSO in the main text.

      "Within the interactome, we identified 46 proteins cross-linked to tubulin, including several not previously annotated as tubulin-binding proteins (Figure 3D)." Confusing! Are all the proteins shown in 3D claimed to be unannotated? There are some proteins that I know from cilia studies, so I am a bit confused. Perhaps clarifying would be helpful for the reader.

      Thank you for the comment, and sorry for the confusion. We are not claiming that all of the proteins shown in 3D are unannotated. Seven of the proteins had not previously been described as tubulin binding. In the figure, none of the proteins are claimed as unannotated; some are not just not described in the current structure.

      "The previous localization of this protein to primary cilia prompted us to stain for the protein in hTERT RPE-1 cells by U-ExM, however we observed no clear localization in the primary cilia of these cells (Figure S5)." Please cite the study that has detected it in primary cilia for the reader to compare. Also, there is some signal in both basal body and daughter centriole. So that should be at least mentioned. Rp1 and SPACA9 are also claimed to not be present in primary cilia, but similar signals from BB and daughter centriole are observed. They are more similar to the situation with DNAJB6. So, I would mention that and be careful with the level of "definitiveness" conveyed by the statements.

      We now cite the study that localized MLF1 to primary cilia in this sentence of the text and now refer to the staining as inconclusive rather than stating the absence of the protein:

      “The previous localization of this protein to primary cilia53 prompted us to stain for the protein in hTERT RPE-1 cells by U-ExM, however our labeling was inconclusive with no clear localization in the primary cilia of these cells (Figure S5).”

      The only experimental/data request from me: "Although the resolution of the average was insufficient for unambiguous molecular identification, prior work has localized proteins such as CEP29064, TMEM6765, and TCTN265 to this region. Our U-ExM data clearly shows TCTN2 localized at the transition zone in a punctate pattern, however, the resolution of the immunostaining does not allow for reliable measurement of center-to-center spacing between individual particles (Figure 5J)." This is very interesting, it would be nice to see Cep290 and TMEM67 U-ExM images for comparison. Do they all show this type of localization indicating a common complex?

      Thank you for the suggestion. For the revision, we attempted U-ExM of both Cep290 and TMEM67. Cep290 clearly localized to the transition zone region, and we now include this as part of Figure 5. Unfortunately, the TMEM67 antibody did not provide any conclusive localization to include.

      "Despite not being able to average the Ylinks, mapping of our ciliary necklace particles and transition zone particles onto the tomograms demonstrated a spatial coordination between the necklace and the transition zone microtubules (Figure 5K)." Not sure what coordination means in this context. I fail to detect clear alignment in the image provided. Can this be made more clear?

      Thank you for pointing out the ambiguity in the statement. We now clarify with the following:

      “Despite not being able to average the Y-links, mapping of our ciliary necklace particles and transition zone particles onto the tomograms demonstrated a spatial coordination between the necklace and the transition zone along the length of the microtubules”

      "Ezrin sits at the base of the ciliary membrane in MCCs, anchoring filamentous actin to the membrane68. This suggests a physical link between the ciliary membrane and IFT assembly machinery." Could this be also interpreted as link between IFT and Actin structures? Also, Ezrin/Actin interactions were also implicated in basal body docking/rootlet tilting and the microvillus structures were shown to resemble microridges. Perhaps these studies would be worth mentioning to contextualize the authors findings.

      While Ezrin links to both IFT and actin, the XL/MS is not temporally resolved, so we cannot say definitively whether IFT could link to actin. Our data does not show a direct link between the two, so we have refrained from commenting on this. It is a good point that ezrin/actin interactions were implicated in basal body docking. We now include a reference to this:

      “In addition to our observations of IFT at the ciliary base, cryo-ET imaging captured several instances of undocked centrioles below the cell’s apical surface– two of which were capped on their distal end by a ciliary vesicle71,72 (Figure 6F-H). In both cases, IFT trains were attached to the distal end of the centriole, beneath the ciliary vesicle. This observation suggests that IFT trains are recruited to the microtubules prior to vesicular docking with the plasma membrane and ciliogenesis. Close-up views of these vesicle-associated trains, template matching, and STA reveal features that are consistent with anterograde IFT trains (Figure 6J-L, S14). This provides in situevidence for IFT trains assembling on undocked centrioles, suggesting they are primed for immediate transport initiation upon ciliogenesis. The ciliary vesicles contained smaller internal vesicles full of dense granular material and were decorated with arrays of membrane densities, some of which are consistent with ciliary necklace proteins (Figure 6I). Considering again the link between Ezrin and IFT88, Ezrin was previously demonstrated to be a core component of microridge-like structures that are essential for basal body rootlet anchoring in MCCs, suggesting the presence of both of the proteins during ciliogenesis73.”

      Reviewer 2:

      1. Please repeat the acronyms for MIP and MAP in the text (page 4). Thank you, we have added this.

      "...we identified a preserved A-tubule MIP..." I'm not sure if preserved is the best word to use here.

      We have now changed this to “conserved”.

      Figure 1 - The figure is beautiful and very informative, however the arrows in panels B, C and E are not informative or do not point to the relevant feature shown. I would suggest improving the annotations in these panels to aid viewers who are not experts in EM. Also, in panel C other views or slices of the tomogram could present the actin repeats in a better way than currently shown. The ciliary membrane should also be present in the legend of panel F.

      Thank you for this comment, we have now modified the figure to make the annotations clearer.

      Figure 2 - Panel A has a black rectangle parallel to the hook region without any reference in the text or legend. In panel B could you comment in the text on the two microtubules at the center of the early axoneme.

      We have added an arrow to the figure to show that the box corresponds to the ring in panel F.

      Figure 4 - Panels A-D could you add labels for the different densities mentioned in the legend. Perhaps switch panels B and C since C is mentioned first in the text.

      Thank you for this comment, we have added labels to the figure that match the text.

      Figure 6 - In panels B-D please specify what is the difference between the black and grey cross-links (if any). Panels F-H could be improved with clearer annotations, for example the centrioles and clear indication of IFTs localization. Panel K - where is the corresponding tomogram slice? Do all the cross-correlation peaks match with IFT densities?

      We now clarify in the caption that the grey crosslinks are intramolecular and the black are intermolecular. We have added centriole labels and additional arrows to IFTs and ciliary necklace.

      Figure S13 is slightly pixelated.

      Thank you, we have fixed this figure.

      The sentence "Each tomogram was acquired from a separate cell and therefore can be considered a biological replicate" is inaccurate and should be removed. Instead, please state how many different cell cultures were used, and each of these can be considered a biological replicate.

      We have now removed this sentence from the text.

      Reviewer 3:

      Docking an AlphaFold NME7 model into a 16 Å density is consistent-with, not a fit; the NME7 assignment is otherwise well supported (position, U-ExM pattern, prior literature), but the docking wording should be softened accordingly.

      We have changed the wording from “docking” to “fitting”.

      Declaration of Interests reads "The other authors declare no competing interests," implying a missing statement for one or more authors. Please correct.

      Thank you, we have fixed this.

      Abstract lists "novel MIPs" among the advances; given points 1 and 2, consider "microtubule-associated proteins" unless lumenal status is established.

      We have changed the wording to “microtubule-associated proteins”.

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      1. "Gene ontology (GO) analysis of these fractions confirmed selective recovery of the intended compartments (Figure 3B), providing the first XL/MS-based interactome of mammalian motile cilia, including the basal body, transition zone, cytoskeletal, and membrane-associated protein networks." The enrichment is not that strong or maybe I have trouble to interpret the graph. Overall this secondary go-enrichment is not a particularly sound method to make that case. We thank the reviewer for this comment. We are just showing that there are more basal body proteins present in the enriched fractions than in the nonenriched fractions.

      2. Could you provide more information on the STA of the various microtubule assemblies and necklace complex? We feel that our methods section is quite complete and describes the processing workflow, we do that have much more that we could add on top of this.

      Have you attempted to change the resolution range for initial CTF estimation (say 50-10 Å) and check whether these are able to improve the STA resolution?

      Thank you for the comment. We did try different resolution search ranges in CTFFIND-4, and it did not consistently improve the CTF fits because the program was not designed to handle tilted data. We also would like to emphasize that this dataset was recorded on a K2 camera with a relatively coarse pixel size (3.52 A/pix), hampering more accurate defocus estimation, especially at the higher tilt angles. Ultimately, only reference-based CTF refinement in RELION-4 did the trick, as described in the methods.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors combine in situ cryo-ET of FIB-milled mouse tracheal epithelial cells with in situ XL/MS on isolated bovine trachea and U-ExM on human airway tissue to build a molecular map of the multiciliated cell ciliary base. They define four microtubule subregions from proximal centriole to early axoneme, assign NME7 to an A-tubule MIP present in basal body and axoneme but absent from the transition zone, describe a transition-zone-specific A-B linker and lumenal MIP helix, quantify the ciliary necklace (about 7 rows, 17.5 nm spacing), and capture IFT trains at the transition zone and, in rare cases, on undocked centrioles beneath ciliary vesicles. An XL/MS interactome of >10,500 cross-links across 1,661 proteins is presented as a community resource, alongside new MIP/MAP candidates (MLF1, DNAJB6, SPAG6) and an actin/intermediate-filament scaffold around the base.

      Major comments

      1. The transition-zone MIP helix is left molecularly unassigned, and the DNAJB6 narrative should not blur that. The MIP helix is one of the headline structural features, yet its identity is unresolved. The manuscript notes the density resembles SPACA9, then shows by U-ExM that SPACA9 is not enriched at the transition zone, then pivots to DNAJB6. This is handled carefully in the text (DNAJB6 is called a "transition-zone-associated microtubule-binding protein," not the MIP helix), but the section structure invites readers to equate the two. Two points need to be explicit: (a) U-ExM cannot place a protein inside versus outside the tubule, so DNAJB6 cannot be called a MIP on this basis, and the MIP helix identity remains unknown; (b) an HSP40 co-chaperone forming an ordered lumenal helix would be structurally unprecedented and should not be implied without direct density-level evidence. Separately, the resemblance-to-SPACA9-but-absence-of-SPACA9 tension is interesting and worth addressing directly: is a SPACA9/lamin-tail family paralog a candidate for the helix? Recommendation: state plainly that the MIP helix is unidentified, and reframe DNAJB6 as a transition-zone MAP candidate of undetermined topology. (Text revision, no new experiments essential.)
      2. Calling MLF1 a MIP is not supported. MIP status rests on a single TUBA cross-link mapped to a lumenal position plus U-ExM, with no corresponding density (the authors themselves note STA fails for sub-stoichiometric binders). One cross-link and expansion microscopy cannot establish lumenal localization. The consistent signal is central-apparatus association. Recommendation: describe MLF1 as an axonemal/central-apparatus-associated microtubule-binding protein and drop or heavily qualify "MIP" unless additional evidence (e.g., a focused average showing lumenal density) can be provided.
      3. Cross-species integration needs explicit justification and caveats. The integrative logic combines a mouse density, a bovine cross-link, and a human localization to make single-protein identity claims. Core ciliary machinery is well conserved and the practical reasons for each choice are defensible (culture, biochemical yield, MucilAir availability), but this is currently unaddressed. Recommendation: add a short paragraph justifying cross-species integration, and flag any identification where conservation cannot be assumed. This is important precisely because the paper's value is the integration.
      4. IFT: tighten "anterograde" and the undocked-centriole interpretation, and report template-matching controls. At 38 Å the trains are identified by overall morphology, yet they are called anterograde "based on their extended morphology"; the morphological criterion should be stated explicitly, since polarity is not resolved at this resolution. The undocked-centriole-plus-vesicle observation (n = 2) is a genuinely nice rare-event capture but should stay framed as anecdotal and hypothesis-generating (the "primed for immediate transport" language is a reasonable interpretation, not a demonstration). Because template matching was initialized with the Chlamydomonas IFT-B reference before re-templating with the native average, please report the false-positive controls (e.g., matching against decoy/rotated references) so readers can judge that the trains are not reference artifacts.
      5. Ciliary necklace: stoichiometry reasoning and coupling language. The claim of "approximately 6 necklace particles per doublet" does not follow from the 17 nm spacing alone without the circumference and doublet count, and it sits awkwardly with the measured 7 {plus minus} 0.68 rows (the "6" appears to derive from the older freeze-fracture rat value rather than from this dataset). Please show the geometric derivation and reconcile the 6-versus-7 numbers. Separately, Y-links are not resolved, so "strongly suggests functionally coupled" and "may tether the transition zone to the ciliary membrane" overstate what the mapback supports. Recommendation: soften to spatial coordination/alignment and present tethering as a model.
      6. For a resource paper, the XL/MS FDR and consensus definition must be specified. Two engines were used at different thresholds (Scout at 1% CSM FDR, xiSearch at 5%), and "consensus" is undefined (union or intersection). Since the deposited cross-link list is a primary deliverable, please define the consensus operation, report the effective FDR of the final set, and give the inter- versus intra-protein breakdown and decoy statistics.

      Minor comments

      The EMDB accession is a placeholder ("XXXXX"); this must be resolved before the interactome and averages can be independently assessed. Docking an AlphaFold NME7 model into a 16 Å density is consistent-with, not a fit; the NME7 assignment is otherwise well supported (position, U-ExM pattern, prior literature), but the docking wording should be softened accordingly. Declaration of Interests reads "The other authors declare no competing interests," implying a missing statement for one or more authors. Please correct. Reference 38 (McCafferty et al., Cell) is "In Press" without volume; update at proofs. Please report STA particle numbers for the actin and intermediate-filament averages in the main text, as done for the necklace and IFT. The low GT335 signal at the transition zone is consistent with Chlamydomonas and nicely used; a one-line note on whether this reflects genuinely reduced glutamylation versus epitope masking would help. Abstract lists "novel MIPs" among the advances; given points 1 and 2, consider "microtubule-associated proteins" unless lumenal status is established.

      Significance

      General assessment: The strength is a native, three-method map of the mammalian MCC ciliary base, with a large and publicly deposited XL/MS interactome and several rarely captured events (IFT on undocked centrioles, necklace on ciliary vesicles). The main limitation is that molecular identity and functional/coupling claims are in several places stronger than the modest STA resolutions and the U-ExM/XL/MS evidence can bear; most are fixable by recalibrating claims rather than by new experiments.

      Advance: Primarily technical and descriptive, extending in situ ciliary-base work from Chlamydomonas (van den Hoek 2022) and mouse ependyma (Ma 2025) to mammalian airway MCCs and adding an orthogonal interactome layer. This is a real advance in scope and as a resource. Conceptual contributions (a proximal-axoneme proteostasis module, necklace-transition-zone coupling) are hypothesis-generating.

      Audience: Specialized (cilia and centriole biology, cryo-ET, integrative structural cell biology), with the deposited datasets useful more broadly, and some relevance to primary ciliary dyskinesia and other ciliopathies.

      Expertise: structural biology of intraflagellar transport and ciliary complexes, cryo-EM/cryo-ET, X-ray crystallography, AlphaFold-based prediction and integrative modeling. I can fully evaluate the structural, IFT, and integrative-modeling content. I am less able to independently assess the fine details of the XL/MS search-engine statistics and the U-ExM immunostaining pipeline.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This work presents an integrative structural analysis of the mammalian multiciliated cell (MCC) ciliary base by combining cryo-ET, in situ XL/MS, and U-ExM. The authors produced a comprehensive molecular atlas, providing new insights into the organization of the basal body, transition zone, ciliary membrane, and surrounding filamentous proteins. The work identifies previously unrecognized microtubule-associated proteins, characterizes the molecular architecture of the ciliary necklace and transition zone, and provides evidence that intraflagellar transport (IFT) trains assemble both at mature cilia and on undocked centrioles prior to ciliogenesis. The study also reveals how actin and intermediate filament networks contribute to mechanical support of the ciliary base. Overall, the methodology applied here is very thorough and technically sound, and the conclusions made by the authors are justified by the experimental data.

      Minor comments:

      Please repeat the acronyms for MIP and MAP in the text (page 4).

      "...we identified a preserved A-tubule MIP..." I'm not sure if preserved is the best word to use here.

      Figure 1 - The figure is beautiful and very informative, however the arrows in panels B, C and E are not informative or do not point to the relevant feature shown. I would suggest improving the annotations in these panels to aid viewers who are not experts in EM. Also, in panel C other views or slices of the tomogram could present the actin repeats in a better way than currently shown. The ciliary membrane should also be present in the legend of panel F.

      Figure 2 - Panel A has a black rectangle parallel to the hook region without any reference in the text or legend. In panel B could you comment in the text on the two microtubules at the center of the early axoneme.

      Figure 4 - Panels A-D could you add labels for the different densities mentioned in the legend. Perhaps switch panels B and C since C is mentioned first in the text.

      Figure 6 - In panels B-D please specify what is the difference between the black and grey cross-links (if any). Panels F-H could be improved with clearer annotations, for example the centrioles and clear indication of IFTs localization. Panel K - where is the corresponding tomogram slice? Do all the cross-correlation peaks match with IFT densities?

      Figure 7 - Panels A, B, H, F - please annotate the segmentations. Panel F it is not easy to identify the actin filaments within the microvilli, perhaps add some transparency to aid in visualization. Panel G - the intermediate filaments could be marked better. Panel J - the STA of the intermediate filament is not very convincing, the hollow tube could be a result of misalignment, reflected by the resolution, perhaps remove this.

      Figure S1 - The micrograph in panel A (bottom right) should have its contrast improved, at the current state it is very difficult to observe the details in the image.

      Figure S2 - very nice gallery, perhaps some annotations or pointing to key features could aid in navigating the images.

      Figure S3 - In panels B and C it's not clear if the A-C linker fits the STA map density. Could you either show a fit into the model, generate an AlphaFold prediction (although I expect this would be quite challenging) or remove altogether since it's very hard to comment on whether this is the correct fit. Panel D - I'm not very convinced by the docked structure, since the resolution just shows a globular density. I would suggest removing this.

      Figure S13 is slightly pixelated.

      The sentence "Each tomogram was acquired from a separate cell and therefore can be considered a biological replicate" is inaccurate and should be removed. Instead, please state how many different cell cultures were used, and each of these can be considered a biological replicate.

      Could you provide more information on the STA of the various microtubule assemblies and necklace complex?

      Have you attempted to change the resolution range for initial CTF estimation (say 50-10 Å) and check whether these are able to improve the STA resolution?

      Significance

      The authors used standard data collection and processing methodologies acceptable in the field. The cryo-ET data collection was performed in a way that was optimized for visualization but not necessarily for STA, nevertheless the tomograms are beautiful, and the STA maps correspond to their reported resolutions.

      As my comments imply my expertise is mainly on the cryo-FIB, cryo-ET and STA parts of the manuscript, and so cannot offer useful comments on the biological significance of the findings themselves.

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      Referee #1

      Evidence, reproducibility and clarity

      This is a nice paper describing new details of multiciliated cell basal bodies and transition zones by an innovative combination of SEM-FIB structural data, proteomics of tubulin interactors and expansion light microscopy for validation of target identities and localizations. It sheds light onto new proteins associated with cilia, their differential use in motile vs. primary cilia and overall MCC cilia architecture. This will be very useful for the airway and cilia communities. The data are mostly clearly presented and the reconstruction and IF images are beautiful. Mostly, the article is well written and does not unnecessarily overstate findings while highlighting potential impact.

      I have no major criticism, mostly some requests for improving clarity and revisiting some statements.

      • In the intro: "Defects in cilium structure and function in MCCs impair fluid movement and cause ciliopathies including primary ciliary dyskinesia15, chronic obstructive pulmonary disease16, and hydrocephalus." The sentence could be improved, by stating first that primary ciliary dyskinesia is a ciliopathy causing impaired MCCs/fluid flows, then stating how it impacts the diseases. PCD is certainly very much distinct in cause and associated genes from COPD (genetic vs. chronic/environmental).
      • In figures 1 and 1 (and associated supplements), I would point towards the structures and name them (C tubules, but also MIPs/MAPs), experts understand, but non-experts likely will have trouble.
      • In S4B, C, the same images are shown as in Fig2J. Perhaps showing another example in the supplements would be good?
      • "To further test NME7 localization, we stained for the protein in hTERT RPE-1 cells using U-ExM and found that it localizes to the centriole exclusively, confirming this protein as both a motile and primary cilia centriolar MIP (Figure S5)." Is this expected that NME7 is in the axoneme of motile cilia only? Mind to comment on that?
      • "Our in situ MCC interactome was generated by adding a membrane permeable, MS cleavable cross-linker to detached cilia and basal bodies." This is now emphasized the second time in the main text. Seems like it would be good to mention its name in the main text.
      • "Gene ontology (GO) analysis of these fractions confirmed selective recovery of the intended compartments (Figure 3B), providing the first XL/MS-based interactome of mammalian motile cilia, including the basal body, transition zone, cytoskeletal, and membrane-associated protein networks." The enrichment is not that strong or maybe I have trouble to interpret the graph. Overall this secondary go-enrichment is not a particularly sound method to make that case.
      • "Within the interactome, we identified 46 proteins cross-linked to tubulin, including several not previously annotated as tubulin-binding proteins (Figure 3D)." Confusing! Are all the proteins shown in 3D claimed to be unannotated? There are some proteins that I know from cilia studies, so I am a bit confused. Perhaps clarifying would be helpful for the reader.
      • "The previous localization of this protein to primary cilia prompted us to stain for the protein in hTERT RPE-1 cells by U-ExM, however we observed no clear localization in the primary cilia of these cells (Figure S5)." Please cite the study that has detected it in primary cilia for the reader to compare. Also, there is some signal in both basal body and daughter centriole. So that should be at least mentioned. Rp1 and SPACA9 are also claimed to not be present in primary cilia, but similar signals from BB and daughter centriole are observed. They are more similar to the situation with DNAJB6. So, I would mention that and be careful with the level of "definitiveness" conveyed by the statements.
      • The only experimental/data request from me: "Although the resolution of the average was insufficient for unambiguous molecular identification, prior work has localized proteins such as CEP29064, TMEM6765, and TCTN265 to this region. Our U-ExM data clearly shows TCTN2 localized at the transition zone in a punctate pattern, however, the resolution of the immunostaining does not allow for reliable measurement of center-to-center spacing between individual particles (Figure 5J)." This is very interesting, it would be nice to see Cep290 and TMEM67 U-ExM images for comparison. Do they all show this type of localization indicating a common complex?
      • "Despite not being able to average the Ylinks, mapping of our ciliary necklace particles and transition zone particles onto the tomograms demonstrated a spatial coordination between the necklace and the transition zone microtubules (Figure 5K)." Not sure what coordination means in this context. I fail to detect clear alignment in the image provided. Can this be made more clear?
      • "Ezrin sits at the base of the ciliary membrane in MCCs, anchoring filamentous actin to the membrane68. This suggests a physical link between the ciliary membrane and IFT assembly machinery." Could this be also interpreted as link between IFT and Actin structures? Also, Ezrin/Actin interactions were also implicated in basal body docking/rootlet tilting and the microvillus structures were shown to resemble microridges. Perhaps these studies would be worth mentioning to contextualize the authors findings.

      Significance

      It sheds light onto new proteins associated with cilia, their differential use in motile vs. primary cilia and overall MCC cilia architecture. This will be very useful for the airway and cilia communities. The data are mostly clearly presented and the reconstruction and IF images are beautiful.

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      Referee #3

      Evidence, reproducibility and clarity

      Liver cancer is a deadly disease that consists of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). CCA can originate from hepatocytes. Interestingly, the NRF2 pathway is hyperactivated in 15% of HCC. To explore this, the authors developed a model of DOX-inducible hyperactivated NRF2T80K in hepatocytes of zebrafish. They suggest that hepatocyte to cholangiocyte transdifferentiation occurs, resulting in expansion of the cholangiocyte compartment. Importantly, they explore these phenotypes in larvae and adult zebrafish, clearly demonstrating that the impact of NRF2-driven cell plasticity is not limited to conditions of organogenesis. Using a pharmacologic inhibitor of BRG1/BRM, they also suggest that NRF2-driven cell plasticity is dependent on the SWI/SNF complex

      Major comments

      A major question regards the involvement of the SWI/SNF complex. The authors state, "NRF2 co-opts the SWI/SNF complex to drive liver cell plasticity". However, these conclusions are largely based on the phenotypes using the BRG1 inhibitor FHD-286. Currently, it is unclear the degree of on-target activity FHD-286 has on BRG1, and it would be important to potentially explore this. Further, did any other BRG1 inhibitors score from the screen, or could they explore BRG1 inhibition using distinct pharmacologic or genetic approaches? These experiments would strengthen the conclusions of the study.

      Significance

      This is a very interesting paper with broad implications. In future studies, it would be interesting to explore how NRF2 potentially impacts the SWI/SNF complex. It would also be interesting to explore which target genes downstream of NRF2 hyperactivation drive expansion of the cholangiocyte compartment. Finally, it would be interesting (in future studies) to explore metabolic changes caused by NRF2 hyperactivation in hepatocytes of zebrafish, and how these changes could cause transdifferentiation of hepatocytes into cholangiocytes

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In the present manuscript, Dr. Ong and colleagues investigated the role of NRF2 activation in promoting liver cell plasticity. To support their conclusions, the authors use models of constitutive NRF2 activation, including KEAP1 KO and expression of the mutated NRF2T80K in HepG2 cells. In addition, they developed a transgenic zebrafish model with hepatocyte-specific inducible expression of mutated NRF2T80K to investigate the effect of NRF2 activation in liver tumour initiation. They found out that NRF2T80K expression promotes expansion of cholangiocyte compartment that resulted from hepatocyte transdifferentiation and provide evidence suggesting the potential role of SWI/SNF chromatin remodeling complex in this process.

      Major comments

      1. The major concern is to rule out the possibility that the observed phenotypes are influenced by off-target effects of doxycycline treatment in zebrafish. Although some control experiments are presented, they were performed at different experimental settings, or the methodology is not sufficiently clear. Therefore, control conditions should be presented more clearly. In particular, the authors should include analyses of liver plasticity markers and NRF2 activity markers in wild-type control animals treated with doxycycline. This would help demonstrate that the observed effects are due to NRF2T80K activation rather than doxycycline treatment itself. In addition, the experimental design is unclear in several panels (e.g. It is not clear if the zebrafish images were taken at 10 dpf and whether that also means the time of NRF2T80K induction).
      2. It would be important to determine whether systemic activation of NRF2WT on KEAP1 KO background produces similar effects.
      3. The data shown with the model keap1a/keap1b crispants needs more detailed explanation.
      4. The authors should better explain the criteria to select the hit from the compound screen. Does the selected compound also have effect on KEAP1 KO or NRF2 OE cells?
      5. Please double check the indications of treatment durations (time points/days) throughout the main text, figure legends, and experimental schemes to ensure consistency. Including a schematic overview that clearly distinguishes the larval and adult experimental settings would improve clarity and help readers follow the experimental design.
      6. The authors conclude the results section with "Together, these data highlight a role for SWI/SNF-dependent chromatin remodelling complex in regulating liver cell plasticity in the context of NRF2 pathway activation." However, the data presented only suggests a possible role of SWI/SNF. The conclusion is based solely on pharmacological treatment, without direct functional validation of the specific components (e.g. through genetic perturbations). Therefore, the current evidence supports an association rather than a definitive role in regulation of liver cell plasticity.
      7. Main conclusions derived from the imaging and gene expression analysis should be further validated at the protein level. Confirming the observed changes in the protein expression would provide stronger support for the proposed biological mechanism.

      Minor comments

      1. Immunoblot of KEAP1 is missing to confirm KO in Fig1B and Immunoblot of NRF2 is missing to confirm OE in Fig1D.
      2. Review whether the data normalization appropriate matches the statistical test applied (In FigS1A, are the reference/control values random set to 1 or the references values used were the average of the controls?).
      3. Please provide a more detailed explanation of the data processing and curation used to generate the graphs on Fig4B.
      4. The legend indication of the panels on FigS4 is wrong. Please revise all figure legends and main text.

      Significance

      The conclusions are relevant for the field of basic research on cancer and cell biology. However, would be essential to clarify the points raised. The major limitation is that conclusions were made based on one model.

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      Referee #1

      Evidence, reproducibility and clarity

      While the study is technically ambitious, challenging in vivo zebrafish, scRNA-seq, and chemical library screen. On the other hand, the NRF2-SWI/SNF causal link is not well supported yet to fully back the claims. This reviewer's background is in redox signaling, so does not comment on the zebrafish technical methodology.

      Major comments

      1. No wild-type NRF2 control is studied. Everything is done only with NRF2T80K, but no WT NRF2 overexpression comparison. Therefore, this reviewer cannot tell if the phenotype is mutant-specific or just a general consequence of NRF2 pathway activation.
      2. FHD-286 does not establish an NRF2-SWI/SNF link. As BRG1/BRM are broadly acting, but not NRF2-specific, identification of FHD-286 does not prove SWI/SNF's action downstream of NRF2. In fact, Figs. S4C & D show that FHD-286 does not fully suppress NRF2 target genes even while blocking trans-differentiation. This disconnect suggests FHD-286 may act on a parallel or downstream pathway, not addressed in this version.
      3. Several other hits in the Fig. 4A screen show similar suppression to FHD-286. The authors need to explain the rationale for picking FHD-286 as the lead compound.
      4. There are no functional cancer assays. Only histology/fluorescence for trans-differentiation were conducted, but no clonogenic assay, anchorage-independent growth, organoids, nor transplant tumor-initiation studies. The link to actual tumorigenesis remains speculative.
      5. The Introduction says NRF2-driven tumorigenesis mechanisms are "poorly understood", but there are substantial existing literatures (e.g., PMID: 38308097, 37364049, 31961719).
      6. No mention about where NRF2T80K mutation was first identified, its prevalence across liver cancer subtypes, nor known functional consequences beyond disrupting KEAP1 binding. This point should be stated more in detail.
      7. In Fig 2A, text describes CCA-like glandular structures after DOX treatment, but they are not marked with arrows or outlines in the figure.

      Minor comments

      1. GSEA results would benefit from heatmaps showing the leading-edge genes driving the enrichment scores.
      2. NQO1 and HMOX1 are the standard markers as NRF2 target genes. Incorporating them would strengthen the validation of NRF2 pathway engagement in this model.
      3. The figure legend for Figure 1G states that it shows magnifications from Figure 1G. This should refer to Figure 1E

      Significance

      This manuscript uses a zebrafish model with inducible NRF2T80K expression, plus HepG2 cells, to show that constitutive NRF2 activation drives hepatocyte-to-cholangiocyte trans-differentiation. This phenotype is cell-autonomous, conserved between zebrafish and human cells, and reversible. The authors conducted a small-molecule screen to identify the BRG1/BRM inhibitor FHD-286 as a suppressor of this trans-differentiation. The authors propose this screening result links NRF2 to the SWI/SNF complex. The phenotypic work is clear and interesting, but the mechanistic experimental design has fundamental gaps that remain unaddressed.

      If the mechanism holds up, this is a meaningful finding for the liver cancer field: it points to hepatocytes as a possible cell-of-origin for NRF2-driven cholangiocarcinoma and suggests SWI/SNF as a druggable vulnerability. The study is technically ambitious, challenging in vivo zebrafish, scRNA-seq, and chemical library screen.

  4. Jul 2026
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      Reply to the reviewers

      Reviewers’ comments:

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This manuscript identifies a potential connection between FASN, endogenous dsRNAs, and innate immune signaling. The authors show that FASN depletion increases cytoplasmic dsRNA staining, enhances IFN transcription and restricts Sindbis virus replication. Overall, the basic observation seems to be solid and well-controlled. Depending on which journal this is being considered for, the central mechanism could be better developed and elucidated to entail more molecular details. As it currently stands, several important questions, especially ones regarding the FASN-RNA interactions remain unresolved.

      Major comments

      The conclusion that FASN regulates dsRNA accessibility rather than dsRNA abundance requires further support. The authors show increased J2 staining without major changes in the global dsRNA landscape by J2-RIP-seq. However, alternative explanations could be: (1) changes in dsRNA structure or length distribution could affect J2 recognition, i.e. epitope masked by FASN binding; (2) differences in RNA-protein complex assembly could influence J2 accessibility; (3) Altered subcellular localization of dsRNAs could increase immunofluorescence signals without substantially affecting RIP-seq recovery. 2. The nature of the FASN-RNA interaction remains unclear. The authors demonstrate that FASN associates with a subset of endogenous RNAs, but it is not clear whether this interaction is sequence-specific, structure-specific, or largely nonspecific. Are there common sequence motifs, secondary structures, repetitive elements, or specific transcript classes enriched among FASN-associated RNAs? Without a clearer understanding of RNA selectivity, it remains difficult to evaluate the biological significance of the observed interactions. 3. The study would be significantly strengthened by complementation experiments. Although the FASN knockout phenotype is convincing, add-back experiments are needed to demonstrate specificity and probe potential mechanisms. Reconstitution with wild-type FASN should rescue the dsRNA and immune phenotypes. Furthermore, domain-specific mutants or catalytic mutants (e.g., palmitoylation) could help distinguish whether the observed effects depend on FASN enzymatic activity, a specific protein domain, or a non-canonical RNA-binding function. Such experiments would greatly improve the mechanistic depth of the study. It will also address whether the observed dsRNA increase phenotype is irreversible.

      The manuscript proposes that increased endogenous dsRNA sensing drives the inflammatory phenotype in FASN-deficient cells. However, direct evidence for enhanced PRR engagement is currently lacking. Although MDA5 and MAVS knockdown experiments suggest a possible contribution of this pathway, the observed knockdown effects are relatively modest and needs to be repeated by CRISPR knockout. Does FASN, MAVS double knockout cells rescue the slow growth phenotype? In addition, direct measurements of PRR activation, such as MDA5-RNA association, MAVS activation, IRF3 phosphorylation, or related downstream signaling events, were not examined. 5. The antiviral phenotype observed during Sindbis virus infection is interesting, but the underlying mechanism remains uncertain. The authors propose that enhanced endogenous dsRNA sensing contributes to viral restriction, yet alternative explanations related to altered lipid metabolism, impaired membrane remodeling, or other consequences of FASN deficiency cannot be excluded. Especially when MDA5 and MAVS knockdown effect appears modest. Additional experiments disrupting interferon signaling or innate immune sensing pathways using the FASN, MAVS double knockout cells would help determine whether the antiviral phenotype is directly linked to the proposed dsRNA sensing mechanism.

      Reviewer #1 (Significance (Required)):

      This manuscript identifies a potential connection between FASN, endogenous dsRNAs, and innate immune signaling. The authors show that FASN depletion increases cytoplasmic dsRNA staining, enhances IFN transcription and restricts Sindbis virus replication. Overall, the basic observation seems to be solid and well-controlled. Depending on which journal this is being considered for, the central mechanism could be better developed and elucidated to entail more molecular details. As it currently stands, several important questions, especially ones regarding the FASN-RNA interactions remain unresolved.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Pasquier and colleagues investigates the role of fatty acid synthase (FASN) in the regulation of innate immune signaling through its interaction with endogenous dsRNAs. The authors build upon their previous study, in which they identified FASN as a potential dsRNA-binding protein. They show that FASN knockout results in increased cytoplasmic dsRNA detection accompanied by the induction of inflammation-related genes. Subsequent multi-omics analyses reveal that FASN associates with dsRNAs derived from nuclear genome-encoded transcripts in multiple contexts. Interestingly, FASN depletion does not alter the overall expression of these dsRNAs but increases their detection by the J2 antibody. Functionally, FASN depletion enhances innate immune responses to dsRNA stimulation and Sindbis virus infection.

      While the study provides evidence supporting an proviral role for FASN, the manuscript lacks mechanistic insight into this phenomenon. In particular, it remains unclear how FASN depletion enhances dsRNA detection without altering endogenous dsRNA abundance. The following points should be addressed:

      1) The main limitation of the manuscript is the absence of a mechanistic explanation for how FASN depletion leads to increased dsRNA detection without affecting expression. Does the increased dsRNA detection require a direct interaction between FASN and dsRNAs? The authors should provide additional evidence to clarify whether FASN binding masks dsRNA epitopes or regulates dsRNA accessibility to recognition by J2 and innate immune sensors.

      2) The authors show that FASN directly binds nuclear genome-encoded transcripts. It would be important to perform RNA-FISH for selected FASN-interacting RNAs to determine whether their cellular localization or J2-detectable signal is altered in FASN-depleted cells.

      3) In Figure 2B, the authors demonstrate enrichment of inflammation-related gene signatures in FASN knockout cells. However, the magnitude of induction is not clear. How does the inflammatory response compare with canonical innate immune activation, such as viral infection or poly(I:C) stimulation? Although Figure 2A partially addresses this point, many of the highlighted genes are not directly associated with inflammatory responses.

      4) For the transcripts directly associated with FASN, do they contain predicted dsRNA-forming regions or known structured RNA elements? The current data do not sufficiently demonstrate whether these transcripts indeed form dsRNA structures. Although some are enriched in J2 immunoprecipitation, additional structural analyses, including computational prediction of dsRNA regions, would strengthen the conclusion.

      5) The authors should further investigate whether the enhanced antiviral activity observed upon FASN depletion is mediated by elevated endogenous dsRNA sensing. Are dsRNA sensors such as PKR, MDA5, or RIG-I more strongly activated in FASN knockout cells? Do these sensors exhibit increased binding to endogenous dsRNAs following FASN depletion?

      Reviewer #2 (Significance (Required)):

      Main strength: Uncover the proviral role of FASN via regulation of endogenous dsRNAs

      Limitations: Molecular mechanism on how FASN depletion increases the accessibility of dsRNAs without affecting their expression. Also, need better characterization of FASN-binding RNAs, especially their secondary structure.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary The manuscript entitled, "Fatty Acid Synthase associates with nuclear-derived cytoplasmic dsRNA molecules and influences antiviral innate immune response," by Pasquier et al. identifies an unexpected function for FASN as an endogenous dsRNA-associated protein and explores the relationship between FASN, cellular dsRNA homeostasis, and antiviral innate immunity. Using FASN knockout cells, the authors demonstrate that depletion of FASN results in accumulation of cellular dsRNA, characterize the endogenous dsRNA species associated with FASN, and show that loss of FASN is accompanied by increased expression of interferon-stimulated genes and restriction of SINV replication. The biochemical characterization of FASN-associated dsRNAs together with the accompanying transcriptomic analyses provide compelling evidence that FASN interacts with a distinct subset of endogenous dsRNAs, representing a novel function for this metabolic enzyme.

      While evidence is presented that FASN can associate with endogenous dsRNA, several aspects of the proposed functional model extend beyond what is directly demonstrated by the data. In particular, the manuscript presents dsRNA accumulation, innate immune activation, and viral restriction as a consequence of FASN-mediated dsRNA regulation. However, it remains unclear whether these downstream phenotypes arise specifically from the newly described RNA-binding activity of FASN or instead reflect broader cellular consequences of FASN deficiency, including alterations in lipid metabolism and protein palmitoylation. Likewise, although the authors identify a population of FASN-associated dsRNAs, the relationship between these transcripts and the dsRNAs that accumulate following FASN depletion remains insufficiently resolved, making it difficult to mechanistically understand how FASN regulates dsRNA homeostasis. Overall, the manuscript provides a number of intriguing observations regarding FASN association with endogenous dsRNA. However, the functional relationship between these findings and the proposed effects on dsRNA homeostasis and innate immunity would benefit from additional clarification before the mechanistic model can be fully supported.

      Major Concerns

      It remains unclear whether the inflammatory and antiviral phenotypes observed following FASN depletion are attributable to FASN's newly described dsRNA-binding activity or to the broader metabolic consequences of losing FASN. The manuscript presents dsRNA accumulation, induction of ISGs, and restriction of SINV as downstream consequences of FASN-mediated dsRNA regulation. While these observations are compelling, they remain correlative and do not distinguish between effects arising from dsRNA regulation versus the numerous metabolic changes expected following loss of FASN. In particular, reduced palmitate production and altered protein palmitoylation are well-established consequences of FASN depletion and have documented roles in MAVS signaling and antiviral immunity. This distinction is important because the principal claim of the manuscript is not simply that FASN depletion alters innate immunity, but that its RNA-binding activity is proposed to underlie these effects. At present, the data support an association between these phenotypes but fall somewhat short of demonstrating that the observed immune response is specifically driven by FASN-mediated regulation of endogenous dsRNA.

      1. While the manuscript does provide evidence that FASN associates with a subset of endogenous dsRNAs, the relationship between these RNAs and those that accumulate following FASN depletion remains unclear. The authors conclude that FASN regulates dsRNA homeostasis, yet also show that transcripts associated with FASN do not measurably accumulate in knockout cells. This creates a disconnect between RNA association and functional regulation that is not adequately resolved.

      An overlap analysis comparing the dsRNA populations identified in wt and KO cells would help clarify whether FASN regulates a stable population of transcripts or whether broader remodeling of the dsRNA landscape occurs following FASN loss. More generally, the manuscript would be stronger if it helps clarify or distinguish the observation that FASN binds endogenous dsrna from the conclusion that it regulates cellular dsRNA homeostasis; the latter is not fully supported with the presented data.

      Minor concerns

      The mitochondrial localization of dsRNA needs additional validation. Specifically, Fig 3A requires quantitative analysis of mitochondrial abundance, while Fig 3C could be strengthened through orthogonal validation of mitochondrial localization (e.g., microscopy-based co-localization or further purification of the mitochondrial fraction). Inclusion of an RNase control would also help demonstrate that the enrichment is specific to dsRNA.

      The role of the ADAR depletion experiments within the overall narrative is somewhat unclear. Consider either integrating these data more directly into the proposed model or reducing their emphasis. Along similar lines, Fig 4B should clarify whether the increased FASN-dsRNA association reflects greater binding or simply increased abundance of both FASN and dsRNA following 5-Aza treatment. • Several aspects of the data presentation needs clarification. These include the rationale for selecting IFIT1 for validation (Fig 2A), the relationship between proteins highlighted in the Fig 4 volcano plot and those selected for immunoblot validation, the apparent reduction in tubulin complicating interpretation of the SINV capsid blot (Fig 5B), and the conclusion that J2-positive puncta are distinct from SINV RNA without co-localization analysis. • Several figures could be improved for presentation. Quantification of the J2 signal in Fig S1C (analogous to Fig 1D) and improved labeling of the volcano plots would improve readability. • Several minor editorial revisions are recommended. These include describing ACP as a carrier/tethering rather than catalytic domain, citing Fig S2 in the Results, defining the long form of NES in the Fig 2 legend, removing redundant panel descriptions in the Fig 3G legend, correcting the missing text on page 14, and resolving the duplicated figures and formatting issues present throughout the manuscript.

      Reviewer #3 (Significance (Required)):

      Strengths Nice extension of FASN biology beyond its canonical metabolic role Introduces FASN as a potential endogenous dsRNA-binding protein Broad interest for the RNA biology and innate immunity communities

      Limitations RNA binding not fully demonstrated with higher-resolution approaches such as more precise CLIP approaches

      Mechanistic link between FASN dsRNA homeostasis and innate immune activation remains incomplete

      Advance Good conceptual advance but is more descritive than mechanistic

      Audience: RNA biology RNA-binding proteins Innate immunity Host-virus interactions Moonlighting metabolic enzymes

      Revision plan:

      1. General Statements:

      We thank the three reviewers for their careful evaluation of our manuscript and for their constructive comments. We are encouraged that all reviewers considered the central observations of the study to be robust and appreciated the conceptual advance provided by the identification of a previously unrecognized association between FASN and endogenous dsRNAs, together with its impact on antiviral innate immunity.

      We also acknowledge the fact that the main limitation of the current manuscript is the mechanistic understanding of how FASN regulates endogenous dsRNA accessibility and how this relates to innate immune activation. We agree that strengthening the mechanistic aspects of the study will substantially improve the manuscript. Nonetheless, dissecting the full mechanism may require more time which goes beyond the scope of this paper.

      Based on the comments of the three reviewers, our revision plan will therefore focus on three major objectives:

      • further characterizing the endogenous RNAs associated with FASN,
      • strengthening the mechanistic aspects linking increased accessibility of endogenous dsRNAs to FASN depletion,
      • providing additional evidence linking endogenous dsRNA accessibility to innate immune priming and antiviral activity. Importantly, we also intend to clarify our proposed model. Our data do not suggest that loss of FASN induces a strong spontaneous interferon response comparable to that triggered by viral infection or poly(I:C) stimulation. We rather propose that FASN depletion establishes a primed state by increasing the accessibility of a subset of endogenous dsRNAs to innate immune sensors, thereby lowering the threshold for antiviral activation upon subsequent challenge. We will revise the manuscript to better communicate this model, improving data presentation throughout the manuscript and discussion to clearly distinguish experimentally supported conclusions from mechanistic interpretations.

      __2. __Description of the planned revisions:

      Further characterization of FASN-associated endogenous RNAs

      To better define the RNA population associated with FASN, we will perform additional bioinformatic analyses using our existing RIP-seq datasets. These analyses will include:

      • characterization of transcript classes (coding and non-coding RNAs);
      • analysis of sequence composition and motif enrichment;
      • prediction of RNA structural features (including GC content, normalized minimum free energy, paired nucleotide fraction and predicted stem length);
      • analysis of overlap with repetitive elements (including Alu, LINE and LTR elements);
      • comparison of predicted structural and sequence features of FASN-associated RNAs and J2-enriched RNAs;
      • additional comparisons between dsRNA populations identified in wild-type and FASN knockout cells. These analyses will help determine whether FASN preferentially associates with specific classes of endogenous RNAs. Based on the shortlisted RNAs, we will also test selected FASN-associated transcripts by RT-qPCR following RNase III treatment, to determine whether they are forming dsRNA with or without FASN depletion.

      • Strengthening the link between endogenous dsRNA accessibility and innate immune activation To provide more evidence that increased accessibility of endogenous dsRNAs results in enhanced innate immune sensing, we plan to further examine activation of dsRNA sensing pathways.

      Specifically, we will assess activation of downstream signaling components (including IRF3 activation) and further investigate the association of endogenous dsRNAs with innate immune sensors by examining MDA5 recruitment to J2-positive dsRNA complexes. Where technically feasible, we will also evaluate the spatial proximity between endogenous dsRNAs and MDA5 by microscopy-based approaches.

      To strengthen the link between endogenous dsRNA sensing and the primed inflammatory phenotype, we also plan to generate polyclonal CRISPR/Cas9 MDA5 or MAVS knockout cell populations in combination with FASN depletion by siRNA treatment, and evaluate the effects on interferon-responsive gene expression such as IFIT1.

      • Relationship between RNA binding and the metabolic functions of FASN Several reviewers raised the important question of whether the observed immune phenotypes arise from FASN-mediated regulation of endogenous dsRNAs or from broader metabolic consequences of FASN deficiency.

      To address this point, we plan to complement our genetic analyses with pharmacological inhibition of FASN catalytic activity using independent inhibitors and assess their effects on endo-dsRNA accumulation and Sindbis virus replication. We also plan to evaluate whether supplementation with exogenous palmitate rescues the observed phenotypes. These experiments will help distinguish catalytic from non-canonical functions of FASN in regulating endo-dsRNA accessibility and antiviral responses.

      Where feasible, we will also attempt complementation experiments using ectopic expression of wild-type FASN in FASN knockout cells.

      Additional manuscript improvements

      We will incorporate the requested additional quantifications, improve figure presentation and labeling, clarify the rationale for selected validation experiments, expand the discussion of alternative mechanistic models, and address the editorial and presentation issues identified by the reviewers.

      __3. __Description of analyses that authors prefer not to carry out:

      Although we agree that additional mechanistic studies would further strengthen the manuscript, we believe that several of the experiments suggested by the reviewers extend beyond the scope of this work.

      In particular, generation and characterization of multiple FASN domain-specific or RNA-binding mutants is currently not feasible. FASN is a large multifunctional enzyme containing several catalytic domains and no canonical RNA-binding domain has yet been defined. Consequently, designing mutants that specifically disrupt RNA binding while preserving enzymatic activities would be possible but difficult to deliver within the timeframe of this revision. Instead, we propose to address the reviewers' concerns through complementary pharmacological approaches (using FASN inhibitors), palmitate supplementation experiments and wild-type FASN complementation where feasible.

      Similarly, while high-resolution CLIP approaches would provide valuable information regarding FASN binding sites on the RNA, these technically demanding experiments fall beyond the scope of the current revision. We believe that the additional computational analyses, biochemical validation and functional experiments described above will substantially strengthen the conclusions regarding the interaction between FASN and endogenous structured RNAs.

      Finally, although generation of stable double knockout cell lines (e.g. FASN/MAVS or FASN/MDA5) could provide additional mechanistic insight, we consider that this could take too much time. Instead, we plan to generate polyclonal CRISPR/Cas9 knockout populations of MAVS or MDA5 in combination with siFASN to strengthen the causal relationship between endogenous dsRNA sensing and the observed primed immune phenotype.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The manuscript entitled, "Fatty Acid Synthase associates with nuclear-derived cytoplasmic dsRNA molecules and influences antiviral innate immune response," by Pasquier et al. identifies an unexpected function for FASN as an endogenous dsRNA-associated protein and explores the relationship between FASN, cellular dsRNA homeostasis, and antiviral innate immunity. Using FASN knockout cells, the authors demonstrate that depletion of FASN results in accumulation of cellular dsRNA, characterize the endogenous dsRNA species associated with FASN, and show that loss of FASN is accompanied by increased expression of interferon-stimulated genes and restriction of SINV replication. The biochemical characterization of FASN-associated dsRNAs together with the accompanying transcriptomic analyses provide compelling evidence that FASN interacts with a distinct subset of endogenous dsRNAs, representing a novel function for this metabolic enzyme. While evidence is presented that FASN can associate with endogenous dsRNA, several aspects of the proposed functional model extend beyond what is directly demonstrated by the data. In particular, the manuscript presents dsRNA accumulation, innate immune activation, and viral restriction as a consequence of FASN-mediated dsRNA regulation. However, it remains unclear whether these downstream phenotypes arise specifically from the newly described RNA-binding activity of FASN or instead reflect broader cellular consequences of FASN deficiency, including alterations in lipid metabolism and protein palmitoylation. Likewise, although the authors identify a population of FASN-associated dsRNAs, the relationship between these transcripts and the dsRNAs that accumulate following FASN depletion remains insufficiently resolved, making it difficult to mechanistically understand how FASN regulates dsRNA homeostasis. Overall, the manuscript provides a number of intriguing observations regarding FASN association with endogenous dsRNA. However, the functional relationship between these findings and the proposed effects on dsRNA homeostasis and innate immunity would benefit from additional clarification before the mechanistic model can be fully supported.

      Major Concerns

      1. It remains unclear whether the inflammatory and antiviral phenotypes observed following FASN depletion are attributable to FASN's newly described dsRNA-binding activity or to the broader metabolic consequences of losing FASN. The manuscript presents dsRNA accumulation, induction of ISGs, and restriction of SINV as downstream consequences of FASN-mediated dsRNA regulation. While these observations are compelling, they remain correlative and do not distinguish between effects arising from dsRNA regulation versus the numerous metabolic changes expected following loss of FASN. In particular, reduced palmitate production and altered protein palmitoylation are well-established consequences of FASN depletion and have documented roles in MAVS signaling and antiviral immunity.

      This distinction is important because the principal claim of the manuscript is not simply that FASN depletion alters innate immunity, but that its RNA-binding activity is proposed to underlie these effects. At present, the data support an association between these phenotypes but fall somewhat short of demonstrating that the observed immune response is specifically driven by FASN-mediated regulation of endogenous dsRNA. 2. While the manuscript does provide evidence that FASN associates with a subset of endogenous dsRNAs, the relationship between these RNAs and those that accumulate following FASN depletion remains unclear. The authors conclude that FASN regulates dsRNA homeostasis, yet also show that transcripts associated with FASN do not measurably accumulate in knockout cells. This creates a disconnect between RNA association and functional regulation that is not adequately resolved.

      An overlap analysis comparing the dsRNA populations identified in wt and KO cells would help clarify whether FASN regulates a stable population of transcripts or whether broader remodeling of the dsRNA landscape occurs following FASN loss. More generally, the manuscript would be stronger if it helps clarify or distinguish the observation that FASN binds endogenous dsrna from the conclusion that it regulates cellular dsRNA homeostasis; the latter is not fully supported with the presented data.

      Minor concerns

      • The mitochondrial localization of dsRNA needs additional validation. Specifically, Fig 3A requires quantitative analysis of mitochondrial abundance, while Fig 3C could be strengthened through orthogonal validation of mitochondrial localization (e.g., microscopy-based co-localization or further purification of the mitochondrial fraction). Inclusion of an RNase control would also help demonstrate that the enrichment is specific to dsRNA.
      • The role of the ADAR depletion experiments within the overall narrative is somewhat unclear. Consider either integrating these data more directly into the proposed model or reducing their emphasis. Along similar lines, Fig 4B should clarify whether the increased FASN-dsRNA association reflects greater binding or simply increased abundance of both FASN and dsRNA following 5-Aza treatment.
      • Several aspects of the data presentation needs clarification. These include the rationale for selecting IFIT1 for validation (Fig 2A), the relationship between proteins highlighted in the Fig 4 volcano plot and those selected for immunoblot validation, the apparent reduction in tubulin complicating interpretation of the SINV capsid blot (Fig 5B), and the conclusion that J2-positive puncta are distinct from SINV RNA without co-localization analysis.
      • Several figures could be improved for presentation. Quantification of the J2 signal in Fig S1C (analogous to Fig 1D) and improved labeling of the volcano plots would improve readability.
      • Several minor editorial revisions are recommended. These include describing ACP as a carrier/tethering rather than catalytic domain, citing Fig S2 in the Results, defining the long form of NES in the Fig 2 legend, removing redundant panel descriptions in the Fig 3G legend, correcting the missing text on page 14, and resolving the duplicated figures and formatting issues present throughout the manuscript.

      Significance

      Strengths

      Nice extension of FASN biology beyond its canonical metabolic role Introduces FASN as a potential endogenous dsRNA-binding protein Broad interest for the RNA biology and innate immunity communities

      Limitations

      RNA binding not fully demonstrated with higher-resolution approaches such as more precise CLIP approaches Mechanistic link between FASN dsRNA homeostasis and innate immune activation remains incomplete

      Advance

      Good conceptual advance but is more descritive than mechanistic

      Audience

      RNA biology RNA-binding proteins Innate immunity Host-virus interactions Moonlighting metabolic enzymes

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Pasquier and colleagues investigates the role of fatty acid synthase (FASN) in the regulation of innate immune signaling through its interaction with endogenous dsRNAs. The authors build upon their previous study, in which they identified FASN as a potential dsRNA-binding protein. They show that FASN knockout results in increased cytoplasmic dsRNA detection accompanied by the induction of inflammation-related genes. Subsequent multi-omics analyses reveal that FASN associates with dsRNAs derived from nuclear genome-encoded transcripts in multiple contexts. Interestingly, FASN depletion does not alter the overall expression of these dsRNAs but increases their detection by the J2 antibody. Functionally, FASN depletion enhances innate immune responses to dsRNA stimulation and Sindbis virus infection.

      While the study provides evidence supporting an proviral role for FASN, the manuscript lacks mechanistic insight into this phenomenon. In particular, it remains unclear how FASN depletion enhances dsRNA detection without altering endogenous dsRNA abundance. The following points should be addressed:

      1) The main limitation of the manuscript is the absence of a mechanistic explanation for how FASN depletion leads to increased dsRNA detection without affecting expression. Does the increased dsRNA detection require a direct interaction between FASN and dsRNAs? The authors should provide additional evidence to clarify whether FASN binding masks dsRNA epitopes or regulates dsRNA accessibility to recognition by J2 and innate immune sensors.

      2) The authors show that FASN directly binds nuclear genome-encoded transcripts. It would be important to perform RNA-FISH for selected FASN-interacting RNAs to determine whether their cellular localization or J2-detectable signal is altered in FASN-depleted cells.

      3) In Figure 2B, the authors demonstrate enrichment of inflammation-related gene signatures in FASN knockout cells. However, the magnitude of induction is not clear. How does the inflammatory response compare with canonical innate immune activation, such as viral infection or poly(I:C) stimulation? Although Figure 2A partially addresses this point, many of the highlighted genes are not directly associated with inflammatory responses.

      4) For the transcripts directly associated with FASN, do they contain predicted dsRNA-forming regions or known structured RNA elements? The current data do not sufficiently demonstrate whether these transcripts indeed form dsRNA structures. Although some are enriched in J2 immunoprecipitation, additional structural analyses, including computational prediction of dsRNA regions, would strengthen the conclusion.

      5) The authors should further investigate whether the enhanced antiviral activity observed upon FASN depletion is mediated by elevated endogenous dsRNA sensing. Are dsRNA sensors such as PKR, MDA5, or RIG-I more strongly activated in FASN knockout cells? Do these sensors exhibit increased binding to endogenous dsRNAs following FASN depletion?

      Significance

      Main strength: Uncover the proviral role of FASN via regulation of endogenous dsRNAs

      Limitations: Molecular mechanism on how FASN depletion increases the accessibility of dsRNAs without affecting their expression. Also, need better characterization of FASN-binding RNAs, especially their secondary structure.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript identifies a potential connection between FASN, endogenous dsRNAs, and innate immune signaling. The authors show that FASN depletion increases cytoplasmic dsRNA staining, enhances IFN transcription and restricts Sindbis virus replication. Overall, the basic observation seems to be solid and well-controlled. Depending on which journal this is being considered for, the central mechanism could be better developed and elucidated to entail more molecular details. As it currently stands, several important questions, especially ones regarding the FASN-RNA interactions remain unresolved.

      Major comments

      1. The conclusion that FASN regulates dsRNA accessibility rather than dsRNA abundance requires further support. The authors show increased J2 staining without major changes in the global dsRNA landscape by J2-RIP-seq. However, alternative explanations could be: (1) changes in dsRNA structure or length distribution could affect J2 recognition, i.e. epitope masked by FASN binding; (2) differences in RNA-protein complex assembly could influence J2 accessibility; (3) Altered subcellular localization of dsRNAs could increase immunofluorescence signals without substantially affecting RIP-seq recovery.
      2. The nature of the FASN-RNA interaction remains unclear. The authors demonstrate that FASN associates with a subset of endogenous RNAs, but it is not clear whether this interaction is sequence-specific, structure-specific, or largely nonspecific. Are there common sequence motifs, secondary structures, repetitive elements, or specific transcript classes enriched among FASN-associated RNAs? Without a clearer understanding of RNA selectivity, it remains difficult to evaluate the biological significance of the observed interactions.
      3. The study would be significantly strengthened by complementation experiments. Although the FASN knockout phenotype is convincing, add-back experiments are needed to demonstrate specificity and probe potential mechanisms. Reconstitution with wild-type FASN should rescue the dsRNA and immune phenotypes. Furthermore, domain-specific mutants or catalytic mutants (e.g., palmitoylation) could help distinguish whether the observed effects depend on FASN enzymatic activity, a specific protein domain, or a non-canonical RNA-binding function. Such experiments would greatly improve the mechanistic depth of the study. It will also address whether the observed dsRNA increase phenotype is irreversible.
      4. The manuscript proposes that increased endogenous dsRNA sensing drives the inflammatory phenotype in FASN-deficient cells. However, direct evidence for enhanced PRR engagement is currently lacking. Although MDA5 and MAVS knockdown experiments suggest a possible contribution of this pathway, the observed knockdown effects are relatively modest and needs to be repeated by CRISPR knockout. Does FASN, MAVS double knockout cells rescue the slow growth phenotype? In addition, direct measurements of PRR activation, such as MDA5-RNA association, MAVS activation, IRF3 phosphorylation, or related downstream signaling events, were not examined.
      5. The antiviral phenotype observed during Sindbis virus infection is interesting, but the underlying mechanism remains uncertain. The authors propose that enhanced endogenous dsRNA sensing contributes to viral restriction, yet alternative explanations related to altered lipid metabolism, impaired membrane remodeling, or other consequences of FASN deficiency cannot be excluded. Especially when MDA5 and MAVS knockdown effect appears modest. Additional experiments disrupting interferon signaling or innate immune sensing pathways using the FASN, MAVS double knockout cells would help determine whether the antiviral phenotype is directly linked to the proposed dsRNA sensing mechanism.

      Significance

      This manuscript identifies a potential connection between FASN, endogenous dsRNAs, and innate immune signaling. The authors show that FASN depletion increases cytoplasmic dsRNA staining, enhances IFN transcription and restricts Sindbis virus replication. Overall, the basic observation seems to be solid and well-controlled. Depending on which journal this is being considered for, the central mechanism could be better developed and elucidated to entail more molecular details. As it currently stands, several important questions, especially ones regarding the FASN-RNA interactions remain unresolved.

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      Reply to the reviewers

      Response to the points raised by the reviewers.

      Once again, we would like to thank the reviewers for their comments. We have systematically addressed their concerns, as detailed below.

      Reviewer #1

      Evidence, reproducibility and clarity

      This study demonstrates that BICD2, previously known as an adaptor protein for dynein, is involved in regulating centriole engagement during mitosis. First, using different antibodies, it was shown that BICD2 localizes near the mother centriole, as observed by super-resolution microscopy. During G1 and S phases, BICD2 localizes slightly outside the Cep152 ring, while in G2 to mitosis, it localizes near the cartwheel component SAS-6. Moreover, analysis of deletion mutants revealed that BICD2 localizes to the centrosome in a CC domain-dependent manner at the C-terminal end. The localization pattern resembling a ring in the cytoplasm was also observed through the CC3 domain. Next, BICD2 knockout (KO) cells were generated to investigate centriole dynamics. In BICD2 KO cells, the distance between the mother and daughter centrioles was observed to increase from G2 to mitosis compared to controls. Along with this, early centriole disengagement and centriole amplification phenotypes were observed. The increased distance phenotype between centrioles was rescued in BICD2 wild-type (WT) and mutant forms lacking the CC1 domain at the N-terminus, suggesting that this function of BICD2 is independent of dynein. Additionally, BICD2 mutants mimicking phosphorylation at the C-terminus showed reduced centrosome localization and were unable to rescue the phenotypes seen in BICD2 KO cells.

      While the study clearly demonstrates BICD2's contribution to centriole engagement, the underlying mechanisms of how BICD2 is involved in centrosome localization and centriole engagement remain unclear. As it is anticipated that the function of BICD2 is independent of dynein, further exploration of this unknown mechanism would enhance the value of the paper. Below are the concerns that should be addressed, including new experiments.

      Main Points:

      1. __ Fig. 1-3: Regarding the localization of BICD2 to centrioles, during the G1-S phase, its localization appears to overlap with PCM. Experimental investigation should be performed to examine whether BICD2's centrosomal localization is influenced by knockdown of PCM components like PCNT, Cep192, or Cep152.__ We now show that BICD2 localization does not depend on pericentrin (Supplementary Figure S4B). We also show that the two proteins do not colocalize (Supplementary Figure S4A and S4D) and are functionally independent (Figure 5).

      We also show that BICD2 localization does depend on the torus protein CEP152 (Figure 8B). Importantly, our data indicate that BICD2 interacts with the N-terminal region of CEP152, suggesting that this interaction places BICD2 at the outer region of the torus (Figures 8C and 8D). We propose that this provides a mechanistic basis for BICD2 function in maintaining mother-daughter centriole engagement.

      __ Fig. 7: The experiments using BICD2 mutants suggest that the function of BICD2 here is independent of dynein. To further investigate whether BICD2's role in centriole engagement is independent of dynein, experiments should be conducted to examine the effect of dynein knockdown on BICD2 localization to the centrosome and centriole engagement.__

      Using Dynapyrazole-A, a fast-acting and potent dynein inhibitor, we demonstrate that acute inhibition of dynein motor activity affects neither the centrosomal localization of BICD2 during G2 and M phases (Supplementary Figure S3A) nor centriole engagement (Supplementary Figure S3B). Together with experiments using BICD2 mutants deficient in dynein interaction (Figure 7B), these data compellingly demonstrate that the recruitment and function of BICD2 at the centriole is dynein-independent.

      __ Fig. 4: The CC4 domain at the C-terminus of BICD2 is important for its centrosomal localization, but identifying the binder/recruiter responsible for BICD2's centrosome localization would be desirable.__

      We thank the reviewer for prompting us to investigate this further. We are delighted that we now identify the torus protein CEP152 as the BICD2 binder/recruiter at the centriole (Figure 8). As we discuss in the manuscript, our observation that the outward-facing N-terminus of CEP152 interacts with the C-terminal region of BICD2 provides a mechanistic basis for understanding BICD2's role in maintaining mother–daughter centriole engagement.

      __ Fig. 7: Rescue experiments using BICD2 mutants suggest that BICD2's functional domains are critical. Further experiments by creating mutants missing parts of CC2 or CC3 could identify functionally important domains of BICD2 by observing any loss-of-function phenotypes at the centrosome.__

      We fully appreciate the reviewer’s suggestion to examine the roles of the CC2 and CC3 regions. We believe that these domains, and particularly the unstructured loop within CC3, are important for both BICD2 localization, function and regulation at the centrosome. However, given that our current data already establish a clear mechanism for BICD2 centriolar recruitment via CEP152 and the CC4 region, we feel these additional structural studies fall outside the core scope of the present manuscript. We hope the reviewer agrees that the current evidence provides a robust foundation for our conclusions, and we look forward to addressing the roles of CC2 and CC3 in a dedicated future study.

      __ Fig. 6: Regarding the BICD2 KO cell phenotype, is there experimental evidence showing an increase in centriole number during mitosis? For instance, while no abnormality in centriole number may occur during G2, a trend of increase in mitosis should be experimentally demonstrated. Also, how should the slight differences in phenotypes between Ndelta4 and Ndelta5 BICD2 KO cells be interpreted?__

      We thank the reviewer for highlighting this point, but we would like to clarify that we do indeed observe a significant increase in centriole number during both mitosis and G2 phase across multiple cell lines in our BICD2 KO models and RNAi experiments (Figure 4E, RPE-1 KO cells, and 4G, U2OS cells, RNAi) and G2 (Figure 5C, both RPE-1 and U2OS, RNAi). As the main text was not explicit enough on this point, we have revised the manuscript to describe these observations more clearly.

      Regarding the phenotypic differences between BICD2 KO lines, we assign them to the clone-to-clone functional heterogeneity often seen in CRISPR/Cas9-generated cell lines. Importantly both clones show a consistent, statistically significant phenotype (e.g., impaired engagement and increased centriole numbers) compared to wild-type controls, confirming that the overall defect is robust and specific to BICD2 loss. We have added a clarifying note on this in the revised manuscript: “Figure 4F; we assign the differences between BICD2-/- cell lines to standard clone-to-clone phenotypic heterogeneity often seen in CRISPR/Cas9-generated cell lines.

      __ Fig. 8: Regarding the phosphorylation of BICD2 at the C-terminus: The phenotypes of mutants where these two phosphorylation sites are changed to alanine should be experimentally observed. It is expected that the removal of BICD2 from the centrosome during mitosis could be rescued. Additionally, the effect of PLK1 or CDK1 inhibitors on the removal of BICD2 from the centrosome should be investigated.__

      We agree with the reviewer that phosphonull mutants should be added to these experiments. As mentioned above we have decided to remove the preliminary data regarding BICD2 phosphorylation from the manuscript data to present a more comprehensive, dedicated study on BICD2 phosphorylation in the near future. In fact, we have already performed the suggested experiments, including the phosphonull mutants and kinase inhibitor treatments, and would be glad to share these additional results if the reviewers would find them helpful. Interestingly, our experiments show that BICD2 centrosomal amounts are not affected by PLK1 inhibition (using BI 2536); CDK1 inhibition (RO-3306), although not significatively changing the amount of BICD2 at the centrosome, slightly diminishes it. We currently favor a model in which BICD2 is predominantly regulated by CDK1, and we are actively defining the precise molecular mechanism governing this regulation.

      Minor Points:

      __ Fig. 1-3: During G1 and S phases, BICD2 localizes near the mother centriole, and from G2 onward, it colocalizes with SAS-6. How can this be explained?__

      We currently do not have a clear explanation for this transition, as our focus has been in understanding BICD2 recruitment to the centriole (and its role in centriole engagement). We view this as a very interesting question that could be studied together with BICD2 regulation through phosphorylation. Our current hypothesis is that most of BICD2 is removed through phosphorylation in late G2 and M, with a pool remaining at the mother-daughter interface, possibly protected by a yet to be understood mechanism. We have added a sentence in the discussion addressing this (“ A pool of protein could be protected and correspond to the observed remnant of BICD2 at the mother-daughter interface.“). This last pool, as we discuss in the manuscript, could be further phosphorylated at the M/G1 transition or cleaved by separase (although this last point is of course highly speculative).

      __ Fig. 4: The GFP-BICD2 488-820 fragment forms cytoplasmic rings, which is interesting. This domain contains the CC4 domain, so it can localize near the centriole, but why does it not form a perfect ring there? Also, which other centriole/centrosome markers were used for colocalization studies? Does knockdown of PCM1 affect BICD2's centrosomal localization?__

      We show in Figures 6G and 6H that BICD2 488-820 can form a ring around the centriole. Indeed this polypeptide contains the CC4 region, which our results indicate it will guide it to the centriole (through an interaction with CEP152). Once the available CEP152 is occupied with BICD2 we assume that BICD2 488-820 forms oligomers that assemble ring-like structures outside the centriole.

      Other centrosomal markers used are SAS-6.

      We now show that PCM1 knockdown does not affect BICD2's centrosomal localization (Supplementary Figure S4C). Although our results indicate that partial forms of BICD2 such as BICD2 488-820 can colocalize with PCM-1 (Figure 6D), full length endogenous BICD2 (or GST-BICD2) does not seem to colocalize with this protein and thus the centriole satellites (Supplementary Figure S4A). We note this discrepancy in the text: “The presence of BICD2 at the centriolar satellites has been suggested previously (Quarantotti et al, 2019); we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 but not the full-length protein”. The relationship between satellites and BICD2 grants further studies. Our data suggests that centriolar localization of the protein may be regulated, possibly by its intramolecular structure, and that regulated binding of BICD2 to a yet to be identified partner may recruit the protein to satellites either for its transport to the centrosome or in order to perform a specific function at the satellites. We have added a sentence to the text to note this: “This suggests that BICD2 satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein.”.

      __ Fig. 4A: What are the aggregates observed in the cytoplasm under the GFP-BICD2 + ice condition? Also, does the 1-575 mutant fail to localize to the centrosome upon ice treatment?__

      We currently do not know the nature of the GFP-BICD2 full length aggregates observed upon microtubule depolymerization. We also observe GFP-BICD2 aggregates in cells that express high amounts of the polypeptide, leading us to hypothesize that it may be insoluble and the disappearance of microtubules may liberate it from motor complexes resulting in its aggregation -although of course more work would be needed to clarify this.

      We now show new data (Supplementary Figure S5), showing that the localization of not only GFP-BICD2 1-575 but also the C-terminal fragments 272-820 and 488-820 are not significantly affected by cold-induced microtubule depolymerization. These last results strongly suggest that BICD2 localization at the centriole is microtubule independent and are compatible with our data showing that BICD2 can directly interact with the centriolar protein CEP152.

      __ Can similar phenotypes be observed in other cell types when BICD2 is knocked down? This should be experimentally validated.__

      Our current manuscript now shows that similar phenotypes regarding centriole separation and amplification are observed upon BICD2 depletion in RPE-1 cells (non-transformed, p53-wildtype) and U2OS cells (transformed). These are shown in Figure 4 (RPE-1 knockout, U2OS RNAi knockdown) and Figure 5 (RPE-1 and U2OS RNAi knockdown).

      __ Are there previous studies suggesting that this function of BICD2 is evolutionarily conserved? This should be addressed.__

      To our knowledge there are no previous studies describing BICD2 function at the centrosome, excepting the recent article by Kuang et al., (Kuang W et al. 2025. BICD2 promotes ciliogenesis by facilitating CP110 removal from the mother centriole. EMBO reports 26:5567–5588. DOI: https://doi.org/10.1038/s44319-025-00597-0), that describes a role for BICD2 during ciliogenesis in non-cycling cells. As we mention in our discussion this new role may be related to the distal pool of protein that we observe using ExM, and we don’t think is related to the function of the proximal pool of BICD2 at the torus in cycling cells that we describe in our manuscript.

      Regarding functional conservation, BICD2 orthologs are widely distributed across metazoans (as reflected in OrthoDB, which lists ~5,000 ortholog genes across ~2,500 species). They share a remarkably conserved C-terminal domain that acts as a docking interface mediating subcellular targeting independently of dynein motor activity (i.e. through binding to Rab6, RanBP2 and, as shown here, CEP152). Cross-species analyses show that this C-terminal domain is preserved in most eukaryotic orthologs, including Drosophila melanogaster BICD (UniProt P16568) and Caenorhabditis elegans BICD-1 (UniProt V6CJ04). Interestingly, several predicted orthologous sequences in public databases retain high C-terminal similarity while completely lacking the N-terminal regions containing the CC1 box motif (residues 29–57 in human BICD2) required for dynein interaction (e.g., predicted isoforms in mouse or camels). Thus, dynein-independent scaffolding functions may represent an ancient, foundational role of the BICD protein family, or alternatively (and perhaps most probably, given that basal metazoans like sponges or Cnidaria do show a conserved N-terminus), these truncated forms may have evolved to fulfill distinct cellular roles operating independently of motor-adaptor activity. We have added a passage at the end of the discussion to reflect this.

      Significance

      In this paper, the identification of BICD2 as a novel factor regulating centriole engagement is of significant importance. However, the mechanisms through which BICD2 controls its localization to the centrosome and regulates centriole engagement remain largely undefined. Further exploration of these mechanisms would likely enhance the value of the paper.

      The findings are likely to be of great interest to researchers in the field of cell biology, particularly those focusing on centrosome biology.

      The above feedback comes from a researcher specializing in centrosome studies.

      __ __

      Reviewer #2

      Evidence, reproducibility and clarity

      Montez-Ruiz and colleagues explore the role of a dynein adaptor BICD2 in the engagement of mother and daughter centrioles. Cells need to maintain centriole engagement in interphase to prevent centriole reduplication and in early mitosis to prevent the formation of aberrant mitosis spindles. The authors demonstrate that BICD2 is a centriolar protein that surrounds the mother centriole adjacent to the daughter centriole. It is removed from centrosomes in mitosis, which, in turn, is responsible for centriole disengagement. Further, they suggest that in BICD2 knock-out G2 and early mitotic cells, centrioles disengage prematurely. By conducting rescue experiments, the authors conclude that BICD2 regions CC2, CC3, and CC4, which are dynein-independent, are essential for their function at the centrosome. Finally, they show that the phosphorylation of S817 and S819 of BICD1 controls its centrosome localization.

      Major comments:

      1. __ Based on F1 and SF1, BICD2 is reduced from centrosomes already in early G2. So, it is hard to square how removing a factor that is not present at the centrosomes at the time of disengagement would dysregulate disengagement. The study at this stage does not explain how BICD2 contributes to centriole engagement only in mitosis, while it does not affect centrioles in S.__ We now present new data obtained using expansion microscopy (ExM) that, together with our super-resolution observations, clarifies this point. As shown in the new Figure 3 and Figure EV2 (and supported by Figures EV3 and EV4), although the total amount of BICD2 at centrosomes is significantly reduced from G2 to M, a pool of BICD2 persists at the mother centriole until late mitosis. Importantly, this pool tends to localize close to the daughter centriole. We note this in the text (“BICD2 remained visible in both diplosomes, associated with the SAS-6 foci (Figure 3, Figures EV2-3). Around anaphase, BICD2 was not detectable in some diplosomes, while others retained some protein (again, close to the SAS-6 foci, which at this point were disappearing from the centrioles as the result of the disassembly of the cartwheel).”). Supported by our BICD2 depletion experiments, we propose that this centriolar pool enables BICD2 to contribute to engagement until late mitosis, when the remaining protein at the centrosome is ultimately removed. We highlight this model in the Discussion section (“In mitosis, when the protein progressively disappears from the centrosomes, BICD2 remains functionally relevant -likely via the small pool that persists at the mother–daughter centriole interface.”).

      Based on our new data demonstrating an interaction with CEP152, we propose that BICD2 forms an outer component of the centriolar torus. Centriole engagement is known to be maintained during S phase by the cartwheel (Huang F et al. 2022. Cartwheel disassembly regulated by CDK1-Cyclin B kinase allows human centriole disengagement and licensing. The Journal of Biological Chemistry 298:102658. DOI: https://doi.org/10.1016/j.jbc.2022.102658; Ito KK et al. 2025. Multimodal mechanisms of human centriole engagement and disengagement. The EMBO journal 44:1294–1321. DOI: https://doi.org/10.1038/s44318-024-00350-8), with the torus playing a role in cohesion later in the cell cycle. We note in our manuscript that this is consistent with our observations and supports a model in which BICD2 functions as part of the torus: “During S phase, mother–daughter centriole cohesion is maintained by the cartwheel (Huang et al, 2022; Ito et al, 2025) and, consistently, does not depend on BICD2.

      __ The interpretation that the longitudinal localization of the BICD2 signal coincides with SAS-6 and procentrioles requires further evidence. BICD2 seems largely localized to the other regions around the mother centriole, and in some examples, it does not colocalize with the site of the daughter centriole or SAS-6 (for instance: F2B second row; SF4B, second row; SF5, fourth row; SF6 upper row).__

      We have added an ExM characterization of BICD2 centrosomal localization in the revised manuscript (Figures 2 and 3), that we think further clarify this point, showing that BICD2 longitudinally coincides with the torus and the daughter centriole. This is supported by new additional superresolution images (Figure 2, Figure EV2).

      Note that ExM revealed an additional stable pool of BICD2 at the distal end of the centrioles that is not detected using standard methanol fixation combined with 3D-SIM. As discussed in the text this distal pool may reflect additional centriolar functions of BICD2.

      __ BICD2 is important for centrosome-nucleus tethering during centrosome separation in G2, and its global removal likely affects the dynamics of the spindle assembly. Is G2 and mitotic progression affected in knockouts? Do the knockout cells show issues with chromosome alignment? Such analyses are critically missing from the manuscript.__

      BICD2 knockout cells indeed show a slightly higher mitotic index than their wild type counterparts, and a higher frequency of lagging chromosomes in anaphase and telophase as well (new data, shown in Figure EV5C). We agree with the reviewer that these might result from the role of BICD2 tethering centrosomes to the nuclear envelope to facilitate their separation during the initial steps of spindle formation. We have added a sentence in the text noting this: “As expected from cells with supernumerary centrioles, BICD2-/- cells showed a slightly higher mitotic index and a higher frequency of lagging chromosomes in anaphase and telophase (Figure EV5C), although these mitotic defects might also be partially attributed to the role of BICD2 in centrosome separation (Splinter et al, 2010; Gallisà-Suñé et al, 2023).

      __ In general, SCLT experiments are ambiguous. Centriole disengagement spontaneously occurs during prolonged prometaphase induced by SCLT. Accordingly, F6D shows that many centriole pairs in the control sample are disengaged after 16h of SCLT treatment. Although the distance between centrioles in knockout cells is, on average, larger, without knowing how BICD2 perturbations affect the dynamics of the mitotic spindles and mitosis progression, SCLT experiments do not provide enough insight.__

      After 16h of SCLT treatment, the authors regularly measure centriole distances in mitosis smaller than 500 nm in all samples. This suggests that the used method (which also needs to be described) cannot reliably assess centriole engagement status. Centrioles can be disengaged but adjacent. The authors reference Shukla et al. 2015 to compare the centriole-to-centriole distances here with those from that publication. However, in Shukla 2015, centriole-to-centriole distances increase from S to M. But here, in F6, the control centriole distances in S, G2, and early M are almost identical and less than 500 nm. This discrepancy needs to be addressed.

      We thank the reviewer for these constructive comments. We appreciate the opportunity to further clarify our methodology and experimental rationale.

      Validity and necessity of STLC treatment (Figures 4D and 7)

      We fully agree with the reviewer that prolonged STLC treatment (16 hours) carries inherent limitations and should not serve as the sole experimental system for studying centriole engagement. As the reviewer notes, 16 hours of STLC treatment results in a baseline population of control cells displaying disengaged centrioles. This population likely represents cells that entered mitosis early during the treatment and remained arrested for the longest duration, or cells with inherently less robust engagement machinery.

      However, we would like to highlight two key observations that validate STLC as a useful comparative tool in our study:

      • The significative increase in the number of cells with higher intercentriolar distances that indicate disengagement in particular experimental conditions. We show that depletion of BICD2 consistently leads to a statistically significant increase in mean intercentriolar distances compared to controls under identical STLC conditions, indicating a distinct weakening of centriole engagement in a substantial number of cells (and thus suggesting that BICD2 is part of the engagement mechanism).

      • Validation in unarrested cells: Crucially, this effect is not an artifact of mitotic arrest. Unarrested, normally cycling mitotic cells also display significantly increased intercentriolar distances in the absence of BICD2 (Figure 4C).

      Following the initial characterization in Figure 4D, we restricted the use of STLC exclusively to experiments requiring cell transfection and recombinant protein expression (Figure 7). Human RPE-1 cells offer the key advantage of being an untransformed, p53-wild-type model. However, they also present technical challenges, including lower transfection efficiencies and sensitivity to experimental manipulation. Capturing a statistically robust sample of transfected, unarrested mitotic cells proved technically challenging. STLC treatment provided a necessary tool to enrich for mitotic cells while allowing clear observation of rescue effects.

      We have explicitly clarified this technical rationale in the manuscript text:

      "Although this treatment inherently increased mean intercentriolar distances, it nevertheless enabled clear observation of the effects of BICD2 ablation, while yielding a sufficient number of mitotic cells expressing the recombinant proteins."

      Assessment of centriole engagement

      We agree that centrioles can occasionally be disengaged while still remaining adjacent. To avoid oversimplifying the observed phenotypes, we chose to report raw intercentriolar distances rather than applying an arbitrary binary classification of "engaged" versus "disengaged." Furthermore, we do not rely solely on distance measurements to assess engagement status. We complemented these data by quantifying c-NAP1-positive centrioles in unarrested, cycling mitotic cells (Figure 4E, F). Because c-NAP1 loading marks centriole-to-centrosome conversion (and thus licensing), this functional readout independently confirms that BICD2 loss promotes premature centriole disengagement.

      Intercentriolar distances across the cell cycle and cell-type variation

      Regarding the comparison with Shukla et al. (2015), we note that their study was conducted in HeLa cells, whereas our primary model is RPE-1 (alongside U2OS cells). Variations in centriole engagement dynamics and distance kinetics can likely be attributed to intrinsic differences among these cell types:

      RPE-1 cells: baseline intercentriolar distances in S-phase control RPE-1 cells (0.4–0.5 µm, measured using centrin) match those reported for HeLa cells in S-phase by Shukla et al. However, in RPE-1 control cells, these distances remain relatively constant from S phase through early M phase (Figure 4).

      U2OS cells: U2OS cells exhibit a slight increase from 0.43±0.01 µm in G2 to 0.50±0.01 µm in M (Figure 5), illustrating that slight variations occur between cell lines.

      Other studies similarly report persistent baseline distances around 0.5 µm through early cell cycle stages. For example, Yaguchi et al. (Yaguchi K et al. 2018. Uncoordinated centrosome cycle underlies the instability of non-diploid somatic cells in mammals. The Journal of Cell Biology 217:2463–2483. DOI: https://doi.org/10.1083/jcb.201701151) observed intercentriolar distances close to 0.5 µm in diploid HAP1 cells throughout mitosis and into early G1 phase, with substantial disengagement (>0.8 µm) occurring only well after cytokinesis onset.

      To address this discrepancy, we have added the following sentence to the manuscript text: "Note that in wild-type S-phase RPE-1 cells, intercentriolar distances measured using centrin as a marker were similar to those described in S-phase HeLa cells (Shukla et al., 2015), namely 0.4–0.5 µm; however, in contrast to HeLa cells, these distances remained fairly constant from S to early M phase in RPE-1 cells." We have also updated the Materials and Methods section to provide a precise description of how intercentriolar distances were measured: “Intercentriolar distances were assessed as the distance between centrin foci of the same diplosome in maximum projections of z-stacks

      __ The authors suggest that BICD2's functions at the centrosome are independent of its dynein functions. They show that GFP-BICD2 1-820 DD rescues centriole engagement among several other mutants. However, it is still possible that the expression of the mutants affects some yet uncovered BICD2 function outside of centrosomes. At least, T821A and S823A should be mutated to Ala. From what I gathered, such mutant should remain associated with mitotic centrosomes. The authors should analyze whether mitotic progression remains unperturbed, and centriole engagement status should be analyzed without SCLT treatment in G2, M, and in ensuing G1.__

      We agree with the reviewer that phosphonull mutants should be added to these experiments. In fact, and as mentioned in the responses to Reviewer 1, we have already performed experiments with the phosphonull mutants, observing that they are more retained at centrosomes than the phosphomimetic counterparts. We would be happy to share these results with the reviewers upon request if helpful. Nevertheless, and as mentioned above, we have decided to remove the preliminary data regarding BICD2 phosphorylation from the manuscript data in order to present a separate and more comprehensive study on BICD2 phosphorylation in the near future.

      Significance

      The question explored is relevant to the centrosome field and beyond since the processes leading to premature centriole disengagement and amplification are not fully understood. The study provides some novel insights. However, at the current stage, the study is preliminary. Additional experiments would be needed to strengthen the conclusion that BICD2 directly regulates centriole disengagement.

      My expertise is in centriole and centrosome assembly and the mechanisms that regulate centrosome homeostasis in human cells.

      __ __

      __Reviewer #3 __

      Evidence, reproducibility and clarity (Required):

      Centrosome duplication is tightly control during cell cycle to prevent loss or amplification of centrosome numbers, which are detrimental for cell proliferation. In preparation for centriole duplication in S-phase, mother and daughter centrioles disengaged late mitosis, a process that functions as a licensing factor for duplication. While several mechanism have been proposed to be important for centriole disengagement, differences between systems and organisms exist, suggesting alternative pathways may play a role.

      In this manuscript, Montes-Ruiz and colleagues investigate the role of the dynein adaptor protein BICD2 during centriole disengagement. They found that BICD2 localises to the centrioles, with a peak in S-Phase. Super resolution microscopy suggests that BICD2 localises to the mother centrioles and is mostly absent in mitosis cells after anaphase, when centrioles are disengaging. KO of BICD2 in REP-1 cells does not some t have strong phenotypes, but the authors found that centriole separation is increased, suggesting a role in centriole cohesion. While there is limited mechanist insight about the regulation of BICD2 and its function at the centrosomes, the data presented suggests a role for BICD2 in centriole cohesion that is independent of dynein interaction. There are however several issues with data presentation, image analyses and data interpretation the authors could improve.

      Major comments

      - On page 5, the authors state that figure 1 and supplementary figure1 data strongly suggest that BICD2 associates with mother centrioles and not the PCM. This is not very clear from the images on these figures. In fact, PCM is often associated with mother centriole as well, thus I am not sure they can make these conclusions based on the data presented in these 2 figures. Also, the fact that PCM is more abundant in G2/M, when BICD2 is not, does not mean it does not localize to the PCM. Higher resolution of expansion will be needed.

      The data presented in supplementary figure 3 does not help the conclusion above as it seems form the images that there is co-localization between BICD2 and pericentrin. It is impossible to conclude also that there is co-localization with the satellite marker PCM-1. In fact, they seem to have no overlap from the images provided. Higher resolution of expansion will be needed.

      Following the reviewer’s suggestion we embarked in a full characterization of BICD2 localization using expansion microscopy (ExM). We think that our new data further clarifies this together with new superresolution data.

      Additionally we now have a figure (Supplementary Figure S4) addressing the relation between pericentrin and the localization of BICD2. We show that pericentrin downregulation does not affect centrosomal BICD2 levels. And that both proteins do not colocalize as observed using 3D-SIM.

      Also regarding pericentrin, the revised version of the manuscript now includes a figure that functionally compares the results of its depletion to those of BICD2 (Figure 5).

      We also provide data showing that BICD2 localization does not significatively change upon PCM-1 depletion (Supplementary Figure S4C). As we mention in the text, previous reports have suggested that BICD2 is indeed in the satellites (Quarantotti V et al. 2019. Centriolar satellites are acentriolar assemblies of centrosomal proteins. The EMBO Journal e101082. DOI: https://doi.org/10.15252/embj.2018101082) . But we only observed clear colocalization of PCM-1 with C-terminal fragments of BICD2. Thus, while GFP-BICD2 488–820 strongly colocalizes with satellites, endogenous BICD2 and full-length GFP-BICD2 do not (Figure 6D). We ignore the reason for this, but the data suggests that satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein. To address this we have added a sentence to the text that now reads: “The presence of BICD2 at the centriolar satellites has been suggested previously (Quarantotti et al, 2019); we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 (but not the full-length protein, see Supplementary Figure S4) colocalize with satellites. This suggests that BICD2 satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein.”.

      - In figure 2, to confirm localization to the mother centrioles, could the authors use a mother centriole marker? Such as a distal appendage protein of ninein? CEP152 localizes to both centrioles in the images provided.

      I was surprised that BICD2 localizes to both distal appendages and linker? These are not close to each other. Can the authors comment on this? In supplementary figure 4C orthogonal view it seems like BICD2 is in between distal appendages and linker?

      We believe that the new ExM data (Figures 2 and 3) directly address the reviewer's concerns.

      Regarding the original supplementary figure S4C, indeed in the orthogonal projections of 3D-SIM images the signal corresponding to BICD2 was observed between distal appendages and linker and was quite broadly distributed. We recognize that this could lead to confusion. We have now removed part of this figure (original Figures 4B and 4C) from the manuscript, as we think that the data is made redundant with our new ExM data. Our new data, with a much higher resolution shows that BICD2 localization corresponds to that of the proximal torus (see new Figures 2D and 2E, and Figure 3). Note that in our new ExM images we use a daughter centriole marker (SAS-6) that (in addition to CEP152) we think helps confirm that BICD2 localizes around the mother centriole.

      -The IF data suggests that BICD2 localization to the centrosome is dynamically regulated during cell cycle. Did the authors consider that this protein could be degraded? Is it a matter of recruitment or total protein levels?

      We agree that protein degradation has to be considered when analyzing cell cycle-dependent localization. However, our data suggest that the dynamic behaviour of BICD2 at the centrosomes does not reflect changes in its total protein amount. We have previously shown that total BICD2 levels are not reduced in mitosis, as assessed by western blot (Gallisà-Suñé N et al. 2023. BICD2 phosphorylation regulates dynein function and centrosome separation in G2 and M. Nature Communications 14:2434. DOI: https://doi.org/10.1038/s41467-023-38116-1). To make this clear in the current manuscript, we have additionally added Figure EV1B depicting BICD2 levels in S, G2 and M phase, and the following note to the text : “Total levels of BICD2 remained constant during the different phases of the cell cycle (Figure EV1B and (Gallisà-Suñé et al, 2023))“.

      - The authors propose that the dynamic localization of BICD2 is associated with licensing. However, it is rather surprising that the phenotype of centriole separation they describe is only observe in mitosis when BICD2 in knockdown and not in S-phase when the levels of BICD2 are higher? If the role of BICD2 is to prevent premature centiole disengagement, shouldn't that be observed in S-phase as well? Why only in mitosis when in control cells BICD2 levels are already very low?

      Recent data supports the notion that in S phase centriole cohesion is maintained by the cartwheel (Huang F et al. 2022. Cartwheel disassembly regulated by CDK1-Cyclin B kinase allows human centriole disengagement and licensing. The Journal of Biological Chemistry 298:102658. DOI: https://doi.org/10.1016/j.jbc.2022.102658; Ito et al. 2025. Multimodal mechanisms of human centriole engagement and disengagement. The EMBO journal 44:1294–1321. DOI: https://doi.org/10.1038/s44318-024-00350-8). Our data, including the new results showing that BICD2 interacts with CEP152, suggests that BICD2 is a dynamic part of the mother centriole torus, a structure that does not seem to be implicated in maintaining cohesion in S. We now note this in the manuscript’s text: “During S phase, mother-daughter centriole cohesion is maintained by the cartwheel (Huang et al, 2022; Ito et al, 2025), and, consistently, does not depend on BICD2.”. Of note, after Ito etl al. BICD2 (and the torus) may have a role in late S if the cartwheel is compromised, something that could be tested in future studies by downregulating cartwheel components and BICD2 simultaneously.

      - The images of C-Nap1 localization in figure 6E are not very convincing to illustrate the pint the authors are making in the main text (additional C-Nap1 foci are visible in the KO cells)

      We would like to note that visualizing C-NAP1 in mitosis is technically challenging, as a significant pool of the protein is displaced from the centrioles after phosphorylation in G2. However, the protein has been widely used as a marker of centriole disengagement (e.g. in the seminal Tsou M-FB et al. 2006. Mechanism limiting centrosome duplication to once per cell cycle. Nature 442:947–951. DOI: https://doi.org/10.1038/nature04985). We therefore consider it a valuable tool to support our conclusions regarding centriole engagement. Regarding extra c-NAP1 foci in BICD2 knockout cells, these may reflect additional centrioles that appear in these cells, as a result of abnormal disengagement and early licensing. To have this into account our data quantifies both c-NAP-1 positive centrioles (increased in KO cells, Figure 4E) and number of c-NAP-1 positive centrioles /total centriole number (with an increase in the abnormal >2:4 configuration in BICD2 KO cells, Figure 4F).

      - The authors propose that PLK1 and CDK1 phosphorylation sites regulate the association of BICD2 with the centrioles. Could this be tested with a PLK1 inhibitor?

      As noted above, we have removed the phosphorylation data from the manuscript, as we aim to report these findings in a dedicated upcoming study. Nevertheless, to address the reviewer's query, we now consider BICD2 to be predominantly regulated by CDK1, supported by data using BI 2536 showing that BICD2 centrosomal levels are unaffected by PLK1 inhibition. In contrast, CDK1 inhibition slightly reduces these levels, though this effect does not reach statistical significance under the tested conditions. We would be glad to share these additional results with the reviewers upon request.

      - On page 13, the authors state that their results do not agree with previous literature showing that pericentrin cleavage can result in disengagement. However, it was unclear from this manuscript what is the evidence to demonstrate that this is the case? The data presented in figure 5B for example only demonstrates that pericentrin levels do not change in the absence of BICD2 in what looks like S-phase cells. Did the authors look at pericentrin levels when they observe centriole disengagement in the ko cells? In G2 or early M-phase?

      We recognize that pericentrin is widely considered a crucial factor in centriole engagement, and have added new data in the manuscript studying the relationship between it and BICD2 (the partially new Supplementary Figure S4), and their relative importances for engagement both in G2 and M (the new Figure 5). Our data suggests that both proteins act independently in a partially redundant manner, BICD2 as part of the torus (key for engagement in G2) and pericentrin of the PCM (more important in M).

      We have updated the Discussion to present this and our view on pericentrin importance for engagement more clearly, specially our concerns that its importance may have been overestimated. Specifically we write that “BICD2 depletion reduces its centrosomal levels to a degree that mirrors those naturally observed during late M and early G1 in unperturbed cells. In contrast, experimental depletion of pericentrin reduces its levels far below physiological baselines across any phase of the cell cycle. This severe reduction produces marked centriole separation in mitosis that is likely amplified by spindle-derived forces. Consequently, the individual contribution of pericentrin to regulating physiological centriole cohesion may be somewhat overestimated under standard experimental knockdowns, and this regulation may rely more heavily on torus components, such as BICD2, than previously appreciated.

      Regarding the phases of the cell cycle in which we quantify pericentrin levels in the original Figure 5B (now Figure EV5B), we realize that the figure could lead to confusion as it was, as they were measured in M (when its amount is maximal, as specified in the figure legend) but the figure did not show examples in this cell cycle phase. We have added new examples of mitotic cells to the figure, and modified the figure labels and wording of the figure legend to clarify this.

      Minor comments

      - A more general reference (review) missing in the second paragraph of the introduction that describe the centrosomes.

      We have added a recent general reference when introducing centrioles (Gönczy P. 2025. Critical constituents and assembly principles of centriole biogenesis in human cells. Nature Reviews Molecular Cell Biology 1–18. DOI: https://doi.org/10.1038/s41580-025-00921-5). Later in the paragraph, when centriole duplication is introduced, we now use this reference plus the also recent Fernandes-Mariano C et al. 2025. Centrosome biogenesis and maintenance in homeostasis and disease. Current Opinion in Cell Biology 94:102485. DOI: https://doi.org/10.1016/j.ceb.2025.102485.

      - Some figures are not well organized, difficult to see which panel they correspond to? The authors could consider labelling panels better to make this clear. For example, figure 4 and 6 could benefit from additional panel labels.

      We have added additional panel labels to Figure 4 (now Figure 6) and Figure 6 (now Figure 4), that we have also slightly reorganized with the aim of making it clearer).

      - On page 7, what the authors mean by: "... we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 but not the fill-length protein co-localize with these pericentriolar structures"?

      By "pericentriolar structures” we were referring to the centriolar satellites. We realize that that was not clear and updated the wording of the sentence that now reads “we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 (but not the full-length protein, see Supplementary Figure S4) colocalize with satellites.”. We subsequently propose a possible a possible explanation for this: "This suggests that BICD2 satellite localization is regulated (possibly via intramolecular autoinhibition) to mediate BICD2 transport or a distinct satellite-specific function of this protein.".

      Reviewer #3 (Significance (Required)):

      In general this work has limited mechanistic insight and BICD2 localization to the centrosomes was known. However, the authors do go into more detail description of the centriole localization of BICD2 . In addition, their established KO cell lines provide some insights into the role of BICD2 in centriole disengagement, which is of interest to the field. But the limited scope of the conclusions does not advance the field significantly as it is.

      this work will interest a specialized audience.

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      Referee #3

      Evidence, reproducibility and clarity

      Centrosome duplication is tightly control during cell cycle to prevent loss or amplification of centrosome numbers, which are detrimental for cell proliferation. In preparation for centriole duplication in S-phase, mother and daughter centrioles disengaged late mitosis, a process that functions as a licensing factor for duplication. While several mechanism have been proposed to be important for centriole disengagement, differences between systems and organisms exist, suggesting alternative pathways may play a role. In this manuscript, Montes-Ruiz and colleagues investigate the role of the dynein adaptor protein BICD2 during centriole disengagement. They found that BICD2 localises to the centrioles, with a peak in S-Phase. Super resolution microscopy suggests that BICD2 localises to the mother centrioles and is mostly absent in mitosis cells after anaphase, when centrioles are disengaging. KO of BICD2 in REP-1 cells does not some t have strong phenotypes, but the authors found that centriole separation is increased, suggesting a role in centriole cohesion. While there is limited mechanist insight about the regulation of BICD2 and its function at the centrosomes, the data presented suggests a role for BICD2 in centriole cohesion that is independent of dynein interaction. There are however several issues with data presentation, image analyses and data interpretation the authors could improve.

      Major comments

      On page 5, the authors state that figure 1 and supplementary figure1 data strongly suggest that BICD2 associates with mother centrioles and not the PCM. This is not very clear from the images on these figures. In fact, PCM is often associated with mother centriole as well, thus I am not sure they can make these conclusions based on the data presented in these 2 figures. Also, the fact that PCM is more abundant in G2/M, when BICD2 is not, does not mean it does not localize to the PCM. Higher resolution of expansion will be needed. The data presented in supplementary figure 3 does not help the conclusion above as it seems form the images that there is co-localization between BICD2 and pericentrin. It is impossible to conclude also that there is co-localization with the satellite marker PCM-1. In fact, they seem to have no overlap from the images provided. Higher resolution of expansion will be needed. In figure 2, to confirm localization to the mother centrioles, could the authors use a mother centriole marker? Such as a distal appendage protein of ninein? CEP152 localizes to both centrioles in the images provided. I was surprised that BICD2 localizes to both distal appendages and linker? These are not close to each other. Can the authors comment on this? In supplementary figure 4C orthogonal view it seems like BICD2 is in between distal appendages and linker? The IF data suggests that BICD2 localization to the centrosome is dynamically regulated during cell cycle. Did the authors consider that this protein could be degraded? Is it a matter of recruitment or total protein levels? The authors propose that the dynamic localization of BICD2 is associated with licensing. However, it is rather surprising that the phenotype of centriole separation they describe is only observe in mitosis when BICD2 in knockdown and not in S-phase when the levels of BICD2 are higher? If the role of BICD2 is to prevent premature centiole disengagement, shouldn't that be observed in S-phase as well? Why only in mitosis when in control cells BICD2 levels are already very low? The images of C-Nap1 localization in figure 6E are not very convincing to illustrate the pint the authors are making in the main text (additional C-Nap1 foci are visible in the KO cells) The authors propose that PLK1 and CDK1 phosphorylation sites regulate the association of BICD2 with the centrioles. Could this be tested with a PLK1 inhibitor? On page 13, the authors state that their results do not agree with previous literature showing that pericentrin cleavage can result in disengagement. However, it was unclear from this manuscript what is the evidence to demonstrate that this is the case? The data presented in figure 5B for example only demonstrates that pericentrin levels do not change in the absence of BICD2 in what looks like S-phase cells. Did the authors look at pericentrin levels when they observe centriole disengagement in the ko cells? In G2 or early M-phase?

      Minor comments

      A more general reference (review) missing in the second paragraph of the introduction that describe the centrosomes. Some figures are not well organized, difficult to see which panel they correspond to? The authors could consider labelling panels better to make this clear. For example, figure 4 and 6 could benefit from additional panel labels. On page 7, what the authors mean by: "... we ignore the reason why in the conditions used in this study only C-terminal fragments of BICD2 but not the fill-length protein co-localize with these pericentriolar structures"?

      Significance

      In general this work has limited mechanistic insight and BICD2 localization to the centrosomes was known. However, the authors do go into more detail description of the centriole localization of BICD2 . In addition, their established KO cell lines provide some insights into the role of BICD2 in centriole disengagement, which is of interest to the field. But the limited scope of the conclusions does not advance the field significantly as it is.

      this work will interest a specialized audience.

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      Referee #2

      Evidence, reproducibility and clarity

      Montez-Ruiz and colleagues explore the role of a dynein adaptor BICD2 in the engagement of mother and daughter centrioles. Cells need to maintain centriole engagement in interphase to prevent centriole reduplication and in early mitosis to prevent the formation of aberrant mitosis spindles. The authors demonstrate that BICD2 is a centriolar protein that surrounds the mother centriole adjacent to the daughter centriole. It is removed from centrosomes in mitosis, which, in turn, is responsible for centriole disengagement. Further, they suggest that in BICD2 knock-out G2 and early mitotic cells, centrioles disengage prematurely. By conducting rescue experiments, the authors conclude that BICD2 regions CC2, CC3, and CC4, which are dynein-independent, are essential for their function at the centrosome. Finally, they show that the phosphorylation of S817 and S819 of BICD1 controls its centrosome localization.

      Major comments:

      1. Based on F1 and SF1, BICD2 is reduced from centrosomes already in early G2. So, it is hard to square how removing a factor that is not present at the centrosomes at the time of disengagement would dysregulate disengagement. The study at this stage does not explain how BICD2 contributes to centriole engagement only in mitosis, while it does not affect centrioles in S.
      2. The interpretation that the longitudinal localization of the BICD2 signal coincides with SAS-6 and procentrioles requires further evidence. BICD2 seems largely localized to the other regions around the mother centriole, and in some examples, it does not colocalize with the site of the daughter centriole or SAS-6 (for instance: F2B second row; SF4B, second row; SF5, fourth row; SF6 upper row).
      3. BICD2 is important for centrosome-nucleus tethering during centrosome separation in G2, and its global removal likely affects the dynamics of the spindle assembly. Is G2 and mitotic progression affected in knockouts? Do the knockout cells show issues with chromosome alignment? Such analyses are critically missing from the manuscript.
      4. In general, SCLT experiments are ambiguous. Centriole disengagement spontaneously occurs during prolonged prometaphase induced by SCLT. Accordingly, F6D shows that many centriole pairs in the control sample are disengaged after 16h of SCLT treatment. Although the distance between centrioles in knockout cells is, on average, larger, without knowing how BICD2 perturbations affect the dynamics of the mitotic spindles and mitosis progression, SCLT experiments do not provide enough insight. After 16h of SCLT treatment, the authors regularly measure centriole distances in mitosis smaller than 500 nm in all samples. This suggests that the used method (which also needs to be described) cannot reliably assess centriole engagement status. Centrioles can be disengaged but adjacent. The authors reference Shukla et al. 2015 to compare the centriole-to-centriole distances here with those from that publication. However, in Shukla 2015, centriole-to-centriole distances increase from S to M. But here, in F6, the control centriole distances in S, G2, and early M are almost identical and less than 500 nm. This discrepancy needs to be addressed.
      5. The authors suggest that BICD2's functions at the centrosome are independent of its dynein functions. They show that GFP-BICD2 1-820 DD rescues centriole engagement among several other mutants. However, it is still possible that the expression of the mutants affects some yet uncovered BICD2 function outside of centrosomes. At least, T821A and S823A should be mutated to Ala. From what I gathered, such mutant should remain associated with mitotic centrosomes. The authors should analyze whether mitotic progression remains unperturbed, and centriole engagement status should be analyzed without SCLT treatment in G2, M, and in ensuing G1.

      Significance

      The question explored is relevant to the centrosome field and beyond since the processes leading to premature centriole disengagement and amplification are not fully understood. The study provides some novel insights. However, at the current stage, the study is preliminary. Additional experiments would be needed to strengthen the conclusion that BICD2 directly regulates centriole disengagement.

      My expertise is in centriole and centrosome assembly and the mechanisms that regulate centrosome homeostasis in human cells.

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      Referee #1

      Evidence, reproducibility and clarity

      This study demonstrates that BICD2, previously known as an adaptor protein for dynein, is involved in regulating centriole engagement during mitosis. First, using different antibodies, it was shown that BICD2 localizes near the mother centriole, as observed by super-resolution microscopy. During G1 and S phases, BICD2 localizes slightly outside the Cep152 ring, while in G2 to mitosis, it localizes near the cartwheel component SAS-6. Moreover, analysis of deletion mutants revealed that BICD2 localizes to the centrosome in a CC domain-dependent manner at the C-terminal end. The localization pattern resembling a ring in the cytoplasm was also observed through the CC3 domain. Next, BICD2 knockout (KO) cells were generated to investigate centriole dynamics. In BICD2 KO cells, the distance between the mother and daughter centrioles was observed to increase from G2 to mitosis compared to controls. Along with this, early centriole disengagement and centriole amplification phenotypes were observed. The increased distance phenotype between centrioles was rescued in BICD2 wild-type (WT) and mutant forms lacking the CC1 domain at the N-terminus, suggesting that this function of BICD2 is independent of dynein. Additionally, BICD2 mutants mimicking phosphorylation at the C-terminus showed reduced centrosome localization and were unable to rescue the phenotypes seen in BICD2 KO cells. While the study clearly demonstrates BICD2's contribution to centriole engagement, the underlying mechanisms of how BICD2 is involved in centrosome localization and centriole engagement remain unclear. As it is anticipated that the function of BICD2 is independent of dynein, further exploration of this unknown mechanism would enhance the value of the paper. Below are the concerns that should be addressed, including new experiments.

      Main Points:

      1. Fig. 1-3: Regarding the localization of BICD2 to centrioles, during the G1-S phase, its localization appears to overlap with PCM. Experimental investigation should be performed to examine whether BICD2's centrosomal localization is influenced by knockdown of PCM components like PCNT, Cep192, or Cep152.
      2. Fig. 7: The experiments using BICD2 mutants suggest that the function of BICD2 here is independent of dynein. To further investigate whether BICD2's role in centriole engagement is independent of dynein, experiments should be conducted to examine the effect of dynein knockdown on BICD2 localization to the centrosome and centriole engagement.
      3. Fig. 4: The CC4 domain at the C-terminus of BICD2 is important for its centrosomal localization, but identifying the binder/recruiter responsible for BICD2's centrosome localization would be desirable.
      4. Fig. 7: Rescue experiments using BICD2 mutants suggest that BICD2's functional domains are critical. Further experiments by creating mutants missing parts of CC2 or CC3 could identify functionally important domains of BICD2 by observing any loss-of-function phenotypes at the centrosome.
      5. Fig. 6: Regarding the BICD2 KO cell phenotype, is there experimental evidence showing an increase in centriole number during mitosis? For instance, while no abnormality in centriole number may occur during G2, a trend of increase in mitosis should be experimentally demonstrated. Also, how should the slight differences in phenotypes between Ndelta4 and Ndelta5 BICD2 KO cells be interpreted?
      6. Fig. 8: Regarding the phosphorylation of BICD2 at the C-terminus: The phenotypes of mutants where these two phosphorylation sites are changed to alanine should be experimentally observed. It is expected that the removal of BICD2 from the centrosome during mitosis could be rescued. Additionally, the effect of PLK1 or CDK1 inhibitors on the removal of BICD2 from the centrosome should be investigated.

      Minor Points:

      1. Fig. 1-3: During G1 and S phases, BICD2 localizes near the mother centriole, and from G2 onward, it colocalizes with SAS-6. How can this be explained?
      2. Fig. 4: The GFP-BICD2 488-820 fragment forms cytoplasmic rings, which is interesting. This domain contains the CC4 domain, so it can localize near the centriole, but why does it not form a perfect ring there? Also, which other centriole/centrosome markers were used for colocalization studies? Does knockdown of PCM1 affect BICD2's centrosomal localization?
      3. Fig. 4A: What are the aggregates observed in the cytoplasm under the GFP-BICD2 + ice condition? Also, does the 1-575 mutant fail to localize to the centrosome upon ice treatment?
      4. Can similar phenotypes be observed in other cell types when BICD2 is knocked down? This should be experimentally validated.
      5. Are there previous studies suggesting that this function of BICD2 is evolutionarily conserved? This should be addressed.

      Significance

      In this paper, the identification of BICD2 as a novel factor regulating centriole engagement is of significant importance. However, the mechanisms through which BICD2 controls its localization to the centrosome and regulates centriole engagement remain largely undefined. Further exploration of these mechanisms would likely enhance the value of the paper.

      The findings are likely to be of great interest to researchers in the field of cell biology, particularly those focusing on centrosome biology.

      The above feedback comes from a researcher specializing in centrosome studies.

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      Referee #4

      Evidence, reproducibility and clarity

      In this manuscript, Dilbaz-Gunden, et al. investigate the localization and function of CSPP1 in multiciliated cells (MCCs). Using a combination of mouse tracheal cell cultures and Xenopus embryos, the authors characterize the localization of CSPP1 in MCCs and use gene knockdowns to analyze CSPP1 function. They find similar protein localization in both models and that loss of CSPP1 disrupts centriole organization, basal body migration/docking, basal body polarity, and cilia formation. Overall, the data is high quality and the presentation is clear. The main conclusion that CSPP1 is a conserved regulator of ciliogenesis in MCCs is supported by the evidence presented.

      Major comments:

      1. For each of the CSPP1 protein localization studies presented in Figure 1, to gauge reproducibility, it would be useful to indicate how many cells were analyzed and how many looked like the representative images.
      2. The conclusion that CSPP1 is required for centriole amplification is confusing since CSPP1 depletion does not alter centriole number (Fig. 2D-E). The centriole defect appears to be due to a MCC morphogenesis defect.
      3. In CSPP1 loss-of-function treatments, the authors find defects in centriole organization, basal body migration, basal body polarization, cilia formation, cilia length, and cilia motility in MCCs. It is not clear whether the authors propose that CSPP1 has distinct functions in each of these processes or these are pleiotropic phenotypes due to earlier defects in MCC morphogenesis. This is particularly confusing since previous work indicates CSPP1 is a regulator of the cell/cytoplasmic cytoskeleton.

      Significance

      Mutations in CSPP1 have been identified in patients with the ciliopathies Joubert syndrome and Meckel-Gruber syndrome, so the significance of studying CSPP1 is high. How mutations in CSPP1 can cause two distinct ciliopathies remains unclear. Previous work has identified CSPP1 as a microtubule-associated protein that is localized to the basal body and ciliary tip of primary cilia and is involved in cilia length regulation. In this context, the finding in the current manuscript that CSPP1 is important for establishing ciliary structure is not new or unexpected. However, this is the first analysis of CSPP1 localization and function in MCCs. This new evidence that loss of CSPP1 can disrupt both primary cilia and motile cilia in MCCs supports a model for how mutations affecting core cilia assembly proteins can underlie multiple ciliopathy phenotypes. This work will appeal to a broad audience in the fields of cell biology and ciliopathy. A limitation to the significance of this work is that it stops short of investigating how CSPP1 mechanistically regulates MCCs.

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      Referee #3

      Evidence, reproducibility and clarity

      The report by Dilbaz-Gunden et al characterizes the roles of Joubert syndrome protein CSPP1 in multiciliogenesis. Joubert syndrome is commonly thought of as a sensory ciliopathy as many regions in the brain that are affected in this disorder do not possess motile cilia. However, the authors note in their introduction that there are pathogenic variants in several "cilia" genes that cause both sensory and motile cilia phenotype in the same individuals. This motivates the study to evaluate the role of CSPP1 in multiciliated cell contexts.

      The strengths of this report are the analyses in multiple systems and the beautiful imaging and quantifications of these images. However, some of the conclusions are less convincing than others as written and this weakens the paper. My suggestions for strengthening this report are as follows:

      1) One major conclusion is that CSPP1 is required for "efficient centriole amplification". This doesn't seem supported by the results in figure 2 as presented. In 2D the conclusion seems to be that the cells have similar number of centrioles, but loss of Cspp1 results in cells with larger apical surfaces that do not fill with centrioles. This doesn't appear to be an inability to make centrioles, but an inability of a cell lacking Cspp1 to recognize that it needs to make more centrioles to fill the larger surface area. Are there other manipulations that increase apical surface area yet the cells compensate appropriately with more centrioles? Given the later results that Cspp1 loss affects the apical microtubule networks, how many of the subsequent defects, (defective basal body docking, apical organization, establishment of rotational polarity, ciliogenesis, and failure of MCCs to integrate properly in the epithelium) are due to that defect alone? The presentation and the interpretations would benefit from thinking about which loss of Cspp1 phenotypes are likely direct roles of Cspp1, versus indirect consequences of an earlier Cspp1 role. The statement on page 20 that "CSPP1 depletion is consistent with a role for CSPP1 in coordinating centriole amplification with apical remodeling" seems more accurate and could be the way to think about reframing the results.

      2) Another major issue is that how quantifications are conducted aren't always clear. For example, saying a control cell has "dense and uniform" centriole distribution and a Cspp1 depletion cell is abnormal and disorganized doesn't clarify how the person doing the analysis decided what was normal versus abnormal. In some cases this can be found in figure legends, but it would be helpful to include an analysis section in materials and methods that explains how decisions were made regarding normal and abnormal for each analysis.

      3) Minor - It would be nice to add some thought to the discussion about why some mutations cause both sensory and motile cilia defects, while others do not.

      4) Figure 1A - please add the ALI stages to the diagram outlining the stages of MCC formation.

      5) Figure 2 B-J it would be helpful to add these category labels to the panels to help the reader understand what each group of stainings is meant to analyze.

      6) Figure 1E bottom panel - the inset isn't from the outlined cell? Why is this?

      7) Figure 1F and S1B - in detached cilia, CSPP1 expression is throughout the axoneme and at the tip, but in other images the concentration at the tip is emphasized as the localization for this protein. Is the staining in detached cilia an artefact? Or is it just less obvious that CSPP1 is throughout the cilia body in intact MCCs?

      8) Figure 2B - As per the major comment above - how did the researcher set the threshold for amplified versus less amplified?

      9) Was there rationale for using the SB versus TB morpholino?

      10) In Figure 3A, why is there so much less ZO1 in the shCspp1 image? Is this a defect with loss of CSPP1 or an imaging plane issue?

      11) Figure 4A add the stage of analysis to the images.

      12) Figure S2A How was quantification of CSPP1 protein levels conducted?

      13) Figure S2E was the new product produced by SB MO sequenced to confirm this is intron retention as opposed to some other splicing defect?

      14) Define GFP-GPI in material and methods. I assume membrane associated GFP marker?

      15) Figure S2F - the traced outlines do not look like the cells indicated by the boxes.

      16) Figure S3B - Which cells is which of the callouts? Why are there no traced outlines as in other figures?

      Significance

      This work is important and will be of interest to the cilia community. The images are fantastic and the ultimate findings of CSPP1 affecting multiciliated cells is novel and intriguing. My issues with the paper have to do with interpretation of the results which seems to be incomplete. The authors should be able to fix the issues I have with text edits and I do not think they need to do additional experiments.

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      Referee #2

      Evidence, reproducibility and clarity

      The paper by Dilbaz-Gunden and colleagues describe the results of experiment to understand the function of the CSPP1 protein in multiciliated cells (MCCs). Mutations CSPP1 have been identified in Joubert syndrome patients and although the protein has been studied with reference to primary cilia, there are clinical issues reported in these patients that suggest functions in motile ciliated cells as well. The authors first describe a detailed localization pattern of CSPP1 in differentiating MCCs, using mouse trachea and Xenopus epidermal MCCs. Then they use knockdown studies to uncover apparently multiple roles of CSPP1 in basal body biogenesis, apical migration and docking as well as axoneme formation and cilia motility. While the localization studies provide solid data, I am not convinced with the loss-of-functions approaches adopted in this study. A stable genetic knockout is essential to properly dissect the role of CSPP1 in MCCs: Either a mouse mutant or knockout in cultured cells then differentiated into MCCs. There are many papers with knockdown studies in cultured cells and Xenopus embryos which do not even up with findings from stable knock outs. Of relevance to this work is the initial description of Deup1 as a protein essential for basal body generation in MCCs using similar knock down studies in mammalian cells and Xenopus embryos (PMID: 24240477), which has been subsequently refuted using stable knockout mice (PMID: 31792378). The other shortcoming is that the data are largely descriptive, with no new or mechanistic insights into CSPP1 function in centriole or cilia formation. Furthermore, the knockdown effects seem to produce pleiotropic effects, so it is not easy to appreciate if CSPP1 indeed has multiple functions or it is principally required for generating proper basal bodies, for example, and failure in this early event in the knockdown conditions triggers other downstream manifestations like problem in their migration and docking and axonemal extension.

      Significance

      The study provides limited conceptual novelty and does not substantially advance mechanistic understanding of CSPP1 function.

      I have expertise in cilia and ciliopathies, particularly multiciliated cells

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      Referee #1

      Evidence, reproducibility and clarity

      The Joubert syndrome protein CSPP1 is a conserved regulator of vertebrate multiciliogenesis and motile cilia function

      By: Irem S. Dilbaz-Gunden, Clothilde Boitel, Jovana Deretic, Marine Touret, Mehmet S. Aydin, Esra N. Yigit, Ozgecan Kayalar, Hasan Bayram, Virginie Thomé, Olivier Rosnet, Nicolas Brouilly, Laurent Kodjabachian, Camille Boutin and Elif Nur FiratKaralar

      I have only some Minor remarks:

      Introduction:

      • Motile cilia not always have 9+2 configuration.
      • Authors present a excellent introduction explaining how a number of findings suggest that ciliogenesis relies on shared molecular modules, including microtubule-associated proteins, that govern organelle biogenesis and function across both primary and motile cilia.

      Results:

      • The authors establish CSPP1 as a conserved component of the multiciliogenesis machinery in mouse, human and Xenopus. Its localization to fibrous granules and deuterosomes during centriole amplification, and to basal bodies and ciliary axonemes in mature cells.

      • Figure 1: is flawless.

      • Figure 2: data from 2 animal models strongly supports that CSPP1 depletion reduced the efficiency of centriole amplification and disrupted formation of the dense centriole arrays characteristic of MCCs.
      • Figure 3: data is robust and presents an excellent quantification in 3 axis of milder defects for centrin signal spacing and stronger defects for polarity direction.

      I would ask the authors to make the figures even more clear by iserting a small diagram of the animal model being used in each case. In all images presented.

      • Figure 4 title: CSPP1 loss leads to defective cilia formation, axonemal structure, and motility (please add in which cells from which animal model) I like the way the authord show the "normal defects" in the control figures and make the comparison with the treatment defects in all cases. Transparency is important. Congratulations. Again, here if one does not read the main text cannot undestand which model is being used. Legends also ommit the model. Please make sure each figure is understandable on its own. Images are of high quality. The number of methodologies used to reach their conclusions is impressive and data is well quantified. The TEM data (h) supports the claims and images were taken at the same cilia longitudinal focus planes supporting sh Cspp1 cilia are indeed very deffective.

      • Figure 5:

      The xenopus experiments are very convincing and robust as cells were stained for the MO lineage tracer (green), the Rescue tracer (RFP, magenta), and cilia (Acetylated alphatubulin, yellow). All images are impecably presented. The authors provide a detailed framework along MCC development for understanding how a Joubert syndrome-associated gene can contribute to respiratory phenotypes.

      Discussion:

      • In the discussion (final paragraph) please add a reference for the HH signalling defects in seen in primary cilia as these were not evaluated here. Are these for the same gene or for other genes? Please specify to make it clearer. In JS patients is there any reason why the symptoms are more related to the upper airways? Perhaps discuss this here. Any data from bronchoscopy available? is this a very mild phenotype in humans?

      Significance

      The manuscript is very well written, supported by great imaging and beautiful figures. I agree that the findings support a model in which disruption of a shared cytoskeletal assembly pathway can link primary and motile ciliopathy phenotypes across multiple ciliary tissues and species. I have some minor remarks that would improve the clariry of the manuscript but I think the work is of excellent quality and will be an important contribution to the field. Publication should be fast.

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      Reply to the reviewers

      Note to readers on BioRxiv: In this response to reviewer comments, we have drafted some display items to illustrate the preliminary analyses that we have performed. If the reader sees a reference to a display item and wishes to view it in its original format, please see the original revision response PDF file. Our response begins below:

      Manuscript number: RC-2026-03518

      Corresponding author(s): Tijana, Ivanovic

      General Statements

      We first provide the general statements from Reviewers #1, 2, and 3 to give a holistic view of their impressions of the paper. Afterwards, we describe our plan to address their comments and the structure of this document.

      Reviewer #1(Evidence, reproducibility and clarity (Required)): Planitzer et al. have developed a clever flow cytometry assay to monitor hemagglutinin (HA)-mediated membrane fusion. This novel approach provides several advantages over bulk and single-particle fusion assays, including dramatically higher throughput and detection of lipid mixing under low efficiency conditions. As such, on purely technical grounds, this manuscript provides a significant advance to the field. The authors then apply their new approach to probe the role of HA-sialic acid binding in the membrane fusion reaction. This reflects an important open question in the field, which, as the authors point out, has been elusive. They conclude that sialic acid binding promotes efficient membrane fusion, probably by increasing the likelihood of the fusion peptide engaging the target membrane after acid-induced conformational changes. Overall, the manuscript is very well written; the data are rigorously analyzed and nicely presented; and the results are important and informative. My comments are, for the most part, minor.

      __Reviewer #1 (Significance (Required)): Planitzer et al. have developed a clever flow cytometry assay to monitor hemagglutinin (HA)-mediated membrane fusion. This novel approach provides several advantages over bulk and single-particle fusion assays, including dramatically higher throughput and detection of lipid mixing under low efficiency conditions. As such, on purely technical grounds, this manuscript provides a significant advance to the field. The authors then apply their new approach to probe the role of HA-sialic acid binding in the membrane fusion reaction. This reflects an important open question in the field, which, as the authors point out, has been elusive. They conclude that sialic acid binding promotes efficient membrane fusion, probably by increasing the likelihood of the fusion peptide engaging the target membrane after acid-induced conformational changes. Overall, the manuscript is very well written; the data are rigorously analyzed and nicely presented; and the results are important and informative.

      __

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): This manuscript by Planitzer et al. investigates how NA activity, HA receptor binding avidity, and the identity and density of sialic acid receptors within the target membrane influence membrane fusion. This is an important question, as functional balance between HA and NA drives the antigenic evolution of seasonal influenza viruses and contributes to the pandemic potential of zoonotic viruses. As discussed in this manuscript, prior studies of how sialic acid receptors contribute to fusion following initial viral attachment are relatively few, and these have frequently reached different conclusions. Many of these discrepancies are likely attributable to details of the experimental system used to address the question, highlighting the importance of approaches in which the receptor identity and density are precisely controlled. Within this context, the sensitive and quantitative method the authors have developed to measure the efficiency and kinetics of virion fusion using flow cytometry ('spCALM') stands out as an important contribution of this manuscript. Using this approach, the authors convincingly demonstrate that engagement of HA with sialic acid receptors during the fusion process promotes efficient fusion, and that conditions that disrupt this engagement (low receptor densities combined with NA activity or receptor-HA mismatch) lead to reduced fusion efficiency by reducing the probability of fusion peptide insertion. Overall, the methods and findings of this paper are a strong contribution to the field and will be of interest to researchers studying viral membrane fusion as well as HA-NA balance. The manuscript is well-written, and the figures are very clear. The supporting figures provide additional context that is valuable. Addressing the comments below would strengthen the manuscript further.

      Reviewer #2 (Significance (Required)): This study establishes a powerful experimental approach to quantitatively dissect fusion between influenza viruses and target membranes across a wide range of experimental conditions. This approach represents a significant methodological advance and its high throughput allows the authors to quantify rare events. Additionally, the manuscript presents convincing data showing that, within the regimes tested here, sustained receptor engagement promotes membrane fusion by increasing the likelihood of fusion peptide insertion. The observation that this is reduced by NA activity at low receptor densities carries implications for HA-NA functional balance.

      While the specific findings may be restricted to the range of experimental conditions tested here, the results will be of interest to researchers studying virus-membrane fusion and functional balance between the influenza virus glycoproteins.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary: The manuscript by Plamitzer et al. investigates influenza A virus hemagglutinin (HA)-mediated membrane fusion using bulk flow cytometry analysis. In the assay, they use red blood cells and liposomes carrying defined, variable densities of sialic acid residues. They used H3N2 and H1N1 model viruses with spherical morphology. The liposomes were produced using double-stranded DNA to attach sialic acid. Similar studies have been done using DNA-tethers to study membrane fusion between liposomes and influenza virions (PMID: 35143209, PMID: 27410740). Some previous studies have shown that reduced HA binding does not influence membrane fusion, while others have shown that binding increases membrane fusion. This work aims to reconcile prior conflicting observations. Based on their results, they conclude that HA-receptor binding avidity and the extent of neuraminidase (NA)-mediated receptor cleavage directly influence lipid mixing, which is used here as a proxy for membrane fusion and allows monitoring of the first step of fusion, namely the hemifusion intermediate. The results of the test whether receptor binding modulates the HA extended intermediate should be interpreted with more caution. The manuscript would benefit from more data on the characterisation of liposome carrying dsDNA sialic acid receptors and from using the TIRF SBP method to validate the flow cytometry.

      __

      __Reviewer #3 (Significance (Required)):

      __

      __Significance: The study attempts to answer an important question and is of interest to the membrane fusion field and the virology field. It provides flow cytometry to study membrane fusion and a method for DNA-based sialic acid receptors that can be displayed at control density.

      Limitations and Advances: 1) Flow cytometry is a high throughput methods which allows to analyze many conditions. Recently published methods on flow virometry are promising methods that allow to study viron morphology. However, it might not be suitable to address specific mechanistic questions of membrane fusion to the molecular details presented in the model and discussed in the discussion. Overall, TIRF microscopy and SBP (Fig S6) present a much more accurate method than flow cytometry as it allows to study individaul events and provides additional information on virus size, fluorescence signal etc. In addition, single-molecule FRET methods could be used to monitor HA conformational changes at varying concentrations of sialic acid receptors. Previous studies have already shown that binding to sialic acid allosterically regulates HA2 conformational dynamics (PMID: 29961575). 2) A limitation is that only lipid mixing is monitored and no full fusion. Hence, one cannot conclude that all lipid mixing here leads to a full fusion pore opening. This should be addressed in the manuscript or at least noted as a limitation noted in the manuscript as a limitation.__

      We thank the reviewers for their careful evaluation of our manuscript and for their thoughtful and constructive comments. We are encouraged that Reviewers 1 and 2 recognized both the methodological advances afforded by the spCALM assay and the importance of the biological insights into the role of HA-receptor interactions in influenza membrane fusion. We also appreciate the critical perspective of Reviewer 3, whose comments prompted us to more carefully articulate the capabilities and limitations of the assay, clarify aspects of its implementation that may not be immediately apparent to readers more familiar with conventional bulk fusion assays or supported planar bilayer/TIRF approaches, and consider alternative mechanistic interpretations of our findings.

      Overall, we believe the reviewers have identified several opportunities to substantially strengthen the manuscript. Briefly, prompted by Reviewer 1 and Reviewer 3, we have developed improved analyses of virion recovery, binding, and liposome occupancy that refine our assessment of virion attachment, reveal previously unrecognized nonspecific particle losses, and will provide more rigorous quality-control metrics throughout the study. These new analyses also strengthen the inference that receptor interactions similarly influence both virion attachment and the lipid-mixing efficiency of bound virions, although the two processes exhibit distinct quantitative dependencies across multiple experiments. In response to Reviewer 2, we will extend the experimental parameter space by attempting measurements at higher receptor densities and with longer DNA receptor linkers. If these experiments cannot be completed successfully, we will better define the experimental regimes over which our conclusions are expected to apply and discuss the corresponding limitations. In response to Reviewer 3, we will revise the manuscript to more clearly describe the single-particle nature and unique advantages of the spCALM assay, explicitly discuss alternative mechanistic interpretations of the receptor-density dependence, attempt experiments to distinguish between competing mechanistic models, and evaluate the effects of soluble receptor on binding and lipid mixing to further probe the role of receptor engagement during membrane fusion.

      In summary, we believe all reviewer comments can be satisfactorily addressed. The revised manuscript will include new analyses, expanded discussion, clarification of the experimental methodology, and even new experiments where feasible. Together, these revisions will strengthen both the technical presentation of the spCALM assay and the mechanistic conclusions regarding the role of receptor engagement in regulating influenza virus lipid mixing.

      We have organized our responses to reviewer comments according to the Revision Plan template, separating planned revisions (Section 2), revisions that are already complete (Section 3), and revisions which we prefer not to carry out (Section 4). Within each section, we present our responses in order from reviewer #1 to reviewer #3 in the original order that they appear in the compiled reviews document. We have included line numbers in this revision plan to indicate where text revisions can be found in the (partially) revised manuscript and have tracked those changes in Microsoft Word. We also cite several publications in our responses and have included a list of references at the end of this revision plan.

      Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Reviewer #1: “In the spCALM assay, the authors indicate that labeled virions were incubated with cells or liposomes at a ratio of "0.15 virions per target". How were the numbers of virions determined for these experiments? The Methods refer to MOIs in units of PFU/cell but do not indicate how the number of physical particles were determined.”

      Thank you for pointing out this omission. We have for now extended our description of the “Viruses and reagents” subsection of Materials and Methods in lines 457-463 to refer to our previous study describing how physical virus particle and vesicle counts were determined.

      However, since this is a relatively new method, in the revision we will add supplemental panels to Figure S1 to show how we count free virions and assess whether they are monodisperse and uniform in both size (mostly spherical) and R18-dye incorporation by flow virometry. Then, these figures will also be referenced in the Methods.

      Reviewer #1: “The authors indicate that the MEDI antibody "increased binding across all tested conditions." But this was not mentioned again. By what mechanism do they expect the antibody enhances virus binding?”

      We thank Reviewer #1 for prompting us to reevaluate this unexpected observation. Upon additional analysis, we found that our original interpretation was incorrect. Rather than increasing receptor-mediated attachment, MEDI increases the recovery of virions in our assay, including both free and liposome-attached particles, while the total liposome concentration remains unchanged (Display Item 1A,B). Consistent with this interpretation, the fraction of virions bound to liposomes is not meaningfully changed by the presence of MEDI (Display Item 1E). We therefore infer that MEDI reduces nonspecific losses of virions, likely through decreased adsorption to plastic surfaces, rather than enhancing receptor-mediated binding. We have added a mention of this point in lines 314-317 and will incorporate these additional analyses in the manuscript revision.

      Display Item 1 caption: MEDI reduces virion losses but does not improve virion attachment. In the experiment presented in Fig 5C and S15, MEDI8852-Fab (MEDI) increased the fraction of liposomes with bound virions because it reduced particle losses. We extracted total virion counts (A), total liposome counts (B), the ratio of total recovered virions to liposomes (C), the percentage of liposomes with bound virions (D), and the percentage of virions bound to liposomes (E) before pH drop. MEDI increased total virion counts (A) but did not affect total liposome counts (B). This increased the actual virion-to-liposome ratio in MEDI relative to no-MEDI condition (C). By instead analyzing attachment efficiency as the percentage of recovered virions which are attached to liposomes (note the liposomes are always in access) (E), we control for differential particle loss with or without MEDI and find no meaningful difference in virion attachment efficiency in the presence or absence of MEDI, as expected. Error bars indicate standard error of the mean.

      While investigating this effect, we also identified an error in our description of a subset of the experimental conditions. Specifically, the experiments shown in Figs. 5A and 5B were performed using 0.5 virions per liposome rather than the previously stated ratio of 0.15. This higher input was intentionally used to increase the number of bound virion-liposome pairs under conditions of very low lipid-mixing efficiency. We will correct the manuscript to accurately report these experimental conditions. Importantly, this did not increase the number of virions bound per liposome (see our response to reviewer 3 #2, Display Item 3).

      Finally, these analyses prompted us to systematically evaluate particle recovery and liposome binding in other experiments in the study (Display Item 2, Display Item 6). We found that low-input virions are subject to nonspecific adsorption losses, explaining why the observed fraction of bound liposomes is often lower than expected from the nominal input ratio (Display Item 2). We will incorporate these new particle recovery and liposome binding analyses into the revised manuscript and extend this systematic evaluation across all experiments.

      Display Item 2 caption: Particle recovery and attachment analysis for the experiment presented in Fig. 4B. From events detected before pH drop, we derive particle recovery metrics, including total virion counts (A), total liposome counts (B), and total virion-to-liposome ratio (C), as well as attachment metrics, including the fraction of liposomes with bound virions (D), the fraction of total virions attached to liposomes (E).

      Reviewer #2: “Related to my first point, a tradeoff in the approach described here is that the observations may be specific to the regime that is being tested. Any effort to expand the range of conditions tested would therefore be valuable. For example, the authors discuss prior work at higher receptor densities (line 364); it seems that this higher-density regime could be evaluated here for direct comparison. Similarly, it is interesting that the authors do not observe differences when they use 12nt vs 24nt for the display of sialic acid on DNA tethers. I would imagine that sustained receptor binding could be detrimental to fusion peptide insertion only if it occurs at too large of a distance from the target membrane. It seems likely that the distances involved here (~4nm vs. 8nm) may not be sufficient to observe this, but testing larger DNA anchors seems feasible. If not, additional discussion of contexts where the findings described here may no longer be applicable would strengthen the manuscript.”

      Thank you for this valuable suggestion. We agree that extending the range of receptor densities and spacer lengths would strengthen this manuscript by more directly comparing our result with previous studies and by testing our proposed mechanism at the high end of receptor densities. We therefore plan to extend our analysis to receptor incorporations of at least 5 mol% and to compare lipid mixing using receptor mimetics displayed on 12 (+4.08 nm), 24 (+8.16 nm), and 32 (+10.88 nm)-nucleotide oligo linkers.

      If these experiments cannot be completed successfully within the revision, we will expand the Discussion to more clearly define the range of conditions over which we expect our conclusions to apply and where they may not.

      Reviewer #3: “1) The conclusions are drawn from a flow cytometry, which allows to analyse samples in bulk. On one hand, it allows analysis of a large number of fusion events; on the other hand, it cannot resolve individual events or the relationship between binding and membrane fusion in individual binding states. The flow cytometry method used in the manuscript should be validated against single-particle fusion events in microscopy-based assays, which would also allow investigation of both the binding area and fusion (as done in the Fig S6). Here, authors could evaluate the effect of neutralizing antibody, NA inhibitor or the presence of soluble sialic acid pretreated virions at receptor concentrations higher than 16%.”

      Thank you for revealing this potential point of confusion. Fundamentally, flow cytometry does measure individual particles which are sampled from a particle suspension; from this, we derive the distribution of liposome-bound virus particles that have undergone R18 dye dequenching within discrete time intervals. It is true that we do not track a single virion from binding to fusion, but we instead measure the distribution of single virions (in virion-liposome pairs) in sampled time bins. In other words, this is not a single-particle tracking experiment (which SPB/TIRF is), but it is nonetheless a single-particle experiment well suited to derive single-particle kinetics and especially efficiencies under attenuation. To address this reviewer’s comment, we will improve our description of this assay to make sure this distinction is clearly made in writing.

      We have developed this novel assay after much experimentation using SPB/TIRF, our key area of expertise (Ivanovic, Choi et al. 2013, Li, Li et al. 2021, Li, Li et al. 2022) and realizing that this more standard approach is critically limited. Some of these limitations can be gleaned from the presented results as follows. Based on the decreases in particle attachment that we observed on supported planar bilayers with 16% receptor saturation with TIRF microscopy in Fig. S6, it would be impractical to evaluate differential effects on lipid mixing from receptor binding when receptor saturation is further reduced. In the spCALM experiment presented in Fig. 4B, which tests lipid mixing by the same virus strain used in the TIRF experiment presented in Fig. S6, the 6’SL receptor did not reduce lipid mixing efficiency until receptor saturation was decreased to values lower than 8%. Therefore, we are unable to assess the relevant receptor saturation regimes using TIRF microscopy; we expect that other treatments which influence receptor binding will likewise restrict virion attachment too severely to be practical. Finally, diffraction-limited optical microscopy methods do not provide any more information on virion-target membrane contact area than our flow cytometry-based method.

      We presented several layers of validation of our assay and summarize them in order. Attachment of PR8 virions to erythrocyte membranes is inhibited by the neutralizing antibody SbH36-26 in the expected concentration range (Fig. 2C, (Partlow, Jaeggi-Wong et al. 2025)). As in SPB/TIRF, PR8 has lower baseline efficiency than X31 in spCALM (Fig. S2, (Otterstrom, Brandenburg et al. 2014)). As is well established including by SPB/TIRF, fusion kinetics and efficiency are pH-dependent in spCALM (Fig. 3E, (Floyd, Ragains et al. 2008, Ivanovic, Choi et al. 2013)). As in SPB/TIRF, Gamma cumulative distribution fitting of lipid mixing vs. time trajectories in spCALM revealed 3-4 underlying steps (Fig. S8, (Floyd, Ragains et al. 2008, Ivanovic, Choi et al. 2013, Otterstrom, Brandenburg et al. 2014, Ivanovic and Harrison 2015, Li, Li et al. 2021, Li, Li et al. 2022).

      Reviewer #3: “2) In particular, authors do not provide data showing that flow cytometry can quantify time-resolved distributions of lipid mixing states among single virions bound to individual cells or vesicles, as claimed in the introduction (Page 3, line 85). Flow cytometry can not distinguish a single virion bound to a 1µm vesicle from multiple virions bound to a 1µm vesicle. Liposomes often can bind to multiple virions, as shown by previous cryo-EM studies. Since authors use liposomes extruded via a 1µm filter, it is expected that these liposomes will be heterogeneous in size and shape, and thus single liposomes can bind a variable amount of viruses, leading to a variable amount of dequnching. In addition, this will lead to aggregation - analogous to a hemagglutination assay, which will complicate the interpretation of flow cytometry results.”

      We thank Reviewer #3 for raising this point – in response, we have performed additional analyses which, as we demonstrate in what follows, formalize our claims. Since flow virometry can readily distinguish single virions from pairs or groups of associated virions by virion fluorescence intensity (bioRxiv: https://doi.org/10.64898/2026.06.15.729605), we decided to analyze fluorescence distributions of free and liposome-associated virions in our experiments to probe whether multi-virion binding to single liposomes occurs with notable frequency. Liposomes with more than one bound virion would, just like clumped virions do, shift upward in fluorescence intensity. We illustrate here with the results of this analysis for the experiment shown in Fig. 5A,B, where virion-liposome input was the highest (0.5 vs. 0.15 in the remaining experiments; also see our response regarding the MEDI8852 experiment to Reviewer 1). We used virus-only and liposome-only controls to define cytometry gates for unbound virions and virion-free liposomes. We extracted the R18 fluorescence distributions for liposomes with and without bound virions, and for unbound virions in the same sample (Display Item 3). The fluorescence distributions for virions and liposome-virion bound complexes strongly overlap and are both considerably higher than the distribution for liposomes without virions. This is consistent with liposomes not appreciably binding more than one virion. We will extend this analysis to the remaining samples in this experiment and to other experiments and present it as a supplementary figure in the revised manuscript. This analysis furthermore excludes the existence of aggregates such as in a hemagglutination assay.

      Display Item 3 caption: Liposomes do not bind multiple particles. (A) Representative sample from the experiment presented in Fig. 5A,B showing that liposome-virion complexes have nearly overlapping R18 fluorescence distribution to unbound virions, and both have higher intensity than liposomes without bound virions. (B) Violin plots showing the distribution of median R18 fluorescence distributions for the specified populations representing all samples from the experiment presented in Fig. 5A,B.

      Reviewer #3: 3) Liposomes with variying concentration of receptors should be characterized by cryo-EM as a quality check. It is not clear what the size distribution of liposomes and multilamellarity is. A fraction of small liposomes can skew the analysis as they would not be gated in the flow cytometry. In addition, cryo-ET could be used to analyze the contact zone area at different concentrations of DNA-linked sialic acid receptors.

      We agree that the size distribution of the liposome preparations used in our study is likely heterogeneous to some extent and that an analysis of the efficiency of lipid mixing for liposomes of different sizes is therefore useful. We thank this reviewer for suggesting we explore this further. In spCALM, liposomes are detected with a range of DiD membrane dye fluorescence and 488-nm side-scatter intensities. For the experiment presented in Fig. 4B, we separately gated subsets of liposomes with low DiD fluorescence and side-scatter intensity, which would comprise smaller liposomes on average, and of liposomes with high DiD fluorescence and side-scatter, which would comprise larger liposomes (Display Item 4A). We compared lipid-mixing efficiency of bound virion-liposomes pairs between the small and large liposome subpopulations to the total liposome population. Differences in efficiency across liposome subpopulations within each replicate were small relative to differences across replicates (Display Item 4B). The smallest ~7-9% of liposomes fused slightly more efficiently at very low or no receptor saturation whereas the largest ~1-3% liposomes tended to fuse somewhat less efficiently overall. The results of the experiment with respect to the effect of receptor density, NA-inhibition, and receptor type are thus practically indistinguishable between the liposome size subpopulations (Display Item 4C). From this, we infer that size heterogeneity in our liposome preparations neither explains nor confounds our interpretations of the regulatory effect of receptor binding on lipid mixing. We will present this analysis as a supplementary figure in the revised manuscript.

      Display Item 4 caption: Liposome size heterogeneity does not produce meaningful differences in lipid-mixing efficiency. (A) We re-analyzed the experiment presented in Fig. 4B by separately analyzing lipid mixing for liposomes with low (Small liposomes) or high (Big liposomes) 488-nm side-scatter (SSC-A) and DiD membrane fluorescence (APC-A). (B) Lipid mixing efficiency for liposomes displaying 3’SL or 6’SL receptors at the indicated range of saturations in the presence or absence of 100 nM NAI; analyses with each liposome size subpopulation are connected by lines for each replicate. (C) For each liposome size subpopulation, logistic curves with a shared Hill slope were fit to the combined data from each condition, with shaded 95% confidence intervals. The overall effect of receptor saturation, receptor type, and NA-inhibition is not meaningfully different between the liposome size subpopulations.

      Reviewer 3: “4) Data on receptor binding promote HA insertion is not cleanly explained and not experimentally justified. Increased binding of virion and liposomes, will potentially lead to an extended binding zone, which could give rise to multiple fusion pores, which could accelerate dequenching and the R18 fluorescence burst. Previous cryo-ET work has shown that interaction with liposomes can engage one to seven HA glycoproteins when 5 mol% gangliosides are present in 200 nm liposomes (PMID: 27572837). This should be considered in the model and the manuscript would largely benefit from having a resolution analysis of the liposomes at varying concentrations of the DNA-sialic acid and the binding area with a virus.”

      We thank reviewer #3 for raising the alternative hypothesis that receptor binding could deform the target membrane, thereby increasing the virion-target contact area and the number of HAs contained within. We agree that this is a plausible mechanism that merits consideration. Our current data do not distinguish between this possibility and the hypothesis that receptor binding promotes HA fusion peptide insertion, and we will revise the interpretation of Figure 5A, B results and the Discussion to more explicitly acknowledge these alternative interpretations.

      To discriminate between these two mechanistic models, we will evaluate whether receptor density differentially affects lipid mixing of spherical and filamentous virions. We hypothesize that if receptor binding promotes lipid mixing by increasing membrane wrapping and thereby expanding the virion-target contact area, this effect would be more pronounced for spherical virions than for filaments. For a spherical particle, relatively small increases in the extent of membrane wrapping produce comparatively large increases in the buried contact area and, consequently, in the number of HA molecules within the virion-target contact zone. In contrast, for a filament, comparable increases in membrane wrapping produce substantially smaller relative changes in buried contact area. Moreover, membrane wrapping around a sphere is expected to incur greater membrane deformation costs than wrapping around a filament, such that increasing receptor density may produce a relatively sharp transition to extensive wrapping for spherical virions, whereas the corresponding response for filaments would be expected to be more gradual. Thus, if receptor density primarily regulates lipid mixing by modulating the extent of membrane wrapping, we would expect the receptor-density dependence to be steeper for spherical virions than for filaments.

      We note that this experiment presents technical challenges. Filamentous virions require larger target membranes to generate sufficient R18 dequenching to resolve unfused and lipid-mixed virion-target pairs, and we therefore anticipate using erythrocyte ghosts rather than liposomes as targets. Receptor density on erythrocyte ghosts would be modulated with bacterial neuraminidase under varying conditions. If these experiments can be completed in time for the revision, we will include them in the revised manuscript. Regardless, we will revise our discussion to explicitly consider membrane deformation as an alternative mechanism underlying the receptor-density dependence observed in Fig. 5AB.

      Can strong binding induce liposome membrane rupture?”

      To test if liposomes are rupturing or aggregating, we examined the total number of detected liposomes before pH drop for the experiment presented in Fig. 4B (Display Item 2B). The total liposome count decreased somewhat at high receptor saturation for liposomes bearing 3’SL but not 6’SL. This effect was associated with a small increase in the fraction of brighter liposomes for 3’SL but not 6’SL liposomes (Display Item 5). From this, we infer that this apparent loss of liposomes is due to aggregation and not rupture. Importantly, the effect on total liposome counts was relatively small and only present for 3’SL-bearing liposomes, for which efficient lipid mixing required greater receptor density than for 6’SL-bearing liposomes. We therefore conclude that this putative aggregation effect is inconsequential for our lipid mixing results.

      Display Item 5 caption: Mild liposome aggregation at high receptor saturation. Representative scatter plots of DiD membrane fluorescence (y-axis) and 488-nm side-scatter (x-axis) before pH drop for liposomes decorated with 0, 16, or 100% saturation with either 3’SL (top) or 6’SL (bottom) receptor mimetics. Gates for overall liposomes, small liposomes, and large liposomes are provided to illustrate the emergence of a larger subpopulation for 3’SL 100%.

      and

      “6) As in 3D space, viruses can also bind several vesicles; membrane fusion would lead to an increase in the size of the vesicles. Can this be detected by flow cytometry? If not, what is the benefit of the flow cytometer over the classical fluorimeter, which has been routinely used to monitor membrane fusion in bulk?”

      As we demonstrate in Display Item 3 and Fig. 2A-B, virions bound to vesicles are distinguishable from vesicles without virions and virion-vesicle pairs that have undergone lipid mixing are distinguishable from pairs which have not. Flow cytometry thus offers a fundamental advantage over bulk fluorimetry because it measures membrane fusion on individual virion-liposome pairs rather than as an ensemble average. Each detected event reports whether a virion is bound to a liposome and whether that virion has undergone lipid mixing. This allows us to determine the probability of lipid mixing among bound virions while independently quantifying binding efficiency. In contrast, bulk fluorimetry reports only the summed fluorescence of the entire sample, making it impossible to distinguish changes in membrane fusion efficiency from changes in the number of virion-target interactions.

      “Also, please comment on the sensitivity of the instrument and required volumes, as well as how acid is added. How was the final pH of the mixture determined so precisely?”

      We thank the reviewer for pointing out that this description was ambiguous. The reported pH values correspond to the fusion-triggering buffer rather than the final reaction mixture, and we have revised the text to make this explicit on lines 116-118, 189, 194, 299, 737-738, 754, 769, 773, 786-787, 801-802, 807-808. Because the neutral pH virion-liposome mixture is diluted approximately 10-fold into the acidic triggering buffer, the final reaction pH is expected to be very close to that of the triggering buffer. We will experimentally verify this and report the result in the revised manuscript.

      Reviewer #3: “7) How efficient is the membrane fusion compared to total disassembly included by a detergent? Normalisation to total dequnching should be used. “

      We thank Reviewer #3 for this comment. We believe there may be a misunderstanding regarding the readout of the spCALM assay. Unlike conventional bulk fluorimetry experiments, spCALM does not quantify the magnitude of ensemble R18 fluorescence dequenching. Instead, it analyzes individual virion-liposome pairs and classifies each virion as either quenched or dequenched within discrete sampling intervals. Thus, the question addressed by spCALM is, "What fraction of virion-liposome pairs have undergone lipid mixing?", whereas bulk fluorimetry asks, "How much total R18 dequenching has occurred?" Lipid-mixing efficiency is therefore determined from the fraction of individual virions occupying the dequenched population and is independent of the absolute fluorescence intensity of the R18 signal. Consequently, normalization to detergent-induced total dequenching, which is appropriate for bulk fluorescence assays, is not applicable to the single-particle measurements reported here because complete lipid mixing is intrinsically represented by complete occupancy of the dequenched state. We will revise the manuscript to make this distinction clearer and avoid potential confusion for readers more familiar with ensemble fusion assays.

      Reviewer #3: “8) HA/NA functional balance is not only constrained by the distribution of sialic species but also by the ratio of the HA/NA and by the NA spatial distribution, which is clustered only at one end and hence it cannot interfere in the binding zone. This is not discussed in the manuscript or introduced.”

      We agree that the spatial organization of HA and NA is an important consideration when interpreting HA/NA functional balance. However, the polarized distribution of NA observed on mature virions might not persist under the low-pH conditions of membrane fusion; polarized NA organization on the virion surface is maintained by the interactions with the underlying M1 matrix which restrict NA mobility (Vahey and Fletcher 2019), and acidification is known to dissociate the underlying M1 matrix layer (Calder, Wasilewski et al. 2010). We had discussed in the original submission that in the context of its polarized distribution, NA might limit receptor availability before the virion-target contact interface is established or, alternatively, it might become mobile at the pH of fusion and contribute from within the interface (lines 400-408). In the revision, we will ensure that the concept of HA/NA polarization is clearly introduced so that it is not overlooked.

      Reviewer #3: “9) Conclusions in the manuscript could be strengthened by providing data on binding and fusion using viruses that were pre-treated with different amounts of soluble sialic acid prior to the fusion kinetics measurement.”

      We thank Reviewer #3 for this interesting suggestion. While soluble receptor (kd for monomeric binding is ~1 - 4 mM) might not effectively compete with the multivalent receptor displays on the target membrane, this experiment is relatively simple to implement, and a positive outcome has a potential to further hone the mechanistic interpretations of our findings. We will perform experiments measuring virion binding and lipid mixing in the presence of varying concentrations of soluble sialyllactose and include the results in the revised manuscript. Depending on the outcome, these experiments may help distinguish whether receptor binding primarily acts through membrane-associated interactions or by directly modulating HA conformation or function. We will discuss the implications of these results for the proposed mechanism by which receptor binding promotes lipid mixing. A negative result (i.e. no effect from the soluble receptors) will be difficult to interpret and will be simply reported as such.

      Reviewer #3: “1) Figure 2 lacks information on time fo the three plots. A time stemp could be also included to the video.”

      We will revise Fig. 2 and Movies 1 and 2 to include information on time.

      Reviewer #3: “2) It is not clear why the model is presented as figure 1 and the density of spikes does not reflect the density on the viruses - it is not drawn to the scale, which in this study might be important.”

      We thank Reviewer #3 for this comment. The purpose of Figure 1 is to provide a schematic illustration of the local conformational changes associated with membrane fusion rather than to accurately represent the surface density or number of HA molecules on influenza virions. We recognize that this was not sufficiently clear in the original figure. To avoid any confusion, we will explicitly indicate in the figure that the illustration is not drawn to scale. We will also revise the legend to clarify that the virion-target contact patch for a representative particle in our experiments is expected to contain on the order of 100 HA molecules, and that the simplified depiction is intended solely to illustrate the key molecular events within the virion-target interface, specifically the fusion cluster and ways HA might be rendered nonparticipating. Depicting the full complement of HA molecules expected within the contact patch would substantially obscure the illustration of the fusion cluster in Fig. 1B and of inhibitor or antibody binding and nonproductive HA refolding in Fig. 1C.

      Reviewer #3: “3) The manuscript structure could be improved, although it is understandable that the final format depends on the journal. Some information on methods is present in the main text, and some in the extended methods in the supplementary data. This makes the manuscript harder to review. Authors could consider redistributing some supplementary information to the main data, and consider publishing the first method and its validation, and another manuscript on biology analysis.”

      We will certainly rearrange the structure of the methods to accommodate the guidelines of the eventual journal of publication. We aimed to structure our main figures to convey the most essential findings of the work but are open to promoting supplementary content to main figures if it can be accommodated within journal guidelines.

      Reviewer #3: “Overall, TIRF microscopy and SBP (Fig S6) present a much more accurate method than flow cytometry as it allows to study individaul events and provides additional information on virus size, fluorescence signal etc. In addition, single-molecule FRET methods could be used to monitor HA conformational changes at varying concentrations of sialic acid receptors. Previous studies have already shown that binding to sialic acid allosterically regulates HA2 conformational dynamics (PMID: 29961575).”

      We respectfully disagree that TIRF microscopy of supported planar bilayers is inherently more accurate than flow cytometry in the ways described in this comment. As we detail in other responses in this document (see our responses to “Reviewer #3: “2) In particular, authors do not provide data showing that…” __and “6) As in 3D space, viruses can also bind several vesicles; …”), flow cytometry in the form of spCALM permits analysis of lipid mixing deriving from fluorescence measurements of single, membrane-attached virions. For analysis of virus particles with sizes below the diffraction limit of visible light, which are the sizes that we study here, flow cytometry provides as much size information per particle as TIRF microscopy. Please see our response to reviewer 3 (“1) The conclusions are drawn from a flow cytometry, …”__) regarding the suitability of SPB/TIRF for the current mechanistic questions.

      We agree that single-molecule FRET has provided important insight into HA conformational dynamics, including the observation that receptor binding shifts HA toward downstream conformational intermediates (Das, Govindan et al. 2018). However, smFRET addresses a fundamentally different mechanistic question than the present study. smFRET reports on the conformational dynamics of individual labeled HA molecules, whereas our experiments measure the functional outcome of the collective action of many HAs during membrane fusion. Moreover, interpretation of smFRET measurements relies on site-specific fluorophore labeling within conformationally sensitive regions of HA, an experimental strategy that may itself influence the native conformational landscape. Thus, while highly complementary, smFRET cannot directly establish how receptor binding alters the probability of productive lipid mixing.

      Implementing and validating an smFRET assay for our virus-membrane fusion system would require substantial development of a highly specialized methodology and is therefore beyond the scope of the present study. Instead, we will expand the Discussion to place our findings in the context of previous smFRET studies. Specifically, we will discuss that, although previous work indicates that receptor binding increases occupancy of later HA conformational intermediates, our data support a different mechanistic interpretation: receptor engagement does not primarily drive HA further along its conformational pathway toward HA extension but instead alters the probability that receptor-bound HAs proceed to membrane insertion following low-pH triggering. In sum, smFRET provides an indication that receptor binding influences conformational HA dynamics whereas our study evaluates the effect of receptor binding on lipid mixing by virions. Both approaches are important, but neither can fully recapitulate the value of the other, nor are they necessarily in conflict.

      __ __

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      Reviewer #1: “A couple points in the text, the authors use the term "distributional dynamics". This term is not defined and could use clarification.”

      We agree that this wording was unclear and have revised our wording for clarity on lines 105-106, 204, 801, and 807.

      Reviewer #1: “Why have the authors used this technique using lipid-ssDNA and receptor-DNA to display sialic acid on the liposomes? In principle, this allows control over the distance between the sialic acid and the membrane. But in the spCALM assay the length of the spacer DNA (12 or 24 nt) had not effect. So, why then not use the simpler approach of incorporating sialylated lipids (e.g. GD1a, GD1b) in the membrane?”

      We thank this reviewer for the opportunity to clarify two additional key advantages of our approach. Our system allows us to display glycans of precisely defined structure, with either α2,3 or α2,6 sialic acid linkage, on identical carbohydrate-lipid conjugates, which ensures identical chemistry and display geometry with only the intended differences. This is not possible with gangliosides familiar to us including GD1a (displays a terminal alpha2,3-linked sialic acid) and GD1b (contains only internal sialic acids, alpha2,8-linked in tandem). Furthermore, to alter receptor density using gangliosides, one must alter membrane lipid composition, while our system allows tuning of receptor density without changing lipid composition.

      We have added a description of these key advantages in lines 161-163. We have furthermore restructured the introductory paragraph in the Results section “Programmable glycan-receptor displays for membrane-fusion studies” to first emphasize programmability of the receptor type, then tunability of density without altering lipid or display chemistry, and the spacer component is now mentioned last (as this, indeed, turned out inconsequential for lipid mixing under our current assay conditions).

      __Reviewer #2: “____While the precise control of receptor identity and density is a strength of this experimental approach, more discussion of how the conditions tested here compare to physiological conditions would strengthen the manuscript. The receptor densities tested here are We agree that placing the tested receptor densities into a physiological context is valuable. Estimates of the density of influenza receptors in the endosomal compartments where membrane fusion takes place are not available, so we instead compare our system with the plasma membrane of human erythrocytes in the Discussion section, lines 361-372. The maximum receptor density used in our experiments is approximately 15-fold lower than the total sialic acid content of the human erythrocyte glycocalyx. However, this comparison almost certainly overestimates the density of physiologically relevant influenza receptors because it includes all sialic acid regardless of glycosidic linkage, underlying glycan structure, or accessibility. Consequently, the density of functional influenza receptors may be substantially closer to the receptor densities explored in our chemically defined liposome system. Moreover, the receptor composition and density within the endosomal lumen remain unknown and might differ considerably from those of the erythrocyte surface. Finally, receptor density may vary widely across potential cell targets in complex systems of natural infections.

      Our aim was to identify the mechanisms by which receptor binding regulates HA-mediated lipid mixing using a molecularly defined receptor display. This approach allowed us to systematically vary receptor density over a controlled range while avoiding the compositional complexity of native membranes, thereby enabling mechanistic interpretation of receptor density-dependent effects.

      Reviewer #2: Regarding the experiments in Figure 4 and S13: what is the anticipated phenotype for the A227T variant? It appears to have the opposite effect of the avidity-increasing E246G mutation, restoring WT avidity in the double mutant (line 245), and forming larger plaques in MDCK cells when introduced alone (in contrast to the smaller plaques formed by E246G). However, in spCALM measurements (Fig 4C), it behaves similarly to WT. The authors mention that this result is surprising (line 259), but more context for this observation would be helpful. Similarly, it is interesting that all of the variants tested exhibit higher peak fusion efficiency than the WT (50-75% vs. 25%). I would be interested how the authors interpret this result.

      We agree that these observations merit additional discussion. We have revised the manuscript to more carefully interpret the plaque assay results, acknowledging that plaque size reflects the combined effects of virus attachment, entry, membrane fusion, replication, and release, and therefore cannot be directly attributed to altered receptor binding or fusion alone (lines 249-256).

      Our reanalysis of the spCALM attachment data (prompted by the Reviewer 1 comment regarding MEDI8852-Fab’s effects on virion attachment) provides additional context for interpreting HA mutants. The attachment phenotypes closely parallel the lipid-mixing results: E246G exhibits the greatest increase in virion binding, A227T alone behaves similarly to WT (maybe slightly reduced binding), and the A227T mutation partially or fully reverses the enhanced binding conferred by E246G (see Display Item 6 below). We have incorporated these observations into the revised Results (lines 256-265) and expanded our interpretations to emphasize that receptor binding and lipid mixing exhibit similar qualitative trends but distinct quantitative dependencies on receptor density (lines 265-271).

      Display Item 6 caption: Trends in virion attachment efficiency mirror those of lipid-mixing efficiency, but attachment is sensitive to receptor density at greater values than for lipid mixing. (A) The percentage of total recovered virions which are attached to liposomes. (B) Efficiency of lipid mixing among bound virion-liposome pairs, replotted from Fig. 4C for ease of comparison.

      Finally, we agree that the increased maximal lipid-mixing efficiencies observed for all three HA variants relative to WT are intriguing. At present, we do not know the molecular basis for this effect, and we do not believe it can be reliably inferred from the available data. We will therefore discuss this as an interesting observation that warrants future investigation while emphasizing that it does not affect the principal conclusion of the study that HA-receptor interactions regulate lipid-mixing efficiency through receptor avidity.

      Reviewer #2: “Line 49: "3-4 neighboring HA2 molecules" should be clarified to indicate that this means 3-4 trimers (as stated a few lines later).”

      Thank you, we have clarified the wording in this clause on line 49.

      __Reviewer #2: “Line 278: "presence of absence" should read "presence or absence".” __

      We thank Reviewer #2 for pointing this out; we found and revised a total of three instances of this typographical error in the main manuscript (lines 289, 309, 769) and one in the supplementary appendix (Fig. S9 legend).

      Reviewer #2: “Line 296: I am surprised that 450nM MEDI8852 is a sub-neutralizing dose; this seems higher than what has been reported elsewhere, although it may be a specific feature of the virus used here. Can the authors clarify how this was determined, i.e., is this based on an infection assay or a lipid mixing assay?”

      Our intended meaning is that the tested dose of MEDI8852-Fab reduces lipid-mixing efficiency and does not fully inhibit it. We agree that this was unclear and have clarified our language on lines 308-309. As an aside, we note here that in vitro membrane fusion experiments seem to be less susceptible to inhibition by MEDI-Fab than infection experiments (i.e. neutralization), as was reported by our lab in 2021 (Li, Li et al. 2021).

      __Reviewer #3: __

      __“5) How does 60% receptor saturation compare to the physiological concentration of receptor density? __

      We agree that placing the tested receptor densities in a physiological context is valuable. As discussed in response to Reviewer #2, we have expanded the Discussion to compare our receptor densities with estimates for the erythrocyte glycocalyx (see lines 362-378). The maximum receptor density used in this study is approximately 15-fold lower than the total sialic acid density on the erythrocyte surface, although this comparison likely overestimates the number of physiologically relevant influenza receptors because it includes all sialic acid species irrespective of glycosidic linkage, glycan structure, or accessibility. The density of accessible influenza receptors within the endosomal lumen, where fusion occurs in vivo, is not known.

      Reviewer #3: “4) Webster ER et al, PMID: 35143209 should be referenced as it showed similar approaches using DNA as a tether to study influenza membrane fusion.”

      We thank Reviewer #3 for pointing out that we did not reference this study. We have modified the text to reference this study on lines 71-73 and 152.

      Reviewer #3: “____A limitation is that only lipid mixing is monitored and no full fusion. Hence, one cannot conclude that all lipid mixing here leads to a full fusion pore opening. This should be addressed in the manuscript or at least noted as a limitation noted in the manuscript as a limitation. “

      We thank Reviewer #3 for highlighting this important distinction. We agree that lipid mixing does not necessarily imply subsequent fusion pore formation and that our assay specifically reports on the lipid-mixing (hemifusion) intermediate rather than full fusion. We have revised the manuscript to make this point more explicit on lines 110–114 and 329-330. Specifically, we now state that our approach probes the lipid-mixing fusion intermediate (i.e., hemifusion), which reports on the preceding HA conformational changes and is an obligate intermediate on the pathway to fusion pore formation.

      __ __

      Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      Reviewer #1: “The authors raise the interesting possibility that neuraminidase (NA) may contribute to virion attachment in the presence of NAI. Would it be worthwhile to test fusion of virions in the absence of NA? Or in the presence of NA where the sialic acid binding site has been mutated?”

      We thank Reviewer #1 for this thoughtful suggestion. We agree that comparing lipid mixing of virions lacking NA or carrying mutations in the NA sialic acid-binding site would provide a direct test of whether receptor-NA interactions contribute to virion attachment and/or lipid mixing. Although NA is a relatively low-abundance virion glycoprotein (there are 4-5 HAs for each NA on the virion surface), it is possible that receptor-NA binding contributes in some way to lipid mixing. We are interested in exploring this in future work. However, production of virions without NA or with defective NA is expected to be challenging because of reduced viral fitness and infectivity. Moreover, this could lead to unintended (e.g. compensatory) differences in virion morphology or glycoprotein composition that would complicate interpretations of any observed effects on lipid mixing. We therefore believe that this represents a substantial follow-up study that is beyond the scope of the current manuscript.

      References to works cited in this revision plan

      Calder, L. J., S. Wasilewski, J. A. Berriman and P. B. Rosenthal (2010). "Structural organization of a filamentous influenza A virus." Proc Natl Acad Sci U S A 107(23): 10685–10690.

      Das, D. K., R. Govindan, I. Nikic-Spiegel, F. Krammer, E. A. Lemke and J. B. Munro (2018). "Direct Visualization of the Conformational Dynamics of Single Influenza Hemagglutinin Trimers." Cell 174(4): 926–937 e912.

      Floyd, D. L., J. R. Ragains, J. J. Skehel, S. C. Harrison and A. M. van Oijen (2008). "Single-particle kinetics of influenza virus membrane fusion." Proc Natl Acad Sci U S A 105(40): 15382–15387.

      Ivanovic, T., J. L. Choi, S. P. Whelan, A. M. van Oijen and S. C. Harrison (2013). "Influenza-virus membrane fusion by cooperative fold-back of stochastically induced hemagglutinin intermediates." Elife 2: e00333.

      Ivanovic, T. and S. C. Harrison (2015). "Distinct functional determinants of influenza hemagglutinin-mediated membrane fusion." Elife 4: e11009.

      Li, T., Z. Li, E. E. Deans, E. Mittler, M. Liu, K. Chandran and T. Ivanovic (2021). "The shape of pleomorphic virions determines resistance to cell-entry pressure." Nat Microbiol 6(5): 617–629.

      Li, Z., T. Li, M. Liu and T. Ivanovic (2022). "Hemagglutinin Stability Determines Influenza A Virus Susceptibility to a Broad-Spectrum Fusion Inhibitor Arbidol." ACS Infect Dis 8(8): 1543–1552.

      Otterstrom, J. J., B. Brandenburg, M. H. Koldijk, J. Juraszek, C. Tang, S. Mashaghi, T. Kwaks, J. Goudsmit, R. Vogels, R. H. Friesen and A. M. van Oijen (2014). "Relating influenza virus membrane fusion kinetics to stoichiometry of neutralizing antibodies at the single-particle level." Proc Natl Acad Sci U S A 111(48): E5143–5148.

      Partlow, E. A., A. Jaeggi-Wong, S. D. Planitzer, N. Berg, Z. Li and T. Ivanovic (2025). "Influenza A virus rapidly adapts particle shape to environmental pressures." Nat Microbiol 10(3): 784–794.

      Vahey, M. D. and D. A. Fletcher (2019). "Influenza A virus surface proteins are organized to help penetrate host mucus." Elife 8.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: The manuscript by Plamitzer et al. investigates influenza A virus hemagglutinin (HA)-mediated membrane fusion using bulk flow cytometry analysis. In the assay, they use red blood cells and liposomes carrying defined, variable densities of sialic acid residues. They used H3N2 and H1N1 model viruses with spherical morphology. The liposomes were produced using double-stranded DNA to attach sialic acid. Similar studies have been done using DNA-tethers to study membrane fusion between liposomes and influenza virions (PMID: 35143209, PMID: 27410740). Some previous studies have shown that reduced HA binding does not influence membrane fusion, while others have shown that binding increases membrane fusion. This work aims to reconcile prior conflicting observations. Based on their results, they conclude that HA-receptor binding avidity and the extent of neuraminidase (NA)-mediated receptor cleavage directly influence lipid mixing, which is used here as a proxy for membrane fusion and allows monitoring of the first step of fusion, namely the hemifusion intermediate. The results of the test whether receptor binding modulates the HA extended intermediate should be interpreted with more caution. The manuscript would benefit from more data on the characterisation of liposome carrying dsDNA sialic acid receptors and from using the TIRF SBP method to validate the flow cytometry. Major comments: 1) The conclusions are drawn from a flow cytometry, which allows to analyse samples in bulk. On one hand, it allows analysis of a large number of fusion events; on the other hand, it cannot resolve individual events or the relationship between binding and membrane fusion in individual binding states. The flow cytometry method used in the manuscript should be validated against single-particle fusion events in microscopy-based assays, which would also allow investigation of both the binding area and fusion (as done in the Fig S6). Here, authors could evaluate the effect of neutralizing antibody, NA inhibitor or the presence of soluble sialic acid pretreated virions at receptor concentrations higher than 16%. 2) In particular, authors do not provide data showing that flow cytometry can quantify time-resolved distributions of lipid mixing states among single virions bound to individual cells or vesicles, as claimed in the introduction (Page 3, line 85). Flow cytometry can not distinguish a single virion bound to a 1µm vesicle from multiple virions bound to a 1µm vesicle. Liposomes often can bind to multiple virions, as shown by previous cryo-EM studies. Since authors use liposomes extruded via a 1µm filter, it is expected that these liposomes will be heterogeneous in size and shape, and thus single liposomes can bind a variable amount of viruses, leading to a variable amount of dequnching. In addition, this will lead to aggregation - analogous to a hemagglutination assay, which will complicate the interpretation of flow cytometry results. 3) Liposomes with variying concentration of receptors should be characterized by cryo-EM as a quality check. It is not clear what the size distribution of liposomes and multilamellarity is. A fraction of small liposomes can skew the analysis as they would not be gated in the flow cytometry. In addition, cryo-ET could be used to analyze the contact zone area at different concentrations of DNA-linked sialic acid receptors. 4) Data on receptor binding promote HA insertion is not cleanly explained and not experimentally justified. Increased binding of virion and liposomes, will potentially lead to an extended binding zone, which could give rise to multiple fusion pores, which could accelerate dequenching and the R18 fluorescence burst. Previous cryo-ET work has shown that interaction with liposomes can engage one to seven HA glycoproteins when 5 mol% gangliosides are present in 200 nm liposomes (PMID: 27572837). This should be considered in the model and the manuscript would largely benefit from having a resolution analysis of the liposomes at varying concentrations of the DNA-sialic acid and the binding area with a virus. 5) How does 60% receptor saturation compare to the physiological concentration of receptor density? Can strong binding induce liposome membrane rupture? 6) As in 3D space, viruses can also bind several vesicles; membrane fusion would lead to an increase in the size of the vesicles. Can this be detected by flow cytometry? If not, what is the benefit of the flow cytometer over the classical fluorimeter, which has been routinely used to monitor membrane fusion in bulk? Also, please comment on the sensitivity of the instrument and required volumes, as well as how acid is added. How was the final pH of the mixture determined so precisely? 7) How efficient is the membrane fusion compared to total disassembly included by a detergent? Normalisation to total dequnching should be used. 8) HA/NA functional balance is not only constrained by the distribution of sialic species but also by the ratio of the HA/NA and by the NA spatial distribution, which is clustered only at one end and hence it cannot interfere in the binding zone. This is not discussed in the manuscript or introduced. 9) Conclusions in the manuscript could be strengthened by providing data on binding and fusion using viruses that were pre-treated with different amounts of soluble sialic acid prior to the fusion kinetics measurement.<br /> Minor comments: 1) Figure 2 lacks information on time fo the three plots. A time stemp could be also included to the video. 2) It is not clear why the model is presented as figure 1 and the density of spikes does not reflect the density on the viruses - it is not drawn to the scale, which in this study might be important. 3) The manuscript structure could be improved, although it is understandable that the final format depends on the journal. Some information on methods is present in the main text, and some in the extended methods in the supplementary data. This makes the manuscript harder to review. Authors could consider redistributing some supplementary information to the main data, and consider publishing the first method and its validation, and another manuscript on biology analysis. 4) Webster ER et al, PMID: 35143209 should be referenced as it showed similar approaches using DNA as a tether to study influenza membrane fusion.

      Significance

      The study attempts to answer an important question and is of interest to the membrane fusion field and the virology field. It provides flow cytometry to study membrane fusion and a method for DNA-based sialic acid receptors that can be displayed at control density.

      Limitations and Advances:

      1. Flow cytometry is a high throughput methods which allows to analyze many conditions. Recently published methods on flow virometry are promising methods that allow to study viron morphology. However, it might not be suitable to address specific mechanistic questions of membrane fusion to the molecular details presented in the model and discussed in the discussion. Overall, TIRF microscopy and SBP (Fig S6) present a much more accurate method than flow cytometry as it allows to study individaul events and provides additional information on virus size, fluorescence signal etc. In addition, single-molecule FRET methods could be used to monitor HA conformational changes at varying concentrations of sialic acid receptors. Previous studies have already shown that binding to sialic acid allosterically regulates HA2 conformational dynamics (PMID: 29961575).
      2. A limitation is that only lipid mixing is monitored and no full fusion. Hence, one cannot conclude that all lipid mixing here leads to a full fusion pore opening. This should be addressed in the manuscript or at least noted as a limitation noted in the manuscript as a limitation.

      Expertise: virology, membrane fusion, cryo-EM

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript by Planitzer et al. investigates how NA activity, HA receptor binding avidity, and the identity and density of sialic acid receptors within the target membrane influence membrane fusion. This is an important question, as functional balance between HA and NA drives the antigenic evolution of seasonal influenza viruses and contributes to the pandemic potential of zoonotic viruses. As discussed in this manuscript, prior studies of how sialic acid receptors contribute to fusion following initial viral attachment are relatively few, and these have frequently reached different conclusions. Many of these discrepancies are likely attributable to details of the experimental system used to address the question, highlighting the importance of approaches in which the receptor identity and density are precisely controlled. Within this context, the sensitive and quantitative method the authors have developed to measure the efficiency and kinetics of virion fusion using flow cytometry ('spCALM') stands out as an important contribution of this manuscript. Using this approach, the authors convincingly demonstrate that engagement of HA with sialic acid receptors during the fusion process promotes efficient fusion, and that conditions that disrupt this engagement (low receptor densities combined with NA activity or receptor-HA mismatch) lead to reduced fusion efficiency by reducing the probability of fusion peptide insertion. Overall, the methods and findings of this paper are a strong contribution to the field and will be of interest to researchers studying viral membrane fusion as well as HA-NA balance. The manuscript is well-written, and the figures are very clear. The supporting figures provide additional context that is valuable. Addressing the comments below would strengthen the manuscript further.

      Major comments:

      • While the precise control of receptor identity and density is a strength of this experimental approach, more discussion of how the conditions tested here compare to physiological conditions would strengthen the manuscript. The receptor densities tested here are <1 mol%. I suspect that this is lower than physiological densities of sialic acid on the cell surface or in the endosome, but it would be helpful to have a direct (approximate) comparison in units of receptors per unit area.
      • Related to my first point, a tradeoff in the approach described here is that the observations may be specific to the regime that is being tested. Any effort to expand the range of conditions tested would therefore be valuable. For example, the authors discuss prior work at higher receptor densities (line 364); it seems that this higher-density regime could be evaluated here for direct comparison. Similarly, it is interesting that the authors do not observe differences when they use 12nt vs 24nt for the display of sialic acid on DNA tethers. I would imagine that sustained receptor binding could be detrimental to fusion peptide insertion only if it occurs at too large of a distance from the target membrane. It seems likely that the distances involved here (~4nm vs. 8nm) may not be sufficient to observe this, but testing larger DNA anchors seems feasible. If not, additional discussion of contexts where the findings described here may no longer be applicable would strengthen the manuscript.
      • Regarding the experiments in Figure 4 and S13: what is the anticipated phenotype for the A227T variant? It appears to have the opposite effect of the avidity-increasing E246G mutation, restoring WT avidity in the double mutant (line 245), and forming larger plaques in MDCK cells when introduced alone (in contrast to the smaller plaques formed by E246G). However, in spCALM measurements (Fig 4C), it behaves similarly to WT. The authors mention that this result is surprising (line 259), but more context for this observation would be helpful. Similarly, it is interesting that all of the variants tested exhibit higher peak fusion efficiency than the WT (50-75% vs. 25%). I would be interested how the authors interpret this result.

      Minor comments:

      Line 49: "3-4 neighboring HA2 molecules" should be clarified to indicate that this means 3-4 trimers (as stated a few lines later).

      Line 278: "presence of absence" should read "presence or absence".

      Line 296: I am surprised that 450nM MEDI8852 is a sub-neutralizing dose; this seems higher than what has been reported elsewhere, although it may be a specific feature of the virus used here. Can the authors clarify how this was determined, i.e., is this based on an infection assay or a lipid mixing assay?

      Significance

      This study establishes a powerful experimental approach to quantitatively dissect fusion between influenza viruses and target membranes across a wide range of experimental conditions. This approach represents a significant methodological advance and its high throughput allows the authors to quantify rare events. Additionally, the manuscript presents convincing data showing that, within the regimes tested here, sustained receptor engagement promotes membrane fusion by increasing the likelihood of fusion peptide insertion. The observation that this is reduced by NA activity at low receptor densities carries implications for HA-NA functional balance.

      While the specific findings may be restricted to the range of experimental conditions tested here, the results will be of interest to researchers studying virus-membrane fusion and functional balance between the influenza virus glycoproteins.

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      Referee #1

      Evidence, reproducibility and clarity

      Planitzer et al. have developed a clever flow cytometry assay to monitor hemagglutinin (HA)-mediated membrane fusion. This novel approach provides several advantages over bulk and single-particle fusion assays, including dramatically higher throughput and detection of lipid mixing under low efficiency conditions. As such, on purely technical grounds, this manuscript provides a significant advance to the field. The authors then apply their new approach to probe the role of HA-sialic acid binding in the membrane fusion reaction. This reflects an important open question in the field, which, as the authors point out, has been elusive. They conclude that sialic acid binding promotes efficient membrane fusion, probably by increasing the likelihood of the fusion peptide engaging the target membrane after acid-induced conformational changes. Overall, the manuscript is very well written; the data are rigorously analyzed and nicely presented; and the results are important and informative. My comments are, for the most part, minor.

      A couple points in the text, the authors use the term "distributional dynamics". This term is not defined and could use clarification.

      In the spCALM assay, the authors indicate that labeled virions were incubated with cells or liposomes at a ratio of "0.15 virions per target". How were the numbers of virions determined for these experiments? The Methods refer to MOIs in units of PFU/cell but do not indicate how the number of physical particles were determined.

      Why have the authors used this technique using lipid-ssDNA and receptor-DNA to display sialic acid on the liposomes? In principle, this allows control over the distance between the sialic acid and the membrane. But in the spCALM assay the length of the spacer DNA (12 or 24 nt) had not effect. So, why then not use the simpler approach of incorporating sialylated lipids (e.g. GD1a, GD1b) in the membrane?

      The authors raise the interesting possibility that neuraminidase (NA) may contribute to virion attachment in the presence of NAI. Would it be worthwhile to test fusion of virions in the absence of NA? Or in the presence of NA where the sialic acid binding site has been mutated?

      The authors indicate that the MEDI antibody "increased binding across all tested conditions." But this was not mentioned again. By what mechanism do they expect the antibody enhances virus binding?

      Significance

      Planitzer et al. have developed a clever flow cytometry assay to monitor hemagglutinin (HA)-mediated membrane fusion. This novel approach provides several advantages over bulk and single-particle fusion assays, including dramatically higher throughput and detection of lipid mixing under low efficiency conditions. As such, on purely technical grounds, this manuscript provides a significant advance to the field. The authors then apply their new approach to probe the role of HA-sialic acid binding in the membrane fusion reaction. This reflects an important open question in the field, which, as the authors point out, has been elusive. They conclude that sialic acid binding promotes efficient membrane fusion, probably by increasing the likelihood of the fusion peptide engaging the target membrane after acid-induced conformational changes. Overall, the manuscript is very well written; the data are rigorously analyzed and nicely presented; and the results are important and informative.

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      Reply to the reviewers

      We thank the two reviewers for their constructive comments regarding our manuscript. Below is our point-by-point response (un-bold) to each reviewer’s comments together with the experiments we propose to carry-out in order to strengthen our main conclusions.

      Reviewer #1

      In this study, the authors investigate the effects of pharmacological inhibition of the spliceosome using the SF3B1 inhibitor pladienolide B in models of platinum-resistant non-small cell lung cancer (NSCLC). Using a combination of cell lines, platinum-resistant derivatives, and patient-derived xenograft (PDX) models, the authors show that spliceosome inhibition sensitizes platinum-resistant tumors to treatment and leads to increased DNA damage accumulation and impaired DNA damage response signaling. Transcriptomic analyses indicate that transcripts encoding DNA damage regulators are particularly sensitive to alternative splicing perturbations, and selected mechanistic experiments suggest involvement of specific regulators such as MLH3. The study further explores links between splicing inhibition, transcriptional activity, and cell cycle progression. Overall, the manuscript presents extensive datasets across multiple experimental systems and provides strong evidence that spliceosome inhibition can sensitize platinum-resistant tumors to DNA damage. However, several aspects of the mechanistic interpretation, data consistency, and presentation require clarification or strengthening to fully support the central claims.

      We thank the reviewer for his/her positive comments on our work and we agree that the manuscript requires clarification to support our main claims.

      Major comments* *

      Conceptual clarity and synthesis of mechanistic model

      The manuscript presents multiple mechanistic observations-including altered splicing of DNA repair genes, increased DNA damage accumulation, transcriptional perturbation, and cell cycle changes-but these are not integrated into a coherent conceptual framework. While it is reasonable that not all mechanistic details are fully resolved, the current presentation leaves the reader uncertain about the relative contributions of these processes. A clearer synthesis of the proposed mechanism, possibly including a summary model figure, would substantially improve the conceptual clarity of the study.

      __We thank the reviewer for his/her comment. We acknowledge that our study provides multiple mechanistic observations. However, all these observations converge towards a more global mechanism by which pladienolide B induces cell death in NSCLC. Hence, we demonstrate that pladienolide B, which targets the SF3B1 protein, a core component of the spliceosome machinery, induces a massive shutdown of the DNA Damage Response signaling pathway, both at the transcriptional and splicing levels, which leads to enhanced genomic instability and cell death notably in NSCLC cells with acquired resistance to platinum salts. More specifically, we identified ATR, DNA-PKcs and MLH3 as novel targets of SF3B1 in NSCLC cell lines, as well as more importantly, in NSCLC Patient-Derived Xenografts. To our knowledge, and as also highlighted by the reviewer, this is the first evidence that pladienolide B slows-down tumor growth by negatively impacting DNA Damage Response in patient-derived xenografts. We agree with the reviewer that providing a summary model figure would improve the clarity of the study. We will provide such a graphical abstract in our revised manuscript. __

      Biological specificity of platinum-resistant cell sensitivity

      A central premise of the study is that platinum-resistant cells exhibit enhanced sensitivity to spliceosome inhibition. However, in several experiments (e.g., cell cycle analysis in Figure 2A), similar responses to pladienolide B appear to occur in both platinum-sensitive and resistant cells. This observation complicates the interpretation that resistant cells exhibit uniquely distinct vulnerability. The authors should clarify how these findings align with the proposed model and more explicitly distinguish shared versus resistance-specific responses.

      __We thank the reviewer for this remark. In this study, we did not want to claim that platinum-salts resistant cells exhibit unique vulnerability to pladienolide B as we fully agree with the reviewer that pladienolide B also exhibits cytotoxic effects in parental sensitive cells, although with a delayed kinetic. We acknowledge that the way we introduced our results section in the former manuscript could have contributed to such a misunderstanding. Rather, we propose a model in which pladienolide B induces cell death in NSCLC cells by massively impairing the expression of key components of the DNA damage and repair signaling pathways, at the transcriptional and/or splicing level. As NSCLC cells with acquired resistance to platinum salts are likely more dependent to these pathways for their survival than parental cells, this explains enhanced susceptibility of these resistant cells to pladienolide B-induced cell death. We think that these results highlight spliceosome targeting compounds as an alternative therapeutic strategy in NSCLC patients who escape chemotherapy. According to the remark of the reviewer, we will modify the way we introduce our results and we will clarify all these points in the discussion section of the revised manuscript. __

      Heterogeneity in PDX responses and lack of platinum-sensitive controls

      The PDX experiments represent a major strength of the study. However, resistant tumors display heterogeneous responses to pladienolide treatment, suggesting the presence of additional determinants of sensitivity.

      __We thank the rewiever for this remark. In this study, we used seven distinct NSCLC PDXs We initially selected these PDXs based on their low responsive rate to cisplatin rather than their mutational status. As discussed in the discussion section, we did not find any common mutation(s) that could predict the differential response of these PDXs to pladienolide B. We only noticed that the LCIM10 PDX, which is the most responsive to pladienolide B, exhibits ATRX mutation. ATRX has been shown to protect stalled replication forks from collapsing. As we found that pladienolide B induces early replicative stress in NSCLC cells, it is tempting to speculate that ATRX mutation might interfere with the replicative stress response and potentiates pladienolide B’s cytotoxic effects. Noteworthy, LCIM10 PDX also displayed higher basal levels of both P-DNA-PKcs(Ser2056) and P-ATR(Thr1989) proteins as compared to LCF26, ML1 and LCIM1 PDXs that were less responsive to pladienolide B (data to be added in the revised version of the manuscript as supplementary data). Therefore, and although this remains to be further clarified, this suggests that NSCLC patients with higher basal level of replicative stress, such as those who escape chemotherapy, might be more susceptible to SF3B1 inhibition. __

      Including platinum-sensitive PDX tumors, if available, would provide valuable baseline comparison and strengthen interpretation of resistance-specific effects. If not feasible, the limitations should be acknowledged and discussed.

      To our knowledge, and as also mentioned by the reviewer, our study provides the first demonstration of the effects of pladienolide B on the growth of NSCLC PDXs. We think that these results pave the way for further investigations in additional NSCLC PDXs and we agree with the reviewer that adding platinum-sensitive PDX tumors would be interesting. However, due to cost limitations and time constraints, we will be unable to repeat them for this specific study. Based on the results we already obtained in 7 platinum salts-resistant PDXs as regard to their response heterogeneity, one might also speculate that the comparisons between numerous sensitive and resistant PDXs should be complicated as sensitive PDXs might also display distinct mutational status. According to the remark of the reviewer, we will discuss these aspects in the discussion section of the revised manuscript.

      Consistency between pharmacological inhibition and genetic depletion

      In Figure 4, the authors compare pladienolide treatment with SF3B1 knockdown to demonstrate target specificity. However, the effects observed with the two perturbations are not entirely consistent-for example, pladienolide affects phosphorylation of DNA-PKcs, while SF3B1 knockdown appears to produce broader effects at both protein and mRNA levels. Additionally, differences are observed between resistant cell lines in the response to SF3B1 knockdown. These discrepancies should be addressed and discussed, as they may reflect mechanistic differences between acute pharmacological inhibition and genetic depletion.

      __We thank the reviewer for this remark and we agree that acute pharmacological inhibition using pladienolide B and genetic depletion using SF3B1 siRNA might produce distinct effects as they do not exhibit the same mechanism of action. Hence, pharmacological inhibitors target the protein while siRNA targets mRNA with effects depending on the basal mRNA level/stability. This could explain why we did not exactly observe the same effects on ATR/DNA-PKcs mRNA and protein levels using both approachs in H460/A549 parental and resistant cells (Fig 3-4). For pladienolide B treatment, the effects were analyzed at “early” timepoint [i.e. after 4-6 hours treatment (Fig 3a-d)] or at a “later timepoint” [i.e. after 24-48 hours treatment (Fig 3e-f)]. At early timepoint, pladienolide B induced replicative stress that correlated with DNA-PKcs phosphorylation, while at later timepoints it decreased ATR and DNA-PKcs mRNA/protein levels. The biological consequences of SF3B1 knock-down were analyzed after 72 hours of transfection in resistant cells. This could explain why SF3B1 knock-down appears to produce broader effects at both protein and mRNA levels. However, we acknowledge the existence of differences between SF3B1 knocked-down-H460 and -A549 cells as regard to the downregulation of ATR and DNA-PKcs that occurs at the mRNA and/or protein level depending on the cell line. Again, this could depend on the time as well as the efficiency of SF3B1 knock-down which was more prominent in H460 resistant cells compared to A549 cells. Nevertheless, and despite these mechanistic differences in cell lines and between pladienolide B and SF3B1 siRNA, our results identify ATR and DNA-PKcs as novel targets of SF3B1 in NSCLC cell lines, including cells with acquired resistance to cisplatin, as well as more importantly in NSCLC PDXs (Fig 9c). To our knowledge, this is the first evidence that pladienolide B or SF3B1 knock-down negatively targets ATR or DNA-PKcs in solid tumors. Owing to the crucial role played by both kinases in the maintenance of genomic stability in cancer cells, we think that this result is of importance. Nevertheless, as suggested by the reviewer, we propose to acknowledge and discuss more in details the discrepancies between cellular models and pladienolide B / SF3B1 knock-down in the revised version of the manuscript. __

      Selection and interpretation of splicing-sensitive transcripts

      Transcriptomic analyses in Figure 5 identify both shared and differential splicing changes between sensitive and resistant cells. However, much of the analysis focuses on transcripts that are commonly affected in both conditions, rather than those uniquely altered in resistant cells. Given that the central phenotype is resistance-specific sensitivity, transcripts uniquely mis-spliced in resistant cells may represent more informative candidates. The authors should clarify the rationale behind focusing on shared events and discuss the implications of resistance-specific versus common splicing changes.

      We thank the reviewer for this remark. Indeed, after obtaining RNA-Seq data, we initially looked for genes which differential expression and/or splicing upon pladienolide B treatment could only be observed in resistant cells but not parental ones. We focused first on the 121 genes belonging to the DNA repair pathways full network (WikiPathway WP4946) since Gene-Ontology analyses based on RNA-Seq data demonstrated enrichment of genes involved in DNA metabolic process, which includes DNA repair, among genes down-regulated after pladienolide B treatment (Fig S3). The focus on DNA repair was also justified by our observation showing accumulation of DNA double strand breaks upon pladienolide B treatment in H460 resistant cells (Fig 2d-f). Doing this comparison, we showed that pladienolide B regulates the expression of 41 (33%) and 47 (39%) genes of this network in H460S and H460R cells respectively, ____which were mostly down-regulated in both H460S (33/41) and H460R (33/47) cells (Table 3). Fifteen genes involved in all DNA repair processes were found to be specifically down-regulated in H460R cells upon pladienolide B treatment, including PARP-1. As a whole, these results demonstrated that pladienolide B down-regulates the expression of numerous DNA repair genes in both NSCLC parental and resistant cells. As discussed above, we propose that the enhanced sensitivity of resistant cells to pladienolide B is related to their increased dependency for survival to functional DNA repair pathways.

      When differentially spliced genes were considered, and focusing on exon skipping events, as they were the more prominent (Fig 5e), we found that pladienolide B regulates the splicing of 107 and 87 genes of the WikiPathway WP4946 in H460 parental and resistant cells, respectively (Table 5). Forty five genes were predicted to be regulated in both cell lines. Only 3 genes, namely DCLRE1C, POLD3 and PNKP, were predicted to be differentially spliced upon pladienolide B treatment in H460R cells only. Trying to increase the number of genes to study, we extended our analysis to genes belonging to another DNA repair database (Human DNA Repair Genes, Resources from Wood laboratory, UT MD Anderson), and we found six additional genes, namely MLH3, MSH5, RAD54L, EME1, SETMAR and SMC6, that were also predicted to be differentially spliced upon pladienolide B treatment in H460R cells only. However, four of these exon skipping events (i.e. PNKP-Ex9, DCLRE1C-Ex11, SETMAR-Ex2, SMC6-Ex6) were not validated and we did not observe clear difference between H460 resistant and parental cells for the others (Fig 5g and Fig S6). Skipping of MLH3-Ex8 was the sole event displaying a slight difference between both cell lines, mainly in term of kinetic of recovery. This is why we decided to further analyze this specific splicing event. Noteworthy, we focused only on genes involved in DNA damage and repair signaling pathways. Therefore, we cannot exclude that genes involved in other biological processes might be differentially transcribed or spliced in response to pladienolide B in H460 parental and resistant cells.

      Transient nature of splicing effects

      The authors report transient alternative splicing effects upon prolonged pladienolide treatment. This observation is counterintuitive, as continued spliceosome inhibition might be expected to produce cumulative splicing defects. While the authors reference studies showing that transient inhibition can produce lasting effects, the current observations involve continuous exposure. This apparent discrepancy should be clarified and discussed.

      We thank the reviewer for this remark. We agree with him/her that the transient effect of pladienolide B on most of the splicing events we studied was unexpected as pladienolide B treatment was prolonged. We do not have a clear explanation for that. One possibility is that pladienolide B is not stable in the cell culture supernatant and is degraded rapidly. Another, not exclusive, possibility relies on the structure of studied transcripts. As discussed in the discussion section, it is possible that the nature of the transcripts involved in DNA damage response/DNA repair which have a long length, a large number of small exons per transcript, and an elevated number of introns could explain why they recover very rapidly from pladienolide B inhibition. Alternatively, upon SF3B1 inhibition, compensatory regulations by other splicing factors might occur. We will discuss these aspects in the revised version of the manuscript.

      Use of unrelated cell lines in reporter assays

      The DNA damage reporter assays (Figure 6A-D) appear to be performed in cell lines not directly linked to platinum sensitivity or resistance. Given the central importance of resistance-specific responses, repeating key reporter assays in both sensitive and resistant paired models would strengthen the conclusions.

      __We initially engineered these cellular models to assess the role of SRSF2, a splicing factor, in DNA repair ____(Khalife M. et al., NAR Cancer, 2025). In these models derived of either H1299 or A549 NSCLC cell lines, DNA double strand breaks (DSBs) are produced after cleavage by the SceI enzyme and the efficiency of repair is assessed based on the expression of either GFP (for homologous recombination) or CD4 (for c-NHEJ). These cellular models were difficult to engineer and to work with as they require a first stable transfection with either PBL174 pDR-GFP (for HR analysis) or PBL230 (for c-NHEJ) plasmid followed by a second transient transfection with the PLBL133 plasmid that encodes SceI enzyme. As an example, we tried to generate stable A549 cells with PBL174 plasmid but we never succeeded. The reverse was true for H1299 cells and transfection with PBL230. In addition, during the time course of this project, we also tried to transiently transfect plasmid encoding MLH3 or MLH3 protein devoid of exon 8-encoding amino acids in H460 or A549 resistant cells but we never obtained good efficiency of transfection, although we tested several transfection reagents. So, it looks like that resistant cells are hardly transfectable. This is why repeating these experiments in resistant models will not be possible. However, and although we agree with the reviewer that the cell lines we used were not directly linked to platinum sensitivity or resistance, the idea behind these experiments was to test whether pladienolide B could have a general impact on DNA repair by homologous recombination or c-NHEJ using these SceI-induced DSBs systems that are already widely used in the DNA repair field. As shown in Fig 6b and 6d, pladienolide B prevented DNA repair in these cellular models, confirming that it widely negatively impacts DNA repair pathways. __

      Interpretation of MLH3 splicing results

      In Figure 7, differences between pharmacological inhibition and SF3B1 knockdown in MLH3 exon 8 regulation are not entirely consistent.

      We do not strictly agree with this comment. __As also discussed above, the differences seen between pharmacological inhibition of SF3B1 using pladienolide B and SF3B1 knock-down in term of MLH3-exon 8 regulation could be related to differences in term of mechanism of action and/or the fact that the effects of SF3B1 knockdown were analyzed after 72 hours treatment (as we obtained the best knock-down efficiency at this time point) while those of pladienolide B were studied between 6 to 48 hours treatment. However, as illustrated in Fig 7a and 7c (right panel), we showed that both pladienolide B and SF3B1 knock-down promote MLH3-exon 8 exclusion in H460 and A549 resistant cells. The effects of SF3B1 knock-down were less pronounced in A549R cells which could be consistent with the decreased efficiency of SF3B1 knockdown as depicted in Fig 7c (left panel). Pladienolide B also promoted MLH3-Ex8 exclusion in H460 and A549 parental cells but the recovery was faster in parental cells as compared to resistant cells (Fig 7a). __

      Furthermore, inclusion levels of regulated and non-regulated exons appear similarly correlated with SF3B1 expression, potentially weakening the argument for exon-specific regulation. These observations should be clarified.

      __As regard to MLH3-exon 5 exclusion, we observed its exclusion in A549 parental and resistant cells upon pladienolide B treatment, while this was not observed in H460 parental and resistant cells (Fig 7a-b). In SF3B1 knocked-down H460R and A549R cells, the exclusion of MLH3-exon 5 was seen in A549R cells. When analyzing MLH3 exon 8 or exon 5 usage in lung adenocarcinoma patients (Fig 7e), we agree with the reviewer that there was a significant correlation between SF3B1 mRNA level and MLH3 exons 5 and 8 usage. Therefore, these and our data indicate that both MLH3 exons 5 and 8 could be regulated by SF3B1 in NSCLC although differences might occur depending on the cell line. To make this point clearer, we will clarify the text of the results for Figure 7 and our conclusion. __

      Combination treatment logic

      In Figure 8, co-treatment experiments with pladienolide B and cisplatin are performed primarily in resistant cells. Performing similar experiments in platinum-sensitive cells would provide an important reference point to distinguish additive versus resistance-specific effects.

      __We thank the reviewer for this important remark. The objective of figure 8 was to investigate whether pladienolide B that induces a shutdown of numerous DNA damage response-related genes could resensitize NSCLC cells with acquired resistance to cisplatin-induced apoptosis. The results presented in Figures 8a and 8b show that this is indeed the case. However, and according to the remark of the reviewer, we propose to illustrate in the revised version of the manuscript the results of the co-treatment experiments in sensitive parental cells also. Indeed, we already had the results for H460 parental cells. They did not show any additive or synergistic effects of the combination in these cells. Rather adding pladienolide B to cisplatin tended to decrease apoptosis as compared to cisplatin alone. We will reiterate these experiments in A549 cells. If confirmed, and to a translational point of view, these results would support the idea that treating NSCLC patients who relapse from chemotherapy with a combination of platinum salts and pladienolide B could provide therapeutic benefits, whereas NSCLC patients that primary respond to platinum salts could less benefit from this combination. __

      Minor comments

      • In Figure 1, pladienolide treatment in platinum-sensitive cells appears to plateau at approximately 50% cell killing. Extending the concentration range may help clarify whether maximal efficacy was reached.

      __ We agree with this remark. We will reiterate our MTS experiments increasing the dose of pladienolide B. __

      In Figure 1H, the difference between 2.5 mg/kg and 5 mg/kg pladienolide in PDX models appears disproportionately large relative to the dose change. This should be discussed or experimentally clarified.

      We agree with the reviewer but these are the results we obtained. In Figure 1h, we illustrated the probability of progression based on Relative Tumor Volume (RTV) = 2. The difference between the two doses was less, although remaining significant, when considering RTV = 4 as a marker of progression (Fig S2d).

      In Figure 5, differential expression and splicing analyses are presented using multiple cutoffs (e.g., log₂FC > 0.4 and >1; ΔPSI thresholds). This introduces redundancy and may obscure key findings. A single well-justified cutoff would improve clarity.

      We agree with the reviewer that in initial Figure 5 we provided graphs illustrating different analysis thresholds based on our transcriptomic analyses. We will select one cut-off for Differentially Expressed Genes [absolute Log2Fold Change ≥ 0.4 and p ≤ 0.05 (Fig 5a)] and Differentially Spliced Genes [absolute percent splice in (PSI) ≥ 0.2 and p ≤ 0.05 (Fig 5e)]. We will remove Fig 5b and 5d for more clarity.

      Several isoform-specific RT-PCR gels are difficult to interpret due to low image clarity. Improving gel presentation or focusing on key timepoints would strengthen data readability.

      We will improve gel presentation.

      Some figure panels appear redundant, showing similar datasets under different analysis thresholds.

      We agree with the reviewer that in Figure 5 we provided graphs illustrating different analysis thresholds based on our transcriptomic analyses. We will select one cut-off for Differentially Expressed Genes [absolute Log2Fold Change ≥ 0.4 and p ≤ 0.05] and for Differentially Spliced Genes [absolute percent splice in (PSI) ≥ 0.2 and p ≤ 0.05]. We will remove Fig 5b and 5d for more clarity.

      A graphical summary model illustrating the proposed mechanism would improve reader comprehension.

      We agree with the reviewer. We will provide such a graphical abstract in the revised version of our manuscript.

      In Figure 3D, representative images of γH2AX foci appear visually similar across conditions, whereas quantification shows large differences. The authors should ensure that representative images accurately reflect quantified trends and clarify selection criteria for displayed images.

      We agree with the reviewer. We will select additional images illustrating more the differences we highlighted after quantification of more than 500 nuclei (Figure 3D, right panel).

      Reviewer #1 (Significance (Required)):

      This study represents a comprehensive investigation of spliceosome inhibition as a therapeutic strategy to overcome platinum resistance in NSCLC. The use of resistant cell lines, PDX models, and functional reporters provides strong experimental depth. The most compelling aspects of the study include the demonstration that spliceosome inhibition enhances DNA damage accumulation and sensitizes resistant tumors to platinum-based therapies. However, several mechanistic interpretations require clarification, and data presentation could be streamlined to improve logical coherence.

      We thank the reviewer for his/her constructive remarks. We hope that our answers and the additional works we now propose to carry-out in order to revise the manuscript will get agreement to him/her and will strengthen our main claims

      Advance

      The work provides evidence that targeting spliceosome function-specifically via SF3B1 inhibition-can sensitize platinum-resistant tumors to DNA-damaging agents. To my knowledge, this represents one of the first comprehensive demonstrations that spliceosome-targeting compounds can effectively overcome acquired platinum resistance in solid tumor models. The study also contributes to the emerging understanding that DNA damage response transcripts may represent particularly sensitive targets of splicing perturbation.

      Audience

      The study will be of interest to researchers in RNA biology, cancer therapeutics, DNA damage response, and translational oncology. It is particularly relevant to scientists investigating therapeutic vulnerabilities in drug-resistant cancers and those exploring RNA processing as a therapeutic target.

      Expertise I have expertise in RNA biology, alternative splicing and cancer models. My expertise is more limited in pharmacological dosing strategies and some aspects of in vivo xenograft modeling.

      Keywords: RNA biology, alternative splicing, spliceosome function, cancer biology, DNA damage response, transcriptomics

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary

      In the manuscript by Jamal-El-Hussein et al., the authors demonstrated that pladienolide B, an inhibitor of splicing factor 3B subunit 1 (SF3B1), inhibits cell viability in NSCLC cells and PDXs with resistance to platinum-based chemotherapy. Mechanistically, they identified that pladienolide B regulates splicing events of genes associated with DNA damage signaling and repair, specifically through exon skipping of MLH3, thus increasing vulnerability to chemotherapy. This study provides therapeutic insights into combining pladienolide B with chemotherapy for overcoming therapy resistance.

      __Major comments __

      The authors showed that both H460R and A549R cell lines are sensitive to pladienolide B; however, they exhibit distinct molecular responses. For example, SF3B1 knockdown downregulates the mRNA expression of ATR and PRKDC in H460R cells, while the expression of these genes remains unchanged in A549R cells (Fig. 4b). Furthermore, exon 8 skipping of MLH3 is not observed in A549R cells with pladienolide B treatment (Fig. 7a). These discrepancies suggest that the two cell lines respond to pladienolide B through different mechanisms. The authors should also perform a bulk RNA-seq on A549 cell line to better understand the different behavior of the two cell lines.

      We disagree with the remark regarding Fig 7a as this figure demonstrated MLH3 exon 8 skipping in A549R cells also after 6 and 24 hours pladienolide B treatment. __Although we agree with the reviewer that some differences exist in term of molecular mechanisms regulated by pladienolide B or SF3B1 knock-down in H460R and A549R cell lines, we identified in this study ATR, DNA-PKcs and MLH3 as novel targets of SF3B1 in both cell lines as well as more importantly in NSCLC PDXs. To our knowledge, this is the first evidence that SF3B1 inhibition negatively impacts ATR or DNA-PKcs and regulates MLH3 alternative splicing in solid tumors. In addition, we found a significant correlation between SF3B1 and DNA-PKCs or ATR protein levels in 77 NSCLC cell lines (Fig 4c) which supports a close relationship between these proteins in lung cancer. As also discussed in the response to reviewer 1, the differences between pladienolide B and SF3B1 knock-down could be related to the distinct timepoints at which we analyzed their effects as well as to the different mechanisms of action between pharmacological and siRNA inhibition. Nevertheless, we propose to acknowledge and discuss more in details the discrepancies between cellular models and pladienolide B or SF3B1 knock-down in the revised version of the manuscript. __

      The authors' central claim is that platinum-based chemotherapy-resistant cells are more sensitive to pladienolide B treatment.

      __We thank the reviewer for this remark. In this study, we did not want to claim that platinum-salts resistant cells exhibit unique vulnerability to pladienolide B as we fully agree with the reviewer that pladienolide B also exhibits cytotoxic effects in parental sensitive cells, although with a delayed kinetic. We acknowledge that the way we introduced our results section in the former manuscript could have contributed to such a misunderstanding. Rather, we propose a model in which pladienolide B induces cell death in NSCLC cells by massively impairing the expression of key components of the DNA damage and repair signaling pathways, at the transcriptional and/or splicing level. As NSCLC cells with acquired resistance to platinum salts are likely more “addict” to these pathways for their survival than parental cells, this might explain enhanced susceptibility of these resistant cells to pladienolide B-induced cell death. We think that these results highlight spliceosome targeting compounds as an alternative therapeutic strategy in NSCLC patients who escape chemotherapy. According to the remark of the reviewer, we will modify the way we introduce our results and we will clarify all these points in the discussion section of the revised manuscript. __

      In their bulk RNA-seq analysis, the authors selected genes commonly regulated by pladienolide B in both parental and resistant cells for further validation. This approach raises a critical concern: the observed sensitivity to pladienolide B may already be present in parental cells rather than representing a mechanism uniquely acquired by the resistant cells. To substantiate their central claim, the authors should analyze the differentially expressed genes between parental and resistant cells to identify resistance-specific molecular alterations that may confer enhanced sensitivity to pladienolide B, thereby providing a more mechanistically rigorous basis for their conclusions.

      We thank the reviewer for this remark and we agree with him/her. Indeed, after obtaining RNA-Seq data, we initially looked for genes which differential expression and/or splicing upon pladienolide B treatment could be only observed in resistant cells but not parental ones. We initially focused on the 121 genes belonging to the DNA repair pathways full network (WikiPathway WP4946) since Gene-Ontology analyses demonstrated enrichment of genes involved in DNA metabolic process, which includes DNA repair, among genes down-regulated after pladienolide B treatment (Fig S3). The focus on DNA repair was also justified by our observation showing accumulation of DNA double strand breaks upon pladienolide B treatment in H460 resistant cells (Fig 2d-f). Doing this comparison, we showed that pladienolide B regulates the expression of 41 (33%) and 47 (39%) genes of this network in H460S and H460R cells respectively, ____which were mostly down-regulated in both H460S (33/41) and H460R (33/47) cells (Table 3). Fifteen genes involved in all DNA repair processes were found to be specifically down-regulated in H460R cells upon pladienolide B treatment, including PARP-1. As a whole, these results demonstrated that pladienolide B down-regulates the expression of numerous DNA repair genes in both NSCLC parental and resistant cells. However, and as discussed above, we propose that the enhanced sensitivity of resistant cells to pladienolide B is related to their increased dependency for their survival to functional DNA repair pathways.

      __When differentially spliced genes were considered, and focusing on exon skipping events, as they were the more prominent (Fig 5e), we found that pladienolide B regulates the splicing of 107 and 87 genes of the WikiPathway WP4946 in H460 parental and resistant cells, respectively (Table 5). Forty five genes were predicted to be regulated in both cell lines. Only 3 genes, namely DCLRE1C, POLD3 and PNKP, were predicted to be differentially spliced upon pladienolide B treatment in H460R cells only. Trying to increase the number of genes to study, we extended our analysis to genes belonging to another DNA repair database (Human DNA Repair Genes, Resources from Wood laboratory, UT MD Anderson), and we found six additional genes, namely MLH3, MSH5, RAD54L, EME1, SETMAR and SMC6, that were also predicted to be differentially spliced in H460R cells only. However, four of these exon skipping events (i.e. PNKP-Ex9, DCLRE1C-Ex11, SETMAR-Ex2, SMC6-Ex6) were not validated and we did not observe clear difference between H460 resistant and parental cells for the others (Fig 5g and Fig S6). Skipping of MLH3-Ex8 was the sole event displaying a slight difference between both cell lines, mainly in term of kinetic of recovery. This is why we decided to further analyze this specific splicing event. Noteworthy, we focused only on genes involved in DNA damage and repair signaling pathways. Therefore, we cannot exclude that genes involved in other biological processes might be differentially transcribed or spliced in response to pladienolide B in H460 parental and resistant cells. __

      The authors conclude that pladienolide B treatment correlates with activation of DNA-PKcs signaling followed by a shutdown of ATR and DNA-PKcs pathways. However, the data presented do not fully support this interpretation. P-ATR levels are already elevated in resistant cells and remain unchanged following pladienolide B treatment (Fig. 3a). However, prolonged pladienolide B treatment leads to decreased total ATR protein and mRNA expression (Fig. 3e-f), suggesting that pladienolide B maintains an initial constitutive ATR activation followed by transcriptional downregulation. Since pladienolide B is a splicing inhibitor, the authors should determine whether ATR and DNA-PKcs mRNA downregulation is a direct consequence of aberrant splicing of their transcripts, or a non-specific effect of prolonged cellular toxicity. To strengthen their mechanistic conclusions, the authors should perform time-course experiments to establish the temporal relationship between these signaling events, analyze splicing changes specifically in ATR and DNA-PKcs transcripts (are they in the differential genes from the bulk RNA-seq analysis?), and also check the downstream targets of the ATR and DNA-PKcs signaling.

      __We thank the reviewer for his/her comment. In our RNA-Seq analyses, we did not recover PRKDC among the differential genes expressed or spliced upon pladienolide B treatment whatever the cell line. However, PRKDC was also not in the full RNA-Seq data list of not significant genes. Therefore, it remains unclear whether PRKDC splicing could account for the decrease of PRKDC mRNA level upon pladienolide B treatment. Concerning ATR, it was not in the RNA-Seq data list of the genes significantly up- or down-regulated upon pladienolide B treatment in either H460 parental or resistant cells. However, transcriptomic analyses were performed after 8 hours pladienolide B treatment while the decrease of ATR mRNA was observed after 24 hours (Fig 3f). Regarding splicing, ATR was predicted to be spliced, skipping of exon 30, upon pladienolide B treatment in both H460 parental and resistant cells. We validated this splicing event after 8 hours treatment with pladienolide B in both H460 cellular models but we did not analyze this splicing event at later timepoints, nor in the A549 parental or resistant cells. Therefore, and according to the remarks of the reviewer, we propose to deepen the temporal relationships between all these signaling events by performing time-course experiments for analysis of ATR exon 30 splicing by RT-PCR, ATR and PRKDC mRNA levels by RT-qPCR, and expression of downstream targets of ATR and DNA-PKcs, such as P-CHK1(Ser345) or P-RPA32(Ser4/8) by immunoblotting. __

      The authors report that skipping of exon 8 of MLH3 leads to the complete absence of the protein (Fig. 7d). However, this observation needs further clarification, as at least two alternative explanations exist. First, the antibody used to detect MLH3 may specifically recognize an epitope encoded by exon 8 or downstream exons, in which case the loss of signal would reflect antibody incompatibility rather than true protein absence.

      __We thank the reviewer for this remark. The anti-MLH3 antibody recognizes the C-terminal part of the MLH3 full-length protein (between amino acids 1228-1453). MLH3 exon 8 is 72 base pair and does not encode for the amino acids recognized by the anti-MLH3 antibody. In ENSEMBL, the MLH3-201 transcript encodes for the full-length protein (1453 amino acids) and the MLH3-202 transcript encodes for a MLH3 protein (1429 amino acids) devoid of the amino acids encoded by exon 8. Nevertheless, the two products have the same C-terminus recognized by the anti-MLH3 antibody used in this study. So, the loss of the signal depicted in Figure 7d is not due to antibody incompatibility. __

      Second, skipping of exon 8 may introduce a premature stop codon, triggering nonsense-mediated mRNA decay (NMD) and consequent loss of the transcript. To distinguish between these possibilities, the authors should perform qPCR using primers targeting sequences both upstream and downstream of the skipped exon, as well as consider NMD inhibition experiments, to clarify whether the observed protein loss occurs at the transcriptional or translational level.

      __As shown in Figure 8f, we demonstrated by RT-qPCR that pladienolide B alone or the combination of pladienolide B with cisplatin does not negatively impact MLH3 mRNA level in both H460R and A549R cells. These results were confirmed in a time-course experiment of pladienolide B treatment performed in H460 and A549 parental and resistant cells, as well as in NSCLC PDXs. Similar results were obtained in H460R or A549R cells deprived of SF3B1. These new data will be added in the revised version of the manuscript. The couple of primers we used for MLH3 amplification was located downstream of exon 8, respectively on constitutive MLH3-exon 9 (forward primer) and MLH3-exon 10 (reverse primer). These results indicate that pladienolide B regulates MLH3 splicing but not MLH3 total mRNA level. Considering NMD, in the FASTER DB database, none of the MLH3 transcripts devoid of exon 8 are predicted to be degraded by NMD. So we do not think that pladienolide B-induced MLH3 exon 8 skipping promotes the synthesis of transcripts recognized by the NMD machinery. As discussed above, the anti-MLH3 antibody does not allow to distinguish between the full length MLH3 protein and the MLH3 product encoded by transcript devoid of exon 8. In addition, only 24 amino acids (around 2-3KDa) differentiate both products which could render difficult their specific detection in SDS-PAGE. So, we speculate that the decrease of MLH3 signal detected by immunoblotting in pladienolide B-treated and SF3B1 knocked-down cells is mostly related to the decrease of MLH3 full-length protein due to the decreased level of MLH3 transcript retaining exon 8 and encoding MLH3 full-length protein. Alternatively, and not exclusively, MLH3 product devoid of exon 8 might also be less stable. __

      The difference shown in Fig. 6b after pladienolide B treatment decreases from 2.2% to 1%. This raises concern about whether the observed difference reflects a true biological effect or is confounded by technical limitations such as low transfection efficiency. The authors should consider optimizing their transfection conditions to achieve a more robust and convincing result.

      We agree with the reviewer’s comment. However, using this SceI-inducible system to analyze DNA double strand breaks repair by homologous recombination, it is very frequent to have only a very low percentage of cells able to perform homologous recombination thereby expressing the GFP protein ____(as examples: Yoshino Y et al., Sci Reports, 2019; Brustel et al., Sci Rep., 2018; Croglio et al., Oncotarget, 2016; Mamouni et al., Mol Cell Biol., 2014). This is why the difference is low between each condition but it is significant. Indeed, these engineered cellular models are not easy to manipulate as we first need to obtain stable clones having incorporated the PBL174 pDR-GFP-plasmid and then to transiently transfect them using a second plasmid encoding the SceI enzyme which creates DNA Double Strand Breaks. The efficiency of the second round of transfection might therefore be decreased as the cells already experienced a first round of transfection.

      __Minor comments __

      The abbreviation "S" in H460S and A549S cells is not defined in the manuscript. As this designation is used throughout the text, the authors should clarify what "S" denotes upon its first appearance.

      __We thank the reviewer for this remark. We will correct the text. __

      The current presentation of Figure S1a does not clearly demonstrate that different NSCLC cell lines exhibit differential sensitivity to pladienolide B. The authors should calculate and report IC50 values for each cell line to enable a more rigorous and quantitative comparison of their respective dose-response relationships.

      We thank the reviewer for this remark and agree with it. We will calculate and report in the revised version of Fig S1a the IC50 for pladienolide B for each cell line.

      In Figure 4c, two dashed black lines are present in the plot but are not described or explained in the figure legend or the main text.

      We thank the reviewer for this remark. The two dashed black lines represent the upper and lower boundaries of the 95% confidence interval for the fitted linear regression line. We have now clarified their meaning in the revised figure legend and indicated section of the main text also.

      In Figure 5a, the authors combine the downregulated and upregulated genes in a single Venn diagram. This approach may obscure biologically meaningful differences, as overlapping genes between conditions could reflect opposing directions of regulation. The authors should separate upregulated and downregulated genes into distinct Venn diagrams to provide a more accurate and interpretable comparison.

      We thank the reviewer for this remark and agree with it. Hence, in Fig 5a-b and Fig S3, we already highlighted the number of genes down-regulated or up-regulated upon pladienolide B treatment in either H460 parental and resistant cells using bar graphs. We will provide new Venn diagrams separating up-regulated and down-regulated genes for both cell lines.

      In the figure legend of Fig. 6a, the panel is incorrectly described as a "quantification." As the panel depicts a schematic representation of the experimental construct rather than numerical data, the term "illustration" or "schematic" would be more accurate and should be used instead.

      We thank the reviewer for this remark and we agree with it. We will modify the legend of Figure 6a accordingly.

      Reviewer #2 (Significance (Required)):

      This manuscript provides evidence that pladienolide B can overcome chemotherapy resistance in NSCLC by modulating splicing events of genes associated with DNA damage signaling and repair. Although the underlying mechanism requires further elucidation, this study offers valuable mechanistic insights into how aberrant splicing regulates therapy resistance, with potential implications for the development of novel therapeutic strategies targeting splicing factors in chemotherapy-resistant cancers.

      My research field is in tumor heterogeneity and tumor microenvironment.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In the manuscript by Jamal-El-Hussein et al., the authors demonstrated that pladienolide B, an inhibitor of splicing factor 3B subunit 1 (SF3B1), inhibits cell viability in NSCLC cells and PDXs with resistance to platinum-based chemotherapy. Mechanistically, they identified that pladienolide B regulates splicing events of genes associated with DNA damage signaling and repair, specifically through exon skipping of MLH3, thus increasing vulnerability to chemotherapy. This study provides therapeutic insights into combining pladienolide B with chemotherapy for overcoming therapy resistance.

      Major comments

      1. The authors showed that both H460R and A549R cell lines are sensitive to pladienolide B; however, they exhibit distinct molecular responses. For example, SF3B1 knockdown downregulates the mRNA expression of ATR and PRKDC in H460R cells, while the expression of these genes remains unchanged in A549R cells (Fig. 4b). Furthermore, exon 8 skipping of MLH3 is not observed in A549R cells with pladienolide B treatment (Fig. 7a). These discrepancies suggest that the two cell lines respond to pladienolide B through different mechanisms. The authors should also perform a bulk RNA-seq on A549 cell line to better understand the different behavior of the two cell lines.
      2. The authors' central claim is that platinum-based chemotherapy-resistant cells are more sensitive to pladienolide B treatment. However, in their bulk RNA-seq analysis, the authors selected genes commonly regulated by pladienolide B in both parental and resistant cells for further validation. This approach raises a critical concern: the observed sensitivity to pladienolide B may already be present in parental cells rather than representing a mechanism uniquely acquired by the resistant cells. To substantiate their central claim, the authors should analyze the differentially expressed genes between parental and resistant cells to identify resistance-specific molecular alterations that may confer enhanced sensitivity to pladienolide B, thereby providing a more mechanistically rigorous basis for their conclusions.
      3. The authors conclude that pladienolide B treatment correlates with activation of DNA-PKcs signaling followed by a shutdown of ATR and DNA-PKcs pathways. However, the data presented do not fully support this interpretation. P-ATR levels are already elevated in resistant cells and remain unchanged following pladienolide B treatment (Fig. 3a). However, prolonged pladienolide B treatment leads to decreased total ATR protein and mRNA expression (Fig. 3e-f), suggesting that pladienolide B maintains an initial constitutive ATR activation followed by transcriptional downregulation. Since pladienolide B is a splicing inhibitor, the authors should determine whether ATR and DNA-PKcs mRNA downregulation is a direct consequence of aberrant splicing of their transcripts, or a non-specific effect of prolonged cellular toxicity. To strengthen their mechanistic conclusions, the authors should perform time-course experiments to establish the temporal relationship between these signaling events, analyze splicing changes specifically in ATR and DNA-PKcs transcripts (are they in the differential genes from the bulk RNA-seq analysis?), and also check the downstream targets of the ATR and DNA-PKcs signaling.
      4. The authors report that skipping of exon 8 of MLH3 leads to the complete absence of the protein (Fig. 7d). However, this observation needs further clarification, as at least two alternative explanations exist. First, the antibody used to detect MLH3 may specifically recognize an epitope encoded by exon 8 or downstream exons, in which case the loss of signal would reflect antibody incompatibility rather than true protein absence. Second, skipping of exon 8 may introduce a premature stop codon, triggering nonsense-mediated mRNA decay (NMD) and consequent loss of the transcript. To distinguish between these possibilities, the authors should perform qPCR using primers targeting sequences both upstream and downstream of the skipped exon, as well as consider NMD inhibition experiments, to clarify whether the observed protein loss occurs at the transcriptional or translational level.
      5. The difference shown in Fig. 6b after pladienolide B treatment decreases from 2.2% to 1%. This raises concern about whether the observed difference reflects a true biological effect or is confounded by technical limitations such as low transfection efficiency. The authors should consider optimizing their transfection conditions to achieve a more robust and convincing result.

      Minor comments

      1. The abbreviation "S" in H460S and A549S cells is not defined in the manuscript. As this designation is used throughout the text, the authors should clarify what "S" denotes upon its first appearance.
      2. The current presentation of Figure S1a does not clearly demonstrate that different NSCLC cell lines exhibit differential sensitivity to pladienolide B. The authors should calculate and report IC50 values for each cell line to enable a more rigorous and quantitative comparison of their respective dose-response relationships.
      3. In Figure 4c, two dashed black lines are present in the plot but are not described or explained in the figure legend or the main text.
      4. In Figure 5a, the authors combine the downregulated and upregulated genes in a single Venn diagram. This approach may obscure biologically meaningful differences, as overlapping genes between conditions could reflect opposing directions of regulation. The authors should separate upregulated and downregulated genes into distinct Venn diagrams to provide a more accurate and interpretable comparison.
      5. In the figure legend of Fig. 6a, the panel is incorrectly described as a "quantification." As the panel depicts a schematic representation of the experimental construct rather than numerical data, the term "illustration" or "schematic" would be more accurate and should be used instead.

      Significance

      This manuscript provides evidence that pladienolide B can overcome chemotherapy resistance in NSCLC by modulating splicing events of genes associated with DNA damage signaling and repair. Although the underlying mechanism requires further elucidation, this study offers valuable mechanistic insights into how aberrant splicing regulates therapy resistance, with potential implications for the development of novel therapeutic strategies targeting splicing factors in chemotherapy-resistant cancers.

      My research field is in tumor heterogeneity and tumor microenvironment.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, the authors investigate the effects of pharmacological inhibition of the spliceosome using the SF3B1 inhibitor pladienolide B in models of platinum-resistant non-small cell lung cancer (NSCLC). Using a combination of cell lines, platinum-resistant derivatives, and patient-derived xenograft (PDX) models, the authors show that spliceosome inhibition sensitizes platinum-resistant tumors to treatment and leads to increased DNA damage accumulation and impaired DNA damage response signaling. Transcriptomic analyses indicate that transcripts encoding DNA damage regulators are particularly sensitive to alternative splicing perturbations, and selected mechanistic experiments suggest involvement of specific regulators such as MLH3. The study further explores links between splicing inhibition, transcriptional activity, and cell cycle progression.

      Overall, the manuscript presents extensive datasets across multiple experimental systems and provides strong evidence that spliceosome inhibition can sensitize platinum-resistant tumors to DNA damage. However, several aspects of the mechanistic interpretation, data consistency, and presentation require clarification or strengthening to fully support the central claims.

      Major comments

      Conceptual clarity and synthesis of mechanistic model

      The manuscript presents multiple mechanistic observations-including altered splicing of DNA repair genes, increased DNA damage accumulation, transcriptional perturbation, and cell cycle changes-but these are not integrated into a coherent conceptual framework. While it is reasonable that not all mechanistic details are fully resolved, the current presentation leaves the reader uncertain about the relative contributions of these processes. A clearer synthesis of the proposed mechanism, possibly including a summary model figure, would substantially improve the conceptual clarity of the study.

      Biological specificity of platinum-resistant cell sensitivity

      A central premise of the study is that platinum-resistant cells exhibit enhanced sensitivity to spliceosome inhibition. However, in several experiments (e.g., cell cycle analysis in Figure 2A), similar responses to pladienolide B appear to occur in both platinum-sensitive and resistant cells. This observation complicates the interpretation that resistant cells exhibit uniquely distinct vulnerability. The authors should clarify how these findings align with the proposed model and more explicitly distinguish shared versus resistance-specific responses.

      Heterogeneity in PDX responses and lack of platinum-sensitive controls

      The PDX experiments represent a major strength of the study. However, resistant tumors display heterogeneous responses to pladienolide treatment, suggesting the presence of additional determinants of sensitivity. Including platinum-sensitive PDX tumors, if available, would provide valuable baseline comparison and strengthen interpretation of resistance-specific effects. If not feasible, the limitations should be acknowledged and discussed.

      (OPTIONAL - would strengthen study but may require substantial additional work.)

      Consistency between pharmacological inhibition and genetic depletion

      In Figure 4, the authors compare pladienolide treatment with SF3B1 knockdown to demonstrate target specificity. However, the effects observed with the two perturbations are not entirely consistent-for example, pladienolide affects phosphorylation of DNA-PKcs, while SF3B1 knockdown appears to produce broader effects at both protein and mRNA levels. Additionally, differences are observed between resistant cell lines in the response to SF3B1 knockdown. These discrepancies should be addressed and discussed, as they may reflect mechanistic differences between acute pharmacological inhibition and genetic depletion.

      Selection and interpretation of splicing-sensitive transcripts

      Transcriptomic analyses in Figure 5 identify both shared and differential splicing changes between sensitive and resistant cells. However, much of the analysis focuses on transcripts that are commonly affected in both conditions, rather than those uniquely altered in resistant cells. Given that the central phenotype is resistance-specific sensitivity, transcripts uniquely mis-spliced in resistant cells may represent more informative candidates. The authors should clarify the rationale behind focusing on shared events and discuss the implications of resistance-specific versus common splicing changes.

      Transient nature of splicing effects

      The authors report transient alternative splicing effects upon prolonged pladienolide treatment. This observation is counterintuitive, as continued spliceosome inhibition might be expected to produce cumulative splicing defects. While the authors reference studies showing that transient inhibition can produce lasting effects, the current observations involve continuous exposure. This apparent discrepancy should be clarified and discussed.

      Use of unrelated cell lines in reporter assays

      The DNA damage reporter assays (Figure 6A-D) appear to be performed in cell lines not directly linked to platinum sensitivity or resistance. Given the central importance of resistance-specific responses, repeating key reporter assays in both sensitive and resistant paired models would strengthen the conclusions.

      (OPTIONAL - likely moderate experimental effort.)

      Interpretation of MLH3 splicing results

      In Figure 7, differences between pharmacological inhibition and SF3B1 knockdown in MLH3 exon 8 regulation are not entirely consistent. Furthermore, inclusion levels of regulated and non-regulated exons appear similarly correlated with SF3B1 expression, potentially weakening the argument for exon-specific regulation. These observations should be clarified.

      Combination treatment logic

      In Figure 8, co-treatment experiments with pladienolide B and cisplatin are performed primarily in resistant cells. Performing similar experiments in platinum-sensitive cells would provide an important reference point to distinguish additive versus resistance-specific effects.

      (OPTIONAL - moderate experimental effort.)

      Minor comments

      • In Figure 1, pladienolide treatment in platinum-sensitive cells appears to plateau at approximately 50% cell killing. Extending the concentration range may help clarify whether maximal efficacy was reached.
      • In Figure 1H, the difference between 2.5 mg/kg and 5 mg/kg pladienolide in PDX models appears disproportionately large relative to the dose change. This should be discussed or experimentally clarified.
      • In Figure 5, differential expression and splicing analyses are presented using multiple cutoffs (e.g., log₂FC > 0.4 and >1; ΔPSI thresholds). This introduces redundancy and may obscure key findings. A single well-justified cutoff would improve clarity.
      • Several isoform-specific RT-PCR gels are difficult to interpret due to low image clarity. Improving gel presentation or focusing on key timepoints would strengthen data readability.
      • Some figure panels appear redundant, showing similar datasets under different analysis thresholds.
      • A graphical summary model illustrating the proposed mechanism would improve reader comprehension.
      • In Figure 3D, representative images of γH2AX foci appear visually similar across conditions, whereas quantification shows large differences. The authors should ensure that representative images accurately reflect quantified trends and clarify selection criteria for displayed images.

      Significance

      This study represents a comprehensive investigation of spliceosome inhibition as a therapeutic strategy to overcome platinum resistance in NSCLC. The use of resistant cell lines, PDX models, and functional reporters provides strong experimental depth. The most compelling aspects of the study include the demonstration that spliceosome inhibition enhances DNA damage accumulation and sensitizes resistant tumors to platinum-based therapies. However, several mechanistic interpretations require clarification, and data presentation could be streamlined to improve logical coherence.

      Advance

      The work provides evidence that targeting spliceosome function-specifically via SF3B1 inhibition-can sensitize platinum-resistant tumors to DNA-damaging agents. To my knowledge, this represents one of the first comprehensive demonstrations that spliceosome-targeting compounds can effectively overcome acquired platinum resistance in solid tumor models. The study also contributes to the emerging understanding that DNA damage response transcripts may represent particularly sensitive targets of splicing perturbation.

      Audience

      The study will be of interest to researchers in RNA biology, cancer therapeutics, DNA damage response, and translational oncology. It is particularly relevant to scientists investigating therapeutic vulnerabilities in drug-resistant cancers and those exploring RNA processing as a therapeutic target.

      Expertise

      I have expertise in RNA biology, alternative splicing and cancer models. My expertise is more limited in pharmacological dosing strategies and some aspects of in vivo xenograft modeling.

      Keywords:

      RNA biology, alternative splicing, spliceosome function, cancer biology, DNA damage response, transcriptomics

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      Reply to the reviewers

      Reviewer #1:

      Major comments:

      1. Lines 103-116 (first paragraph of the results section) describe mainly published data that is more suitable for the introduction section. It is annoying to refer to different published articles in the Results section to strengthen the results instead of showing them. The same goes for paragraphs two and three. Why mention those data in the Results section if they are already published and known?

      We have reorganized this material by moving some background information to the Introduction. Our intention was not to incorporate published data to strengthen our results, but rather to provide essential context for interpreting our findings. We have therefore left some of this foundational information in the results section to create a clear narrative flow, enabling readers to understand the basis for our experimental design and interpretations without needing to recall details from earlier paragraphs in the Introduction. For example, we considered it crucial to restate the earlier report of the BiP:sfGFP:HDEL phenotype in Atlastin mutants, since our results supporting luminal ER protein displacement contradict the previous fragmentation model.

      The following concept was in line 103 in the Results section, and is now in the introduction in lines 82-92: "Conventional light microscopy, commonly used in studies of neuronal ER structure, lacks the resolution necessary to visualize individual ER tubules in small structures, such as presynaptic terminals. The ER is highly sensitive to fixation, and live imaging experiments in neurons in vivo have been conducted on upright microscopes using water dipping objectives with a typical axial resolution limit of >300 nm, which cannot distinguish the densely packed ER tubules at presynaptic terminals (3,8,21,28,39-42). Electron microscopy offers higher resolution, but cannot be used in live samples and has typically been limited to thin 2D sampling (in which it is difficult to distinguish ER cross-sections from synaptic vesicles) (8,20,22)."

      Figure Legends-(in all Figures): The number of experimental repeats must be mentioned in the figure legends.

      This information is provided in Supplementary Table 1, which contains detailed information about the genotype, statistical analysis, and number of larvae and NMJs analyzed. If the journal requires this information in figure legends, we can move it.

      The way the figures are labeled is worrisome; supplementary figures are not ordered numerically.

      We will be happy to rename supplementary figures according to journal guidelines.

      The tubule extension in Figure 2D is not convincing. Is there a movie showing those changes? Better images are needed. It is essential to show which supplemental movie corresponds to which panel.

      We have now included a corresponding video of the same neuron used as an example of tubule extension. We also added another frame to the figure to provide further information on the tubule event we captured. (Figure 2D, Movie S10)

      This is unnecessary in the results section: "To investigate the relationship between ER structure and function at synapses, we examined mutants of Atlastin, a GTPase that regulates ER tubule fusion. Drosophila has a single homolog while mammals have three Atlastin homologs, with Atlastin-1 enriched in the brain (Rismanchi et al., 2008)."

      This information was moved to the introduction.

      "This reduction in ER membrane marker intensity has also been observed in other HSP mutants, suggesting this is a common feature of ER shaping mutants and could indicate changes in ER membrane composition, integrity, or tubule thickness (Perez-Moreno et al., 2023)." This comparison is important and should be shown in the same settings as for the Atlastin mutant rather than referring to published data.

      We agree with the reviewer that it is important to determine whether other ER-shaping proteins, besides Atlastin, also show a decrease in tdTomato:Sec61b to support our claim that this could be a common feature among ER-shaping mutants. To do this, we examined mutants of another ER-shaping protein, Reticulon 1, which regulates membrane bending and stabilization in ER tubules. These loss-of-function mutants were a gift from Dr. Cahir O'Kane at the University of Cambridge and were used in his lab's Pérez-Moreno et al., 2023 publication. We found that in our hands tdTomato:Sec61b levels were reduced in Reticulon 1 mutants, consistent with the results reported by Pérez-Moreno et al. (2023). These results are in Figure 3E-F. We also examined the synaptic distribution of the luminal ER marker, BiP:sfGFP:HDEL, in Reticulon 1 mutants to see if it is displaced to the cytosol. Notably, it remained ER-associated, unlike in Atlastin mutants. These results are in Figure 6F-G, results lines 267-270, and discussion lines 542-545.

      Does the distribution of the luminal ER marker in Figure 6F diffuse due to mislocalization or reflux after being localized to the ER and then refluxed to the cytosol as was previously shown for the ER to Cytosol signaling (ERCYS) mechanism? Could you assess other ER-luminal protein localization biochemically? It is highly recommended to look at another soluble ER-protein localization in the Atlastin mutant without overexpression, which can be an artifact.

      ER stressors can induce ERCYS, in which some luminal proteins, including PDIA3, DNAJB11, ERp29, and an eroGFP reporter, reflux by 30-70% to the cytoplasm without subsequent degradation (unlike ERAD (ER-associated degradation). This phenomenon has only previously been observed in yeast and glioblastoma tumor cells from mice and human . We believe that our work provide the first suggestion that this may occur in neurons, and particularly in a neurological disease model.

      We do not believe that the reflux phenotype for BiP:sfGFP:HDEL is due to its overexpression for two reasons: (1) we observe reflux in our neuronal Atlastin knockdown experiments, even when the levels of BiP:sfGFP:HDEL are significantly reduced artificially because of titration of the GAL4 between the RNAi and the reporter (Figure 7A), and (2) BiP:sfGFP:HDEL overexpression somewhat suppresses endogenous BiP upregulation ((Figure 10 and see Reviewer 1.10), arguing that the transgene does not induce ER stress). We included a new "limitations of the study" section to be transparent about the caveats of the BiP:sfGFP:HDEL reporter (lines 639-664).

      Identifying potential endogenous neuronal ERCYS substrates in our in vivo preparation poses several challenges. First, biochemical approaches, such as fractionation, are not possible in our complex in vivo sample because neuronal ER proteins would mix with ER from other tissues upon homogenization. Second, detecting endogenous proteins with antibodies requires fixation and permeabilization, which notoriously disrupts ER structure and even causes our reporter BiP:sfGFP:HDEL to collapse from a smooth distribution, as visualized by live imaging and FRAP, to a punctate distribution. Third, using antibodies rather than neuronally restricted transgenes makes it challenging to determine whether the signal originates from the neuron or from dense ER structures in the surrounding muscle. Fourth, some ER luminal proteins can displace as little as 30% in the ERCYS examples cited above, and the sensitivity of our imaging assays may limit our ability to detect these small changes. Finally, the limited availability of tagged transgenes and antibodies specific to Drosophila luminal ER proteins (see next paragraph) poses additional challenges. These limitations highlight the need for future studies to develop novel tools and techniques to more definitively test whether we are indeed observing ERCYS. We have included a paragraph on these future challenges in our discussion in lines 639-664. Identifying endogenous targets of ERCYS in fly neurons is a worthwhile goal, but beyond the scope of the current study. These next steps will particularly benefit from identifying the machinery involved in the reflux of our BiP:sfGFP:HDEL reporter.

      Tools we tested: We investigated several options: (1) a tagged PDI transgene (a gift from Karen Hibbard), which was not detectable at presynaptic terminals, (2) a tagged BiP (FlyORF; F000956) that did not localize to the ER, and (3) full-length endogenous BiP detected by antibody staining. We did not detect obvious reflux of endogenous BiP to the cytoplasm (Figure 9), with the caveat that in fixed samples, the BiP signal was not tightly co-localized with the ER marker even under control conditions. However, we did use this antibody to detect an increase in BiP in Atlastin mutant presynaptic terminals, indicating ER stress (see Reviewer 1.10).

      Though we have not identified endogenous targets, we believe that our studies with the exogenous reporter will be of great interest to the field, as they clarify the previously reported Atlastin phenotype and provide the first report of a new defect in a human disease animal model.

      In comparison to Summerville et al. (2016) in Figure 7, the experiment was not done in the same way. It is important to keep the same settings for comparison

      In Figure 7D-E, we compare the distribution of BiP:sfGFP:HDEL in cell bodies, axons, and muscles between controls and Atlastin mutants. To clarify the experimental approach relative to Summerville et al. (2016): while both our studies examined the same cellular compartments (cell bodies, axons and nerve terminals) using the BiP:sfGFP:HDEL reporter, we employed super-resolution Airyscan microscopy. This enhanced resolution was critical for definitively demonstrating that this is a functional rather than a structural phenotype and that ER displacement is progressive, and repeating this experiment at lower resolution as previously reported does not provide any new information. We identified two distinct distribution phenotypes in Atlastin mutants expressing BiP:sfGFP:HDEL, which were not described in the Summerville et al., 2016 paper. From our manuscript (lines 249-251): "We identified two distinct ER network phenotypes in Atlastin mutants expressing BiP:sfGFP:HDEL: "Partial loss" NMJs retained both diffuse signal and identifiable ER network structures, while "Complete loss" NMJs showed no visible ER network structures. Note that the "Complete loss" phenotype in Atlastin mutants reflects the absence of detectable luminal marker signal in organized ER structures, but not the complete absence of ER membranes, as demonstrated by our ER membrane marker tdTomato:Sec61β results."

      Does the Atlastin mutant induce the unfolded protein response and stress within the ER? It is necessary to look for UPR markers in those settings. It was shown previously that ER stress leads to protein reflux from the ER to the cytosol. Is there a difference in the ER stress markers in the presynaptic terminal?

      The reviewer suggested that Atlastin mutant synapses may exhibit ER stress. To address this, we examined levels of the ER chaperone BiP, a well-established ER stress marker whose expression increases during UPR activation. We first validated that our BiP antibody can detect changes in ER stress by feeding control larvae with 50mM DTT for 24 hours. These results are in the new Figure 10A. Note that we were unable to test sensitivity to ER stress in this way in Atlastin mutant larvae because they did not consume the DTT-treated food, as assessed by blue food coloring in the larvae's guts.

      Using this antibody, we measured baseline BiP levels at NMJs of Atlastin mutants on normal food, and found they were slightly increased compared to controls. We conclude from these experiments that Atlastin mutant synapses have mild ER stress. Notably however, Atlastin mutants co-expressing UAS-BiP:sfGFP:HDEL or UAS-tdTomato:Sec61b did not show significantly increased endogenous BiP levels, suggesting that transgene expression at least partly suppresses the mild ER stress response, even though there is extensive cytosolic displacement. These results argue (1) that the mild ER stress in Atl mutants does not strictly correlate with the reflux phenotype, and (2) that the reflux phenotype is not an artifact of overexpression-induced stress. These results are described on lines 430-436 in the results section and shown in Figure 10B-E, and their implications discussed on lines 585-598.

      We also explored another strategy to detect ER stress by assessing eIF2α phosphorylation, a key event in the Unfolded Protein Response (UPR) pathway. We obtained a phospho-eIF2α antibody (Cell Signaling; #3597) that was reported to work in Drosophila. However, when we tested this antibody by Western blot, we were unable to detect a band at the expected molecular weight for phosphorylated eIF2α, even in positive-control samples treated with DTT to induce ER stress. We therefore concluded that this antibody is not suitable for reliably detecting ER stress in our experimental system. The failure of this antibody highlights the challenges of finding robust tools to measure ER stress in Drosophila.

      It is important to add biochemical experiments to show that no fragmentation of the ER membrane occurred. It can be simply demonstrated by looking at the redox state of the ER, which would change if it were mixed with the reducing cytosol. Moreover, this can be shown by using an ER-targeted redox-sensitive fluorescent protein that is tethered to the ER membrane to follow changes in the redox state of the ER.

      The reviewer asked us to test whether the redox state of the ER is disrupted, which could indicate exchange between the cytosol and ER due to membrane rupture. As noted above, biochemical approaches such as fractionation are not possible in this in vivo sample. We attempted to address this concern by creating a UAS-Sec61β:roGFP construct, using the roGFP sequence from Igbaria et al. (2019) to monitor the ER lumen redox environment in Atlastin mutants. Since Sec61β is membrane-tethered, it should remain in the ER and not undergo reflux, making it an ideal sensor for detecting any mixing between the reducing cytosolic environment and the oxidizing ER lumen that would occur if membrane fragmentation and/or ruptures were present. We tested this approach in wild-type Drosophila S2 cells and used the Gal4-UAS binary expression system to co-express Actin-Gal4 (to drive expression of UAS constructs), UAS-Sec61β:roGFP (redox sensor), and UAS-BiP:Halo:HDEL (as a control reporter insensitive to DTT treatment).

      Our experiments showed no detectable changes in the fluorescent properties of UAS-Sec61β:roGFP following 30 min 10mM DTT treatment compared to DMSO vehicle control, including no increase in 405-nm excitation fluorescence or changes in 488nm/405nm excitation ratios. These results suggest that either the roGFP sensor requires further optimization for sensitivity in this cellular system or that additional controls and calibration steps are needed to establish the dynamic range of the assay. We believe this experiment falls beyond the scope of the current study, given the extensive optimization required. However, it represents an important future direction for testing membrane fragmentation as a mechanism underlying the phenotypes observed in Atlastin mutants. The possibility of ER integrity defects is mentioned in the discussion on lines 547-559.

      Minor comments:

      1. It is important to call figures by order. Figure 2C is called before 2A-B. Figure 2B is called before Figure 2A.

      The revised manuscript has all figures in order of appearance in the text.

      Figure legends (Figure 2): "The same control dataset used in E-G was used in Figure 5 and Figure 5_Supplement." Why is this relevant?

      We wanted to be transparent about reusing the same control dataset across multiple figures to avoid any appearance of data duplication. This notation clarifies that, although the data appear in different contexts (Figures 2 and 5. This version does not contain a Figure 5_Supplement), it represents the same biological samples analyzed for different parameters, ensuring readers understand that these are not independent datasets.

      Figure 4F is called before Figure-4D-E which are not called.

      We revised our manuscript and reorganized Figure 4 to ensure that all figure panels are referenced in sequential order and that panels 4D-E, which were previously not cited in the text, are now properly referenced when discussing their corresponding results.

      Figure 5B is called before the previous ones. Same for Figure 5A supplement.

      We referenced Figure 5A in lines 211-212, which precedes our discussion of Figure 5B. To clarify the figure order, we removed the early references to Figures 2D-G and Movies 7-14, which were mentioned only to indicate that we were analyzing the same dataset in different ways.

      The revised manuscript has all figures in order of appearance in the text.

      Referees cross-commenting

      I agree with the comments raised by reviewer2 and 3. Basically it is highly important to validate those data by genetic rescue. Moreover, it is essential to know the source of the displaced luminal marker to the cytosol. Is it mislocalization or it is a reflux of pre-existing protein to the cytosol after insertion to the ER. It is also recommended by me and the reviewers and me to test the endogenous protein rather than overexpression.

      We have addressed these points in our responses to the following reviewer questions:

      • Genetic rescue: Please see our responses to Reviewer 1/Question #10 and Reviewer 2/Question #1.
      • Source of displaced luminal marker: We provide some evidence addressing this in our response to Reviewer 3/Question #1.
      • Endogenous protein localization: We have examined this and detailed our findings in our responses to Reviewer 1/Question #7 and Reviewer 2/Question #6.

        Reviewer #1 (Significance (Required)):

      General assessment: This interesting paper shows that proteins can escape the ER under special conditions. However, the authors need more evidence to show that and rely less on the overexpression system, especially of BIP-GFP, which can cause proteostasis stress within the ER. Advance: The results have been oversimplified in their explanations, and some points and complexities of the study need to be addressed further to make the most of them. These are often some of the more interesting concepts in the paper. I think many points can be addressed in the text by the authors being clear and concise with their reporting. At the same time, other experiments would turn this paper from an observational one into a very interesting mechanistic one. This paper is based on previously published articles from the group and other groups, and it is a nice progression. However, as mentioned, this paper depends primarily on published data, and the novelty is somehow lost between all the comparisons to other published data instead of emphasizing that. Without a substantial mechanistic improvement, the paper would remain observatory.

      Audience: The microscopy tools can be great addition to researchers in the field to monitor protein trafficking especially Cell biologists (basic research)

      My expertise: ER homeostasis, protein trafficking, cell biology

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary The endoplasmic reticulum (ER) is a continuous organelle that extends throughout neurons to regulate fundamental processes. The analysis of ER dynamics at synaptic terminals is limited by the challenge of imaging these structures at high resolution. In this manuscript, the authors use super-resolution (~170 nm) live imaging and a combination of membrane and luminal ER markers at the Drosophila larval NMJ, an important model synapse, to investigate dynamic ER architecture in vivo. They report a detailed characterization of the presynaptic ER organization and dynamics at wild-type and GTPase Atlastin mutant NMJs. Their analysis using the ER membrane marker tdTomato:Sec61b reveals the presence of an intact ER network in Atlastin mutants. This contrasts with the apparent ER fragmentation phenotype previously reported and replicated here when using a luminal marker. Their findings instead point to the progressive displacement of luminal proteins to the cytosol in Atlastin mutants specifically at synapses. The authors propose that the disruption of ER protein dynamics at synapses is a compartment-specific ER stress response. The manuscript is well written, results are clearly presented, and experiments are technically rigorous.

      Major comments

      1. The baseline ER phenotypes in Atlastin mutants are mild with complete loss of ER network only observed in terminal boutons. This interesting and unexpected result should be further confirmed by genetic rescue. The authors can use a UAS rescue line previously reported in PMID: 19341724.

      We tested the UAS-Atl-myc rescue line and unfortunately found that even in wild-type neurons, overexpression of Atlastin produced strong ER organization defects that precluded the rescue experiment. Instead, to confirm the cell autonomy of the phenotype and to test it wth an independent tool, we performed a presynaptic knockdown of Atlastin by RNAi and found that BiP:sfGFP:HDEL is displaced, as observed in the Atlastin null mutant. These results are in now shown in Figure 7A-C.

      Lines 204-7: It's not clear how a greater coefficient of variation indicates that the marker is more concentrated in subsynaptic structures or what is meant by 'subsynaptic structures.'

      We added the following text to explain, in lines 181-183: "A higher CoV indicates an uneven distribution of tdTomato:Sec61β within the presynaptic terminal, with some areas showing higher concentrations than others (in contrast to the uniform, diffuse signal expected from fragmentation)." To avoid confusion with postsynaptic structures called the subsynaptic reticulum, we have removed the term "subsynaptic". The intended meaning is distinct structures found within the presynaptic terminal.

      There's a mistake in Figure 6C and the associated text. The summed percentage of the three phenotypic categories adds up to 110% for Atlastin mutants.

      The reviewer noted that the summed percentage of the three phenotypic categories in Figure 6C adds up to 110% for Atlastin mutants, which appears to be a mathematical error. However, this is not an error, but rather a reflection of our quantification methodology, in which a single bouton can exhibit more than one type of ER dynamics per movie recorded. Our quantification counts each phenotype independently, so boutons displaying multiple phenotypes contribute to more than one category. This approach provides a more comprehensive view of the range of ER dynamics present in Atlastin mutants, as restricting the analysis to mutually exclusive categories would underrepresent the complexity of the phenotypes observed. To make this point clear, we made the following change to the text in lines 257-259: "We note that the sum of these percentages exceeds 100% because one NMJ exhibited multiple phenotypes: one branch had a complete loss, while the other branch had no phenotype. These phenotypes were counted separately."

      Figure 8: the ER looks fragmented in 1st instar controls and mutants. The authors should address this difference from more mature NMJs.

      We would like to clarify that the bulk of experiments in this manuscript (including all ER dynamics, luminal marker redistribution, and membrane marker analyses discussed throughout the Results) were performed in 3rd instar larvae, which are more mature larval NMJ preparations standard in the field. Figure 8 was included specifically to test whether the Atlastin mutant phenotype we describe throughout the paper is also detectable at an earlier developmental stage, not to replace or reinterpret our primary findings.

      Regarding the specific observation that the ER appears more fragmented in Figure 7F-H relative to the more mature NMJs shown elsewhere: this fragmentation, observed similarly in both control and Atlastin mutant 1st instar larvae, likely reflects technical challenges associated with dissecting these smaller, more delicate early-stage specimens rather than a genotype-specific effect. Because fragmentation occurred similarly in both genotypes, we could still reliably assess the redistribution of BiP:sfGFP:HDEL as our primary phenotypic readout in this experiment. We have added the following text (lines 306-309) to clarify this point: "Note that in 1st instar larvae, both normal networks in controls and residual networks in Atlastin mutants appeared more fragmented than in 3rd instar preparations, likely due to the technical challenges of dissecting these smaller, more delicate specimens. Since ER fragmentation occurred similarly in both genotypes, we could still reliably assess the redistribution of BiP:sfGFP:HDEL as our primary phenotypic readout.

      The images in figure 9B do not seem representative of the quantification in Figure 9D. Specifically, the partial loss Atlastin NMJ appears to have recovered as fully as the complete loss Atlastin NMJ.

      The images showed FRAP recovery across the entire bouton, but we photobleached only a small region within each bouton and quantified only this region. We have now added outlines to clearly delineate the specific FRAP regions that were analyzed in each image, which clarify that the partial loss Atlastin showed less recovery than the overall bouton. We have also reordered the figures to more clearly convey our message (Figure 9 is now Figure 8).

      We also made a few changes to the paragraph on lines 347-350 to clarify our experimental reasoning: "We photobleached en passant boutons using a defined region of 6.8 x 7.8 microns (dashed box in Figure 8D) to ensure that BiP:sfGFP:HDEL could recover from the ER networks surrounding the FRAP region (Movies S20-S23)."

      We also added this sentence to the figure legends of Figure 8: "The dashed boxes in (D) indicate areas that were photobleached and analyzed for recovery quantification in (E-F)."

      Optional: An overexpressed luminal marker is displaced to the cytoplasm in Atlastin mutants. It would be interesting to know and increase the significance of the findings if the same is true of endogenous luminal proteins under biological stress conditions.

      As noted in our response to Reviewer #1 suggested that Atlastin mutant synapses may exhibit ER stress. To address this, we examined levels of the ER chaperone BiP, a well-established ER stress marker whose expression increases during UPR activation. We first validated that our BiP antibody can detect changes in ER stress by feeding control larvae with 50mM DTT for 24 hours. We were unable to perform this experiment in Atlastin mutant larvae because they did not consume the DTT-treated food, as assessed by blue food coloring in the larvae's guts. These results are in Figure 10A. In the future, it will be of interest to establish a protocol to examine Atlastin mutants by feeding or treating larval fillets with DTT.

      We measured BiP levels at NMJs of Atlastin mutants and found they were slightly increased compared to controls. Atlastin mutants co-expressing UAS-BiP:sfGFP:HDEL or UAS-tdTomato:Sec61b did not show significantly increased endogenous BiP levels, suggesting that transgene expression suppresses the mild ER stress response. We conclude from these experiments that Atlastin mutant synapses have mild ER stress. These results are in Figure 10B-E).

      Optional: Applying this approach in stimulated conditions (high potassium, increased temperature) might reveal a greater activity-dependent role for Atlastin at synaptic terminals.

      This is a very interesting idea, as we have only examined synapses at rest. However, this is beyond the scope of this paper.

      Minor Comments

      1. Line 16: Atlastin should be italicized.

      Thank you for catching this typo. We have fixed it.

      Figure 5A: Based on the relative intensities, it appears that control and mutant images are not contrast matched but this isn't stated.

      Thank you for catching this omission. We added to the figure legend: "Control and Atlastin mutant images are not contrast matched."

      Line 822: The number of static Atlastin mutant boutons used for analysis is missing.

      Thank you for catching this omission. We have fixed this supplementary table.

      Figure 9: The blue arrows are not annotated in the figure legend.

      Thank you for catching this omission. We have fixed this figure legend.

      Reviewer #2 (Significance (Required)):

      Atlastin is linked to Hereditary Spastic Paraplegia (HSP) and this study changes our understanding of the compartment-specific impacts of its loss. This study reveals the importance of using both membrane and luminal ER markers to accurately interpret phenotypes as well as the importance of considering compartment-specific effects on ER. These findings represent significant mechanistic and conceptual advances. The lack of genetic rescue is a limitation and adding an investigation of an endogenous luminal protein under basal and stress conditions would add significantly to our understanding of Atlastin dysfunction in HSP. Notably, the in vivo imaging approach introduced here can be adapted broadly for live imaging of Drosophila larvae. Thus, this work will be of interest to both neuronal cell biologists and the wider Drosophila community. This review is based on our expertise in neuronal cell biology.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, the authors investigate the structural dynamics of the endoplasmic reticulum (ER) in Drosophila neurons and examine the role of the ER-shaping protein Atlastin in ER morphology. Their discovery on the neuromuscular junction (NMJ)-specific contribution of Atlastin to ER integrity is intriguing and may provide valuable insights into the pathological mechanisms underlying Atlastin mutations associated with hereditary spastic paraplegia (HSP) and hereditary sensory neuropathy. The key observation on ER protein showing an aberrant cytoplasmic localisation in mutant cells appears convincing. Though this phenomenon's characterisation stays at the point of primary observation with its mechanics unclarified, establishing this new and unexpected functional rather than structural Atl effect is important and useful for the field. The observation that ER is structurally preserved in this mutant with absolute lack of Atl are also extremely useful.

      It is unclear if the cytoplasmic localisation affects an exogenous overexpressed ER marker or endogenous protein would also appear in cytoplams, the authors should consider adding an immunostaining data to test that.

      Authors offer speculations on potential reasons for the cyto localisation of the ER marker suggesting that relocation at the cell periphery specifically combined with slow clearance there is the most likely explanation (still unclear what stops the marker from spreading through the entire cell). They suggest that decrease in cotranslational translocation is unlikely as this would result in somatic accumulation of the marker. However, if the clearance in the periphery is less efficient than in soma, the accumulation there might reflect a compromised translocation. Any clarifying experiments, if practical, to directly demonstrate how ER proteins in relocates to the cytoplasm in atl mutant would help understanding better the phenomenon. For example, would proteasomal inhibition make the marker accumulate more across the cell? Authors also suggest links to ER stress. Would stress induction phenocopy the mutant?

      Reviewer #3 asked whether defective proteasomal clearance underlies the cytosolic accumulation of BiP:sfGFP:HDEL in Atlastin mutants. We addressed this directly. First, proteasome function appears intact in the mutants: baseline ubiquitinated protein levels (FK1 antibody) were comparable between control and Atlastin mutants, and MG132 treatment produced a similar increase in ubiquitination in both genotypes, confirming both antibody specificity and normal proteasome activity. We then examined BiP:sfGFP:HDEL directly. In controls, MG132 caused the marker to accumulate at axons and presynaptic terminals, showing that it is normally cleared from these compartments by the proteasome. Critically, this accumulated marker remained associated with intact ER networks: MG132 did not induce diffuse cytosolic BiP:sfGFP:HDEL in any compartment (cell bodies, axons, or presynaptic terminals), even where levels rose substantially. Thus, blocking proteasomal clearance raises ER-localized marker but does not generate the cytosolic pool seen in Atlastin mutants, indicating that impaired clearance is not sufficient to cause the displacement phenotype. We separately noted that BiP:sfGFP:HDEL was already elevated in Atlastin mutant axons without MG132, paralleling the axonal tdTomato:Sec61β accumulation in Figure 4, consistent with reduced baseline clearance specifically in mutant axons, but this does not lead to cytosolic displacement. This experiment is now shown in Figure 11, described in Results (lines 445-475), and discussed in lines 576-581.

      Minor comments:

      Line 146:

      "fast dynamics (Thank you for catching this mistake. We have corrected it.

      Fig. 2D: The data representation of "Tubule displacement" image is unclear. The ER tubule indicated by the red arrow does not seem to show any changes over time (like static). time 0 in stamp appears behind the image.

      Thank you for catching the typo. We have fixed it. Additionally, we added black arrows to highlight a tubule that is not moving, allowing the reader to compare it with the moving tubule. We also included a video of all types of ER tubule dynamics to ensure the reader can also look at the raw data (Movies S9-11).

      Line 157-158 (and relevant method sections):

      The definition of static and dynamic boutons is ambiguous. The author should describe in more detail this point including how long they observed the structure to define the changes in ER tubule dynamics.

      We provide in the methods (lines 779-791) a detailed explanation of how we categorized boutons as dynamic or static. In addition, we added the following to explain in the results section how we defined static vs dynamic:

      Old sentence: We qualitatively categorized boutons as "static" if we observed no change in ER network structure or "dynamic" if we observed at least one change.

      New sentence in lines 143-147: "We imaged boutons for 40 sec at 0.92 sec intervals to capture ER dynamics over this observation period. Boutons were qualitatively categorized as "static" if we observed no detectable changes in ER network structure throughout the entire 40 sec imaging session, or "dynamic" if we observed at least one of the three defined dynamic events during this time window."

      Fig. 2E: What n=75 and n=29 represent is unclear, are these the number of boutons in en passant and terminal subjected for qualitative analysis?

      We removed these n values from the figure and added this information to the Supplementary Table 1, which contains detailed information about the genotype, statistical analysis, and number of larvae and NMJs analyzed.

      Fig. 2: What the qualitative analysis represents is unclear, are the points pulled from different experiments?

      The data in Fig. 2 E-F comes from movies acquired in the same experiment. The number of independent animals and NMJs imaged is described in Table 1.

      * *Line 231: Regarding "...we found a small but significant reduction in dynamic boutons in Atlastin mutants (76%), ...", how do the authors assess significance. If proportion of static/dynamic ER in boutons was obtained from multiple experiments, it should be presented e.g. as in average {plus minus} standard deviation, or clarify that the proportion is representative of x independent experiments.

      The videos used for this figure were acquired from a single experiment. We use a chi-square test to determine significance relative to the "expected" distribution of dynamics types from controls, as these are categorical rather than continuous data (see PMID 31145670). Information regarding genotype, statistical analysis and number of larvae and NMJs can also be found in Supplementary Table 1.

      Line 267-269 and Fig. 6B: The author's conclusion that "Complete loss of ER network structure in NMJ of BiP:sfGFP:HDEL overexpressing Atl mutant" seem to be based on the lack of signal from luminal marker, which may be undetectable due to changes to tubular volume or marker loss to the cytoplasm, as suggested by the authors, while the membranous ER structure is intact. It would be useful to discuss this point and potentially add ER membrane-stained control.

      We agree with the reviewer that Atlastin mutants categorized as 'complete loss mutants' do not actually lack ER at synapses. We think this is an important point so we added the following to the results in lines 251-254: "Note that the "Complete loss" phenotype in Atlastin mutants reflects the absence of detectable luminal marker signal in organized ER structures, not the complete absence of ER membranes, as demonstrated by our ER membrane marker tdTomato:Sec61β results."

      We attempted to co-label the ER membrane and ER lumen, but these crosses yielded very few live larvae (in either controls or Atlastin mutants, and those that survived had severely deformed NMJs. We added Figure 6-Supplement showing the results of this experiment, and described them on lines 270-273.

      Fig. 6C: In Atl mutant, why does the total of the proportion exceed 100% (10 + 45 + 55)?

      The reviewer noted that the summed percentage of the three phenotypic categories in Figure 6C adds up to 110% for Atlastin mutants. This is not an error, but rather a reflection of our quantification methodology because a single bouton can exhibit more than one type of ER dynamics per movie recorded. Our quantification counts each phenotype independently, so boutons displaying multiple phenotypes contribute to more than one category. This approach provides a more comprehensive view of the range of ER dynamics present in Atlastin mutants, as restricting the analysis to mutually exclusive categories would underrepresent the complexity of the phenotypes observed. To make this point clear, we made the following change to the text in lines 257-259: "We note that the sum of these percentages exceeds 100% because one NMJ exhibited multiple phenotypes: one branch had a complete loss, while the other branch had no phenotype. These phenotypes were counted separately."

      Fig. 9C, line 342-344: In FRAP experiment using CD8, it seems that the Partial loss Atl mutant shows slower recovery that control. There seems to be a mismatch in triangle symbols of Partial loss Atl mutant between legend and plot (one is filled and the other is empty). This should be clarified.

      Thank you for catching this mistake. We have fixed the figure.

      fig. 10 is a clever way to verify the cytoplasmic localization of the ER marker; however, its description and annotation can be improved, and it would be stronger if 4 curves in F for mutant and controls with the trap and normal were shown.

      The reviewer suggested merging our graphs but we believe that keeping them separate is clearer.

      Line 495: Drosophila have ReepA and ReepB, but not Reep1-4. If the authors discuss their speculation based on their observation (using Drosophila), the gene names should be unified in the same species, and explain the corresponding genes to mammalian cells.

      We made the following changes to address the reviewer's concern about gene nomenclature consistency (lines 502-506): "These ER-derived vesicles are likely to involve ReepA and ReepB, the Drosophila orthologs of mammalian REEP1-4, which regulate ER vesicle formation in mammalian cells (67). Notably, while overexpression of Atlastin can regulate REEP vesicle fusion in mammalian systems (67), it is not essential for vesicle formation, suggesting similar regulatory relationships may exist between Atlastin and Reep genes in Drosophila."

      Line 548; should UPR be Unfolded Protein Response?

      Thank you for catching the typo. We have fixed it.

      Reviewer #3 (Significance (Required)):

      This study advances the understanding of how ER morphogens affect neuronal cells specifically, the lack of which limits researchers ability to comprehend the neuronal pathologies associated with ER structure-function. The observation on ER content aberrant localisation caused by the lack of key structural protein should be of a great interest for cell and neuronal biologists and researchers of the associated diseases and shows the field a new direction. Though, mechanistic details remain to be unraveled, it constitutes a fundamental, conceptual advance.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the authors investigate the structural dynamics of the endoplasmic reticulum (ER) in Drosophila neurons and examine the role of the ER-shaping protein Atlastin in ER morphology. Their discovery on the neuromuscular junction (NMJ)-specific contribution of Atlastin to ER integrity is intriguing and may provide valuable insights into the pathological mechanisms underlying Atlastin mutations associated with hereditary spastic paraplegia (HSP) and hereditary sensory neuropathy. The key observation on ER protein showing an aberrant cytoplasmic localisation in mutant cells appears convincing. Though this phenomenon's characterisation stays at the point of primary observation with its mechanics unclarified, establishing this new and unexpected functional rather than structural Atl effect is important and useful for the field. The observation that ER is structurally preserved in this mutant with absolute lack of Atl are also extremely useful.

      It is unclear if the cytoplasmic localisation affects an exogenous overexpressed ER marker or endogenous protein would also appear in cytoplams, the authors should consider adding an immunostaining data to test that.

      Authors offer speculations on potential reasons for the cyto localisation of the ER marker suggesting that relocation at the cell periphery specifically combined with slow clearance there is the most likely explanation (still unclear what stops the marker from spreading through the entire cell). They suggest that decrease in cotranslational translocation is unlikely as this would result in somatic accumulation of the marker. However, if the clearance in the periphery is less efficient than in soma, the accumulation there might reflect a compromised translocation. Any clarifying experiments, if practical, to directly demonstrate how ER proteins in relocates to the cytoplasm in atl mutant would help understanding better the phenomenon. For example, would proteasomal inhibition make the marker accumulate more across the cell? Authors also suggest links to ER stress. Would stress induction phenocopy the mutant?

      Minor comments:

      Line 146: "fast dynamics (<1 sec)" a velocity should be presented as distance/time

      Fig. 2D: The data representation of "Tubule displacement" image is unclear. The ER tubule indicated by the red arrow does not seem to show any changes over time (like static). time 0 in stamp appears behind the image.

      Line 157-158 (and relevant method sections): The definition of static and dynamic boutons is ambiguous. The author should describe in more detail this point including how long they observed the structure to define the changes in ER tubule dynamics.

      Fig. 2E: What n=75 and n=29 represent is unclear, are these the number of boutons in en passant and terminal subjected for qualitative analysis?

      Fig. 2: What the qualitative analysis represents is unclear, are the points pulled from different experiments?

      Line 231: Regarding "...we found a small but significant reduction in dynamic boutons in Atlastin mutants (76%), ...", how do the authors assess significance. If proportion of static/dynamic ER in boutons was obtained from multiple experiments, it should be presented e.g. as in average {plus minus} standard deviation, or clarify that the proportion is representative of x independent experiments.

      Line 267-269 and Fig. 6B: The author's conclusion that "Complete loss of ER network structure in NMJ of BiP:sfGFP:HDEL overexpressing Atl mutant" seem to be based on the lack of signal from luminal marker, which may be undetectable due to changes to tubular volume or marker loss to the cytoplasm, as suggested by the authors, while the membranous ER structure is intact. It would be useful to discuss this point and potentially add ER membrane-stained control.

      Fig. 6C: In Atl mutant, why does the total of the proportion exceed 100% (10 + 45 + 55)?

      Fig. 9C, line 342-344: In FRAP experiment using CD8, it seems that the Partial loss Atl mutant shows slower recovery that control. There seems to be a mismatch in triangle symbols of Partial loss Atl mutant between legend and plot (one is filled and the other is empty). This should be clarified

      fig. 10 is a clever way to verify the cytoplasmic localisatoin of the ER marker, however its description and annotation can be improved, and it would be stronger if 4 curves in F for mutant and controls with the trap and normal were shown.

      Line 495: Drosophila have ReepA and ReepB, but not Reep1-4. If the authors discuss their speculation based on their observation (using Drosophila), the gene names should be unified in the same species, and explain the corresponding genes to mammalian cells.

      Line 548; should UPR be Unfolded Protein Response?

      Significance

      This study advances the understanding of how ER morphogens affect neuronal cells specifically, the lack of which limits researchers ability to comprehend the neuronal pathologies associated with ER structure-function. The observation on ER content aberrant localisation caused by the lack of key structural protein should be of a great interest for cell and neuronal biologists and researchers of the associated diseases and shows the field a new direction. Though, mechanistic details remain to be unraveled, it constitutes a fundamental, conceptual advance.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The endoplasmic reticulum (ER) is a continuous organelle that extends throughout neurons to regulate fundamental processes. The analysis of ER dynamics at synaptic terminals is limited by the challenge of imaging these structures at high resolution. In this manuscript, the authors use super-resolution (~170 nm) live imaging and a combination of membrane and luminal ER markers at the Drosophila larval NMJ, an important model synapse, to investigate dynamic ER architecture in vivo. They report a detailed characterization of the presynaptic ER organization and dynamics at wild-type and GTPase Atlastin mutant NMJs. Their analysis using the ER membrane marker tdTomato:Sec61b reveals the presence of an intact ER network in Atlastin mutants. This contrasts with the apparent ER fragmentation phenotype previously reported and replicated here when using a luminal marker. Their findings instead point to the progressive displacement of luminal proteins to the cytosol in Atlastin mutants specifically at synapses. The authors propose that the disruption of ER protein dynamics at synapses is a compartment-specific ER stress response. The manuscript is well written, results are clearly presented, and experiments are technically rigorous.

      Major comments

      1. The baseline ER phenotypes in Atlastin mutants are mild with complete loss of ER network only observed in terminal boutons. This interesting and unexpected result should be further confirmed by genetic rescue. The authors can use a UAS rescue line previously reported in PMID: 19341724.
      2. Lines 204-7: It's not clear how a greater coefficient of variation indicates that the marker is more concentrated in subsynaptic structures or what is meant by 'subsynaptic structures.'
      3. There's a mistake in Figure 6C and the associated text. The summed percentage of the three phenotypic categories adds up to 110% for Atlastin mutants.
      4. Figure 8: the ER looks fragmented in 1st instar controls and mutants. The authors should address this difference from more mature NMJs.
      5. The images in figure 9B do not seem representative of the quantification in Figure 9D. Specifically, the partial loss Atlastin NMJ appears to have recovered as fully as the complete loss Atlastin NMJ.
      6. Optional: An overexpressed luminal marker is displaced to the cytoplasm in Atlastin mutants. It would be interesting to know and increase the significance of the findings if the same is true of endogenous luminal proteins under biological stress conditions.
      7. Optional: Applying this approach in stimulated conditions (high potassium, increased temperature) might reveal a greater activity-dependent role for Atlastin at synaptic terminals.

      Minor Comments

      1. Line 16: Atlastin should be italicized.
      2. Figure 5A: Based on the relative intensities, it appears that control and mutant images are not contrast matched but this isn't stated.
      3. Line 822: The number of static Atlastin mutant boutons used for analysis is missing.
      4. Figure 9: The blue arrows are not annotated in the figure legend.

      Significance

      Atlastin is linked to Hereditary Spastic Paraplegia (HSP) and this study changes our understanding of the compartment-specific impacts of its loss. This study reveals the importance of using both membrane and luminal ER markers to accurately interpret phenotypes as well as the importance of considering compartment-specific effects on ER. These findings represent significant mechanistic and conceptual advances. The lack of genetic rescue is a limitation and adding an investigation of an endogenous luminal protein under basal and stress conditions would add significantly to our understanding of Atlastin dysfunction in HSP. Notably, the in vivo imaging approach introduced here can be adapted broadly for live imaging of Drosophila larvae. Thus, this work will be of interest to both neuronal cell biologists and the wider Drosophila community. This review is based on our expertise in neuronal cell biology.

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      Referee #1

      Evidence, reproducibility and clarity

      In the present manuscript, the authors address an important question related to the ultrastructure and the dynamics of the ER in HSP. In contrast to previous studies, the authors here show (by using a membrane and luminal protein markers) that in the presynaptic terminals, the overexpressed BIP "mislocalizes" to the cytosol without affecting (or with minimal effect) the integrity of the ER membrane. Although they used an artificial system by overexpressing (overexpression) of BIP-sfGFP-HDEL (fused protein), the findings lack validation of the endogenous protein by biochemical and fluorescent tools.

      Concerns:

      I am worried about how the article is presented, mainly in the results section, as most of it refers to published data. The Results section is reserved for presenting new findings without external interpretation or comparison. The paper is written mainly as a comparison paper with other studies or relies on previous studies to strengthen their findings rather than coming up with novel findings. Up to figure 4, I missed the relevance of the new findings. The manuscript needs rewriting to emphasize its novelty and significance without comparing it to previous data. Moreover, the manuscript emphasizes the technology and the findings of the localization of the ER luminal proteins to the cytosol (which is not novel and was previously reported in other settings). Those two aspects were not given enough focus. Here are the main major and minor comments:

      Major comments:

      1. Lines 103-116 (first paragraph of the results section) describe mainly published data that is more suitable for the introduction section. It is annoying to refer to different published articles in the Results section to strengthen the results instead of showing them. The same goes for paragraphs two and three. Why mention those data in the Results section if they are already published and known?
      2. Figure Legends-(in all Figures): The number of experimental repeats must be mentioned in the figure legends.
      3. The way the figures are labeled is worrisome; supplementary figures are not ordered numerically.
      4. The tubule extension in Figure 2D is not convincing. Is there a movie showing those changes? Better images are needed. It is essential to show which supplemental movie corresponds to which panel.
      5. This is unnecessary in the results section: "To investigate the relationship between ER structure and function at synapses, we examined mutants of Atlastin, a GTPase that regulates ER tubule fusion. Drosophila has a single homolog while mammals have three Atlastin homologs, with Atlastin-1 enriched in the brain (Rismanchi et al., 2008)."
      6. "This reduction in ER membrane marker intensity has also been observed in other HSP mutants, suggesting this is a common feature of ER shaping mutants and could indicate changes in ER membrane composition, integrity, or tubule thickness (P.rez-Moreno et al., 2023)." This comparison is important and should be shown in the same settings as for the Atlastin mutant rather than referring to published data.
      7. Does the distribution of the luminal ER marker in Figure 6F diffuse due to mislocalization or reflux after being localized to the ER and then refluxed to the cytosol as was previously shown for the ER to Cytosol signaling (ERCYS) mechanism? Could you assess other ER-luminal protein localization biochemically? It is highly recommended to look at another soluble ER-protein localization in the Atlastin mutant without overexpression, which can be an artifact
      8. "(data not shown)" in line 288. This affects the process of judging those data.
      9. In comparison to Summerville et al. (2016) in Figure 7, the experiment was not done in the same way. It is important to keep the same settings for comparison
      10. Does the Atlastin mutant induce the unfolded protein response and stress within the ER? It is necessary to look for UPR markers in those settings. It was shown previously that ER stress leads to protein reflux from the ER to the cytosol. Is there a difference in the ER stress markers in the presynaptic terminal?
      11. It is important to add biochemical experiments to show that no fragmentation of the ER membrane occurred. It can be simply demonstrated by looking at the redox state of the ER, which would change if it were mixed with the reducing cytosol. Moreover, this can be shown by using an ER-targeted redox-sensitive fluorescent protein that is tethered to the ER membrane to follow changes in the redox state of the ER.

      Minor comments:

      1. It is important to call figures by order. Figure 2C is called before 2A-B. Figure 2B is called before Figure 2A.
      2. Figure legends (Figure 2): "The same control dataset used in E-G was used in Figure 5 and Figure 5_Supplement." Why is this relevant?
      3. Figure 4F is called before Figure-4D-E which are not called.
      4. Figure 5B is called before the previous ones. Same for Figure 5A supplement.

      Referees cross-commenting

      I agree with the comments raised by reviewer2 and 3. Basically it is highly important to validate those data by genetic rescue. Moreover, it is essential to know the source of the displaced luminal marker to the cytosol. Is it mislocalization or it is a reflux of pre-existing protein to the cytosol after insertion to the ER. It is also recommended by me and the reviewers to test the endogenous protein rather than overexpression.

      Significance

      General assessment: This interesting paper shows that proteins can escape the ER under special conditions. However, the authors need more evidence to show that and rely less on the overexpression system, especially of BIP-GFP, which can cause proteostasis stress within the ER.

      Advance: The results have been oversimplified in their explanations, and some points and complexities of the study need to be addressed further to make the most of them. These are often some of the more interesting concepts in the paper. I think many points can be addressed in the text by the authors being clear and concise with their reporting. At the same time, other experiments would turn this paper from an observational one into a very interesting mechanistic one. This paper is based on previously published articles from the group and other groups, and it is a nice progression. However, as mentioned, this paper depends primarily on published data, and the novelty is somehow lost between all the comparisons to other published data instead of emphasizing that. Without a substantial mechanistic improvement, the paper would remain observatory.

      Audience: The microscopy tools can be great addition to researchers in the field to monitor protein trafficking especially Cell biologists (basic research)

      My expertise: ER homeostasis, protein trafficking, cell biology

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      Reply to the reviewers

      Manuscript number: RC-2026-03460

      Corresponding author(s): Louise, Walport

      1. General Statements

      We thank the reviewers for their critical reading and insightful comments on the manuscript and for highlighting the relevance of our data to developmental biology audiences.

      Below we have included a detailed point-by-point response to each reviewer comment, divided into those that are linked with revisions in the manuscript, and those that are not. As well as this we have written two general statements regarding the recently published high-resolution structures of the CPLs and our findings linking the CPLs to other oocyte structures, both of which address multiple reviewer comments.

      High resolution CPL structure papers

      While this manuscript was under review, several papers were published in Nature or deposited on bioRxiv detailing high-resolution structures of the CPLs (DOIs: 10.1038/s41586-026-10360-7 ,10.1038/s41586-026-10513-8, 10.1038/s41586-026-10442-6, 10.64898/2026.03.22.713481). These structural studies validate the key scaffolding function of PADI6 in CPL formation, as well as the association of ubiquitination machinery (including the SCF complex) to the CPLs, which we also determined in this work through mass spectrometry. Our work is complementary to these studies. The published structures provide detailed insight into how the CPLs form and function. However, while the new papers identify the structure and composition of the core CPL fiber, due to the requirement for particle averaging, they cannot resolve the identity of proteins beyond this core that interact or are stored on the CPLs sub-stoichiometrically. This limitation is addressed by our sub-cellular single oocyte proteomic workflow which pools all CPLs within each oocyte, identifying proteins which become soluble upon CPL dissolution, whether due to presence in the core fibre or to association with the fibres. Our manuscript therefore provides a complementary atlas of the CPL-associated proteome as a resource for further studying the function of CPLs in early development. To place our work in the context of the new manuscripts we have added the following to the discussion:

      • “During preparation of this manuscript, several high-resolution structures of the CPLs were reported80–83. Our proteomic workflow identified all CPL proteins resolved in these published structures as CPL-associated, including the newly identified SCF complex components and several F-box proteins. Notably, whilst highly informative, the structural studies cannot resolve the precise identity of individual proteins from families of structurally homologous proteins that form the CPL core, or of proteins which associate with the CPLs sub-stoichiometrically. One key example is the F-box proteins which offer substrate specificity to the SCF complex. Our proteomic analysis identified 10 different F-box proteins to have CPL-association. By contrast each published structure reports only a single or a few F-box proteins as part of the core complex. Notably different F-box proteins are reported in different structures. Our data provides an explanation for this variation, suggesting that even the core CPL fibres are non-homogeneous in the cell with different F-box proteins present in different locations (Fig 6B). Similarly, the close homology of a/b-tubulin proteins prevents identification of exact isoforms in the reported structures. Our data suggests that many different isoforms contribute to the CPLs (Fig 5E). Additionally, it is proposed that the CPLs act as storage hubs for a much wider range of proteins30. By allowing identification of proteins associated with the CPLs even at low occupancy our CPL-associated proteome provides a list of potential CPL-associated proteins for further structural and functional characterisation of CPL function.” CPLs and other oocyte structures

      The presence of proteins from other large cellular structures in the CPL-enriched protein dataset was highlighted by reviewers 1 and 3, in particular reviewer 3: “the physical meshwork of CPLs may prevent loss of CPL-associated proteins as well as cytoplasmic protein complexes or organelles that are too large to escape the cytoplasm through the CPLs. This is particularly a concern for the authors' conclusions regarding high amounts of ELVA-associated and mitochondrial and mitochondria-associated proteins associated with the CPLs given the large size of these organelles/structures. Is there any evidence by an alternative method of direct association of ELVAs or mitochondria with CPLs? Others have not detected mitochondrial proteins associated with CPLs”.

      Functional cross-talk with ELVA:

      Regarding an association with the ELVA, we should clarify that we believe that the relation between CPLs and the ELVA is most likely not a direct association but a previously unknown functional interaction and we have revised the text to more clearly reflect this view. We identify proteins involved in protein degradation, most notably the SCF complex, in our CPL-enriched dataset. In support of their presence being likely due to direct CPL association, rather than indirect trapping of the large ELVA, the recently published CPL structures similarly identify numerous proteins involved in protein degradation as associated with the CPLs, suggesting these proteins are sequestered and inactivated on the CPLs. As we do, these other works also propose that the CPLs are critical hubs for proteostasis in the oocyte and early embryo, similar to the proposed function of the ELVAs. However, as discussed below in the context of the mitochondria, we acknowledge that it is possible that the high level of these proteins observed in our dataset could alternatively be due to reduced cytoplasmic escape and we have updated the limitations section of the manuscript to reflect this caveat in our interpretation.

      • “Whilst we interpret proteome differences in Triton X-100 treated Padi6-KO oocytes to indicate an association with the CPLs, in the case of proteins associated with other large cellular structures such as mitochondria or the ELVA it is possible that this difference is instead due to physical entrapment of these structures by the CPLs during the precipitation step.” Interestingly, we identify RUFY1 in the CPL-enriched fraction. RUFY1 is a key marker of the ELVA where it functions as a scaffolding protein. In the manuscript we discussed the possibility that RUFY1 could therefore perform a similar function on the CPLs. In a recent work, it was reported that the number and size of RUFY1 compartments was increased in Padi6-null oocytes (DOI: 10.1038/s41594-026-01758-y). As RUFY1 is the key marker of the ELVAs this shows that their morphology is likely affected in the absence of Padi6. Together these data point towards a functional crosstalk between the two, potentially via the CPLs storing protein degradation machinery required by the ELVAs, as well as also storing/sequestering RUFY1, which could explain the increase in RUFY1 compartments in the absence of the CPLs.

      Mitochondria:

      Whilst it has been reported that there are defects in mitochondrial localisation in the absence of PADI6 and the CPLs (10.1016/j.ydbio.2010.11.033), we agree with the reviewers that it is possible these are not due to direct interactions between the CPLs and mitochondria, but rather from two separate roles of PADI6 and that our observation of mitochondrial proteins in the CPL-enriched set is due to increased physical entrapment of the large mitochondria by the CPLs rather than association. As additional links between the mitochondria and CPLs are not present in the literature unlike protein degradation machinery, we have removed the section entitled ‘The CPLs are associated with the oocyte mitochondria’, along with supplementary Figures 6E and 6F and added to the text the caveat that proteins associated with large cellular structures in our datasets could be due to physical entrapment. As we did not further discuss links between the CPLs and mitochondria in either the abstract or discussion, we do not believe the removal of this section significantly affects either the findings or novelty of this work.

      PADI6 Catalytic Activity

      Both reviewers 1 and 2 ask that we experimentally validate the loss of catalytic activity in the PADI6-C663A mutant. Along similar lines, reviewer 3 questions the rationale for the Padi6-C663A mouse line based on the lack of in vitro catalytic activity. We would like to reiterate that prior to this work there has been contradictory evidence regarding the catalytic activity of PADI6. Early work into PADI6 (10.1016/j.mce.2007.05.005) detected citrulline by IHC specifically in the oocytes of ovarian sections, which was absent in PADI6 knock-out ovaries. Later, it was shown by IF using anti-Citrulline antibodies that there is nuclear citrulline staining in 2-cell and 4-cell embryos that is ablated by a PADI inhibitor, in that study attributed to PADI1 (PADI1 presence detected by antibody-based techniques such as IF, 10.1038/srep38727). Together these manuscripts all posit that there is an active deiminase in oocytes and early embryos. We do not detect transcripts or protein for any PADI aside from PADI6 at the 2-cell stage and before suggesting any citrullination in the 2-cell embryo or before could only be a result of deimination by active PADI6 (Figure S4E).

      However, we and others, have confirmed that wild-type PADI6 is not active in vitro under the same conditions as the other PADIs (DOIs: 10.1016/j.csbj.2024.08.019, 10.4236/abb.2011.24044). Whilst this may be interpreted as overall lack of catalytic activity, an alternative explanation is that incorrect assay conditions have been used. For in vitro assays, supra-physiologically high calcium concentrations are required to activate catalysis by PADIs 1 to 4. We have previously shown that these calcium binding residues are not conserved in PADI6 and PADI6 does not bind calcium. It is therefore possible that PADI6 has evolved to be activated by other as yet unknown activating signals so as to not have its function disrupted during the large calcium transient post-fertilization. In the absence of the correct activating signals, in vitro activity would not be expected, even if the enzyme is catalytically active in vivo.

      We believe that this, together with the conservation of the catalytic tetrad residues, and the contradictory evidence regarding citrullination in vivo demonstrated that based on the current literature a catalytic function of PADI6 cannot be ruled out in vivo. This was our rationale for developing the Padi6-C663A mouse model in this work, to disentangle the dramatic phenotypes observed from full knockout of PADI6 from a possible catalytic function. Based on our previous structural characterization of human PADI6 (DOI: 10.1016/j.csbj.2024.08.019), if PADI6 were to have catalytic activity it would be through cysteine 663. Our data conclusively shows that catalytic activity of PADI6 through cysteine 663 is not required for murine female fertility, but that mice with this mutation are also not fully wildtype. Given the lack of large-scale structural damage loss of this cysteine imparts on the protein (Supplementary Figure 1), the observed phenotypes point towards either the disruption of a previously unidentified non-essential catalytic function or to more subtle changes to function from this mutation, for example through alterating the binding affinities of proteins that interact with PADI6 lacking this cysteine.

      2. Point-by-point description of the revisions

      Description of revisions incorporated into the manuscript along with discussion of reviewer comments

      Reviewer 1:

      • Major Comment 1: “1- The methods for differential gene expression analysis are insufficiently described. Were these calculated using Scanpy? If so, the rationale for this choice should be provided, as the number of replicates and the nature of the data appear more suited to standard bulk differential expression frameworks such as DESeq2 or edgeR.”
      • Response and Incorporated Revision: We chose the Scanpy framework for analysis because it allows for quality control, PCA, normalization, and differential expression within a single Python environment. To calculate differential gene expression, we performed Student's t-test on log-normalized counts for each gene followed by correction for multiple testing using the Benjamini-Hochberg procedure. We have added this further clarification to the methods section:

      “To identify dysregulated genes, for each stage, mean log2CPM ratios (test vs. wild-type) and q-values were calculated for each gene using a custom Python script (see Data and Code Availability), extracted and plotted using GraphPad Prism. In brief, an independent Student’s t-test was applied to the normalized expression values and p-values were corrected for multiple testing using the Benjamini-Hochberg procedure.”

      • Major Comment 2:

      *“2- In Figure 4C, it is unclear what correlation is being calculated. Additionally, the methods state that differential protein abundance was determined using the same approach as the scRNA-seq analysis. Given the lack of methodological clarity noted above, it is difficult to evaluate these results. The authors should justify whether the chosen method is compatible with the normalization approach used for the proteomics data.”

      *

      • Response and Incorporated Revision: We apologize for the lack of clarity on the calculated correlation and to improve this have amended the Figure legend for 4C to the following: “(C) Pearson correlation values for protein abundances in wild-type, Padi6-C663A and Padi6-KO GV oocyte and 2-cell embryo replicates when compared to all other replicates of the same condition. Samples of the same genotype and stage show high correlation in the abundance of individual proteins.”

      Regarding the compatibility, dysregulated proteins and transcripts were determined using the same statistical method, independent Student’s t-tests corrected for multiple testing with the Benjamini-Hochberg procedure.

      • Major Comment 4:

      “4- A notable result is the complete lack of correspondence between differential RNA-seq and proteomics in 2-cell embryos. The authors state that "This is consistent with data showing that minimal translation occurs in the 2-cell embryo, with the first large translational wave only occurring in the morula," citing Israel et al. 2019. This statement is factually incorrect. The cited study did not measure translation directly. Recent work that specifically measured translation during preimplantation development has demonstrated that translation is highly dynamic throughout these stages (Ozadam et al. 2023, Nature). This claim should be corrected and the results discussed in the context of these more recent findings.”

      • Response and Incorporated Revision: We have revised the claim, discussing our results in the context of the more recent Ozadam et al. 2023, Nature paper. The amended text reads: “This is consistent with data showing that protein abundance does not correlate with RNA level, but with ribosome occupancy and translation efficiency in the zygote58,59. Our results show that this lack of correlation in RNA and protein is maintained in the 2-cell embryo with RNA dysregulation and protein dysregulation uncoupled in Padi6-KO embryos, suggesting that transcriptomic and proteomic dysregulation need not be linked at early developmental stages.”

      • Major Comment 6:

      *“6- A large fraction of the manuscript interprets changes in the PADI6 knockout proteomics data as direct evidence that CPLs are associated with specific protein complexes (e.g., ribosomes) or cellular structures (e.g., mitochondria). However, these results are indirect and could alternatively be explained by roles of PADI6 that are independent of CPLs. These conclusions should be tempered, or this limitation should be explicitly acknowledged.”

      *

      • Response and Incorporated Revision:

      We have addressed this comment in the General Statements section discussing links between the CPLs and the mitochondria. Given the previously identified links between protein degradation machinery and ribosomes with the CPLs, we believe the changes are due to the loss of CPLs. We have however included the following statement acknowledging that these defects could be due to alternate function of PADI6 in the Limitations of the study section: “…It is also possible that some differences are due to an alternate defect that alters protein solubility caused by the absence of PADI6 independent of CPL formation.”

      • Major Comment 7:

      “7- Relatedly, the authors conclude that CPLs are associated with mitochondria based on the CPL proteomics data. Several questions arise: Do the EM images show mitochondria in close proximity to CPLs? Is mitochondrial morphology affected in Padi6-null oocytes? Given that respiratory chain complexes are large, membrane-bound assemblies, how might they associate with CPLs? Could the Triton-insoluble CPL fraction be contaminated with other oocyte-specific superstructures, as the authors themselves allude to? Do the EM images show mitochondria in close proximity to CPLs? Is mitochondrial morphology affected in Padi6-null oocytes?

      Reviewer 2:

      • Major Comment 5: “5) The authors found that CPLs are associated with mitochondria. How do CPLs in the cytosol interact with the components of the electron transport chain in the mitochondria? Do CPLs directly interact with these proteins in the cytosol or attach to the outer mitochondrial membrane? Does the loss of PADI6 affect the morphology, membrane potential, and ROS production of mitochondria in oocytes?”
      • Combined response to reviewer 1 major comment 7 and reviewer 2 major comment 5 and Incorporated Revision: As stated in the general statement, whilst it has been reported that there are defects in mitochondrial localisation in the absence of PADI6 and the CPLs (10.1016/j.ydbio.2010.11.033), it is possible these are not due to direct interactions. Presence of mitochondrial proteins in the CPL-enriched proteome could instead be caused by physical entrapment of the mitochondria by the CPLs. Whilst mitochondrial localization is known to be disrupted in PADI6 knockout oocytes (DOI: 10.1016/j.ydbio.2010.11.033), as additional links between the mitochondria and CPLs are not present in the literature unlike protein degradation machinery, we agree further work would be required to support this claim and have therefore removed the section entitled ‘The CPLs are associated with the oocyte mitochondria’, along with supplementary Figures 6E and 6F. As we did not further discuss links between the CPLs and Mitochondria in either the abstract or discussion, we do not believe the removal of this section significantly affects both the findings and novelty of this work.

      • Major Comment 8:

      “8-The authors should discuss their findings in the context of Liu et al. 2026 (Nature), which elucidates the structural basis of PADI6 in CPL formation. This comparison would be particularly informative given the overlapping scope of the two studies.”

      • Response and Incorporated Revision: As discussed in the general statement, we have included a new discussion of how the published CPL structure papers compare to our manuscript in the general comments section above. We have incorporated the following in the Discussion section of our manuscript: “During preparation of this manuscript, several high-resolution structures of the CPLs were reported80–83. Our proteomic workflow identified all CPL proteins resolved in these published structures as CPL-associated, including the newly identified SCF complex components and several F-box proteins. Notably, whilst highly informative, the structural studies cannot resolve the precise identify of individual proteins from families of structurally homologous proteins that form the CPL core, or of proteins which associate with the CPLs sub-stoichiometrically. One key example is the F-box proteins which offer substrate specificity to the SCF complex. Our proteomic analysis identified 10 different F-box proteins to have CPL-association. By contrast each published structure reports only a single or a few F-box proteins as part of the core complex. Notably different F-box proteins are reported in different structures. Our data provides an explanation for this variation, suggesting that even the core CPL fibres are non-homogeneous in the cell with different F-box proteins present in different locations (Fig 6B). Similarly, the close homology of a/b-tubulin proteins prevents identification of exact isoforms in the reported structures. Our data suggests that many different isoforms contribute to the CPLs (Fig 5E). Additionally, it is proposed that the CPLs act as storage hubs for a much wider range of proteins30. By allowing identification of proteins associated with the CPLs even at low occupancy our CPL-associated proteome provides a list of potential CPL-associated proteins for further structural and functional characterisation of CPL function.”

      • Minor Comment 1:

      “1- For the scRNA-seq analysis, please expand the methodology for PCA. Specifically, how are read counts normalized prior to PCA? We assume some form of log-normalization was applied, but this is not described in the methods or figure legends.”

      • Incorporated Revision: We apologize for this lack of clarity; log normalization was applied. The methods have been updated to the following to describe normalization prior to PCA: “PCA analysis of log normalized CPM values was performed using ScanPy on the full dataset, as well as oocyte, zygote, and 2-cell split data.”

      • Minor Comment 2:

      “2- Please clarify the z-score calculation for results shown in Figure 3. While the general approach can be inferred from context, the exact calculation is not provided.”

      • Incorporated Revision: Z-scores were calculated with the ScanPy scale function, the following has been added into the methods to clarify this: “Transcript Z-scores were calculated using the pp.scale function in the Python package ScanPy…”

      • Minor Comment 6:

      “6- It is unclear why the authors conclude that PADI6 regulates UHRF1 at the protein level (Figure S5). The observation that UHRF1 levels are reduced in Padi6-null oocytes could simply reflect reduced maternal deposition. The evidence does not appear sufficient to support a specific claim of protein-level regulation by PADI6.”

      • Incorporated Revision: We agree that it is possible that the reduced UHRF1 levels could be due to reduced maternal deposition, for example by reduced protein translation levels of UHRF1, and therefore we have amended our conclusion to the following: “…suggesting PADI6 regulates UHRF1 at the protein level or conceivably at the translational level during maternal deposition.”

      Reviewer 2:

      • Major Comment 4: “4) The authors also found that several proteasome subunits, as well as LAMP1 and RUFY1, were enriched in CPLs. These proteins are known to localize to ELVAs in GV and MII oocytes (Zaffagnini et al., 2024), suggesting functional crosstalk between CPLs and ELVAs. The authors should confirm that these components localize to the CPLs as well as ELVAs by immunostaining or using fluorescently labeled proteins. Are the morphology and localization of ELVAs affected by the loss of PADI6? Do CPLs colocalize or interact with ELVAs during oocyte maturation? It was reported that ELVAs were disassembled when RUFY1 was removed by Trim-Away in oocytes. Does the loss of RUFY1 affect CPL formation?”
      • Response and Incorporated Revision: Unfortunately, the only way to directly validate protein localisation to the CPLs is by expansion microscopy, which we do not have the technical capacity to do and therefore cannot perform these experiments in an informative manner. Reviewer #3 agrees with this conclusion: “Although some of the suggestions by Reviewer #2 could provide interesting information, the immunofluorescence experiments suggested in my view are not likely to provide definitive information regarding association of specific proteins or structures with CPLs. Instead, higher resolution technologies such as proximity ligation or immuno-EM might be required. These experiments seem like good ways to extend the findings beyond the current manuscript, but I think are not essential for the major take home points.”

      Regarding the morphology and localization of ELVAs in the absence of PADI6, it was recently reported that the number and size of RUFY1 compartments was increased in Padi6-null oocytes (DOI: 10.1038/s41594-026-01758-y). As RUFY1 is the key marker of the ELVAs this suggests that their morphology is likely affected, further pointing towards a functional crosstalk between the two. To highlight this we have added the following sentence in the discussion: “Additionally, recent work identified an increase in the number and size of RUFY1 and ProteoStat positive compartments in Padi6-null oocytes, further pointing towards a functional crosstalk between the ELVA and CPLs.” However, testing whether RUFY1 loss affects CPL formation is beyond the scope of this work investigating the functions of PADI6.

      Reviewer 3:

      • Major Comment 4: ‘4- Proteomics analysis: The authors carried out proteomics analysis on oocytes treated with Triton X-100 so that they would retain only cytoskeleton-associated proteins. As a control, Padi6-null oocytes (lacking CPLs) were used, and the authors interpret the proteins identified in the WT and not in the Padi6-null as CPL-associated proteins. It is not clear to me that this is a reasonable interpretation of the results. My concern is that the physical meshwork of CPLs may prevent loss of CPL-associated proteins as well as cytoplasmic protein complexes or organelles that are too large to escape the cytoplasm through the CPLs. This is particularly a concern for the authors' conclusions regarding high amounts of ELVA-associated and mitochondrial and mitochondria-associated proteins associated with the CPLs given the large size of these organelles/structures. Is there any evidence by an alternative method of direct association of ELVAs or mitochondria with CPLs? Others have not detected mitochondrial proteins associated with CPLs (see J. Li et al., doi 10.1038/s41594-026-01758-y).”
      • Response and Incorporated Revision: We have addressed this comment in the General Statements section entitled “CPLs and other oocyte structures”. We believe links between the CPLs, and protein degradation machinery and the ELVA are well supported by both our data, and the recent and past literature covering the CPLs (DOIs: 10.1038/s41594-026-01758-y, 10.1038/s41586-026-10360-7 ,10.1038/s41586-026-10513-8, 10.1038/s41586-026-10442-6, 10.64898/2026.03.22.713481, 10.1016/j.cell.2024.01.031, 10.1016/j.cell.2023.10.003). As additional links between the mitochondria and CPLs are not present in the literature unlike protein degradation machinery, we have removed the section entitled ‘The CPLs are associated with the oocyte mitochondria’, along with supplementary Figures 6E and 6F. As we did not further discuss links between the CPLs and Mitochondria in either the abstract or discussion, we do not believe the removal of this section significantly affects both the findings and novelty of this work.

      Responses to other reviewer comments including analyses that the authors prefer not to carry out

      Reviewer 1:

      • Major Comment 3: “3-The single-embryo proteomic measurements are an important aspect of the paper. However, additional quality control data are needed to assess data quality. In particular, a more systematic comparison to Ye et al. would strengthen confidence in these measurements.”
      • Response: Unfortunately, the Ye et al. work has not released a list of the proteins identified in oocytes and early embryos, or their intensities, therefore we cannot compare in this manner. In terms of overall number of proteins identified the two approaches are comparable, however they differ in both their sample preparation and MS acquisition methods.

      • Major Comment 5:

      “5- The manuscript refers to the C663A mutation as a "catalytic mutant." While the structural and homology-based rationale is compelling, the entire paper's conclusions depend on this interpretation. Experimental validation of the inferred loss of catalytic activity would substantially strengthen the study.”

      • Reviewer 2 Major Comment 1: “1) There is insufficient evidence to conclude that the C663A mutant is catalytically inactive. The authors should conduct an in vitro citrullination assay to show whether wild-type PADI6 has peptidyl arginine deiminase activity, but the C663A mutant loses it.”
      • Combined response to reviewer 1 major comment 5 and reviewer 2 major comment 1: We are pleased the Reviewer 1 finds our structural and homology-based rationale for the design of the C663A mutation compelling. As discussed in the general statements, prior to this work no catalytic activity of PADI6 had been observed in vitro, despite contradictory in vivo data regarding its activity. It was this challenge in replicating in vivo conditions that might be required for protein activation in an in vitro assay that directly led us to develop our in vivo mouse model in this work. It is therefore not possible to experimentally validate any change in activity following the C663A mutation as wild-type PADI6 is also inactive under the in vitro assay conditions used for other PADI isozymes. Except when discussing the design of the mouse itself we have been careful to always describe the mouse based on its mutation rather than as a catalytic mutant and have used terms such as “putative” or “potential” in the manuscript to make clear that there is no direct evidence for any catalytic activity that could then be abolished.

      • Minor Comment 3:

      “3- MII oocytes are used for scRNA-seq experiments and GV oocytes for proteomics. Please provide a rationale for the use of two different developmental stages.”

      • Response: The reasons for using MII oocytes over GV oocytes in the RNA-seq experiments was due to availability and sample number requirements. GV oocytes were used in place of MII oocytes for proteomic experiments as it was possible to gather many more GV oocytes per mouse than MII oocytes. Therefore, to increase the number of replicates in the single oocyte/embryo proteomics workflow developed in this work, we chose GV oocytes to increase confidence and show reproducibility.
      • Minor Comment 4:

      “4- While the data support the conclusion that maternal RNA and minor EGA mRNA degradation is defective, none of the experiments directly measure mRNA degradation. Direct experimental validation, even for a few select targets using standard decay assays, would strengthen this claim.”

      • Response: Whilst our data does not directly measure mRNA degradation, we believe our data is sufficient evidence to state that mRNA degradation is defective in the absence of PADI6, in line with other work (DOI: 10.1101/gad.351238.123 and consequently that it would not be appropriate to use further mice for these experiments in line with the 3Rs.
      • Minor Comment 5:

      *“5- The statement "Together these results indicate that we have established a powerful sub-cellular proteomic workflow from single mouse oocytes capable of identifying proteins associated with the CPLs" overstates the findings. The results are consistent with this interpretation, but the approach described is not a sub-cellular proteomic workflow in the spatial proteomics sense. This language should be revised.”

      *

      • Response: We agree that our workflow is not a sub-cellular proteomic workflow in the spatial proteomics sense, but we believe our statement and discussion does not claim that our workflow is a spatial proteomic workflow at any point. We therefore do not believe any revision of language is necessary.
      • Minor Comment 7:

      “7- Many ribosomal, proteasomal, and mitochondrial proteins appear to associate with CPLs in a PADI6-dependent manner. Could an alternative explanation be that maternal deposition of these proteins is globally reduced in Padi6-null oocytes, rather than their association with CPLs being specifically affected?”

      • Response: A global reduction in the maternal deposition of CPL-associated proteins would be reflected by a decrease in the levels of these proteins in intact oocytes. As the protein levels of the majority of CPL-associated proteins are not reduced in Padi6-KO oocytes (Figure 6A-B and Figure S6B), and for those that are reduced the effect is generally subtle, this discounts a global reduction in their maternal deposition.

        Reviewer 2:

      • Major Comment 2: “2) The author found that the levels of key CPL scaffolding proteins from the SCMC (OOEP, TLE6, NALP5, and KHDC3) were not affected by the loss of PADI6. It should be examined whether the subcellular localization of these proteins is affected in Padi6-deficient and C663A mutant embryos using immunostaining.”

      • Major Comment 3: “3) The authors found that CPLs contain components of the SKP1-CUL1-F-box protein (SCF) ubiquitin ligase complex. They also found that hPADI6 interacted with CUL1 when it was transiently transfected into HEK-293T cells. It is important to examine whether the stability or subcellular localization of these proteins is affected by the loss of PADI6 in oocytes.”

      • Combined response to reviewer 2 major comment 2 and 3: From our intact GV oocyte proteomics experiments, we know that the stability of the SKP1-CUL-F-box proteins is not affected by the loss of PADI6, similar to the key CPL scaffolding proteins highlighted in major comment 2. Regarding localization, it was reported by Jentoft et al. in 2023 that due to the cytoplasmic abundance of CPL proteins, their true cellular distribution can only be measured by IF using a Halo-tag knock-in line to circumvent the use of secondary antibodies which aggregate at the subcortex. Alternatively, expansion microscopy could be used to determine differences in localization. Unfortunately, we do not have the capacity to generate these lines or perform expansion microscopy and therefore we are not able to conduct these experiments in an informative manner. Reviewer #3 agrees with this conclusion: “Although some of the suggestions by Reviewer #2 could provide interesting information, the immunofluorescence experiments suggested in my view are not likely to provide definitive information regarding association of specific proteins or structures with CPLs. Instead, higher resolution technologies such as proximity ligation or immuno-EM might be required. These experiments seem like good ways to extend the findings beyond the current manuscript, but I think are not essential for the major take home points.”
      • Major Comment 6:

      “6) Figure 6C, G, and I.

      Statistical analysis should be done.”

      • Response: We have performed statistical analysis of Figure 6G and demonstrate that the PADI6-N598S variant binding to UHRF1 is statistically significantly impaired (see below). However, the immunoprecipitation assay performed is a largely qualitative assay and we don’t believe that detailed quantification of it is appropriate. Similarly for Figures 6C and 6I we don’t think quantification is necessary as the assay represents presence or absence of a protein in a sample.

      Reviewer 3:

      • Major Comment 1: “Padi6 catalytic activity: Given that PADI6 was previously shown not to have catalytic activity in vitro, the rationale for doing the experiment mutating Padi6 function is weak. The authors claim that a homologous mutation in human PADI6 does not "significantly damage the folded state of PADI6", but this conclusion does not necessarily mean that a scaffolding or protein interaction function could not be affected by the mutation. The authors provide zero evidence of catalytic activity in the WT oocytes (which I agree would be technically quite challenging given the poor quality/specificity of antibodies that recognize citrullinated proteins and low amount of protein available for mass spec analysis) but still include a full paragraph in the Discussion regarding the potential catalytic activity and why it might be important. This focus implies that the underlying data support the concept, even though the authors do frame the paragraph carefully.”
      • Response: We have primarily addressed this comment in the General Statements section of this document. Regarding scaffolding or interactional functions, as shown in our published X-Ray crystal structure of PADI6 (DOI: 10.1016/j.csbj.2024.08.019), the proposed catalytic cysteine is buried, not surface exposed, and doesn’t appear to be involved in structural interactions or disulphide bonds. We cannot however rule out subtle structural changes around the active caused by the C663A mutation resulting in altered protein-protein interaction binding affinities at proteins interacting near to the proposed PADI6 active site. To account for this possibility the following sentences have been added/amended in the discussion to read:

      “Given the lack of large-scale structural damage the loss of C663 imparts on PADI6, the possibility of a non-essential catalytic function of PADI6 in oogenesis and early embryo development cannot be ruled out, potentially in the epigenetic regulation of transcription similar to PADI4. Alternatively, it is possible that the C663A substitution alters protein binding affinities for interactions on or near to the proposed PADI6 active site.”

      • Major Comment 2:

      *“2. Padi6 mutant embryo development: The Padi6 mutant embryo development findings are minimally different from WT controls. The embryos were all cultured in vitro and it is unclear if they would have developed fine in vivo, which is suggested from the lack of a difference in litter sizes, which if anything were slightly higher in the Padi6 mutant females. In the absence of additional useful information regarding why the development was slightly lower, this experiment does not seem to add to the conclusions of the paper but seems more like an incomplete side note that should be more deeply investigated.”

      *

      • Response: An explanation for the lack of difference in litter sizes but difference in developmental potential has been discussed in the text: “This phenomenon (significant decrease in early embryo numbers in one mouse line over another despite litter sizes remaining comparable) has been observed previously and is attributed to mice, and other species, producing greater numbers of eggs and pre-implantation embryos than the uterus can accommodate, with excess embryos lost during the pre-implantation stage50–52.” The difference in developmental potential is statistically significant for Padi6-C663A embryos. Without an impaired function of PADI6 we do not see how in vitro culture of the embryos would result in such a difference in developmental potential of the mutant compared to the wild type embryos as they were cultured under the same conditions. We agree with the reviewer that we haven’t yet determined the underlying cause for this difference but we think nonetheless that it is an important finding to highlight - that a single cysteine mutation in the active site of PADI6, which does not affect protein structure significantly impacts the development of early-stage embryos.

      • Major Comment 3:

      “3. Padi6 mutant 2C embryo EGA timing: The altered transcription in the Padi6 mutant 2C embryos appears to indicate that they are ahead in development relative to the WT based on the PCA plot and the relative downregulation of minor ZGA genes and upregulation of major ZGA genes. The 1-cell embryos were collected from spontaneously ovulating mice and the time of development was not controlled in any way. Mouse embryos are quite variable in their exact timing of development, even across different embryos in the same mouse. I find these changes in transcription likely to be explained by differences in developmental timing and I don't think the authors have robustly shown "dysregulation of EGA". Similarly, the delay in development of the Padi6-null embryos from zygote to 2C (Figure 1D) explains why the maternal mRNAs are upregulated in the Padi6-null mice - they are simply delayed in development.

      • Response: When harvesting 2-cell embryos, samples from all four mice were harvested on different days at the same time of day. If the differences between samples were only due to differences in developmental timing we would anticipate as significant differences between the embryos from mice with the same genotype as between those from different genotypes which is not what we observe. Given the significant developmental defects observed in these embryos (Figure 1), it is highly likely these are associated with defects on the transcriptional level. Furthermore, we observed a small but significant delay in Padi6-C663A embryos reaching the 2-cell stage (Figure 1D) which we believe makes it highly unlikely that the embryos from both C663A females were further along in development compared to those from both wild-type females.

      The reviewer states that the upregulation of major EGA genes and downregulation of minor EGA genes further points toward an advancement in development. If this is the case, then it would be expected that there would also be increased degradation of maternal transcripts which decrease between the zygote and 2-cell stage in wild-type embryos. We do not see a further decrease of these transcripts in Padi6-C663A embryos. Finally, the reviewer notes the PCA plot as a reason for being advanced in development – PCA only measures differences between samples, not developmental time.

      Taking into account the above, we do not agree with the reviewer’s interpretation of our data. Regarding Padi6-null mice, disrupted EGA has been reported in Padi6-null mice in other work (DOI: 10.1101/gad.351238.123.).

      • Minor Comment 1:

      “1. What was the point of splitting up the 2C embryo blastomeres rather than treating them as single embryos?”

      • Response: 2C embryos were split up to investigate whether defects in Padi6-null 2-cell embryos were due to asymmetric inheritance of transcripts given the significant mis-localization of various oocyte structures in the absence of PADI6. As this was not clear in the text, we have added the following sentence clarifying the rationale and referencing Figure S4C-D where the transcriptomic correlation between blastomeres is shown: “The transcriptomes of separated blastomeres of the same 2-cell embryo showed high levels of correlation for embryos of each genotype suggesting asymmetric inheritance of transcripts is not a cause of PADI6 associated developmental defects (Figure S4C-D).”.

      • Minor Comment 2:

      “2. Proteomics - Because PADI6 makes up a significant fraction of total oocyte protein, and the Padi6-null oocytes don't have any PADI6, does this artificially increase the relative amount of the remaining proteins?”

      • Response: Any such effect would have been corrected during data normalization.
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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      PADI6 is a highly abundant oocyte-specific protein and component of cytoplasmic lattices (CPLs). The current study attempts to better characterize mouse PADI6 function and CPL-associated proteins using a novel Padi6 mutant allele (predicted to be catalytically dead), RNA sequencing, and single oocyte proteomics approaches. Key findings reported are:

      1. Validation of known Padi6-null female mouse phenotypes including infertility, disruption of CPL formation, impaired embryonic genome activation, and abnormal maternal mRNA degradation.
      2. Mice homozygous for the Padi6-mutant allele, when mated to WT males, have a slight impairment in preimplantation embryo development in vitro but have normal fertility as indicated by average litter sizes.
      3. CPLs are enriched in ribosomal proteins, proteins involved in protein degradation including ubiquitination proteins, and mitochondria. Several of the protein interactions with PADI6 were validated by showing co-immunoprecipitation of epitope-tagged proteins expressed in HEK-293 cells (UHRF1, UBE2D2, and CUL1).

      Major comments:

      • Are the key conclusions convincing?

      • Padi6 catalytic activity: Given that PADI6 was previously shown not to have catalytic activity in vitro, the rationale for doing the experiment mutating Padi6 function is weak. The authors claim that a homologous mutation in human PADI6 does not "significantly damage the folded state of PADI6", but this conclusion does not necessarily mean that a scaffolding or protein interaction function could not be affected by the mutation. The authors provide zero evidence of catalytic activity in the WT oocytes (which I agree would be technically quite challenging given the poor quality/specificity of antibodies that recognize citrullinated proteins and low amount of protein available for mass spec analysis) but still include a full paragraph in the Discussion regarding the potential catalytic activity and why it might be important. This focus implies that the underlying data support the concept, even though the authors do frame the paragraph carefully.

      • Padi6 mutant embryo development: The Padi6 mutant embryo development findings are minimally different from WT controls. The embryos were all cultured in vitro and it is unclear if they would have developed fine in vivo, which is suggested from the lack of a difference in litter sizes, which if anything were slightly higher in the Padi6 mutant females. In the absence of additional useful information regarding why the development was slightly lower, this experiment does not seem to add to the conclusions of the paper but seems more like an incomplete side note that should be more deeply investigated.
      • Padi6 mutant 2C embryo EGA timing: The altered transcription in the Padi6 mutant 2C embryos appears to indicate that they are ahead in development relative to the WT based on the PCA plot and the relative downregulation of minor ZGA genes and upregulation of major ZGA genes. The 1-cell embryos were collected from spontaneously ovulating mice and the time of development was not controlled in any way. Mouse embryos are quite variable in their exact timing of development, even across different embryos in the same mouse. I find these changes in transcription likely to be explained by differences in developmental timing and I don't think the authors have robustly shown "dysregulation of EGA". Similarly, the delay in development of the Padi6-null embryos from zygote to 2C (Figure 1D) explains why the maternal mRNAs are upregulated in the Padi6-null mice - they are simply delayed in development.
      • Proteomics analysis: The authors carried out proteomics analysis on oocytes treated with Triton X-100 so that they would retain only cytoskeleton-associated proteins. As a control, Padi6-null oocytes (lacking CPLs) were used, and the authors interpret the proteins identified in the WT and not in the Padi6-null as CPL-associated proteins. It is not clear to me that this is a reasonable interpretation of the results. My concern is that the physical meshwork of CPLs may prevent loss of CPL-associated proteins as well as cytoplasmic protein complexes or organelles that are too large to escape the cytoplasm through the CPLs. This is particularly a concern for the authors' conclusions regarding high amounts of ELVA-associated and mitochondrial and mitochondria-associated proteins associated with the CPLs given the large size of these organelles/structures. Is there any evidence by an alternative method of direct association of ELVAs or mitochondria with CPLs? Others have not detected mitochondrial proteins associated with CPLs (see J. Li et al., doi 10.1038/s41594-026-01758-y).
      • Are the data and the methods presented in such a way that they can be reproduced?

      The authors should be congratulated on the clarity and thoroughness of the Methods and Results descriptions in this manuscript. I have rarely seen this done so nicely. - Are the experiments adequately replicated and statistical analysis adequate?

      The experiments were replicated and analyzed appropriately.

      Minor comments:

      Points for clarification:

      1. What was the point of splitting up the 2C embryo blastomeres rather than treating them as single embryos?
      2. Proteomics - Because PADI6 makes up a significant fraction of total oocyte protein, and the Padi6-null oocytes don't have any PADI6, does this artificially increase the relative amount of the remaining proteins?

      Referees cross-commenting

      It was interesting to read the additional reviews on this manuscript. Regarding Reviewer #1's points, we seem to be in agreement, in particular regarding major comment 7 regarding the possibility that the Triton-insoluble CPL fraction may be contaminated with other oocyte-specific superstructures. We all had concerns regarding the lack of evidence that PADI6 has catalytic activity and the point mutant does not, which impacts interpretation of many of the embryo development experiments.

      Although some of the suggestions by Reviewer #2 could provide interesting information, the immunofluorescence experiments suggested in my view are not likely to provide definitive information regarding association of specific proteins or structures with CPLs. Instead, higher resolution technologies such as proximity ligation or immuno-EM might be required. These experiments seem like good ways to extend the findings beyond the current manuscript, but I think are not essential for the major take home points.

      Significance

      There are several very recent publications on CPLs, including two recently accepted papers (March 2026) reporting the structure of CPLs and associated proteins detected in situ or after CPL purification using cryo-EM and mass spec analysis (Chi et al., doi 10.1038/s41586-026-10442-6; Liu et al., doi 10.1038/s41586-026-10360-7). The associated proteins include UHRF1, tubulin proteins, and ubiquitin ligase components, similar to what was shown in the current manuscript. A third paper, also published in March 2026 (J. Li et al., doi 10.1038/s41594-026-01758-y), used cryo-EM and IP-mass spec combined with mutagenesis to draw similar conclusions. This manuscript specifically comments on the lack of associated mitochondrial proteins, conflicting with the current manuscript. Finally, a preprint (Y. Li et al, doi 10.64898/2026.03.30.715190) on the same topic was posted on bioRxiv April 1, 2026; similar methods were used and similar conclusions were drawn. The current manuscript stands out for using Padi6-null oocytes as a control, which in theory could improve the mass spec results to improve information regarding the extent and identity of associated proteins over what was done in these manuscripts (though see above for concerns related to this point). Further, if the authors were able to conclusively show that PADI6 catalytic activity played a role in preimplantation embryo development, it would be quite distinct from these papers that are focused on CPL structure and associated proteins.

      • State what audience might be interested in and influenced by the reported findings.

      The audience for this paper would be reproductive biologists or clinical infertility scientists, given the relevance to human Padi mutations. Should the Padi6 catalytic activity be defined and relevant to oocytes and early embryos, interest would be broadened to more general epigenetics of development and nuclear reprogramming. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Field of expertise: Reproductive biology, oocyte physiology, embryonic genome activation, epigenetics Lack of expertise: Proteomics

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, the authors investigated the role of PADI6, which belongs to the peptidyl arginine deiminase (PADI) family and is involved in the formation of cytoplasmic lattices (CPLs) in oocytes, in mouse female fertility. The authors generated Padi6-deficient mice and C663A mutant mice, which are assumed to lose the catalytic activity of PADI6, and examined these mice in detail. The authors found that Padi6-/- mice were infertile, but the C663A mutant mice were not, although they showed a small reduction in their developmental potential. The authors also found that CPLs normally exist in GV oocytes of C663A mutant mice, whereas they disappear in PADI6-deficient GV oocytes. In addition, the authors conducted scRNA-seq analyses on MII oocytes, zygotes, and blastomeres from 2-cell embryos and found that Padi6-deficient and C663A mutant embryos resulted in different transcriptome defects, leading to defective EGA. They also performed single-cell proteomic analyses of wild-type and mutant oocytes and embryos and found that the loss of PADI6 caused drastic protein dysregulation. Finally, the authors conducted proteomic analysis of CPLs isolated from control, PADI6-deficeit and mutant oocytes and revealed that PADI6 mediates the association of some components of the ubiquitination machinery, ELVAs, and mitochondria with CPLs. These results suggest that PADI6 functions as a scaffold protein in CPL formation and regulates translation, respiration, and protein degradation in oocytes and early embryos. A detailed analysis of PADI6 functions in mouse oocytes and embryos and the establishment of a proteomic workflow for a single oocyte and embryo are appreciated, and the potential consequences of this work are interesting. However, since the role of PADI6 in CPL formation, early development, and female fertility has already been reported, further analysis is necessary to offer novelty and mechanistic insights that go beyond previous research.<br /> I have enlisted several major concerns that may help the authors address these issues.

      Major comments

      1) There is insufficient evidence to conclude that the C663A mutant is catalytically inactive. The authors should conduct an in vitro citrullination assay to show whether wild-type PADI6 has peptidyl arginine deiminase activity, but the C663A mutant loses it.

      2) The author found that the levels of key CPL scaffolding proteins from the SCMC (OOEP, TLE6, NALP5, and KHDC3) were not affected by the loss of PADI6. It should be examined whether the subcellular localization of these proteins is affected in Padi6-deficient and C663A mutant embryos using immunostaining.

      3) The authors found that CPLs contain components of the SKP1-CUL1-F-box protein (SCF) ubiquitin ligase complex. They also found that hPADI6 interacted with CUL1 when it was transiently transfected into HEK-293T cells. It is important to examine whether the stability or subcellular localization of these proteins is affected by the loss of PADI6 in oocytes.

      4) The authors also found that several proteasome subunits, as well as LAMP1 and RUFY1, were enriched in CPLs. These proteins are known to localize to ELVAs in GV and MII oocytes (Zaffagnini et al., 2024), suggesting functional crosstalk between CPLs and ELVAs. The authors should confirm that these components localize to the CPLs as well as ELVAs by immunostaining or using fluorescently labeled proteins. Are the morphology and localization of ELVAs affected by the loss of PADI6? Do CPLs colocalize or interact with ELVAs during oocyte maturation? It was reported that ELVAs were disassembled when RUFY1 was removed by Trim-Away in oocytes. Does the loss of RUFY1 affect CPL formation?

      5) The authors found that CPLs are associated with mitochondria. How do CPLs in the cytosol interact with the components of the electron transport chain in the mitochondria? Do CPLs directly interact with these proteins in the cytosol or attach to the outer mitochondrial membrane? Does the loss of PADI6 affect the morphology, membrane potential, and ROS production of mitochondria in oocytes?

      6) Figure 6C, G, and I. Statistical analysis should be done.

      Significance

      This study provide the information of PADI6 function in CPL formation, early development, and female fertility.

      The authors established single oocyte proteomic analysis and CPL isolation method, which are useful to examine the protein content in mouse oocytes and embryos.

      This study is likely to be of interest to basic and clinical researchers.

      My expertise is developmental and cell biology.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This study characterizes two mouse models with mutations in Padi6, a protein central to maternal protein storage and cytoplasmic lattice formation. The authors perform proteomics and transcriptomics experiments using a knockout and a C663A point mutant, framing their results effectively within the substantial existing literature on PADI6. The most significant advance is the generation and characterization of the C663A mutation, which is interpreted as a catalytic-dead variant. While the structural and homology-based rationale for this interpretation is compelling, several conclusions would benefit from additional experimental validation or more cautious framing. The computational methods also require greater methodological detail.

      Major comments:

      1. TThe methods for differential gene expression analysis are insufficiently described. Were these calculated using Scanpy? If so, the rationale for this choice should be provided, as the number of replicates and the nature of the data appear more suited to standard bulk differential expression frameworks such as DESeq2 or edgeR.
      2. In Figure 4C, it is unclear what correlation is being calculated. Additionally, the methods state that differential protein abundance was determined using the same approach as the scRNA-seq analysis. Given the lack of methodological clarity noted above, it is difficult to evaluate these results. The authors should justify whether the chosen method is compatible with the normalization approach used for the proteomics data.
      3. The single-embryo proteomic measurements are an important aspect of the paper. However, additional quality control data are needed to assess data quality. In particular, a more systematic comparison to Ye et al. would strengthen confidence in these measurements.
      4. A notable result is the complete lack of correspondence between differential RNA-seq and proteomics in 2-cell embryos. The authors state that "This is consistent with data showing that minimal translation occurs in the 2-cell embryo, with the first large translational wave only occurring in the morula," citing Israel et al. 2019. This statement is factually incorrect. The cited study did not measure translation directly. Recent work that specifically measured translation during preimplantation development has demonstrated that translation is highly dynamic throughout these stages (Ozadam et al. 2023, Nature). This claim should be corrected and the results discussed in the context of these more recent findings.
      5. The manuscript refers to the C663A mutation as a "catalytic mutant." While the structural and homology-based rationale is compelling, the entire paper's conclusions depend on this interpretation. Experimental validation of the inferred loss of catalytic activity would substantially strengthen the study.
      6. A large fraction of the manuscript interprets changes in the PADI6 knockout proteomics data as direct evidence that CPLs are associated with specific protein complexes (e.g., ribosomes) or cellular structures (e.g., mitochondria). However, these results are indirect and could alternatively be explained by roles of PADI6 that are independent of CPLs. These conclusions should be tempered, or this limitation should be explicitly acknowledged.
      7. Relatedly, the authors conclude that CPLs are associated with mitochondria based on the CPL proteomics data. Several questions arise: Do the EM images show mitochondria in close proximity to CPLs? Is mitochondrial morphology affected in Padi6-null oocytes? Given that respiratory chain complexes are large, membrane-bound assemblies, how might they associate with CPLs? Could the Triton-insoluble CPL fraction be contaminated with other oocyte-specific superstructures, as the authors themselves allude to?
      8. The authors should discuss their findings in the context of Liu et al. 2026 (Nature), which elucidates the structural basis of PADI6 in CPL formation. This comparison would be particularly informative given the overlapping scope of the two studies.

      Minor comments:

      1. For the scRNA-seq analysis, please expand the methodology for PCA. Specifically, how are read counts normalized prior to PCA? We assume some form of log-normalization was applied, but this is not described in the methods or figure legends.
      2. Please clarify the z-score calculation for results shown in Figure 3. While the general approach can be inferred from context, the exact calculation is not provided.
      3. MII oocytes are used for scRNA-seq experiments and GV oocytes for proteomics. Please provide a rationale for the use of two different developmental stages.
      4. While the data support the conclusion that maternal RNA and minor EGA mRNA degradation is defective, none of the experiments directly measure mRNA degradation. Direct experimental validation, even for a few select targets using standard decay assays, would strengthen this claim.
      5. The statement "Together these results indicate that we have established a powerful sub-cellular proteomic workflow from single mouse oocytes capable of identifying proteins associated with the CPLs" overstates the findings. The results are consistent with this interpretation, but the approach described is not a sub-cellular proteomic workflow in the spatial proteomics sense. This language should be revised.
      6. It is unclear why the authors conclude that PADI6 regulates UHRF1 at the protein level (Figure S5). The observation that UHRF1 levels are reduced in Padi6-null oocytes could simply reflect reduced maternal deposition. The evidence does not appear sufficient to support a specific claim of protein-level regulation by PADI6.
      7. Many ribosomal, proteasomal, and mitochondrial proteins appear to associate with CPLs in a PADI6-dependent manner. Could an alternative explanation be that maternal deposition of these proteins is globally reduced in Padi6-null oocytes, rather than their association with CPLs being specifically affected?
      8. Although the authors discuss limitations of their study, more pertinent caveats, such as those raised above regarding indirect inference, would be appropriate for the limitations section.
      9. We were unable to access the zenodo link. Please make sure this is available for the code to be reviewed.

      Significance

      General Assessment: The principal strength of this study is the generation and characterization of the PADI6 C663A point mutant. The authors contextualize their findings within the extensive existing literature on PADI6 and CPLs. The recent structural characterization of PADI6 in CPL formation by Liu et al. 2026 provides a complementary perspective that should be discussed alongside the current findings. Several key conclusions rest on indirect inference. The catalytic-dead interpretation of the C663A mutant, while structurally well-motivated, also lacks direct experimental validation. Additionally, the computational methods require better description to allow proper evaluation of the differential expression analyses.

      Advance: The generation and characterization of a mouse model with a putative catalytic-dead PADI6 mutant represents a significant advance and will be an important contribution to the field.

      Audience: This work will be of primary interest to a specialized audience studying oocyte biology, and preimplantation embryonic development. In our opinion, the study is unlikely to attract broad attention beyond these fields.

      Reviewer Expertise: Computational biology, gene expression in early embryonic development

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      1. General Statements

      We thank the reviewers for their constructive criticisms and helpful suggestions.

      2. Description of the planned revisions

      Point-by-point reply explaining what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Reviewer #1

      (Note: to see the Figures in our response we refer to the document Revision-plan_RC-2026-03552)

      Summary: This study examines how simultaneous expression of high levels of alpha and beta tubulin impact microtubule organization, cell cycle progression and stress response. The main conclusions are that a/b tubulin overexpression 1) increases the density of microtubules in cells with mild effects on growth rate and catastrophe frequency; 2) causes mitotic spindle defects and cell cycle disruption; 3) alters the transcriptome and proteostasis; 4) causes mitochondrial stress by binding mitochondrial import proteins; 5) disrupts cellular stress response.

      Major comments:

      The results generally use an appropriate number of cells and technical replicates.

      There are several instances in the manuscript where the data do not support the authors' conclusions. These include:

      Page 6. "We detected a mild increase in MT growth rates in Dox-treated cells (Fig. 2b), which was observed in all of the growing MTs examined (Fig. 2c), consistent with an increase in soluble tubulin levels in all Dox-induced cells." The data do not support this statement. The histogram in Figure 2C shows that only a small portion of growth rate measurements are faster in the overexpression cells, and the statistical test in 2b suggests that the datasets are unlikely to be different. It is unclear from the presentation of the data why a small population of overexpression cells exhibit faster growth rates. The data in Figure 2b should be plotted as a superplot could show whether the faster data points are from specific cells or technical replicates. The same concern applies to the data in Figure 2D.

      Answer:

      We agree that an overlaid density plot as currently shown in Fig. 2c might visually suggest that only the non-overlapping tail differs between conditions, but this is not what the underlying data show. Comparison of individual comet measurements indicates that the entire Dox distribution is shifted toward faster speeds (as we state in the text), not a small subpopulation: a randomly selected Dox comet is faster than a randomly selected Ctrl comet in 62% of pairwise comparisons (50% expected if the groups were identical), and 69% of Dox comets exceed the Ctrl median speed. We have added a cumulative distribution plot (see Figure below) that makes this more explicit — the Dox curve runs to the right of Ctrl across essentially the entire range, rather than only in a discrete high-speed segment. We acknowledge that our previous Figure panel may not have represented the data properly and propose to replace current Fig. 2c with this new plot.

      New Fig. 2c. Cumulative distribution plot of EB3-GFP comets (displacements in time).

      Note: to see the Figures in our response we refer to the document Revision-plan_RC-2026-03552

      Regarding the statistic in Fig. 2b: the reported P = 0.057 is from a test comparing group means. However, the Dox distribution is right-skewed and borderline non-normal (Shapiro-Wilk P = 0.054), and we realized that this is a condition under which a mean-comparison test is not well powered. A Mann-Whitney U test, which compares the full rank distribution and is more appropriate for data of this shape, gives P = 0.013 (Kolmogorov-Smirnov P = 0.0043). We will add these results and the effect-size measures mentioned above to a revised manuscript, and will report the rank-based test as the primary statistic for this comparison in the revised manuscript. All in all, we hope this clarifies that the effect is a genuine population-wide shift in comet speed. Note that we agree with the reviewer that the data should be plotted as a superplot in Fig. 2b, and we will do this in a revised manuscript.

      For the data presented in Fig. 2d (duration of EB3-GFP displacements), we performed a similar analysis as described above. In contrast to Fig. 2c no significant changes are seen in duration, and here the concern of the reviewer is justified. We will indicate this clearly in the text. It does not change any of our conclusions.

      Page 11. "Moreover, DNA content was highly aberrant after 48 hr, with large proportions of cells containing 4n chromosomes (Fig. 4d). Thus, persistent overexpression of tubulin affects the cell cycle and after 48 hr it results in severe DNA abnormalities, suggestive of CIN". The plot in Figure 4D shows increased propidium iodide signal below the 2N peak and above the 4N peak, but it is quite likely that this could represent signal from apoptotic cells. The authors should specifically stain for an apoptotic marker to test this possibility. This would suggest that prolonged overexpression of a/b tubulin leads to apoptosis, which could be important evidence for later conclusions in the study.

      Answer:

      We agree with the reviewer. We actually thought along the same lines and already performed the apoptosis experiment by staining tubulin overexpressing cells (dox) and control cells (ctrl) with Annexin, an apoptosis marker, and propidium iodide. We then quantified double-stained cells. The experiment was performed in triplicate. As shown in the Figure below (averages ± SEM), the data do not reveal differences in apoptosis. We therefore did not include these results in the original manuscript. However, based on the comment of the reviewer we will now include these data in a revised version of the manuscript. The conclusion is that tubulin overexpression does not lead to increased apoptosis.

      Apoptosis Figure.

      Note: to see the Figures in our response we refer to the document Revision-plan_RC-2026-03552

      Page 11. "Combined, our data show that tubulin overexpression affects all three major cell cycle checkpoints, i.e. G1/S, G2/M, and the SAC in mitosis." The data do not fully support this conclusion. While the evidence for SAC-mediated delay in overexpression cells is strong, the evidence for other checkpoints is weak. The phosphor-RB experiment in Figure S3b lacks a positive control. The γH2A-X results in Figure 4f and g show that the difference is driven by a minor population of dim foci in control cells. Similar to the point above, these data should be plotted as a superplot to explore the possibility that this minor population arises from a small number of cells or a specific technical replicate.

      Answer:

      With respect to the “inclusion of a positive control”, we are not sure what is meant by the reviewer. Total RB is shown underneath the phosphor-RB lane, and underneath that lane we show a tubulin blot. Moreover, mass spectrometry data are included in Table 1, showing the levels of many other proteins. Note that both the western blot and proteomics data reveal similar RB1 levels in Ctrl and Dox cells.

      New Fig. 4g. Cumulative distribution plot of 𝛾-H2AX foci intensity.

      Note: to see the Figures in our response we refer to the document Revision-plan_RC-2026-03552

      With respect to the γH2A-X results in Figure 4f and g, we re-analyzed and re-plotted the data as described above for Fig. 2c. A cumulative distribution plot, depicted above, clearly shows separation of the two populations. We ran several statistical tests, which all show highly siginificant values (Welch P = 1.5e-17, Mann-Whitney P = 3.2e-17, KS P = 1.1e-14). We propose to change the current Fig. 4g with the plot shown above, and we will indicate statistical tests.

      We then tested the reviewer's proposal (driven by a minor population of dim foci in control cells) by fitting a 2-component Gaussian mixture to each condition separately. The table below shows close to the opposite of the reviewer's hypothesis. Both Ctrl and Dox contain the same two-population structure in similar proportions (roughly 75%/25%). The minor subpopulation is therefore not unique to Ctrl, but a feature of both conditions. Perhaps it is a cell-cycle-linked fraction. When we next compared the matched components directly we observed that the majority/lower-intensity component is shifted (102 → 112 AU, P = 1.5e-30), while the minor/higher-intensity component is statistically indistinguishable between conditions (143 vs. 146 AU, P = 0.11, ns). Thus, if anything, the effect is carried by the bulk population, and the minor subpopulation is the part that does not differ. All data are summarized in the table below.

      2-component Gaussian mixture decomposition

      Ctrl weight

      Ctrl mean (AU)

      Dox weight

      Dox mean (AU)

      Lower-intensity component

      ~78%

      101.7

      ~71%

      111.9

      Higher-intensity component

      ~22%

      143.3

      ~29%

      146.3

      Page 19. "This, in turn, lowers EEF1A1 causing attenuation of translation and dampened elongation rates. That other initiation and elongation factors are mildly down in Dox-induced cells, as are ribosomal proteins (Fig S6c, Table S1), indicates a mild ISR and general translation inhibition. We propose that tubulin overexpression results in mitochondrial dysfunction leading to stress and attenuated translation elongation. We observe the start of an ISR in these cells, which prevents a proper hypoxic response." This conclusion seems to be at odds with the earlier conclusion drawn from Figure 5e, where ISR genes are significantly decreased in overexpression cells. The authors should reconcile these results in the discussion.

      Answer:

      Our proteomics data suggest that attenuated translation elongation caused by lower EEF1A1 levels hampers the integrated stress response (ISR) in Dox-induced cells. One can intuitively understand this: translation is already lowered, it does not need to be lowered much further via the ISR. Thus, in Dox-induced cells the ISR is hampered compared to control cells, which do not suffer from mito-stress. In other words, the ISR is set in motion in Dox-induced cells but less compared to control cells. This is exactly what we see in the RNA-Sequencing experiments, where in Dox-induced cells mRNAs encoding proteins in volved in HIF-1 and PKR signaling (reflecting ISR) are down (Fig. 5e, right hand panel). Thus, ISR is hampered at the RNA level when tubulin is overexpressed. While we thought we had explained our view well enough on page 19, we realize that this may not have been the case and we will provide an improved explanation in the Discussion section of a revised manuscript.

      Figure 7. This experiment lacks a key control - identifying peptides in a pull down from cells that do not overexpress the tagged tubulin. Without this control, it is impossible to discern which peptides bind to the beads, independent of tubulin. This concern is amplified by the results in Figure 7b where TIMM50 binding is tested in a follow-up experiment. Here the negative control is employed and it shows that TIMM50 pulls down in the absence of tagged tubulin. This indicates that TIMM50 may not bind to tubulin and calls into question any conclusions related to TIMM50 and mitochondrial proteostasis. Before making any conclusions from the pull down experiment, the authors must include the negative control and carefully assess which peptides are likely to be false positives.

      Answer:

      In contrast to what the reviewer states a negative control was included. As described on page 19: “Using beads coupled to ALFA-tag antibodies, we affinity-purified recombinant tubulin and tubulin-associated proteins (TAPs) from cell lysates of tubulin overexpressing cells, using non-induced cells as controls”. To make this more clear we propose to include a plot in Figure 7 showing the fold enrichment of TAPs in the dox-induced versus Ctrl cells.

      The reason that TIMM50 enrichment in the recombinant tubulin pull down is not high is explained on page 21 of the manuscript: “We note that the relatively weak enrichment of TIMM50 on beads containing recombinant tubulins (Fig. 7b, lower blot, Table S1) as compared to the enrichment of the recombinant tubulins themselves (Fig. 7b, upper and middle blots, Table S1) is well explained, first by a low affinity of the tubulin-TIMM50 interaction itself, and second by competition for TIMM50 between recombinant tubulins, which are enriched after washing, and the much larger reservoir of endogenous tubulins, which are lost (together with TIMM50) after washing”.

      While we believe that the weak enrichment of TIMM50 is well explained in the current set-up, we nevertheless feel that the tubulin-TIMM50 interaction should be further corroborated and we propose to perform a new experiment where we tag both TIMM50 (and AIFM) in addition to our dual tubulin constructs and show - via dual affinity purification - that the proteins do interact.

      Figure 8. This figure and the associated text feel rather disconnected from the rest of the study. The authors do not make clear conclusions from these results. Perhaps these experiments should be further developed in a separate study?

      Answer:

      The physiological relevance of autoregulation is still largely unknown. Our analysis in Figure 8, where we show that autoregulation is activated upon stress (i.e. hypoxia and Gln deprivation), provides a clue. Moreover, we show that disruption of normal tubulin levels, which induces stress, dampens the hypoxic response. We believe these reciprocal effects fit nicely. However, since reviewer 2 is of the same opinion as this reviewer we are willing to remove the data in a revised version of the manuscript.

      Page 25. "We found that tubulin overexpression induces mitochondrial dysfunction." The data do not strongly support this conclusion. The proteomic data in Figure 6 indicate that some mitochondrial proteins are less abundant in tubulin overexpressing cells, but the study does not include any experiments that actually test mitochondrial function. The authors could test this by including new experiments to measure mitochondrial membrane potential, or respiration activity, etc. These would be an important addition to the study.

      Answer:

      We agree with this criticism and will perform additional experiments in a revised version of the manuscript to describe mitochondrial dysfunction.

      Page 26. "We provide evidence here that surplus tubulin slows general translation, increasing the time window for a TTC5-nascent tubulin interaction." The data do not demonstrate a decrease in translation rate. It is unclear what evidence the authors refer to with this statement. The proteomic analysis in Figure 6 measures protein abundance, and a decrease could be due to either decreased transcription, decreased translation, or increased protein degradation. The number of proteins that are shown to be downregulated in tubulin overexpressing cells is rather small (100s) which seems to argue against a general decrease in translation, despite the decrease in several translation regulators. To make this conclusion, the authors would need to add new experiments that measure translation rate.

      Answer:

      While we agree with the reviewer that “protein decrease could be due to either decreased transcription, decreased translation, or increased protein degradation”, we provide evidence below that, at least in our view, excludes transcription and degradation as mechanisms. We do agree that we should provide more compelling evidence that attenuated translation is at work in tubulin overexpressing cells. We will therefore perform an assay to measure translation rates in tubulin overexpressing and control cells.

      Having stated which new experiment we will perform in a revised version of the manuscript, we now would like to extensively react to the above comment of the reviewer using our existing data. First, the reviewer argues that “the number of proteins that are shown to be downregulated in tubulin overexpressing cells is rather small (100s)”. However, this applies to the signficantly differentially expressed proteins (DEPs) and "only hundreds of proteins pass significance" is a power argument, not an effect-size argument. In Fig. 7c (heatmap) and Fig. S6b (probability distributions) we show that the whole population of 8224 proteins is down-regulated in Dox-induced cells.

      We visualize probability distributions again in the Figure below, but this time we analyzed both proteomic data and matching transcriptomic data.

      New Fig. S6. Probability distributions of proteomes and transcriptomes (a) and directionality of changes (b) comparing Dox-induced cells (Dox) to control cells.

      Note: to see the Figures in our response we refer to the document Revision-plan_RC-2026-03552

      At the protein level the median protein log2-fold change (L2FC) is -0.144. The mean L2FC is -0.149, which is highly similar to the median, indicating that the whole population shifts in a similar manner (panel a). Indeed, as shown in panel b the directionality of the change at the protein level is 91%. Thus, 91% of proteins is down in tubulin overexpressing cells. This effectively rules out protein degradation as a mechanism for decreased protein levels, as degradation is highly unlikely to be so aspecific.

      Using our RNA-Seq dataset we analyzed the mRNA levels corresponding to the 8224 proteins in the proteomics analysis. In panel a of the Figure it can be seen that the median mRNA log2-fold change (L2FC) is 0.008, which is close to zero. Panel b shows that directionality of change is 45.7%, which is close to 50%, and is essentially what one would expect from distributions around zero. The Wilcoxon effect size (r) is 0.76 for protein, which is large, and 0.09 for mRNA, which is trivial. These data strongly suggest that transcription plays no role in the downregulation of the 8224 proteins in tubulin overexpressing cells. Thus, neither protein degradation nor transcription appear to play a role in the downregulation of proteins in tubulin overexpressing cells. We propose to add these new data to our revised manuscript and change current Fig. S6b for the new Fig. S6.

      Minor comments:

      Page 10." We also observed "extra-mitotic" centrioles at the onset of mitosis after Dox-treatment, indicative of mitotic arrest and aneuploidy, which eventually resolved into normal metaphase plates (Supplementary Videos 3, 4)." It is unclear whether these foci are centrioles. The SiR-tubulin reagent used in these experiment labels tubulin. It would be more accurate to describe these as "tubulin foci".

      Answer:

      We thank the reviewer for this comment. We will modify the text.

      Page 13. "Analysis of TUBG1 reads revealed downregulation in tubulin overexpressing cells, albeit weakly (Fig. 5c)." These datapoints appear to be quite similar. Please provide a statistical analysis.

      Answer:

      The reviewer is correct, datapoints are quite similar. DESeq2 analysis reveals the difference in expression is minimal but significant (Dox vs Ctrl, log2fold change -0.111, p-value 0.016). These data are shown in Table S1 (sheet: DESeq2 24 hr).

      Reviewer #2

      Note: to see the Figures in our response we refer to the document Revision-plan_RC-2026-03552

      Summary: This study develops an inducible HEK293F cell model that modestly overexpresses αβ-tubulin heterodimers. Using a dual expression system, the authors introduce exogenous TUBB3 and TUBA1A and combine cell biology, live-cell imaging, RNA-seq, proteomics, flow cytometry, and biochemistry to investigate the consequences of increased tubulin abundance. They suggest that, despite downregulation of endogenous tubulin transcripts, excess tubulin still accumulates in cells. This is accompanied by increased microtubule mass, changes in plus-end protein composition, defects in mitosis, and alterations in cell-cycle progression. The study further links tubulin overexpression to replication stress and broader changes in cellular physiology, including reduced abundance of mitochondrial respiratory proteins, altered translation-associated factors, and an attenuated response to hypoxic stress. In addition, the authors confirm previous observations that oxygen and nutrient deprivation reduce tubulin and microtubule abundance. Based on these findings, the authors propose that tubulin is not merely a structural component of microtubules but also contributes to the regulation of cellular homeostasis.

      The possibility that tubulin abundance itself influences broader aspects of cellular physiology is conceptually interesting and warrants further investigation. At the same time, many of the mechanistic links proposed in the manuscript remain insufficiently resolved. A large number of cellular processes are associated with tubulin overexpression, but it is often unclear which phenotypes represent primary consequences of altered tubulin abundance and which are secondary responses. For instance, conclusions regarding mitochondrial dysfunction and translation attenuation are inferred from omic signatures rather than direct functional tests. Similarly, while the authors propose that altered tubulin abundance contributes to many of the observed phenotypes, the causal relationship between tubulin abundance and the downstream cellular defects is not fully established.

      Despite being largely descriptive, the study has several strengths. The inducible dual-tubulin expression system is technically elegant and provides a useful platform to investigate the consequences of altered tubulin isotype composition and/or abundance. The work also indicates that relatively modest increases in tubulin levels can have measurable biological consequences, offering a possible explanation for why tubulin abundance is tightly regulated. Finally, the breadth of approaches-including transcriptomics, proteomics, imaging, and cell-cycle analyses-provides a valuable resource for the field. While many of the proposed connections remain to be mechanistically tested, the study raises a number of interesting hypotheses and highlights several promising directions for future work.

      Answer:

      We thank the reviewer for these positive comments.

      Major comments:

      Figure 1:

      The dual expression system that the authors use to introduce exogenous tubulins is well described and characterized, representing an elegant strategy and useful tool for the field. The current version of the manuscript, however, is missing a quantitative assessment of the overall tubulin levels (endogenous + exogenous) upon doxycycline treatment (e.g., using western blot). Given that the article is centered on cellular roles of surplus soluble tubulin, the authors must quantify both the soluble and polymerized fractions of total cellular tubulin in control and doxycycline-treated samples.

      Answer:

      We will perform the assays requested by the reviewer and add them to a revised version of the manuscript.

      Throughout the manuscript, the authors extrapolate the effects of TUBB3+TUBA1A overexpression to "tubulin overexpression". But how can they distinguish isotype-specific from general effects? If endogenous tubulins are massively downregulated upon the expression of exogenous TUBB3+TUBA1A, the tubulin isotype composition is presumably dramatically altered. If the authors wish to retain the claims later on in the manuscript that the observed phenotypes are associated with excess tubulin, they need to provide evidence by testing other isotype combinations. Alternatively, the authors should tone their claims to reflect the possibility that what they observe downstream of TUBB3+TUBA1A overexpression may be isotype-specific rather than general.

      Answer:

      Making new stable lines with other tubulin isotypes and analyzing the many downstream effects takes months and is beyond the scope of the present manuscript (see also below, Description of analyses that authors prefer not to carry out). Hence, we will tone down our claims by acknowledging that observations were made with one set of tubulin isotypes.

      Figure 2:

      While changes at the level of EB1, and to a lesser extent CLIP-170 upon TUBB3+TUBA1A overexpression are convincing, the claimed changes in microtubule dynamics are not supported by the data (small, but statistically insignificant trends are observed). The authors should tone down their claims and adapt the results subheadings and figure captions accordingly. Likewise, the authors state "our results convincingly show that soluble tubulin levels control +TIP composition at MT ends," which should be toned down, as the authors do not provide any experiments to probe this model (e.g., mild tubulin downregulation to restore normal protein levels and then characterize the +TIP composition).

      Answer:

      With respect to changes in microtubule dynamics we refer this reviewer to our answers to comments of reviewer #1 (pages 1,2). Briefly, by displaying a cumulative distribution plot in combination with appropriate tests we show that effects are statistically significant. We propose to include these data in a revised version.

      We furthermore propose to perform dose-dependent doxycycline experiments (see also next comment) and test whether these cause dose-dependent +TIP composition shifts. If this is the case we maintain our current statement, if not, we will tone down this statement.

      Figure 3.

      Similar to Figure 2, the authors state that tubulin overexpression leads to mitotic defects. But this model is never challenged in rescue experiments, and the claims should therefore be toned down.

      Answer:

      We do not completely understand this comment. We simply turn on (or off) tubulin expression, and in our view the “rescue” experiment is the control situation, i.e. non-induced cells. To addres this criticism, we nevertheless propose to perform dose-dependent doxycycline experiments and test whether these cause dose-dependent mitotic effects.

      The methodology that the authors chose to measure metaphase duration is inadequate, since it is difficult, if not impossible, to precisely measure metaphase duration with a tubulin label alone. A DNA label is required. Likewise, measurements of spindle length should be done in living, but not fixed cells. These parameters can be extracted from the same movies with a tubulin and a DNA stain.

      Answer:

      We will perform additional experiments where we add a DNA label (in addition to a tubulin label) to more accurately measure metaphase duration (and we take along spindle length).

      We do not understand the claim of the reviewer that “measurements of spindle length should be done in living, but not fixed cells”. We and others have measured spindle length in fixed cells with immunostaining (PMID: 38117947, PMID: 40353778; PMID: 25568341), and it therefore appears to be an accepted method. Note that in these measurements we do include a DNA label.

      The unaligned chromosomes in metaphase represent a striking phenotype associated with TUBB3+UBA1A overexpression. A close inspection of the mitotic spindle staining, however, doesn't seem to show a much denser microtubule network in dox-treated samples. This is surprising, and the authors should provide a quantification to support their model (e.g., tubulin fluorescence intensity normalized to an internal control), or at the very least discuss this paradox in the manuscript.

      Answer:

      We thank the reviewer for this observation and will analyze this in more detail.

      Figure 4.

      The authors convincingly demonstrate the alterations in cell cycle progression upon TUBB3+TUBA1A overexpression using flow cytometry. An orthogonal approach would strengthen this claim, while allowing the authors to mechanistically test the premature G1/S transition model that they propose. As is, the reduced G1 population could also be explained by increased G2/M population in these cells. Well-established time-resolved approaches exist for directly measuring G1/S transition timing, including FUCCI systems for live imaging and EdU pulse-chase methods for temporal cell cycle analysis. Without employing these validated methodologies, the inference of premature G1/S remains untested, leaving a key mechanistic link in the claim insufficiently supported.

      Answer:

      We thank the reviewer for this comment. We note that the flow cytometry data are supported both by RNA-Seq and proteomics results, as stated in the manuscript. Hence, orthogonal approaches were to some extent already performed. However, we do feel that additional evidence should be provided to support our premature G1/S transition model. Based on the suggestion of the reviewer we will perform an EdU pulse-chase experiment as orthogonal approach.

      Figures 5-6.

      The authors use next-generation sequencing to reveal transcriptional changes associated with TUBB3+TUBA1A overexpression. While these analyses appear comprehensive, it remains unclear how many of the alterations in gene expression are a direct consequence of TUBB3+TUBA1 overexpression and how many are a downstream consequence of the altered cell cycle profile. The authors should, at the very least, acknowledge this in the manuscript.

      Answer:

      This is actually acknowledged in the manuscript, on page 15: “These results are consistent with our FACS analysis and suggest that shifts in cell cycle fractions partly underlie gene expression differences”.

      Similarly, the authors provide a careful proteomics profiling of cells overexpressing TUBB3+TUBA1 in both normoxia and hypoxia. But as with transcriptomics, it is unclear what the contribution of the altered cell cycle profile is in these analyses. This should, at the very least, be acknowledged in the manuscript.

      Answer:

      Again, this is actually stated in the manuscript, on page 18: “Metascape analysis revealed upregulation of terms associated with mitosis and G2/M transition, including the PLK1 pathway, in the Dox-induced cells, whereas G1/S-specific, and DNA repair terms were down (Fig. 6d, Table S1). These data are consistent with our FACS (Fig. 4a, b) and RNA-seq results (Fig. 5e)”.

      The authors then leverage their omics data to propose a functional link between tubulin overexpression and translation and mitochondrial function. A limited number of targeted functional assays would substantially strengthen the manuscript and help distinguish between primary and secondary effects. For example, direct measurements of protein synthesis (e.g., puromycin incorporation or OPP labeling) would provide evidence for the proposed translation defects. Likewise, direct assessment of mitochondrial function (e.g., oxygen consumption, mitochondrial membrane potential, or ATP production) would strengthen claims regarding mitochondrial dysfunction. These experiments are standard in the field and could realistically be completed within several weeks to a few months, depending on local expertise and instrumentation. Moreover, they would test conclusions already central to the manuscript without opening entirely new directions.

      Answer:

      We thank the reviewer for these comments and helpful suggestions. We will perform experiments measuring translation and mitochondrial (dys)function (see also our rebuttal to reviewer #1 on pages 6 and 7), and include them in a revised version of the manuscript.

      Figure 7.

      The authors use proteomics-based approaches to characterize the partners of the overexpressed tubulins, revealing novel interactors. These data present an interesting but incomplete picture. For example, can the identified novel interactors bind any tubulin isotype, or are they specific to the TUBB3/TUBA1A used in this study? The enrichment of TIMM50 does not appear very strong. How reproducible are these data? The authors should provide quantifications from several independent biological replicates and perform statistical analyses.

      Answer:

      We thank the reviewer for these comments. As stated on page 22 of our manuscript: “Thus, AIFM1 and TIMM50 are consistently identified as TAPs using different purification strategies, tags, and cell lines”. For the revised version of the manuscript we will perform pull down experiments with other recombinant tubulins.

      With respect to the relatively weak TIMM50 enrichment we refer to our answer to reviewer #1 on pages 5, 6 of this rebuttal. Briefly, we will perform additional pull down experiments (dual affinity purifications) to solidify our claims.

      Figure 8.

      While convincing, this figure largely confirms previous studies. In addition, it addresses a question that is different from the rest of the manuscript. The reviewer feels like this part could be taken out of the manuscript to reduce complexity and focus the scope of this work.

      Answer:

      Since reviewer #1 is of a similar opinion as this reviewer we are willing to remove the data presented in Fig. 8 in a revised version of the manuscript. We agree that this reduces complexity and focusses the scope of this work.

      Minor comments:

      There is a discrepancy between the manuscript and the methods that makes it unclear in which cells the transcriptomic analyses were done. The authors should clarify this.

      Answer:

      We are not sure what the reviewer means here, there is but one Dox-inducible cell line in which our own transcriptomic analyses was done. On page 37 we list the GEO numbers from which we retrieved expression data for Figure 8. Note that this part of the Methods section will be removed in a revised version, as we will not show Fig. 8 anymore.

      The authors report "We also observed "extra-mitotic" centrioles...". However, they appear to refer to movies of SiR-tubulin-stained cells. This staining dows not allow visualization of centrioles, and the authors should revise their manuscript to either provide a clarification or correct this claim.

      Answer:

      We thank the reviewer for pointing out this mistake. We will correct this.

      • *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      NA.

      4. Description of analyses that authors prefer not to carry out__ __

      Reviewer #2

      Throughout the manuscript, the authors extrapolate the effects of TUBB3+TUBA1A overexpression to "tubulin overexpression". But how can they distinguish isotype-specific from general effects? If endogenous tubulins are massively downregulated upon the expression of exogenous TUBB3+TUBA1A, the tubulin isotype composition is presumably dramatically altered. If the authors wish to retain the claims later on in the manuscript that the observed phenotypes are associated with excess tubulin, they need to provide evidence by testing other isotype combinations. Alternatively, the authors should tone their claims to reflect the possibility that what they observe downstream of TUBB3+TUBA1A overexpression may be isotype-specific rather than general.

      Answer:

      We thank the reviewer for this comment. Making new stable lines with other tubulin isotypes and analyzing the many downstream effects takes months and is beyond the scope of the present manuscript. Hence, we will tone down our claims by acknowledging that observations were made with one set of tubulin isotypes.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      This study develops an inducible HEK293F cell model that modestly overexpresses αβ-tubulin heterodimers. Using a dual expression system, the authors introduce exogenous TUBB3 and TUBA1A and combine cell biology, live-cell imaging, RNA-seq, proteomics, flow cytometry, and biochemistry to investigate the consequences of increased tubulin abundance. They suggest that, despite downregulation of endogenous tubulin transcripts, excess tubulin still accumulates in cells. This is accompanied by increased microtubule mass, changes in plus-end protein composition, defects in mitosis, and alterations in cell-cycle progression. The study further links tubulin overexpression to replication stress and broader changes in cellular physiology, including reduced abundance of mitochondrial respiratory proteins, altered translation-associated factors, and an attenuated response to hypoxic stress. In addition, the authors confirm previous observations that oxygen and nutrient deprivation reduce tubulin and microtubule abundance. Based on these findings, the authors propose that tubulin is not merely a structural component of microtubules but also contributes to the regulation of cellular homeostasis.

      The possibility that tubulin abundance itself influences broader aspects of cellular physiology is conceptually interesting and warrants further investigation. At the same time, many of the mechanistic links proposed in the manuscript remain insufficiently resolved. A large number of cellular processes are associated with tubulin overexpression, but it is often unclear which phenotypes represent primary consequences of altered tubulin abundance and which are secondary responses. For instance, conclusions regarding mitochondrial dysfunction and translation attenuation are inferred from omic signatures rather than direct functional tests. Similarly, while the authors propose that altered tubulin abundance contributes to many of the observed phenotypes, the causal relationship between tubulin abundance and the downstream cellular defects is not fully established.

      Despite being largely descriptive, the study has several strengths. The inducible dual-tubulin expression system is technically elegant and provides a useful platform to investigate the consequences of altered tubulin isotype composition and/or abundance. The work also indicates that relatively modest increases in tubulin levels can have measurable biological consequences, offering a possible explanation for why tubulin abundance is tightly regulated. Finally, the breadth of approaches-including transcriptomics, proteomics, imaging, and cell-cycle analyses-provides a valuable resource for the field. While many of the proposed connections remain to be mechanistically tested, the study raises a number of interesting hypotheses and highlights several promising directions for future work.

      Major comments:

      Figure 1:

      1. The dual expression system that the authors use to introduce exogenous tubulins is well described and characterized, representing an elegant strategy and useful tool for the field. The current version of the manuscript, however, is missing a quantitative assessment of the overall tubulin levels (endogenous + exogenous) upon doxycycline treatment (e.g., using western blot). Given that the article is centered on cellular roles of surplus soluble tubulin, the authors must quantify both the soluble and polymerized fractions of total cellular tubulin in control and doxycycline-treated samples.
      2. Throughout the manuscript, the authors extrapolate the effects of TUBB3+TUBA1A overexpression to "tubulin overexpression". But how can they distinguish isotype-specific from general effects? If endogenous tubulins are massively downregulated upon the expression of exogenous TUBB3+TUBA1A, the tubulin isotype composition is presumably dramatically altered. If the authors wish to retain the claims later on in the manuscript that the observed phenotypes are associated with excess tubulin, they need to provide evidence by testing other isotype combinations. Alternatively, the authors should tone their claims to reflect the possibility that what they observe downstream of TUBB3+TUBA1A overexpression may be isotype-specific rather than general.

      Figure 2:

      1. While changes at the level of EB1, and to a lesser extent CLIP-170 upon TUBB3+TUBA1A overexpression are convincing, the claimed changes in microtubule dynamics are not supported by the data (small, but statistically insignificant trends are observed). The authors should tone down their claims and adapt the results subheadings and figure captions accordingly. Likewise, the authors state "our results convincingly show that soluble tubulin levels control +TIP composition at MT ends," which should be toned down, as the authors do not provide any experiments to probe this model (e.g., mild tubulin downregulation to restore normal protein levels and then characterize the +TIP composition).

      Figure 3.

      1. Similar to Figure 2, the authors state that tubulin overexpression leads to mitotic defects. But this model is never challenged in rescue experiments, and the claims should therefore be toned down.
      2. The methodology that the authors chose to measure metaphase duration is inadequate, since it is difficult, if not impossible, to precisely measure metaphase duration with a tubulin label alone. A DNA label is required. Likewise, measurements of spindle length should be done in living, but not fixed cells. These parameters can be extracted from the same movies with a tubulin and a DNA stain.
      3. The unaligned chromosomes in metaphase represent a striking phenotype associated with TUBB3+UBA1A overexpression. A close inspection of the mitotic spindle staining, however, doesn't seem to show a much denser microtubule network in dox-treated samples. This is surprising, and the authors should provide a quantification to support their model (e.g., tubulin fluorescence intensity normalized to an internal control), or at the very least discuss this paradox in the manuscript.

      Figure 4.

      1. The authors convincingly demonstrate the alterations in cell cycle progression upon TUBB3+TUBA1A overexpression using flow cytometry. An orthogonal approach would strengthen this claim, while allowing the authors to mechanistically test the premature G1/S transition model that they propose. As is, the reduced G1 population could also be explained by increased G2/M population in these cells. Well-established time-resolved approaches exist for directly measuring G1/S transition timing, including FUCCI systems for live imaging and EdU pulse-chase methods for temporal cell cycle analysis. Without employing these validated methodologies, the inference of premature G1/S remains untested, leaving a key mechanistic link in the claim insufficiently supported.

      Figures 5-6.

      1. The authors use next-generation sequencing to reveal transcriptional changes associated with TUBB3+TUBA1A overexpression. While these analyses appear comprehensive, it remains unclear how many of the alterations in gene expression are a direct consequence of TUBB3+TUBA1 overexpression and how many are a downstream consequence of the altered cell cycle profile. The authors should, at the very least, acknowledge this in the manuscript.
      2. Similarly, the authors provide a careful proteomics profiling of cells overexpressing TUBB3+TUBA1 in both normoxia and hypoxia. But as with transcriptomics, it is unclear what the contribution of the altered cell cycle profile is in these analyses. This should, at the very least, be acknowledged in the manuscript.
      3. The authors then leverage their omics data to propose a functional link between tubulin overexpression and translation and mitochondrial function. A limited number of targeted functional assays would substantially strengthen the manuscript and help distinguish between primary and secondary effects. For example, direct measurements of protein synthesis (e.g., puromycin incorporation or OPP labeling) would provide evidence for the proposed translation defects. Likewise, direct assessment of mitochondrial function (e.g., oxygen consumption, mitochondrial membrane potential, or ATP production) would strengthen claims regarding mitochondrial dysfunction. These experiments are standard in the field and could realistically be completed within several weeks to a few months, depending on local expertise and instrumentation. Moreover, they would test conclusions already central to the manuscript without opening entirely new directions.

      Figure 7.

      1. The authors use proteomics-based approaches to characterize the partners of the overexpressed tubulins, revealing novel interactors. These data present an interesting but incomplete picture. For example, can the identified novel interactors bind any tubulin isotype, or are they specific to the TUBB3/TUBA1A used in this study? The enrichment of TIMM50 does not appear very strong. How reproducible are these data? The authors should provide quantifications from several independent biological replicates and perform statistical analyses.

      Figure 8.

      While convincing, this figure largely confirms previous studies. In addition, it addresses a question that is different from the rest of the manuscript. The reviewer feels like this part could be taken out of the manuscript to reduce complexity and focus the scope of this work.

      Minor comments:

      1. There is a discrepancy between the manuscript and the methods that makes it unclear in which cells the transcriptomic analyses were done. The authors should clarify this.
      2. The authors report "We also observed "extra-mitotic" centrioles...". However, they appear to refer to movies of SiR-tubulin-stained cells. This staining dows not allow visualization of centrioles, and the authors should revise their manuscript to either provide a clarification or correct this claim.

      Significance

      This study addresses a long-standing question in cytoskeletal biology: what are the consequences of altering tubulin abundance independently of acute perturbations of the microtubule cytoskeleton? The work is primarily a conceptual and technical advance, providing an inducible system to investigate tubulin dosage and suggesting that tubulin abundance may influence cellular processes beyond microtubule assembly. The findings build on earlier work demonstrating that tubulin expression responds to physiological and environmental cues and raise the possibility that tubulin homeostasis interfaces with broader cellular stress-response pathways. While several of the proposed mechanistic links remain to be established, the study will be of interest to researchers working on the cytoskeleton, protein homeostasis, cell-cycle regulation, and cellular stress responses.

      My expertise lies in cytoskeletal biology, mitosis, and quantitative cell biology. I am less qualified to evaluate in depth the aspects of the manuscript related to mitochondrial physiology and hypoxic signaling.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: This study examines how simultaneous expression of high levels of alpha and beta tubulin impact microtubule organization, cell cycle progression and stress response. The main conclusions are that a/b tubulin overexpression 1) increases the density of microtubules in cells with mild effects on growth rate and catastrophe frequency; 2) causes mitotic spindle defects and cell cycle disruption; 3) alters the transcriptome and proteostasis; 4) causes mitochondrial stress by binding mitochondrial import proteins; 5) disrupts cellular stress response.

      Major comments:

      The results generally use an appropriate number of cells and technical replicates.

      There are several instances in the manuscript where the data do not support the authors' conclusions. These include: Page 6. "We detected a mild increase in MT growth rates in Dox-treated cells (Fig. 2b), which was observed in all of the growing MTs examined (Fig. 2c), consistent with an increase in soluble tubulin levels in all Dox-induced cells." The data do not support this statement. The histogram in Figure 2C shows that only a small portion of growth rate measurements are faster in the overexpression cells, and the statistical test in 2b suggests that the datasets are unlikely to be different. It is unclear from the presentation of the data why a small population of overexpression cells exhibit faster growth rates. The data in Figure 2b should be plotted as a superplot could show whether the faster data points are from specific cells or technical replicates. The same concern applies to the data in Figure 2D.

      Page 11. "Moreover, DNA content was highly aberrant after 48 hr, with large proportions of cells containing < 2n or > 4n chromosomes (Fig. 4d). Thus, persistent overexpression of tubulin affects the cell cycle and after 48 hr it results in severe DNA abnormalities, suggestive of CIN" The plot in Figure 4D shows increased propidium iodide signal below the 2N peak and above the 4N peak, but it is quite likely that this could represent signal from apoptotic cells. The authors should specifically stain for an apoptotic marker to test this possibility. This would suggest that prolonged overexpression of a/b tubulin leads to apoptosis, which could be important evidence for later conclusions in the study.

      Page 11. "Combined, our data show that tubulin overexpression affects all three major cell cycle checkpoints, i.e. G1/S, G2/M, and the SAC in mitosis." The data do not fully support this conclusion. While the evidence for SAC-mediated delay in overexpression cells is strong, the evidence for other checkpoints is weak. The phosphor-RB experiment in Figure S3b lacks a positive control. The γH2A-X results in Figure 4f and g show that the difference is driven by a minor population of dim foci in control cells. Similar to the point above, these data should be plotted as a superplot to explore the possibility that this minor population arises from a small number of cells or a specific technical replicate.

      Page 19. "This, in turn, lowers EEF1A1 causing attenuation of translation and dampened elongation rates. That other initiation and elongation factors are mildly down in Dox-induced cells, as are ribosomal proteins (Fig S6c, Table S1), indicates a mild ISR and general translation inhibition. We propose that tubulin overexpression results in mitochondrial dysfunction leading to stress and attenuated translation elongation. We observe the start of an ISR in these cells, which prevents a proper hypoxic response." This conclusion seems to be at odds with the earlier conclusion drawn from Figure 5e, where ISR genes are significantly decreased in overexpression cells. The authors should reconcile these results in the discussion.

      Figure 7. This experiment lacks a key control - identifying peptides in a pull down from cells that do not overexpress the tagged tubulin. Without this control, it is impossible to discern which peptides bind to the beads, independent of tubulin. This concern is amplified by the results in Figure 7b where TIMM50 binding is tested in a follow-up experiment. Here the negative control is employed and it shows that TIMM50 pulls down in the absence of tagged tubulin. This indicates that TIMM50 may not bind to tubulin and calls into question any conclusions related to TIMM50 and mitochondrial proteostasis. Before making any conclusions from the pull down experiment, the authors must include the negative control and carefully assess which peptides are likely to be false positives.

      Figure 8. This figure and the associated text feel rather disconnected from the rest of the study. The authors do not make clear conclusions from these results. Perhaps these experiments should be further developed in a separate study?

      Page 25. "We found that tubulin overexpression induces mitochondrial dysfunction." The data do not strongly support this conclusion. The proteomic data in Figure 6 indicate that some mitochondrial proteins are less abundant in tubulin overexpressing cells, but the study does not include any experiments that actually test mitochondrial function. The authors could test this by including new experiments to measure mitochondrial membrane potential, or respiration activity, etc. These would be an important addition to the study.

      Page 26. "We provide evidence here that surplus tubulin slows general translation, increasing the time window for a TTC5-nascent tubulin interaction." The data do not demonstrate a decrease in translation rate. It is unclear what evidence the authors refer to with this statement. The proteomic analysis in Figure 6 measures protein abundance, and a decrease could be due to either decreased transcription, decreased translation, or increased protein degradation. The number of proteins that are shown to be downregulated in tubulin overexpressing cells is rather small (100s) which seems to argue against a general decrease in translation, despite the decrease in several translation regulators. To make this conclusion, the authors would need to add new experiments that measure translation rate.

      Minor comments:

      Page 10." We also observed "extra-mitotic" centrioles at the onset of mitosis after Dox-treatment, indicative of mitotic arrest and aneuploidy, which eventually resolved into normal metaphase plates (Supplementary Videos 3, 4)." It is unclear whether these foci are centrioles. The SiR-tubulin reagent used in these experiment labels tubulin. It would be more accurate to describe these as "tubulin foci".

      Page 13. "Analysis of TUBG1 reads revealed downregulation in tubulin overexpressing cells, albeit weakly (Fig. 5c)." These datapoints appear to be quite similar. Please provide a statistical analysis.

      Significance

      This study explores how increasing a/b tubulin expression impacts microtubule-dependent functions in cells and broadly explores other impacts on cell function. How tubulin expression changes impact cells is an important question because there are well-established developmental, cell cycle, and disease contexts where tubulin levels strongly increases. Furthermore, tubulin levels are known to be regulated in part by an 'autoregulation' mechanism that degrades tubulin mRNAs when soluble protein levels are high, but little is known about how this mechanism might impact other processes beyond tubulin.

      This study seeks to add new knowledge on both points by using a system previously developed by the authors to conditionally and simultaneously overexpress alpha and beta tubulin. The use of this system is an advance for the field, which has previously relied on drugs that alter the soluble-polymer equilibrium of tubulin, rather than expression levels, to address these questions.

      The study is also strengthened by combining experiments that validate the activity and assembly effects of overexpressed tubulin with exploratory experiments that measure impact across the transcriptome and proteome. This is an advance for the field. Previous studies have examined home tubulin-targeting drugs affect the transcriptome, but this study goes further by directly modulating tubulin levels and measuring both transcriptomic and proteomic impact. In addition, previous studies overexpressing a/b tubulin in budding yeast found similar effects on mitosis but did not examine broader impacts on the cell.

      The major weakness of the study is that several of the main conclusions are not strongly supported by the current results. These are detailed above.

      The audience for this study is a broad population of basic researchers, including the microtubule, mitosis, hypoxia, cell stress, translational regulation fields. The findings will open questions about how tubulin expression changes that occur in development and disease contexts might impact all of these processes, and what pathways might be used to harmonize them.

      My expertise is in tubulin biology, including the tubulin gene family in development, tubulin proteostasis, and mitosis.

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      Reply to the reviewers

      Reviewers' comments:

      Reviewer #1

      Evidence, reproducibility and clarity

      The study entitled "Human bone marrow adipocytes drive prostate cancer bone metastasis progression via lipid-mediated induction of Angiopoietin-like 4" by Hernandez et al. investigates the role of the bone marrow adipocytes (BMAds) from red hematopoietic rich-areas in the progression of prostate cancer (PCa) bone metastases. To do so, the authors used an elegant three-dimensional culture of primary human rBMAds isolated from the femoral bone marrow of patients undergoing hip surgery then cultured with PCa cell lines. The authors claim that PCa cells take up FFAs released by rBMAds which enhance the epithelial-mesenchymal transition and motility through upregulation of ANGPTL4. The article is well written, the figures are very clear and the methodology is perfectly described. To further improve the understanding of this mechanism, the authors should address 1) how PCa cells take up the FFAs, 2) whether PCa cells enhance non-canonical FFAs release and 3) the specificity of the rBMAds in their ability to upregulate ANGPTL4 compared to other adipocytes.

      We thank this reviewer for their positive and constructive feedbacks. Below, we provide detailed responses addressing each of their specific comments either with additional data and/or writing.

      Major comments:

      The paper's conclusions are overall convincing but the following supplementary experiments or discussion points could strengthen their claims.

      1) Is ANGPTL4 upregulation in PCa cells specific to rBMAds or could it also be seen with other adipocytes, such as periprostatic adipocytes or SCAds (used in Fig 2)? Co-culture of PCa cells with SCAds should offer insights.

      More precisely, the article does not state if ANGPTL4 upregulation could be induced prior to bone invasion, by the periprostatic adipose tissue FFA release for example, explaining why ANGPTL4-high cells could be found in bone metastasis. In that case, increased motility and migration could be BMAds independent and trigger the first metastatic events originating from the primary prostatic tumor.

      To address this important question, we performed additional co-culture experiments using adipocytes isolated from human periprostatic adipose tissue (PPAT) and rBMAds. Although PPAT adipocytes also induced ANGPTL4 expression in PCa cells compared with non-cocultivated cells (NC), this induction was approximately 2.5-fold greater in PCa cells co-cultured with rBMAds (new Figure 6A). Consistent with these in vitro findings, analysis of publicly available PCa datasets revealed higher ANGPTL4 expression in bone metastases than in primary tumors (new Figure 6B). Together, these results suggest that although ANGPTL4 expression may be initiated within the primary tumor microenvironment, it is markedly amplified following colonization of the bone marrow niche.

      In addition, as suggested by Reviewer 3, we assessed the clinical relevance of ANGPTL4 expression at different stages of disease progression. High ANGPTL4 expression in primary PCa tumors was not associated with either overall or disease-free survival (new Figure 6C). In contrast, elevated ANGPTL4 expression in bone metastases was significantly associated with poorer survival (new Figure 6D), indicating that the clinical relevance of ANGPTL4 is primarily linked to the metastatic bone microenvironment rather than the primary tumor. We therefore propose that, although ANGPTL4 expression may be initiated at the primary tumor site, it is markedly amplified within the bone marrow adipocyte-rich niche, where it promotes PCa cell migration and metastatic outgrowth. We have revised the manuscript accordingly (see lines 559-583).

      2) The article hypothesizes an ANGPTL4-associated increase of motility and migration of PCa cells upon interaction with bone marrow adipocytes in the first metastatic bone, inducing enhanced invasion of said bone and secondary metastasis sites. However, the article lacks evidence that ANGPTL4 is also upregulated in cells from secondary metastasis sites in patients. It would be interesting to explore in the existing data set the expression of ANGPTL4 in secondary metastasis of patients who had previous bone metastasis. These points could be addressed in the concluding remarks.

      We thank the reviewer for this interesting comment. Bone represents 80% of prostate cancer metastases (Gandaglia et al, Prostate, 2014, PMID: 24132735) and the bone metastases have been shown to act as a source of further metastatic dissemination (Gundem et al, Nature, 2015, PMID: 32728210; Hong et al, Nature commun, 2015, PMID: 25827447). Unfortunately, currently available public datasets only provide the anatomical site of metastatic lesions and do not distinguish between primary and secondary metastatic sites. Nevertheless, we examined ANGPTL4 expression in metastatic lesions. ANGPTL4 expression is maintained in liver, lung and lymph nodes (new Supplementary Figure 6A) suggesting that it might contribute to metastatic dissemination beyond bone. The manuscript has been revised accordingly (lines 575–580).

      Additional experiments or qualifying the claims:

      3) Fig 1 : It would be interesting to have an insight on the mechanism by which FFAs are uptaken by PCa.

      Several evidences demonstrate that membrane-associated transport proteins facilitate and regulate this process, including CD36 (fatty acid translocase) and members of the fatty acid transport protein family (FATPs/SLC27A1–5). We evaluated the expression of these transporters implicated in the uptake of long-chain FFAs in our cellular models. CD36 expression was not detected at either the mRNA or protein level in all three prostate cancer cell lines (PC3, Du145, and LNCaP). The validity of our detection strategy was confirmed using a CD36-positive control (U937 leukemic cell line) and a corresponding negative control (U937 cells KO for CD36) (Supplementary Fig. F and G added). Among the FATP family members, FATP4 (SLC27A4 gene) was the most highly expressed transporter in all the three PCa cell lines (Supplementary Fig. 1H, added). We therefore investigated its potential contribution to FFA uptake by silencing SLC27A4 expression using siRNA in PC3 cells and knockdown efficiency was confirmed at both mRNA (Supplementary Fig. 1I added) and protein (Supplementary Fig. 1J added) level. Silencing of FATP4 reduced the uptake of exogenous fluorescent oleate (Supplementary Fig. 1K) but not palmitate (Supplementary Fig. 1L, added) showing a selectivity of FFA uptake depending on their nature. However, FATP4 knockdown did not affect lipid transfer in the co-culture model (Supplementary Fig. 1M, added), where several FFA species are present. Collectively, these findings indicate that the uptake rBMAd-derived FFAs is unlikely to be primarily mediated by FATP4 suggesting that alternative mechanisms, such as extracellular vesicle-mediated transfer or passive diffusion, may account for their uptake. These results have been included in Supplementary Figure 1F–M, and the manuscript has been revised accordingly (lines 396–411).

      4) Fig 1D : It would be interesting to add an extra control of NC PCa treated with BODIPY to measure basal BODIPY uptake of PCa

      To address this point, we performed an additional control experiment in which PC3 cells were directly exposed to 5 µM BODIPY FLC16, corresponding to the concentration used to preload rBMAds in the co-culture experiments (Fig. 1D). As expected, direct exposure resulted in higher BODIPY FL C16 uptake compared with co-culture with preloaded rBMAds.

      This difference is expected since, during direct treatment, the fluorescent FFA is immediately available at 5 µM in the culture medium. In contrast, BODIPY FLC16 represents only a tracer when rBMAds are preloaded, and the released of FFAs during co-culture consist of a mixture of fluorescent and non-fluorescent species. The figure can be included in the manuscript upon referee’s request.

      5) Fig 2 : The figure efficiently states that rBMAds undergo non-canonical lipolysis in comparison to SCAds, without PCa cells. Glycerol dosage and/or pan-lipase inhibitor treatment of rBMAds in co-culture with PCa would confirm rBMAds still show non-canonical lipolysis in bone metastasis environment.

      To address this point, we quantified glycerol and FFAs released into the culture medium from PC3 cells alone or from rBMAds co-cultured with PC3 cells, in the presence or absence of the pan-lipase inhibitors paraoxon ethyl or orlistat. As shown in the new Supplementary Figure 2B, pan-lipase inhibition markedly reduced FFA release, whereas glycerol release remained unchanged. These results confirm that, even in the presence of PCa cells, FFA release from rBMAds occurs independently of canonical lipolysis.

      6) Fig2 : Is ANGPTL4 upregulation in PCa cells specific to rBMAds or could it also be seen with other adipocytes, such as periprostatic adipocytes or SCAds ? Co-culture of PCa cells with SCAds should offer insights.

      This comment was addressed in point 1.

      7) Fig 2 : Could the authors specify if there is a possibility the lipolysis kinetics is slower in rBMAds instead of an actual incomplete lipolysis?

      To determine whether the reduced glycerol release reflected delayed rather than incomplete lipolysis, we extended isoproterenol stimulation up to 24 h in rBMAds, using SCAds as a control. As shown in the new Supplementary Figure 2A (commented in the text, lines 425–430), glycerol release did not increase over time in rBMAds, whereas it progressively increased in SCAds. These results do not support delayed lipolysis in rBMAds but rather indicate that lipolysis remains incomplete despite prolonged stimulation.

      7) Fig 3B/C : The author should provide tables to show : 1) all significantly up and down GO term 2) the top 20 genes DEG 3) any GO terms/genes related to EMT (table 1 should be associated with figure 3)

      As proposed by the reviewer, we added a Table 1 in the revised manuscript with all significant upregulated and downregulated GO terms including gene names and the proportion of genes related to each GO term and p-value. We also added the top 20 of upregulated and downregulated genes.

      8) Fig 5 : To complete experiences of KD of ANGPTL4 and PPARy inhibition, the author should also include the mRNA levels of EMT associated genes in these conditions.

      We thank the reviewer for this suggestion. The EMT-associated genes originally included in the manuscript were selected based on the literature in pancreatic cancer as descriptive markers (Gordon et al, BMC Cancer, 2023, PMID: 37291514). In the revised manuscript, to further investigate the mechanisms underlying ANGPTL4-dependent cell migration, we reconstructed an ANGPTL4-associated gene network in Cytoscape using RNA-seq-derived differentially expressed genes enriched in cell migration GO terms and their interactions with ANGPTL4. Interestingly, the gene signature identified in our conditions do not clearly overlaps with canonical EMT-associated genes described in the literature. This analysis revealed a highly interconnected network rather than a linear signaling cascade (new Supplementary Figure 5L) suggesting that ANGPTL4 promotes cell migration by orchestrating a coordinated transcriptional program involving multiple complementary signaling pathways. While this systems-level analysis reveals a coherent migration-associated network, it does not aim to establish a direct functional role for each individual gene, but rather to define the global transcriptional context of ANGPTL4-dependent migration. In line with this, we added and discussed the Cytoscape network of ANGPTL4-associated migration genes in the revised manuscript (new Supplementary Figure 5L and commented in the text lines 538-545).

      To better the clarity of the paper, the author should indicate in table S1 which patients are used in which figures. Also, what is the rationale for not using female patients in all the experiments?

      We thank the reviewer for this helpful suggestion. We have revised Table S1 to improve its clarity by indicating, for each experiment, the corresponding patient information, including age, sex, and BMI. In this study, rBMAds from both male and female donors were used to establish and validate the 3D culture model and to characterize the mechanisms of FFA release. However, for the co-culture experiments investigating the crosstalk between rBMAds and PCa cells, only rBMAds from male donors were used. Since PCa is a male-specific disease, restricting these experiments to male donors minimizes the potential confounding effects of sex-dependent differences in BMAd biology and ensures that the model remains physiologically relevant.

      The suggested additional experiment should be achievable in 6 months.

      *9) Statistical analyses: The authors state in the Materiel and Methods that they used Student's t-test, one-way ANOVA or two-way ANOVA. These tests are applied to parametric data sets, validated for their normal distribution. Usually, n To determine the appropriate statistical test and post hoc analysis, data normality was first assessed using the Shapiro-Wilk test. Parametric tests were applied when data followed a normal distribution, whereas non-parametric tests were used when normality assumptions were not met. The specific statistical tests used for each analysis have now been indicated in the corresponding figure legends.

      Minor Comments:

      10) In Fig 1F, the author should discuss the discrepancy between the significant increase of TG uptake in PCa shown in 1B and the non-significant effect seen in 1F, given that TG represents 85% of the total FFAs shown in 1E and 1F.

      Figures 1B and 1F correspond to two distinct experimental readouts and therefore should not be directly compared. Figure 1B measures the intracellular accumulation of TG in PCa cells following coculture with adipocytes. These TG are formed after the uptake of adipocyte-derived FFAs and their subsequent re-esterification into neutral lipids stored in lipid droplets. In contrast, Figure 1F quantifies the concentration of FFAs remaining in the culture medium at the end of the coculture period. Although extracellular FFA levels appear slightly higher in the presence of PCa cells than with rBMAds alone, this difference is not statistically significant. This is likely because FFAs released by rBMAds are continuously taken up by PCa cells during coculture, thereby limiting their accumulation in the medium. Therefore, the lack of a significant increase in extracellular FFAs does not contradict the marked intracellular accumulation of TG observed in Figure 1B.

      *11) Fig 3 : In Fig 3G and 4E, staining quantification would be necessary. *

      The quantification of the area of lipid droplets per nucleus depending on exogenous FFA treatment was done according to reviewer comments and added in a Figure 4E. Concerning Figure 3G, the images shown were intended as representative illustrations of the cytoskeletal and morphological changes observed following co-culture, rather than as a quantitative endpoint. The conclusions of this section are primarily supported by the quantitative functional assays measuring cell migration (Fig. 3D-E) and invasion (Fig. 3F), which directly assess the biological consequences of these morphological changes.

      12) In Fig3/Sup Fig3, why is the invasion assay not shown for LNCaP cell line ?

      According to reviewer comments, invasion assay was performed on LNCaP and added in the manuscript (new Supplementary Fig. 3D) on the revised manuscript. Coculture of rBMAds with LNCaP stimulate invasion as other PCa cell lines (PC3 and Du145).

      *13) 5F: Is there a reason why the sum of FFA has a stronger effect than the individual ones ? *

      We observed that the combination of palmitate, oleate, and linoleate induced a stronger increase in lipid accumulation (Figure 4E, only statistically significant when compared palmitate alone with the mix) and target gene expression (Figure 5F) than each FA alone. Although we did not investigate the underlying mechanism in the present study, we speculate that the combination is likely to better mimic the FA environment of rBMAds and may promote more efficient downstream signaling than individual FA. This aspect is also discussed in response to the point 4 of the referee 2.

      14) 5E/F : Rationale for changing FFA treatment duration between 5E and 5F (72h then 24h) ?

      While most FFA treatment and co-culture experiments with rBMAds were performed for 72 h, experiments involving ANGPTL4 siRNA or the PPARγ inverse agonist T0070907 were limited to 24 h. This shorter duration was chosen to preserve the efficacy of T0070907 and to capture the optimal window of siRNA-mediated knockdown. Importantly, we observed that FFA treatment was already sufficient to induce ANGPTL4 expression after 24 h, allowing us to investigate the contribution of ANGPTL4 and PPARγ signaling under conditions where the pharmacological inhibition and gene silencing remained effective. We have clarified this rationale in the revised manuscript (lines 255-256).

      *15) For the discussion: the authors clearly describe the two different BMAds subtypes (cBMAds vs rBMAds), would it be interesting to question the role of cBMAds in PCa progression as well ? *

      We thank the reviewer for this suggestion and agree that this is an important point to address. Because PCa bone metastases predominantly localize to red bone marrow areas, our study focused on the role of rBMAds in PCa progression. Nevertheless, we cannot exclude a potential contribution of constitutive bone marrow adipocytes, which reside primarily in yellow bone marrow, and whose effects on PCa cells may be similar to or distinct from those of rBMAds. We have now included this point in the concluding remarks of the Discussion (lines 599–601).

      The studies are referenced appropriately.

      *16) Please, could the authors indicate the co-culture duration with rBMAds in the legends of Figure 5. Excepted this point, the text and figures are clear and accurate. *

      We added in the revised manuscript the duration time of coculture for the different experiments in Figure 5.

      Significance

      General assessment:

      the authors used an elegant three-dimensional culture of primary human rBMAds isolated from the femoral bone marrow of patients undergoing hip surgery then co-cultured with PCa cell lines. The authors claim that PCa cells take up FFAs released by rBMAds which enhance the epithelial-mesenchymal transition and motility through upregulation of ANGPTL4. The most important aspects of this study are 1) the description of the non-canonical FFAs release from rBMAds, 2) the induction of EMT/motility of PCa cells induced by FFAs uptake. To further improve the understanding of this mechanism, the authors should address 1) how PCa cells take up the FFAs, 2) whether PCa cells enhance non-canonical FFAs release and 3) the specificity of the rBMAds in their ability to upregulate ANGPTL4 compared to other adipocytes.

      This study extends the knowledge in the field of prostate cancer metastasis.

      The authors have developed an elegant 3D culture model of primary human rBMAds, which is highly relevant for investigating the role of adipocytes in cancer cell behavior. Indeed, most models used in the literature rely on adipocytes differentiated from bone marrow mesenchemal stem cells. Although adipocytes differentiation models have recently been improved by incorporating 3D structure, the model developed by the authors is most physiological, as it is based on the direct use of primary bone marrow adipocytes. In this way, this model represents a valuable tool to advance our understanding of mechanisms involved in bone metastasis in breast/prostate cancers.

      The type of audience that will be interested by this research include both basic and specialized researchers. Moreover, this study should improve 1) the understanding of the impact of rBMAds on prostate cancer behavior and 2) the robustness and physiological relevance of adipocyte-based studies.

      Our expertise lies in the interaction between normal or pathological hematopoiesis with the components of bone marrow microenvironment.

      We sincerely thank the reviewer for the constructive comments and suggestions, which have significantly contributed to improving the mechanistic depth and clarity of our manuscript. We believe that the additional data and revisions provided in this response address the main points raised and further strengthen the conclusions of our study.

      Reviewer #2

      Evidence, reproducibility and clarity

      Summary: This manuscript focuses on evaluation of effects of bone adipocytes on prostate cancer and their potential role in prostate cancer bone metastases progression. Mechanistically, the results show that fatty acid (FFA) secreted by bone marrow adipocytes increased expression of ANGPTL4 in prostate cancer cell lines in vitro, and this increase was dependent on PPARgamma signaling, resulting in increased migration and invasiveness of the cells. Evaluation of data from SU2C data set, was also included to support the conclusions, showing that increased levels of ANGPTL4 expression in bone metastases are negatively correlated with patients' survival, suggesting that ANGPTL4 could be a therapeutic target for prostate cancer bone metastasis.

      The authors previously investigated and published on crosstalk/interaction between periprostatic adipocytes and prostate cancer cells, where FFAs induced released by adipocytes elicited increased aggressiveness and dissemination of prostate cancer cells, with mechanisms involving increase in NOX5 expression. However, specifically bone marrow adipocytes effect on prostate cancer cell have not been investigated in details. The experiments are logically sequenced and data clearly presented, in general supporting the conclusions that FFAs secreted by bone marrow adipocytes affect prostate cancer cells migration and invasiveness in vitro.

      Major comments:

      1) The authors state: "These data collectively demonstrate that PCa cells can trigger the release of FFAs from rBMAds cultured in 3D (Fig 1), which are then rapidly taken up and re-esterified into TGs by cancer cells." There are no data presented to show that prostate cells trigger the FFAs release from adipocytes- the data only show that FFA from adipocytes are taken up by prostate cancer cells. Use of condition media in combination with the coculture experiments would address this point. It is not clear how the authors came to the following conclusions: " In co-culture, the levels of these FFAs tended to increase (Fig. 1E), a trend also observed in total FFA content (Fig. 1F). The data presented in this figure have no statistical significance to support this statement, and moreover, the relative abundance in the PC3 COC appears to be simply addition of the abundance of PC NC and rBMAds NC, no alterations of secretion.

      We agree that our data do not directly demonstrate that prostate cancer (PCa) cells induce the release of FFAs from rBMAds. Rather, our results show that FFAs present in the co-culture system are efficiently taken up and re-esterified into triglycerides by PCa cells (Fig. 1B-D). Accordingly, we have revised the manuscript to attenuate our interpretation and avoid implying a causal effect of PCa cells on induction of FFA release by rBMAds. We have also modified the corresponding text describing Figures 1E and 1F to underline that there are no changes in FFA present in the culture medium between rBMAds alone or cocultivated with cancer cells (lines 392-395).

      2) GO analysis presented in this manuscript indicates that prostate cancer cells grown in bone marrow adipocytes condition media exhibit increased expression of gene sets associated with cell migration, cell adhesion, etc. were significantly induced under these conditions (Fig 3C). However, it seems contradictory that while locomotion and invasion were increased so were the gene associated with adhesion, as one would speculate that with increased invasion and mobility you will not have increased adhesion. This analysis also show that downregulated gene sets were mainly associated with proliferation regulation. However, the data included in Fig 3 H and I and supp figure show no effects on proliferation of the prostate cancer cells. This discrepancy should be discussed.

      We agree with the reviewer that the concomitant upregulation of the “cell adhesion” and the “cell migration” GO terms may appear contradictory. However, genes annotated within the “cell adhesion” category are not exclusively involved in stable cell–cell adhesion; many of them like integrins subunits (ITGA3, 5, 6, ITGB1, 5) or actin cytoskeleton genes (ACTB1, ACTG1, ACTN1, ACTN4) also participate in the dynamic remodeling of cell–matrix and cell–cell interactions that are required for efficient cell migration. In this context, increased expression of adhesion-related genes can be consistent with enhanced migratory capacity. Similarly, the “regulation of cell population proliferation” GO term includes both pro- (ID2, LRP5) and anti-proliferative genes such as cell cycle inhibitor genes (CDKN2A, CDKN2B, CDKN2C, CDKN3, TGFB3, TGFBR2) reflecting a complex and balanced transcriptional response. This may explain why no significant changes in prostate cancer cell proliferation were observed in functional assays under co-culture conditions (Fig. 3H–I). As requested by the Reviewer 1, the list of genes associated with each GO term has now been added in Table 1 and we modify the manuscript accordingly (line 456-460).

      3) Some of the genes associated with effects of adipocytes condition media or treatment with FFAs were confirmed by PCR and western blot in PC3 cells, but not in DU145 or LNCaP. These experiments should be done, and results in the other two cell lines should be included to demonstrated the generality of the mechanism of the observed effects. This is important specifically, as LNCaP cells is the only cell line used that expresses androgen receptor that is the hallmark of prostate cancer, and majority of bone metastasis express androgen receptor (PC3 and DU145 do not). Similarly, no migration results under coculture of LNCaP and rBMAds were included.

      We would like to clarify that several of the points raised are supported by data already included in the manuscript. Specifically, we showed that both coculture with rBMAds and treatment with exogenous FFAs increase ANGPTL4 expression in PC3 cells (Fig. 5B–F) as well as in Du145 cells (Supplementary Fig. 5A–E). In contrast, although LNCaP cells exhibited increased migration (Supplementary Fig. 3B) and invasion (new Supplementary Fig. 3D) following coculture with rBMAds, ANGPTL4 expression was undetectable in this cell line under both basal conditions and after coculture (data not shown). These findings indicate that the enhanced migratory and invasive properties of LNCaP cells are mediated through an ANGPTL4-independent mechanism, highlighting the biological heterogeneity of prostate cancer and indicating that not all cell lines recapitulate the same molecular pathway. The manuscript was revised accordingly (lines 514-518). Importantly, the relevance of ANGPTL4 is further supported by our analysis of clinical prostate cancer datasets, which showed increased ANGPTL4 expression in bone metastases in comparison to primary sites and its association with poor prognosis in metastatic disease (see response to Reviewer 1 points 1 and 2 and new Fig. 6).

      4) An important question concerns the clinical relevance of the observed effects of FFAs. In the in vitro experiments mainly migration and invasion, FFAs were used at 10 µM, including conditions in which three different FFAs were combined, each at a concentration of 10 µM. Notably, the magnitude of the effects was similar when FFAs were applied individually or in combination, suggesting possible saturation of the signaling pathway. Therefore, lower FFA concentrations, such as those found in the bone marrow microenvironment should be used to better reflect physiological conditions. Moreover, the FFAs were measured in adiposities condition media but only relative abundance was included in figure 1E, not actual levels.

      We agree that the clinical relevance of the FFA concentrations used should be carefully discussed. We would first like to clarify that all experiments with exogenous FFAs were performed using 100µM of each FFA (palmitate, oleate, or linoleate), including the mixture in which each FFA was added at 100µM. The 10 µM concentration mentioned in the review appears to result from a misunderstanding. As the reviewer points out, no additive effect was observed when the three FFAs were combined compared with individual treatments in the migration (Fig. 4F and Supplementary Fig. 4A–B) and invasion assays (Fig. 4H), suggesting that the signaling pathways involved may already be saturated under these experimental conditions.

      To address the reviewer's concern regarding physiological relevance, we estimated the amount of FFAs released by rBMAds after 3 days of culture to be approximately 100µM total FFAs. Lipidomic analysis (Fig. 1E) further showed that palmitate, oleate, and linoleate account for approximately 20%, 30%, and 20% of the total FFAs, corresponding to physiological concentrations of approximately 20µM palmitate, 30µM oleate and 20µM linoleate, respectively__. __We therefore performed additional migration experiments using these physiological concentrations. Under these conditions, treatment with each individual FFA did not significantly affect PC3 cell migration. In contrast, the combination of the three FFAs at their physiological concentrations significantly increased migration, suggesting that although individual FFAs are insufficient at physiological levels, their combined presence reaches a threshold required to promote prostate cancer cell migration. We are ready to include theses results in the manuscript upon referee request.

      *5) The authors used SU2C data sets to evaluate ANGPTL4 expression in bone metastases and associated increased expression worse better survival. However, in this data set, it is clear that ANGPTL4 exhibits significantly higher expression in liver metastases than in bone metastasis. Some discussion about this aspect would increase the potential translational indication. *

      We thank the reviewer for this important comment (also raised by Reviewer 1, point 2). While approximately 80% of prostate cancer metastases are located in bone, these metastases can acquire the capacity to generate secondary metastatic sites and contribute to further metastatic dissemination (Gundem et al, Nature, 2015, PMID: 32728210; Hong et al, Nature Communications, 2015, PMID: 25827447). Analysis of prostate cancer metastatic datasets, in which ANGPTL4 expression is assessed across different metastatic sites, revealed that ANGPTL4 expression is maintained and even increased in metastases located at secondary sites, such as liver and lung, compared with bone metastases (new Supplementary Fig. 6A). These findings suggest that high ANGPTL4 expression is not restricted to the bone microenvironment but may persist or become amplified during metastatic progression and contribute to late-stage metastatic dissemination. These results have been added to the revised manuscript and discussed accordingly (lines 575-580).

      Minor comments:

      6) Preparation of the gel description stipulates "One hundred microliters of SCAds or rBMAds were added and homogenized quickly with 100µL of thrombin....." Number of cells added for normalization should be added to the methods.

      We have now included in the method part (lines 137-138), the estimated number of adipocytes within the gel, based on cell counts performed in a subset of samples (Shin et al, Cell Reports, 2026, PMID: 42348417).

      7) In this sentence- is it correct to assume that oleate is also uM and not uL? "One day after seeding, cells were treated with 100μM palmitate (Cayman #29558), 100μL oleate (Sigma Aldrich #O3008), 100μM linoleate (Sigma Aldrich #L9530), or with the mix of 3 FFAs (100μM palmitate/100μM oleate/100μM linoleate) during 3 days.

      We agree with the reviewer, it is a mistake. It is a concentration of oleate and not a volume. It was corrected in the manuscript by 100µM oleate (line 215).

      8) There is a description of an experiment: "PC3 and Du145 were transiently transfected with ANGPTL4 siRNA pool (final concentration 25nmol/L) using ON-TARGETplus SMARTpool (Thermo Scientific Dharmacon) or ON-TARGETplus nontargeting pool used as a control (Thermo Scientific Dharmacon). Transfection was done according to the manufacturer's instructions with Lipofectamine RNAiMAX (Invitrogen Life Technologies) followed by 24h of coculture with rBMAds. After coculture, a second transfection was done and gene extinction, migration and invasion were evaluated." The rationale for second transfection (assuming still with siRNA) has not been provided and it is not clear.

      We agree with the reviewer that clarification is needed. A second siRNA transfection was performed 24 h after coculture with rBMAds to maintain ANGPTL4 knockdown during the subsequent 24 h migration and invasion assays. For more clarity, the section materials and methods was corrected accordingly (lines 255-256).

      9) Figure 6. Survival: HR and number of patients at each time point should be added to the graph. Some English editing is needed

      The number of patients in each section “low ANGPTL4 expression” (n=8) and “high ANGPTL4 expression” (n=8) was added in the legend of the figure 6D (line 705).

      Significance

      There are multiple studies focusing on adipocytes and FFAs effects on cancer, in general, as well as on prostate cancer specifically. Effects on migration and invasiveness are well documented in literature. The novel aspect of the current manuscript is specifically looking at red bone marrow adipocytes, but it is not clear that the effects of FFAs would be different than when other adipocytes are used.

      We agree that adipocyte-derived fatty acids have been implicated in cancer progression in a large number of study as we recently reviewed (Attané and Muller Trends in Cancer, 2020, PMID: 32610069). However, the novelty of our study is that it demonstrates that the origin and metabolic identity of adipocytes determine the prostate cancer (PCa) response. Most previous studies have focused on adipocytes from primary tumor sites, while studies investigating bone metastasis have mainly relied on in vitro-differentiated bone marrow adipocytes (like OP9) or mesenchymal stem cell-derived models, which do not fully reproduce the properties of mature human bone marrow adipocytes (Shin et al, Cell Reports, 2026, PMID: 42348417).

      Using primary human rBMAds, we show that bone marrow adipocytes display specific biological features, including a non-canonical lipolytic pathway, and induce a distinct molecular response in PCa cells compared with periprostatic adipocytes. Specifically, rBMAds induce stronger ANGPTL4 expression, whereas NOX5, previously identified by our team as a mediator of PPAT adipocyte-induced effects (Laurent et al, Mol Cancer Res, 2019, PMID: 30606769), is not increased following rBMAd coculture. This demonstrates that adipocytes from different anatomical niches are not functionally equivalent and activate distinct tumor signaling pathways. The relevance of ANGPTL4 is further supported by our analysis of clinical PCa datasets, which showed increased ANGPTL4 expression in bone metastases in comparison to primary sites and its association with poor prognosis in metastatic disease.

      Reviewer #3

      (Evidence, reproducibility and clarity (Required)):

      The study identifies a role for free fatty acids released by bone marrow adipocytes from red hematopoietic rich-areas in promoting prostate cancer cell migration and viability by inducing the expression of ANGPTL4. To achieve this the authors use to cancer cell-lines (LNCaP, PC3 and DU145) and a coculture model for their phenotypic assays and perform RNA-seq on one line (n=5 per condition) subjected to co-culture activation or not. The selection of ANGPTL4 is supported by its significant association with shorter survival times in clinical RNA-seq data from a metastasis cohort (SU2C) when highly expressed.

      *1) The paper is concise and clear. The authors also quantify free fatty acid release and neutral lipid accumulation. It would be interesting to have a more comprehensive analysis (pathway and coexpression) of the RNA-seq data with RT-PCR validation of a number of significant transcripts that are up and downregulated. *

      In the revised manuscript, we further strengthened the analysis of the RNA-seq data by reconstructing an ANGPTL4-associated gene network in Cytoscape using RNA-seq-derived differentially expressed genes enriched in cell migration GO terms and their interactions with ANGPTL4. This analysis revealed a highly interconnected network rather than a linear signaling cascade (Supplementary Fig. 5L) suggesting that ANGPTL4 promotes cell migration by orchestrating a coordinated transcriptional program involving multiple complementary signaling pathways. While this systems-level analysis reveals a coherent migration-associated network, it does not aim to establish a direct functional role for each individual gene, but rather to define the global transcriptional context of ANGPTL4-dependent migration. In line with this, we added and discussed the Cytoscape network of ANGPTL4-associated migration genes in the revised manuscript (lines 538-545).

      2) Also a survival analysis in a localised PCa cohort using publicly available RNA-seq data.

      As suggested, we assessed the clinical relevance of ANGPTL4 expression at different stages of disease progression. High ANGPTL4 expression in primary PCa tumors was not associated with either overall or disease-free survival (new Figure 6C). In contrast, elevated ANGPTL4 expression in bone metastases was significantly associated with poorer survival (Figure 6D), indicating that the clinical relevance of ANGPTL4 is primarily linked to the metastatic bone microenvironment rather than the primary tumor. We have revised the Discussion accordingly (lines 569–575).

      (Significance (Required)): Explores an interesting biological question but in a limited number of pre-clinical models. Interesting as a startpoint for further studies.

      Prostate cancer research is inherently limited by the availability of representative experimental models, with only a limited number of well-characterized prostate cancer cell lines compared with other cancer types (Cunningham et al, J Biol Methods, 2015, PMID: 26146646). The major prostate cancer models used in the field, PC3, Du145, and LNCaP, were included in our study. Importantly, our study goes beyond classical cancer cell line models by incorporating ____primary human rBMAds____, which remain rarely used due to their limited accessibility and the technical challenges associated with their culture. Using this physiologically relevant model, we demonstrate that adipocyte origin and metabolic identity critically influence the tumor response. Indeed, rBMAds display specific biological properties, including a non-canonical lipolytic pathway, and induce a distinct molecular response in prostate cancer cells compared with periprostatic adipocytes. Specifically, rBMAds induce stronger ANGPTL4 expression and elevated ANGPTL4 expression in bone metastases was significantly associated with poorer survival (Figure 6D). In contrast, high ANGPTL4 expression in primary PCa tumors was not associated with either overall or disease-free survival (new Figure 6C) indicating that the clinical relevance of ANGPTL4 is primarily linked to the metastatic bone microenvironment rather than the primary tumor. Together, these results support the biological and translational relevance of our model and identify a bone marrow adipocyte-specific signaling axis involved in prostate cancer progression.

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      Referee #3

      Evidence, reproducibility and clarity

      The study identifies a role for free fatty acids released by bone marrow adipocytes from red hematopoietic rich-areas in promoting prostate cancer cell migration and viability by inducing the expression of ANGPTL4. To achieve this the authors use to cancer cell-lines (LNCaP, PC3 and DU145) and a coculture model for their phenotypic assays and perform RNA-seq on one line (n=5 per condition) subjected to co-culture activation or not. The selection of ANGPTL4 is supported by its significant association with shorter survival times in clinical RNA-seq data from a metastasis cohort (SU2C) when highly expressed.

      The paper is concise and clear. The authors also quantify free fatty acid release and neutral lipid accumulation. It would be interesting to have a more comprehensive analysis (pathway and coexpression) of the RNA-seq data with RT-PCR validation of a number of significant transcripts that are up and downregulated. Also a survival analysis in a localised PCa cohort using publicly available RNA-seq data.

      Significance

      Explores an interesting biological question but in a limited number of pre-clinical models. Interesting as a startpoint for further studies.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: This manuscript focuses on evaluation of effects of bone adipocytes on prostate cancer and their potential role in prostate cancer bone metastases progression. Mechanistically, the results show that fatty acid (FFA) secreted by bone marrow adipocytes increased expression of ANGPTL4 in prostate cancer cell lines in vitro, and this increase was dependent on PPARgamma signaling, resulting in increased migration and invasiveness of the cells. Evaluation of data from SU2C data set, was also included to support the conclusions, showing that increased levels of ANGPTL4 expression in bone metastases are negatively correlated with patients' survival, suggesting that ANGPTL4 could be a therapeutic target for prostate cancer bone metastasis. The authors previously investigated and published on crosstalk/interaction between periprostatic adipocytes and prostate cancer cells, where FFAs induced released by adipocytes elicited increased aggressiveness and dissemination of prostate cancer cells, with mechanisms involving increase in NOX5 expression. However, specifically bone marrow adipocytes effect on prostate cancer cell have not been investigated in details. The experiments are logically sequenced and data clearly presented, in general supporting the conclusions that FFAs secreted by bone marrow adipocytes affect prostate cancer cells migration and invasiveness in vitro.

      Major comments: The authors state: "These data collectively demonstrate that PCa cells can trigger the release of FFAs from rBMAds cultured in 3D (Fig 1), which are then rapidly taken up and re-esterified into TGs by cancer cells." There are no data presented to show that prostate cells trigger the FFAs release from adipocytes- the data only show that FFA from adipocytes are taken up by prostate cancer cells. Use of condition media in combination with the coculture experiments would address this point.

      It is not clear how the authors came to the following conclusions: " In co-culture, the levels of these FFAs tended to increase (Fig. 1E), a trend also observed in total FFA content (Fig. 1F). The data presented in this figure have no statistical significance to support this statement, and moreover, the relative abundance in the PC3 COC appears to be simply addition of the abundance of PC NC and rBMAds NC, no alterations of secretion.

      GO analysis presented in this manuscript indicates that prostate cancer cells grown in bone marrow adipocytes condition media exhibit increased expression of gene sets associated with cell migration, cell adhesion, etc. were significantly induced under these conditions (Fig 3C). However, it seems contradictory that while locomotion and invasion were increased so were the gene associated with adhesion, as one would speculate that with increased invasion and mobility you will not have increased adhesion. This analysis also show that downregulated gene sets were mainly associated with proliferation regulation. However, the data included in Fig 3 H and I and supp figure show no effects on proliferation of the prostate cancer cells. This discrepancy should be discussed.

      Some of the genes associated with effects of adipocytes condition media or treatment with FFAs were confirmed by PCR and western blot in PC3 cells, but not in DU145 or LNCaP. These experiments should be done, and results in the other two cell lines should be included to demonstrated the generality of the mechanism of the observed effects. This is important specifically, as LNCaP cells is the only cell line used that expresses androgen receptor that is the hallmark of prostate cancer, and majority of bone metastasis express androgen receptor (PC3 and DU145 do not). Similarly, no migration results under coculture of LNCaP and rBMAds were included.

      An important question concerns the clinical relevance of the observed effects of FFAs. In the in vitro experiments mainly migration and invasion, FFAs were used at 10 µM, including conditions in which three different FFAs were combined, each at a concentration of 10 µM. Notably, the magnitude of the effects was similar when FFAs were applied individually or in combination, suggesting possible saturation of the signaling pathway. Therefore, lower FFA concentrations, such as those found in the bone marrow microenvironment should be used to better reflect physiological conditions. Moreover, the FFAs were measured in adiposities condition media but only relative abundance was included in figure 1E, not actual levels. The authors used SU2C data sets to evaluate ANGPTL4 expression in bone metastases and associated increased expression worse better survival. However, in this data set, it is clear that ANGPTL4 exhibits significantly higher expression in liver metastases than in bone metastasis. Some discussion about this aspect would increase the potential translational indication.

      Minor comments:

      Preparation of the gel description stipulates "One hundred microliters of SCAds or rBMAds were added and homogenized quickly with 100µL of thrombin....." Number of cells added for normalization should eb added to the methods. In this sentence- is it correct to assume that oleate is also uM and not uL? "One day after seeding, cells were treated with 100μM palmitate (Cayman #29558), 100μL oleate (Sigma Aldrich #O3008), 100μM linoleate (Sigma Aldrich #L9530), or with the mix of 3 FFAs (100μM palmitate/100μM oleate/100μM linoleate) during 3 days.

      There is a description of an experiment: "PC3 and Du145 were transiently transfected with ANGPTL4 siRNA pool (final concentration 25nmol/L) using ON-TARGETplus SMARTpool (Thermo Scientific Dharmacon) or ON-TARGETplus nontargeting pool used as a control (Thermo Scientific Dharmacon). Transfection was done according to the manufacturer's instructions with Lipofectamine RNAiMAX (Invitrogen Life Technologies) followed by 24h of coculture with rBMAds. After coculture, a second transfection was done and gene extinction, migration and invasion were evaluated." The rationale for second transfection (assuming still with siRNA) has not been provided and it is not clear.

      Figure 6. Survival: HR and number of patients at each time point should be added to the graph. Some English editing is needed

      Significance

      There are multiple studies focusing on adipocytes and FFAs effects on cancer, in general, as well as on prostate cancer specifically. Effects on migration and invasiveness are well documented in literature. The novel aspect of the current manuscript is specifically looking at red bone marrow adipocytes, but it is not clear that the effects of FFAs would be different than when other adipocytes are used.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The study entitled "Human bone marrow adipocytes drive prostate cancer bone metastasis progression via lipid-mediated induction of Angiopoietin-like 4" by Hernandez et al. investigates the role of the bone marrow adipocytes (BMAds) from red hematopoietic rich-areas in the progression of prostate cancer (PCa) bone metastases. To do so, the authors used an elegant three-dimensional culture of primary human rBMAds isolated from the femoral bone marrow of patients undergoing hip surgery then cultured with PCa cell lines. The authors claim that PCa cells take up FFAs released by rBMAds which enhance the epithelial-mesenchymal transition and motility through upregulation of ANGPTL4. The article is well written, the figures are very clear and the methodology is perfectly described. To further improve the understanding of this mechanism, the authors should address 1) how PCa cells take up the FFAs, 2) whether PCa cells enhance non-canonical FFAs release and 3) the specificity of the rBMAds in their ability to upregulate ANGPTL4 compared to other adipocytes.

      Major comments:

      The paper's conclusions are overall convincing but the following supplementary experiments or discussion points could strengthen their claims. Is ANGPTL4 upregulation in PCa cells specific to rBMAds or could it also be seen with other adipocytes, such as periprostatic adipocytes or SCAds (used in Fig 2) ? Co-culture of PCa cells with SCAds should offer insights. More precisely, the article does not state if ANGPTL4 upregulation could be induced prior to bone invasion, by the periprostatic adipose tissue FFA release for example, explaining why ANGPTL4-high cells could be found in bone metastasis. In that case, increased motility and migration could be BMAds independent and trigger the first metastatic events originating from the primary prostatic tumor. The article hypothesizes an ANGPTL4-associated increase of motility and migration of PCa cells upon interaction with bone marrow adipocytes in the first metastatic bone, inducing enhanced invasion of said bone and secondary metastasis sites. However the article lacks evidence that ANGPTL4 is also upregulated in cells from secondary metastasis sites in patients. It would be interesting to explore in the existing data set the expression of ANGPTL4 in secondary metastasis of patients who had previous bone metastasis. These points could be addressed in the concluding remarks.

      Additional experiments or qualifying the claims:

      Fig 1 : It would be interesting to have an insight on the mechanism by which FFAs are uptaken by PCa. Fig 1D : It would be interesting to add an extra control of NC PCa treated with BODIPY to measure basal BODIPY uptake of PCa <br /> Fig 2 : The figure efficiently states that rBMAds undergo non-canonical lipolysis in comparison to SCAds, without PCa cells. Glycerol dosage and/or pan-lipase inhibitor treatment of rBMAds in co-culture with PCa would confirm rBMAds still show non-canonical lipolysis in bone metastasis environment. Fig2 : Is ANGPTL4 upregulation in PCa cells specific to rBMAds or could it also be seen with other adipocytes, such as periprostatic adipocytes or SCAds ? Co-culture of PCa cells with SCAds should offer insights. Fig 2 : Could the authors specify if there is a possibility the lipolysis kinetics is slower in rBMAds instead of an actual incomplete lipolysis ? Fig 3B/C : The author should provide tables to show : 1) all significantly up and down GO term 2) the top 20 genes DEG 3) any GO terms/genes related to EMT (table 1 should be associated with figure 3) Fig 5 : To complete experiences of KD of ANGPTL4 and PPARy inhibition, the author should also include the mRNA levels of EMT associated genes in these conditions. To better the clarity of the paper, the author should indicate in table S1 which patients are used in which figures. Also, what is the rationale for not using female patients in all the experiments ?

      the suggested additional experiment should be achievable in 6 months.

      Satistical analyses: The authors state in the Materiel and Methods that they used Student's t-test, one-way ANOVA or two-way ANOVA. These tests are applied to parametric data sets, validated for their normal distribution. Usually, n < 30 implies the use of non-parametric tests. The authors should better justify the rationale for their choices. Also, the details of tests for each figure is not indicated in the legends as stated in the Mat&Med.

      Minor Comments:

      In Fig 1F, the author should discuss the discrepancy between the significant increase of TG uptake in PCa shown in 1B and the non-significant effect seen in 1F, given that TG represents 85% of the total FFAs shown in 1E and 1F. Fig 3 : In Fig 3G and 4E, staining quantification would be necessary. In Fig3/Sup Fig3, why is the invasion assay not shown for LNCaP cell line ? 5F: Is there a reason why the sum of FFA has a stronger effect than the individual ones ? 5E/F : Rationale for changing FFA treatment duration between 5E and 5F (72h then 24h) ? For the discussion : the authors clearly describe the two different BMAds subtypes (cBMAds vs rBMAds), would it be interesting to question the role of cBMAds in PCa progression as well ?

      The studies are referenced appropriately.

      Please, could the authors indicate the co-culture duration with rBMAds in the legends of Figure 5. Excepted this point, the text and figures are clear and accurate.

      Significance

      General assessment:

      the authors used an elegant three-dimensional culture of primary human rBMAds isolated from the femoral bone marrow of patients undergoing hip surgery then co-cultured with PCa cell lines. The authors claim that PCa cells take up FFAs released by rBMAds which enhance the epithelial-mesenchymal transition and motility through upregulation of ANGPTL4. The most important aspects of this study are 1) the description of the non-canonical FFAs release from rBMAds, 2) the induction of EMT/motility of PCa cells induced by FFAs uptake. To further improve the understanding of this mechanism, the authors should address 1) how PCa cells take up the FFAs, 2) whether PCa cells enhance non-canonical FFAs release and 3) the specificity of the rBMAds in their ability to upregulate ANGPTL4 compared to other adipocytes.

      This study extends the knwoledge in the field of protaste cancer metastasis. The authors have developed an elegant 3D culture model of primary human rBMAds, which is highly relevant for investigating the role of adipocytes in cancer cell behavior. Indeed, most models used in the literature rely on adipocytes differentiated from bone marrow mesenchemal stem cells. Although adipocytes differentiation models have recently been improved by incorporating 3D structure, the model developed by the authors is most physiological, as it is based on the direct use of primary bone marrow adipocytes. In this way, this model represents a valuable tool to advance our understanding of mechanisms involved in bone metastasis in breast/prostate cancers.

      The type of audience that will be interested by this research include both basic and specialized researchers. Moreover, this study should improve 1) the understanding of the impact of rBMAds on prostate cancer behavior and 2) the robustness and physiological relevance of adipocyte-based studies.

      Our expertise lies in the interaction between normal or pathological hematopoiesis with the components of bone marrow microenvironment.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      This manuscript presents a comprehensive experimental analysis of the calcineurin pathway in the fungal pathogen Cryptococcus neoformans, a pathway that is important for thermoregulation and virulence. Calcineurin is a calcium-dependent protein phosphatase complex that is conserved in eukaryotes and that is required for growth at high temperature in fungi, and thus for fungal pathogenesis in humans. The work uses a range of approaches including genetics, proteomics, and transcriptomics to establish important new insight into the pathway.

      Genetic screens for growth of C. neoformans H99 / KN99 at high temperature in the absence of calcineurin discover suppressors including inactive alleles of the Yak1 kinase. Extensive mutagenesis and phosphoproteomics assays confirm Yak1 as the primary kinase that opposes calcineurin-dependent dephosphorylation, an important result in the field. This complements and extends the authors' recently published work in C. deneoformans (Yadav et al., 2025, https://doi/10.1073/pnas.2503751122), which identified but did not follow up Yak1 mutations as suppressing calcineurin deletion. The importance of that study, including Yak1, was highlighted in a news and views article (Mitchell, 2025, https://doi.org/10.1073/pnas.2511623122).

      Proximity-labeling proteomics with TurboID discover interactors of calcineurin, a dataset complementary to phosphoproteomics (in wild-type, calcineurin deletion, yak1 deletion) in mapping the signaling pathway. Transcriptomics and translatomics assays aim to map gene expression regulation downstream of calcineurin. Furthermore, molecular functions of the Cna1 component of calcineurin are dissected with a series of mutants, including their impact on thermoregulation and Cryptococcus virulence in mice.

      Major comments

      The main result, that "Yak1 as the kinase that acts antagonistically to calcineurin at 37{degree sign}C", is thoroughly supported by multiple lines of evidence. The genetic suppressor screens identify 8 distinct alleles of Yak1 that allow cnb1∆ strains to grow at 37{degree sign}C. They confirm the suppressive effect of yak1∆ in two different strain backgrounds and in the presence of FK506 and CsA drugs. The phosphoproteomics experiments show 409 overlapping phosphorylation events that are down in yak1∆ compared to up in cna1∆, although a quantitative analysis e.g. enrichment of these shared targets is not presented.

      The proximity-labeling proteomics is an important assay with appropriate controls, and generates several hypotheses that are then followed up on. The methods section on the proteomics results is detailed and comprehensive.

      In my opinion, the results in the rest of the manuscript (validation of interactions witn microtubules etc, transcriptomics and translatomics, Cna1 mutant series) are not clearly integrated with the Yak1 kinase and proteomics results. Also, the discussion does little to critically synthesize the results presented within the context of wider knowledge including the authors' own recent work. The authors could choose to publish a more focused manuscript that concentrates on identifying Yak1 as the suppressor, or they could more clearly synthesise the wider set of results that they present.

      Overall the figures are well presented both for the Cryptococcus microbiology (spot assays, mating, etc.) and omics experiments (e.g. consistent evidence from PCA that 3 biological replicates cluster together). Experimental design schematics help the reader,

      Some claims are made superficially and need to be critically evaluated, notably the question of mitochondrial targets of the apparently cytosolic calcineurin complex.

      Furthermore, the manuscript lacks critical details and data sharing for some experiments and analyses, notably the transcriptomics and translatomics assays that are not possible to evaluate as currently presented. No data sharing (e.g. reviewer tokens) was available for my review. DNA sequencing is listed as shared on NCBI project PRJNA1306639, and TurboID data listed as shared on PRIDE project PXD067478. There's no mention of data sharing for phosphoproteomics, nor for transcriptomics/translatomics. I consider it appropriate to share data along with preprints, and thorough data sharing is required for publication in any reputable journal.


      Mitochondrial targets. The literature suggests that Calcineurin is located in the cytosol, however this manuscript reports differential calcineurin-dependent phosphorylation of mitochondrial proteins, including some that are encoded in the mitochondrial genome. I did not find the discussion of this in the paper convincing. It seems there are four possibilities (a) "calcineurin dephosphorylates [some mitochondrial proteins] prior to their import into the mitochondria"; (b) calcineurin dephosphorylates cytosolically exposed peptides of mitochondrial outer membrane proteins; (c) a subpopulation of Calcineurin is in mitochondria; or (d) calcineurin affects via a cascade or indirect effects mitochondrial kinases and/or phosphatases.

      Dephosphorylation prior to import is inconsistent with current models of co-translational mitochondrial import, unless calcineurin were acting co-translationally? I do not think interactions with eIFs support this argument, because eIFs are release from the mRNA early in translation elongation.

      Calcineurin acting on and interacting with cytosolically exposed peptides should be addressable by analysing current datasets to find differential phosphorylation and/or biotinylation specifically of cytosolic segments of mitochondrial proteins. I strongly recommend that the authors do this analysis and discuss the results.

      The authors tried to assess Calcineurin localisation in mitochondria, briefly.

      Otherwise, a cascade or indirect effects seem more likely.

      Transcriptomics and translatomics: the data analysis descriptions, data presentation, and data sharing are not sufficient for publication. See e.g. MINSEQE guidlines https://doi.org/10.5281/zenodo.5706412, and https://doi.org/10.1093/bib/bbz124. Experimental descriptions are thorough. However, the Ribo-seq/ribosome profiling protocol described, with pre-incubation of cells with harringtonine and cycloheximide then slow lysis, is far from best practice established in the extensive literature on ribosome profiling methods (see papers by Gloria Brar, Nick Ingolia, Sebastian Leidel, and Vadim Gladyshev, etc.), a limitation that should be discussed. Sequencing preparation and execution should be described accurately, e.g. it does not make sense for Ribo-seq data with ~30nt inserts to use 150PE sequencing. Data analysis steps should be described sufficient to reproduce the analysis. Key QC should be reported: read depth for each dataset, read length and frame information critical for interpreting ribosome profiling data, scatter plots or comparisons of TPMs or other gene-level summaries beyond the "TE" . PCA plots must describe what the PCA is calculated on (TPMs, log2 fold-change, or all or some genes, etc.). Data should be shared including raw reads and summarised gene-level reads.

      OPTIONAL: beyond reporting the data, there is scope for additional insights from analysing these data. For example, are Crz1-dependent genes differentially expressed or translated?

      Discussion. The short 5-paragraph discussion mostly reiterates the results, with limited engagement with wider context. Even the authors' recent related work in C. deneoformans, where cytokinesis factors suppress calcineurin phenotypes, gets only 3 sentences. To increase its impact, the manuscript would benefit from a considerably expanded critical discussion, including some of:

      • relating their C. neoformans and C. deneoformans results to each other.
      • discussing evidence for and against the conservation of Yak1 being antagonistic to Cna1 amongst fungi or more broadly, e.g. was there any precedent for this result from work by other groups?
      • discussing evidence for Cna1 and Yak1 having shared direct targets, including quantitatively from the new data and in reference to any other studies in other organisms.
      • discussing localisation of Cna1, and how that might affect interpretation of TurboID data and of truncation mutants.
      • expanding the discussion of mitochondrial targets with alternative hypotheses and wider literature engagement.
      • evaluating transcriptomics and translatomics results in light of other studies in Cryptococcus and beyond.
      • engaging with the role of calcineurin in translation: could it dephosphorylate translation initiation factors?

      Minor comments

      On novelty of TurboID in C. neoformans: it would be appropriate to cite Kalem, Panepinto, and co-authors' work which was to my knowledge the first TurboID work in this fungus (https://doi.org/10.1101/2022.01.13.475903).

      On Cna1 interactors in the spliceosome: it would be appropriate to compare with the work by Madhani and colleagues that map the C. neoformans spliceosome and its function (https://doi.org/10.1016/j.cub.2021.09.004).

      On Ribo-seq in C. neoformans: it would be appropriate to compare key results (e.g. TPMs, TE) and QC metrics to the only published Ribo-seq dataset in C. neoformans by Wallace, Maufrais, et al. (http://.doi.org/10.1093/nar/gkaa060). Disclosure: I am a lead author on that study. Likewise, for the arguments on splicing regulation it would be appropriate to compare with datasets from Wallace, Maufrais et al, and others from Guilhem Janbon lab, that conduct RNA-seq at different temperatures.

      OPTIONAL: Do the truncation mutations of Cna1 affect its localisation or interactions?

      Materials and Methods: Different parts are described in different levels of detail and this should be thoroughly checked so that a reasonable colleague could reproduce the experiments and analyses. The genetic screen and proteomics descriptions, for example, are thorough. All data analysis should be described for all assays, including methods used in software for sequence and image analysis beyond "Geneious prime" or "ImageJ", and software cited.

      All strains should be thoroughly described, currently also in different levels of detail - e.g. what is the sequence of Cna1-TurboID tag (source of tag, linker, etc.).

      Referee cross-commenting

      All reviewers agree on the importance of Yak1-calcineurin interactions. All reviewers also agree on the need for synthesis of the data, and to address the current emphasis on the manuscript of associations/correlations/data magnitude over mechanism and synthesis.

      Reviewer 1's point that "cna1∆ strain clustered apart from inactive mutants and even the epitope-tagged wild-type behaved differently from the true wild-type" is important. Reviewer 2's point about testing the functionality and localisation of Cna1-TurboID is also important, and aligns with the request to describe this strain in detail. These suggest additional experiments and/or recognizing limitations in the discussion, are needed.

      Significance

      Overall, the study represents an important advance in the field, that everyone working on calcium signaling in fungi, on Cryptococcus virulence mechanisms more generally, or on the calcineurin pathway more generally, will need to read and cite. Both the experimental results and the large-scale data presented are major contributions, notably the discovery of Yak1 kinase as the primary antagonist to calcineurin phosphatase at higher temperatures.

      Thorough experimental and analysis descriptions and data sharing would make this study far more valuable. Synthesising the results, including with an upgraded critical discussion and literature engagement, would increase the impact of the study.

      "Please define your field of expertise:" I'm a quantitative biologist working on gene expression regulation in fungi, using approaches including molecular microbiology and 'omics data, and have published on thermoregulation, translational control and its evolution, and C. neoformans.

      Review signed by Edward Wallace, University of Edinburgh.

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      Referee #2

      Evidence, reproducibility and clarity

      This study defines a comprehensive thermoregulatory network in Cryptococcus neoformans, revealing that calcineurin and its antagonistic kinase Yak1 regulate thermotolerance through shared substrates. The authors further show that calcineurin mediates thermal adaptation via novel interactions with translation initiation factors, spliceosome components, and mitochondrial proteins, and that distinct structural domains of calcineurin differentially regulate thermotolerance, meiosis, and virulence.

      Overall, the study presents an extensive dataset integrating RNA-seq, TurboID proximity labeling, phosphoproteomics, and genetic screens. The experimental scope is impressive, but the analysis often emphasizes data magnitude rather than biological interpretation. The manuscript would benefit from deeper synthesis of what these datasets reveal about calcineurin function beyond its global impact.

      Major Comments

      1. The role of Yak1 mutations in spontaneous suppressors is not directly validated by allelic reconstruction or complementation in the original genetic backgrounds. Establishing causality is essential to link Yak1 loss with calcineurin-related thermotolerance phenotypes.
      2. The interaction claims rely on the TurboID proximity map generated using a Cna1-TurboID fusion, which shows good replicate consistency and clear temperature-specific enrichments. However, the study does not show that the fusion protein is functional or correctly localized under heat stress. Calcineurin is known to relocalize during thermal stress at 37{degree sign}C, moving from diffuse cytoplasmic distribution to ER-associated puncta and the mother-bud neck. Without complementation of calcineurin-dependent phenotypes or independent localization data for the Cna1-TurboID fusion, the interactome identified in Fig. 2E-2G could reflect stress-induced relocalization or tag artifacts rather than calcineurin's native interaction landscape. The Western blots in Supplementary Fig. S2A-S2B confirm expression and biotinylation but not functional integrity or localization fidelity. Demonstrating that the fusion complements calcineurin loss is critical to attribute the 37{degree sign}C interactome to native calcineurin activity.

      Minor Comments

      Fig. S1C is missing a color legend.

      Lines 106-108: The text does not match the referenced figure (Fig. S1D).

      Fig. S1E: The authors clarify what distinguishes the two different knockout strains.

      Fig. 2G: The authors state that "the most enriched biological processes included proteins from the translation initiation complex, the spliceosome, the proteasome machinery, and mitochondria." Please clarify what analysis supports these enrichments, as the GO term analysis in Fig. 2F does not include these processes.

      Fig. 5F: Control wild-type crosses should be shown for comparison.

      Panels in Fig. 5 should be reordered to match their sequence in the main text.

      Significance

      This study presents a comprehensive map of the thermoregulatory network in Cryptococcus neoformans, integrating genetic screens, proximity labeling, phosphoproteomics, and ribosome profiling to define a broad signaling framework controlling thermal stress adaptation. By combining these approaches, the authors provide a systems-level view of how calcineurin orchestrates thermotolerance, extending beyond individual pathways to reveal network-wide regulation. Through two independent genetic screens, the kinase Yak1 is identified as the principal antagonist of calcineurin, establishing a clear opposing relationship within the thermotolerance pathway and highlighting Yak1 as a key regulator of heat-stress signaling. Using TurboID proximity labeling, the study also uncovers previously unrecognized roles for calcineurin in microtubule organization, spliceosome function, and mitochondrial translation, substantially broadening the known functional scope of this conserved phosphatase.

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      Referee #1

      Evidence, reproducibility and clarity

      This impressive manuscript combines several different "omic" technologies to broadly survey the functions of calcineurin in Cryptococcus neoformans. First, it utilizes genetic screens to identify YAK1 protein kinase as an antagonist of calcineurin signaling, mutations in which reverse the temperature-sensitive phenotypes of calcineurin deficiency. The phospho-proteomes of yak1∆, cna1∆, and wild-type strains were also determined at 25{degree sign} and 37{degree sign}, indicating potential instances of convergence of the regulatory enzymes on common substrates that could regulate thermotolerance. TurboID was optimized and implemented to further understand the proteins in close physical proximity to calcineurin, a number of which contain docking motifs for binding of calcineurin as well as phospho-sites that undergo calcineurin-sensitive changes. A new role for calcineurin in regulation of microtubule functions was deduced and validated to some degree using fluorescent probes of nuclear morphology and alterations to benomyl susceptibility. RNA-seq was utilized to establish roles of calcineurin in transcription and splicing of mRNAs. Ribo-seq was also utilized to establish roles of calcineurin in the efficiency of translation of subsets of mRNAs, particularly those encoding mitochondrial proteins. Then a series of strains expressing calcineurin proteins with epitope tags and C-terminal truncations were analyzed in great detail for variation in signaling outputs. C-terminal tags on Cna1 selectively abolished its role in virulence. Some truncation mutants had gained activity through loss of auto-inhibitory domains and other regulatory inputs. Others had lost activity almost completely. Phospho-proteomes of all these strains were analyzed at two temperatures and found to be consistent with some expectations. Curiously, the cna1∆ strain clustered apart from inactive mutants and even the epitope-tagged wild-type behaved differently from the true wild-type. Temperature caused a rather large shift in phospho-proteomes that was mostly independent of calcineurin. Wow, what an enormous resource for the field (and playbook to followed in other systems)!

      Major concerns.

      Despite the massive breadth of the multi-omics data, there was no real pinpointing of new substrates of calcineurin that could directly impact thermotolerance or the other phenotypes. Normally this achieved through site-directed mutagenesis of the phospho-sites within the substrate and careful analysis of the relevant phenotypes along with some directed analyses of calcineurin-dependent changes in phosphorylation of the substrate. The "validation" by enhanced benomyl sensitivity, for example, is not really a validation because the finding could be explained by calcineurin-mediated effects on drug import/export/metabolism as opposed to effects on the cytoskeleton. In the end, the manuscript produces a wealth of associations and correlations but falls short of advancing any specific molecular mechanism of regulation. This is the most prominent weakness of the manuscript.

      Minor concerns.

      1. Typos in fig 6F and 6G (hyperphosrylated?)
      2. Why exactly do the inactive truncations of Cna1 look so different from cna1∆ mutants in terms of the phosphoproteomes at both temperatures (Fig. 6D)?
      3. Why wasn't a Cna1-dead mutant (mutation in active site) also tested? What is the predicted behavior in phosphoproteomes and other methods?
      4. Does the yak1∆ phosphoproteome resemble those of gain-of-function Cna1 alleles? Yak1∆ should be included in the principal components analysis of Fig. 6D.
      5. Fig. 6E (clustering) seems to have missed the mark by failing to recapitulate some of the main associations in 6D. Can this be improved by tweaking the parameters? Or removed altogether?
      6. Temperature seemed to cause a major shift of the phospho-proteome independent of calcineurin. Somehow that message was not obvious in the Discussion or Abstract.
      7. More discussion of how C. deneoformans differs from C. neoformans is needed. How drastic is the "rewiring" of calcineurin, and what does this mean for the evolvability of calcineurin signaling across the tree of life?

      Significance

      The study takes on the difficult challenge of understanding how an environmental microbe has evolved to thrive at elevated temperatures of the human body, causing fungal disease. Calcineurin was previously shown to be necessary for proliferation of C. neoformans at such high temperature, but the mechanism was unclear. Though this study did not pinpoint an output of calcineurin signaling responsible for thermotolerance, it provided a vast survey of the numerous factors that could be involved and a resource for deeper studies in the future.

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      Reply to the reviewers

      Manuscript number: RC-2026-03566

      Corresponding author(s): Mandë Holford

      1. General Statements [optional]

      The authors would like to thank the reviewers for their thorough analysis of our study and the critical perspective for improvements. We appreciate their time and effort, and expert feedback. Below we list all comments and suggestions of the reviewers, together with our detailed point-by-point reply (in blue).

      2. Description of the planned revisions

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary

      The manuscript entitled « Neurons, Muscles, and Venom: Elucidating a Neural-to-Secretory Pathway in Cephalopod Predation » by Kirchhoff et al. investigates the anatomical and molecular basis of venom release in coleoid cephalopods. The authors used a combination of histology, immunohistochemistry, micro-computed tomography, comparative phylogenetics, and ex vivo calcium imaging to conclude that venom secretion from the posterior salivary gland (PSG) is under neuromuscular control through cholinergic and potentially dopaminergic regulation. They also show that this is a conserved structural feature across octopus, squid, and cuttlefish species.

      Overall, this manuscript addresses an understudied topic, namely the mechanisms regulating venom secretion, which is poorly understood in most venomous systems despite their ecological and evolutionary significance. As such, this work provides an important anatomical and molecular characterization of cephalopod posterior salivary glands and generates valuable hypotheses regarding the neural regulation of venom secretion. I personally appreciate the quality of the histology work, a methodology largely neglected nowadays in a world thirsty for the use of high-tech, fancy instrumentations.

      Although I command the authors for this elegant and visually pleasing study, I have some reservations about the major mechanistic conclusions (especially the cholinergic and dopaminergic regulation), which to me rely on indirect evidence, as no experiment directly demonstrates neural control of venom secretion. Therefore this manuscript would benefit from additional functional validation and certainly a more cautious interpretation of the findings.

      Major Comments

      • The principal conclusion of the manuscript is that venom release is under neuronal and neuromuscular regulation, which is highly likely given the authors convincingly demonstrate the presence of actin-positive muscular structures, neuronal markers within the PSG, cholinergic-associated markers, spontaneous calcium activity. Unfortunately, none of these experiments directly demonstrate control of venom secretion ! The presented evidence establishes anatomical proximity between neural and muscular elements but does not demonstrate causality. For instance, no experiments measure venom release following neuronal stimulation or receptor activation/inhibition.

      Therefore I suggest that the authors substantially temper these conclusions or provide functional experiments demonstrating secretion following pharmacological or electrical stimulation.

      Author’s response: We agree with the reviewer’s comments that our findings “convincingly demonstrate” that venom release is under neuronal control, but without experimental evidence cannot definitely make this claim. In vivo experimental verification of venom secretion after neuronal stimulation are complex studies with anatomical, analytical and animal welfare limitations. Even though we agree with the reviewer that these would be the ultimate verification for the neuronal control and neurotransmission systems involved in venom release, this is out of scope for the present study which is meant to be descriptive in providing a foundational overview of potential mechanisms for venom release. In this vein, we will revise the manuscript to “temper our conclusions” as the reviewer requested. Specifically, we will revise the contextualization of our data and make clear the level of insights that they allow versus their limitations.

      • Furthermore, the conclusion that acetylcholine is a major neurotransmitter regulating the PSG is based on α-bungarotoxin labeling, ChAT expression and phylogenetic identification of nicotinic receptor homologs.

      My concerns is that α-bungarotoxin binding specificity in cephalopods has not been demonstrated. Although I agree that α-bungarotoxin binding is generally associated with nAChRs binding (not only muscle-type, but also α-7 and α-9 containing neuronal receptors), there are also evidences (weaker, I admit) that it may also interact with GABAA receptors, as well as some VGICs (potassium, sodium). There are also confirmed non-canonical protein interactions, such as with Ly6-family proteins. So first, it must be determined what α-bungarotoxin is binding to in cephalopods. In addition, ChAT expression alone does not prove cholinergic neurotransmission, and to be fair, the receptor phylogeny does not establish receptor expression in the specific cells observed histologically (only that it is found in the venom gland transcriptome). Importantly, from what I could see, no co-localization is shown between, cholinergic neurons, receptor-positive cells, and muscle fibers.

      The cholinergic circuit remains hypothetical, although it can be defended as a strong hypothesis. To reflect this, the authors should moderate their interpretation and explicitly distinguish between evidence for cholinergic potential and/or demonstrated cholinergic signaling.

      Author’s response: While we agree with the reviewer that the target specificity of each marker needs to be verified in new species and taxa under study, we would like to point towards a study in the branchial heart in Sepia officinalis (Gebauer & Versen, 1998) showing strong inhibition of nicotinic signaling by alpha-bungarotoxin addition even in presence of high concentrations of the agonists. Additionally, alpha-bungarotoxin is widespread in use for muscle type nAChR identification and characterization across taxa, and the binding affinity has been proven to be exceptionally high for these receptors, with off-target affinities being significantly lower. Therefore, as the reviewer mentions, we have a “strong hypothesis” to suggest a cholinergic circuit is present in the venom gland. To highlight these target specificities and affinities for the markers used, we will add an additional supplementary section on their known molecular targets and their verification in cephalopods. Further, we will extend the discussion to point towards the limited knowledge on off-targets, receptor sequence/structural similarity ambiguities between vertebrates and invertebrates, and the resulting limitations on the data interpretation.

      • The authors repeatedly suggest dopaminergic participation in venom release. Here, however, the evidence consists solely of transcriptomic identification of receptors clustering with dopamine-gated channels in phylogenetic analyses. Quid of dopamine synthesis enzymes, dopamine localization, dopaminergic neurons, dopamine-dependent physiological responses?

      I believe the statements implying a role for dopamine in venom secretion are premature, so the authors should restrict discussion of dopamine to a candidate pathway requiring future validation.

      Author’s response: We agree with the reviewer that this conclusion needs a more tempered tone aligning it with the indicators obtained from the phylogenetic analysis and will revise the text to indicate the need for future validation. However, we also would like to highlight that recently dopamine-gated channels have been verified and functionally characterized in the optic lobe of cephalopods [1], and these are clustering together with the PSG transcripts of the channels found in all three species of cephalopods included in this analysis, providing strong evidence for our hypothesis that there is dopaminergic participation in venom release.

      [1] Courtney, A., Styfhals, R., Van Dijck, M., Boulanger, J., Geenen, L., Lanoizelet, M., ... & Schafer, W. R. (2025). Novel dopaminergic neurotransmission in the Octopus visual system. bioRxiv, 2025-04.

      I also have some less important or minor comments:

      • The distinction between secretory and smooth-striated tubules is a central component of the proposed secretion model. Yet, the functional interpretation of tubular subtypes remains uncertain, as the authors acknowledge that differentiation was clear in octopus, but incomplete or uncertain in squid, and micro-CT identification of secretory tubules in E. berryi was inconclusive.

      Given this uncertainty, the proposed model of distal production and proximal transport remains speculative and additional histochemical characterization or molecular markers would strengthen these interpretations.

      Author’s response: As highlighted by the reviewer, we stated that the functional organization within the gland in secretory and transportation regions is only one potential scenario and needs cross-species comparison and functional verification. In order to ensure that this differentiation is a proposed scenario of the functional architecture of the gland, we will rephrase the corresponding sections. We will revise the text to clarify that the findings in the octopus is a proposed model that needs to be confirmed in squids and cuttlefish.

      • p6 The reference for the statement that « holocrine secretion is proposed for cone snail » is not convincing (Vonk et al. deals with scorpions, and only a schematic drawing is presented to illustrate the different secretion routes for various venomous animals) so the authors should provide a research article as reference, but I am not aware of any published study that explicitly tested this hypothesis and found positive evidence (need to show that whole-cell disintegration contributes substantially to venom release). « Insoluble granules » are seen inside secretory cells and sometimes also retrieved in milked venoms, but not always. If holocrine secretion occurs, they should always be there, if not, merocrine secretion might also be at work.

      Author’s response: There seems to be some confusion about the cone snail holocrine secretion statement and we’d like to clarify. As written in the manuscript, “holocrine secretion is proposed for cone snails,” which means it is not definitive and is an ongoing topic of discussion in the field. A recent publication on cone snail venom release also suggests a holocrine release, and we will revise the text to mention this manuscript and update the references to include this citation [2].

      [2] Rogalski, A., Himaya, S. W. A., & Lewis, R. J. (2023). Coordinated adaptations define the ontogenetic shift from worm-to fish-hunting in a venomous cone snail. Nature Communications, 14(1), 3287.

      • A minor but possibly relevant comment on the fact that most antibodies were originally developed for vertebrate systems. Although some validation controls are shown, stronger evidence for specificity in cephalopod tissues would be desirable, particularly for NeuN, α-actin, and neurofilament (SMI-31).

      Author’s response: As discussed for the bungarotoxin comment above, we agree on the importance of highlighting the target specificity of the histological markers in cephalopod tissue. Further, for monoclonal antibodies a single epitope is being targeted determining the high target specificity, thus target presence is assumed with signal recording. For polyclonal antibodies, such as the NeuN antibody, several epitopes are being targeted given better cross-taxa applicability and higher sensitivity, but increases the chance of off-target binding. Therefore, we will further establish a supplementary section on the molecular targets, tested control tissues for contextualization of PSG signal observations, and previous studies assessing the targets of these markers in cephalopods or molluscs.

      Reviewer #1 (Significance (Required)):

      Overall this manuscript provides an important anatomical and molecular characterization of cephalopod posterior salivary glands and generates valuable hypotheses regarding the neural regulation of venom secretion. The comparative dataset is extensive and likely to be of interest to researchers in cephalopod biology, neurobiology, and venom evolution. However, the major mechanistic conclusions currently exceed the strength of the evidence so that significant revision is therefore required to align the claims with the data.

      Reviewer #2 (Evidence, reproducibility and clarity (Required):

      The paper uses modern approaches to address a fascinating question that has been relatively unexplored since J.Z. Young's morphological work in the 1960s: how venom release is controlled in the posterior salivary gland (PSG) of coleoid cephalopods. The authors' work uses five species spanning the three major coleoid groups: octopus (Octopus bimaculoides), squid (Doryteuthis pealeii, Euprymna berryi), and cuttlefish (Ascarosepion bandense, Sepia officinalis), and employs a broad toolkit: synchrotron micro-CT, a panel of histological and immunohistochemical stains, multiplexed HCR, a comparative phylogeny of cys-loop ligand-gated ion channels, and an ex vivo calcium-imaging proof of concept.

      Structurally, they identified a shared two-tubule architecture (proximal-striated and distal-secretory), a peri-tubular actin layer they interpret as a possible circular smooth-muscle layer, and dense innervation labeled by neurofilament and synapsin. They also show α-bungarotoxin-positive nicotinic receptor signal and ChAT expression near putative neuromuscular contacts in some of the species examined. In the PSG transcriptome datasets with confirmed LGIC recovery, acetylcholine- and dopamine-gated cation-channel groups are prominent. A live E. berryi PSG explant shows spontaneous calcium activity. From these findings, authors propose a working model in which cholinergic and dopaminergic input mediate secretory tubule activity and contraction of the surrounding muscle to move venom toward the beak.

      The study is mainly descriptive and comparative. My major points address matching the strongest claims to the data presented. The minor points are figure, reference, and reproducibility fixes, several of which simply need a careful pass.

      Major comments:

      1. The calcium imaging is described inconsistently between the abstract and the rest of the paper. The abstract says "ex vivo stimulation of the PSG elicits calcium signaling throughout the gland" (L34-35), but no stimulation was applied. The methods describe only dissection, incubation in CAL-520, and epifluorescence imaging. The octomedia is physiological (not depolarizing), and the analysis step refers to "spontaneously active regions." Everywhere else the authors are more cautious. The section heading only "suggests" calcium signaling activation (L216), and the results call the activity spontaneous and the experiment a pilot study, noting that "further analyses are required to determine if this activity is neural or muscular" (L222-228). "Throughout the gland" also overstates a result localized near the gland edge.
      2. Required either way: drop "stimulation" and "elicits" from the abstract, describe what was actually done, and soften "consistent with neural regulation" for a spontaneous signal of unknown cellular origin.
      3. Optional (only if the authors want to keep the neural-regulation speculation) the explant preparation and the CAL-520 protocol already exist, so a depolarizing stim with a cholinergic agonist and antagonist control, recorded the same way, would show whether the signal is evokable and start to separate neural from muscular sources. This is an extension of the existing assay, not a new experimennt.
      4. Reframing the section as spontaneous pilot activity resolves the point just as well. I am not asking for new data as a condition of publication, and either is fine.
      5. The Results subheading "Live staining of squid venom gland explant" (L216) is imprecise. CAL-520 is a calcium indicator rather than a stain, and the experiment is live-tissue calcium imaging of an ex vivo explant, so the heading reads as histology on living tissue and underdescribes what was actually done.

      Author’s response: The reviewer identified this analysis correctly as a pilot study and a bridging analysis between the histology based molecular indicators and the follow-up in vivo venom secretion stimulation study. We will rephrase the corresponding sections to ensure that it is consistently stated as a pilot study for spontaneous calcium activity in explants.

      We would also like to point out that currently the molecular agonists of the nicotinic receptors in cephalopods, and even most of the other receptor types currently identified in these lineages, have not yet been determined. This functional characterization and ligand identification could be a stand-alone study and out of scope for the present study. In our current manuscript we provide preliminary evidence for potential calcium signaling in the gland. For our revised manuscript, we are attempting to include additional experiments to describe the variation of calcium activity in explanted venom glands upon addition of acetylcholine in comparison to the baseline spontaneous activity previously observed.

      Replication and quantification are not reported. The descriptive observations are convincing overall, but the comparative language ("conserved," L36, L90; "stereotyped," L30) rests on single representative images, and I couldnt find n (animals, sections) for any stain or any quantification of the spatial and cross-species claims. For a claim about conservation across lineages, that support is necessary, and does not require new experiments.

      • Give n (animals and sections) per stain per species.

      • Where the claim is about spatial pattern (peripheral versus central, peri-tubular versus luminal), add basic quantification.

      • Several claims are clearly meant to be qualitative. Say so, and be explicit about which conclusions are based on representative examples vs replicated or quantified observations.

      • This should be achievable from the existing data.

      Author’s response: We agree the replication of the experiments is crucial to ensure reproducibility and data interpretation and we will revise the corresponding sections to reflect quantification of experimental data, and where appropriate indicate if results are purely qualitative. While several tissue sections per animal have been stained, replication is most representative across specimens. This has been challenged by the accessibility of the study species, but also their maturity stage and related body sizes. We address these and other limitations in a dedicated supplementary section.

      The actin antibody needs to be identified clearly. The main text calls Abcam ab119952 an "α-actin" marker, but the supplement consistently call it pan actin: the section is titled "Alpha-Pan Actin" and the body refers to the "alpha-pan actin antibody" and the "anti-pan actin antibody (ab119952)" (suppl. L140). The product documentation is ambiguous: ab119952 (clone 4A4) is described both as a pan-actin antibody and as an alpha smooth-muscle actin antibody, with an ACTA2 immunogen. As written, the methods describe a pan-actin antibody, which does not support "confirms a circular smooth muscle layer".

      • Essential and non-experimental: state exactly which epitope was detected, and soften "confirms" to match the results text, which already says the staining "provides molecular evidence for the presence of [a] proposed circular smooth muscle layer"

      • Optional: support the smooth-muscle claim with an independent smooth-muscle marker or a proper control.

      Author’s response: We would like to thank the reviewer for this critical perspective on the marker used for smooth muscle actin. After a discussion with the scientific support of Abcam, advising that while smooth muscle actin is detected with higher affinity, skeletal muscle and gamma actin can also be detected. Therefore, we will repeat these experiments aiming for separate staining of smooth muscle actin and F-actin detecting across all isoforms and with higher species coverage. Accordingly, these sections will be revised to address the actin isoform differentiation, in addition to the previously mentioned marker target section in the supplements.

      The title and abstract claim more than the present data show. The paper establishes molecular and structural underpinnings for neuromuscular control- tubule differentiation, peri-tubular actin, dense innervation, nicotinic receptor signal at putative junctions, ChAT expression in a subset of species, a cationic acetylcholine and dopamine channel repertoire, and spontaneous calcium activity. It does not show signal moving from neuron to muscle or secretory epithelium to secretion. "Elucidating a Neural-to-Secretory Pathway," may be too strong. The discussion is careful and uses the right tone ("working model," "potential"). Please bring the title and abstract in line with the hedging you already use. No new experiments are needed.

      Author’s response: In agreement with the reviewer, we have revised the title to better reflect the nature of our results. Our new title is: Neurons, Muscles, and Venom: Molecular and Structural Insights into Venom Release in Cephalopods. Additionally, we have revised the abstract to be more tempered about our findings.

      The phylogeny needs node support reported for the load-bearing nodes, and one label should be reconsidered. The interpretation rests on a few specific sister relationships, especially PSG unknown 3 sitting next to the cephalopod chemotactile receptors, which is what the chemotactile-trigger idea is built on. The absence of cephalopod sequences from the glycine, serotonin, and anionic ACh groups is also relevant, since it is used to rule signaling modes in or out. The methods report 1,000 ultrafast bootstraps (L536), so the values exist. They just don't appear on Figure 4 or in the text for the nodes that matter.

      • Report bootstrap values for the nodes the interpretation depends on, and soften where support is weak. A small supplementary subtree of the PSG-unknown clades with their nearest neighbors would probably be easier to read than annotating Figure 4 directly.

      • "PSG unknown 1" is described as a "cephalopod-specific subfamily," but its 11 sequences are all from A. bandense (L190-191). Is "A. bandense-specific in this dataset" not more accurate? PSG unknown 2 spans all three species and does support the cephalopod-wide label.

      __Author’s response: __As proposed by the reviewer we will add bootstrap values to the phylogeny, and address the clade naming as needed. We would like to highlight that the PSG unknown 1 cluster includes only PSG sequences from A. bandense but further non-PSG sequences from E. berryi and Octopus sinensis cluster within this clade, and thus cephalopod-specific subfamily remains accurate. We will revise the result section to clarify this point.

      Reproducibility:

      Methods are strong. The supplement is detailed on staining protocols, the fixative comparison, the transcriptome pipeline, and the phylogenetics, with software versions throughout. The gaps that would stop someone reproducing the work are the replication numbers (above), acquisition and processing settings for any quantitative fluorescence comparison, and two deposition items. The data-availability statement says the paper reports no original code, but the calcium analysis used a custom Python script. Deposit the script or correct the statement. On the transcriptomes, two of four PSG datasets have public SRA accessions (D. pealeii SRX14223474, O. bimaculoides SRX1045409), but E. berryi and A. bandense are listed only as "unpublished." A. bandense in particular is included in the phylogeny, so accessions for those two would help.

      Author’s response: __We thank the reviewer for pointing out the missing python script that has been used to normalize and visualize the calcium activity in the regions of interest, and we will add this information in the revised manuscript. For the transcriptome dataset, we will add the accession numbers in the revised manuscript as these datasets have been uploaded to NCBI SRA. __

      Minor comments:

      Figure legends and callouts need a careful consistency pass. Most are small and several are probably production or copy-editing artifacts..

      By figure:

      Figure 1.

      • Panel b: legend lists "venom duct, salivary papilla and buccal mass," but the panel labels Buccal Mass, ASG, PSG, Digestive Gland, and Oesophagus. Reconcile.

      • Panel c (L683-684): the color key is hard to follow. The legend gives "distal-secretory (pink)" and "digestive gland (purple)," but in the image the saturated magenta is the distal-secretory tubules and the digestive gland looks grey-lavender. The inset confirms distal in magenta, proximal in green. The "corresponding color code" does not carry across panels: in e the key uses green for proximal tubule lumen and purple for digestive gland (L690), while in c the magenta marks the distal tubules. Make the color assignments panel-specific.

      • Panel d : "PSGPT" in the image, "PSGT" in the legend. The supplement text uses PSGT, while supplementary Fig. S1 labels it PSGPT. Pick one and use it throughout.

      • Panel a: the tree is titled "venomous animal taxa" but includes non-venomous outgroups (sponges, echinoderms). "Major animal lineages, with venomous coleoids highlighted" would be more accurate.

      __Author’s response: __We thank the reviewer for the critical view on the details of the figures. We will ensure that the small discrepancies in the figure labels and the color codes are being addressed in the revised manuscript.

      Figure 2.

      • The g-i legend has the wrong panel letters: it reads "E. berryi (e), O. bimaculoides (f), and Ascarosepion bandense (g)", but the TEM panels are g, h, and i (in-image labels EB, OB, AB confirm). The letters should be (g), (h), (i).

      Author’s response: Will be corrected in the revised manuscript.

      Figure 3.

      • Panel r legend (L720): "regions 1-3 highlighted in p" should be "in q." The regions are marked on the CAL-520 projection (q), not the O. bimaculoides ChAT panel (p).

      • Panel h is a chromogenic (DAB) Synorf-1 image with no DAPI, but the d-h legend describes synapsin "along with nuclear staining (DAPI: grey)." That holds for d-g. Note the exception for h.

      __Author’s response: __Will be correct in the revised manuscript.

      Text, references, and supplement.

      • The species epithet is inconsistent: "Ascarosepion bandense" in the main text and Figs 2-3, "Ascarosepion bandensis" in supplementary Fig. S3a, and "Sepia bandensis (Ascarosepion bandense)" in Table S1. The genus is inconsistent in the literature, so just be consistent here

      __Author’s response: __We thank the reviewer for catching these typos. As indicated the genus is inconsistent in the literature and was revised as we were writing the manuscript. We will correct the inconsistency in the revised manuscript.

      • Small writing fixes: "predominancy" should be "predominance," "mucopolysaccharids" should be "mucopolysaccharides," "communication sides" should be "sites," and the supplementary Fig. S2 title "two differentiate tubule types" should likely read "two differentiated tubule types."

      __Author’s response: __We will correct these typos in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      General assessment and nature of the advance: This is mainly a descriptive and comparative study, and its strengths are breadth and integration. The advance is partly conceptual and substantially technical. I'm not aware of an existing cross-lineage molecular and structural survey of PSG neuromuscular organization across octopus, squid, and cuttlefish. The idea that venom release is under neuromuscular control has been proposed previously, but what is new here is putting it on a molecular and cross-lineage footing, with marker localization, receptor-like signal, and a channel repertoire across the three groups. Synchrotron micro-CT, multiplexed HCR and immunohistochemistry, a comparative cys-loop LGIC phylogeny, and a live ex vivo calcium recording together provide quite a versatile platform, and the paper will be a great resource and a methodological template. Its limitation is the gap between the structural and molecular evidence and the functional language used to describe it, which the major comments describe and can be straightforward to address.

      Context. The relevant prior work includes Young, House, and the more recent histology and transcriptomics in S. officinalis and O. vulgaris, all of which the authors cite. The study also sits within a wider literature on venom-gland innervation in spiders, scorpions, snakes, and centipedes, which the authors use to argue that neuronal control of venom release is a recurring strategy implemented with different molecular components. The most novel proposal, a chemotactile route to PSG activation through the superior buccal lobe that could link the arms and oral sensory systems to venom release, connects to van Giesen et al. (2020) and the group's own micro-CT work. On the current data this remains speculative and should be framed as a hypothesis.

      Audience. Comparative and evolutionary neurobiology, invertebrate zoology, venom biology, and cephalopod biology, with some reach into neurosecretion and neuromuscular-control work. The integrative approach and the resource value should extend its readership beyond the immediate field.

      Reviewer expertise. Comparative and molecular neuroscience of cephalopods, the octopus peripheral nervous system, calcium imaging, multiplexed HCR, and transcriptomics. I am qualified to evaluate the molecular labeling, imaging, and comparative-neurobiology claims. I cannot independently assess the micro-CT reconstruction pipeline or the finer points of the ligand-gated ion channel phylogenetics, and I have weighted my comments on those sections accordingly.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      This is a comprehensive study of the cephalopod venom gland by tissue staining , micro-CT imaging, calcium imaging and phylogenetics. The study detects markers of muscular and neuronal tissues in the venom gland along with excitatory acetylcholine- and dopamine-gated receptors suggesting neuro-muscular control of venom release.

      I have only minor comments:

      Table S1: It is unclear why not all the experiments where performed with all the species. Alternatively, why didn't the authors run all the experiments on one representative species?

      Author’s response: We thank the author for this question and as we will highlight in the revised manuscript the experimental coverage across species was related to the availability of specimens for the corresponding analyses. When working with non-traditional model systems, it’s not possible to always have access to specimens of the adequate size and/or maturity stage. We aim to include coverage of techniques for all species used, but this is not achievable due to the complexity of the approaches and the mentioned accessibility of specimens.

      We opted not to focus on a single species because cephalopods comprise approximately 800 coleoid species in the three major lineages of squid, octopus and cuttlefish. The posterior salivary gland(s) have been shown to produce toxic secretions and are to date assumed to be the primary venom producing organ. The community is still assessing the histological and functional diversification of these glands, but already recorded multiple differences in structural organization, paired or single glands, size and protein content across the species-tree but even across ontogenesis. Therefore, for a general understanding on the innervation pattern of the venom gland in cephalopods a cross-species and cross-coleoid comparison has higher potential to give a comprehensive picture of the functional organization within the gland and finally of the secretion process itself and brain-bite connection.

      Line 52: "dating back 500 million years" - cnidarians are older than 500 million years, so it would be more precise to say "dating back at least 500 million years".

      __Author’s response: __Will be corrected in the revised manuscript.

      Figure 1a: even though some sponges are toxic, they don't have a toxin-injection apparatus, so they probably cannot be called venomous.

      __Author’s response: __The species of the genus Haliclona have been shown to recruit nematocytes from cnidaria, employing them for prey capture. This recruitment mechanism is thought to be enough to categorize them as venomous is similar to other kleptocyte venomous species.

      Line 120: a full stop or an "and" is missing after D. pealeii

      __Author’s response: __Will be corrected in the revised manuscript.

      Reviewer #3 (Significance (Required)):

      This study will be mainly interesting to zoologists and venom researchers. The study advances our understanding of how the venom release is controlled in one of the important marine animals. While this work provides important insights into the anatomy as well as molecular and cellular basis of the cephalopod venom gland function, it could be strengthened by in vivo experiments showing correlation of the calcium signals with venom release and the role of the ion channels in this process (e.g. incubation of the venom gland in high concentrations of acetylcholine and dopamine).

      __Author’s response: __We would like to thank the reviewer for his positive evaluation of our work, and agree that future studies are required to characterize the receptors found in the PSG with the neuronal control of the secretion process with verified venom secretion. We believe that the in vivo experiments that would be necessary to demonstrate this are out of scope for the present study, however an ex vivo experiment observing the activity induced by high neurotransmitter exposure could give additional insights. We are currently attempting these experiments and will revise the manuscript with our findings if the experiments are conclusive.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      __Author’s response: __To ensure a timely revision of the manuscript we have addressed the minor typos and grammatical issues pointed out by the reviewers. Additionally, we’ve revised the title and abstract to tone down references to neuronal control and secretory release of cephalopod venom. Experimentally, we are currently working on additional histological tissue stainings for a more comprehensive comparative framework across the cephalopod lineages used in the study. As mentioned previously, this is based on availability of specimens and we will add any additional species coverage we can achieve. As stated in the replies above and addressing the concern regarding target specificity of the cellular markers used in our study, we revised the manuscript to include a section in the supplementary material section, and will further integrate these affinity vs limitations in the final revised discussion. Additionally, we are conducting additional ex vivo implant experiments of venom gland calcium imaging using acetylcholine and dopamine perturbations versus spontaneous signaling to characterize receptor activity. The revision of the discussion and figures will be finalized after completing the additional experiments.

      4. Description of analyses that authors prefer not to carry out

      __Author’s response: __The reviewers pointed out that a final verification of in vivo neuronal stimulation with subsequent venom secretion would be crucial to elucidate the mechanism of neuronal control of the venom gland. While we agree that this is the ultimate verification, the complexity of these analyses and the required additional functional receptor characterization and neurotransmitter system identification are out of scope of our study. However, we believe that the revision of our manuscript to highlight the molecular and structural foundations, and additional histological and ex vivo calcium activity experiments will provide a more comprehensive comparison of the venom gland functionality across cephalopod lineages. Together with tempering the conclusions about neuronal control and secretory venom release, these changes improve the strength of our study to be accepted for publication.

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      Referee #3

      Evidence, reproducibility and clarity

      This is a comprehensive study of the cephalopod venom gland by tissue staining , micro-CT imaging, calcium imaging and phylogenetics. The study detects markers of muscular and neuronal tissues in the venom gland along with excitatory acetylcholine- and dopamine-gated receptors suggesting neuro-muscular control of venom release.

      I have only minor comments:

      Table S1: It is unclear why not all the experiments where performed with all the species. Alternatively, why didn't the authors run all the experiments on one representative species?

      Line 52: "dating back 500 million years" - cnidarians are older than 500 million years, so it would be more precise to say "dating back at least 500 million years".

      Figure 1a: even though some sponges are toxic, they don't have a toxin-injection apparatus, so they probably cannot be called venomous.

      Line 120: a full stop or an "and" is missing after D. pealeii

      Significance

      This study will be mainly interesting to zoologists and venom researchers. The study advances our understanding of how the venom release is controlled in one of the important marine animals. While this work provides important insights into the anatomy as well as molecular and cellular basis of the cephalopod venom gland function, it could be strengthened by in vivo experiments showing correlation of the calcium signals with venom release and the role of the ion channels in this process (e.g. incubation of the venom gland in high concentrations of acetylcholine and dopamine).

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      The paper uses modern approaches to address a fascinating question that has been relatively unexplored since J.Z. Young's morphological work in the 1960s: how venom release is controlled in the posterior salivary gland (PSG) of coleoid cephalopods. The authors' work uses five species spanning the three major coleoid groups: octopus (Octopus bimaculoides), squid (Doryteuthis pealeii, Euprymna berryi), and cuttlefish (Ascarosepion bandense, Sepia officinalis), and employs a broad toolkit: synchrotron micro-CT, a panel of histological and immunohistochemical stains, multiplexed HCR, a comparative phylogeny of cys-loop ligand-gated ion channels, and an ex vivo calcium-imaging proof of concept. Structurally, they identified a shared two-tubule architecture (proximal-striated and distal-secretory), a peri-tubular actin layer they interpret as a possible circular smooth-muscle layer, and dense innervation labeled by neurofilament and synapsin. They also show α-bungarotoxin-positive nicotinic receptor signal and ChAT expression near putative neuromuscular contacts in some of the species examined. In the PSG transcriptome datasets with confirmed LGIC recovery, acetylcholine- and dopamine-gated cation-channel groups are prominent. A live E. berryi PSG explant shows spontaneous calcium activity. From these findings, authors propose a working model in which cholinergic and dopaminergic input mediate secretory tubule activity and contraction of the surrounding muscle to move venom toward the beak. The study is mainly descriptive and comparative. My major points address matching the strongest claims to the data presented. The minor points are figure, reference, and reproducibility fixes, several of which simply need a careful pass.

      Major comments:

      1. The calcium imaging is described inconsistently between the abstract and the rest of the paper. The abstract says "ex vivo stimulation of the PSG elicits calcium signaling throughout the gland" (L34-35), but no stimulation was applied. The methods describe only dissection, incubation in CAL-520, and epifluorescence imaging. The octomedia is physiological (not depolarizing), and the analysis step refers to "spontaneously active regions." Everywhere else the authors are more cautious. The section heading only "suggests" calcium signaling activation (L216), and the results call the activity spontaneous and the experiment a pilot study, noting that "further analyses are required to determine if this activity is neural or muscular" (L222-228). "Throughout the gland" also overstates a result localized near the gland edge.
        • Required either way: drop "stimulation" and "elicits" from the abstract, describe what was actually done, and soften "consistent with neural regulation" for a spontaneous signal of unknown cellular origin.
        • Optional (only if the authors want to keep the neural-regulation speculation) the explant preparation and the CAL-520 protocol already exist, so a depolarizing stim with a cholinergic agonist and antagonist control, recorded the same way, would show whether the signal is evokable and start to separate neural from muscular sources. This is an extension of the existing assay, not a new experimennt.
        • Reframing the section as spontaneous pilot activity resolves the point just as well. I am not asking for new data as a condition of publication, and either is fine.
        • The Results subheading "Live staining of squid venom gland explant" (L216) is imprecise. CAL-520 is a calcium indicator rather than a stain, and the experiment is live-tissue calcium imaging of an ex vivo explant, so the heading reads as histology on living tissue and underdescribes what was actually done.
      2. Replication and quantification are not reported. The descriptive observations are convincing overall, but the comparative language ("conserved," L36, L90; "stereotyped," L30) rests on single representative images, and I couldnt find n (animals, sections) for any stain or any quantification of the spatial and cross-species claims. For a claim about conservation across lineages, that support is necessary, and does not require new experiments.
        • Give n (animals and sections) per stain per species.
        • Where the claim is about spatial pattern (peripheral versus central, peri-tubular versus luminal), add basic quantification.
        • Several claims are clearly meant to be qualitative. Say so, and be explicit about which conclusions are based on representative examples vs replicated or quantified observations.
        • This should be achievable from the existing data.
      3. The actin antibody needs to be identified clearly. The main text calls Abcam ab119952 an "α-actin" marker, but the supplement consistently call it pan actin: the section is titled "Alpha-Pan Actin" and the body refers to the "alpha-pan actin antibody" and the "anti-pan actin antibody (ab119952)" (suppl. L140). The product documentation is ambiguous: ab119952 (clone 4A4) is described both as a pan-actin antibody and as an alpha smooth-muscle actin antibody, with an ACTA2 immunogen. As written, the methods describe a pan-actin antibody, which does not support "confirms a circular smooth muscle layer".
        • Essential and non-experimental: state exactly which epitope was detected, and soften "confirms" to match the results text, which already says the staining "provides molecular evidence for the presence of [a] proposed circular smooth muscle layer"
        • Optional: support the smooth-muscle claim with an independent smooth-muscle marker or a proper control.
      4. The title and abstract claim more than the present data show. The paper establishes molecular and structural underpinnings for neuromuscular control- tubule differentiation, peri-tubular actin, dense innervation, nicotinic receptor signal at putative junctions, ChAT expression in a subset of species, a cationic acetylcholine and dopamine channel repertoire, and spontaneous calcium activity. It does not show signal moving from neuron to muscle or secretory epithelium to secretion. "Elucidating a Neural-to-Secretory Pathway," may be too strong. The discussion is careful and uses the right tone ("working model," "potential"). Please bring the title and abstract in line with the hedging you already use. No new experiments are needed.
      5. The phylogeny needs node support reported for the load-bearing nodes, and one label should be reconsidered. The interpretation rests on a few specific sister relationships, especially PSG unknown 3 sitting next to the cephalopod chemotactile receptors, which is what the chemotactile-trigger idea is built on. The absence of cephalopod sequences from the glycine, serotonin, and anionic ACh groups is also relevant, since it is used to rule signaling modes in or out. The methods report 1,000 ultrafast bootstraps (L536), so the values exist. They just don't appear on Figure 4 or in the text for the nodes that matter.

      6. Report bootstrap values for the nodes the interpretation depends on, and soften where support is weak. A small supplementary subtree of the PSG-unknown clades with their nearest neighbors would probably be easier to read than annotating Figure 4 directly.

      7. "PSG unknown 1" is described as a "cephalopod-specific subfamily," but its 11 sequences are all from A. bandense (L190-191). Is "A. bandense-specific in this dataset" not more accurate? PSG unknown 2 spans all three species and does support the cephalopod-wide label.

      Reproducibility:

      Methods are strong. The supplement is detailed on staining protocols, the fixative comparison, the transcriptome pipeline, and the phylogenetics, with software versions throughout. The gaps that would stop someone reproducing the work are the replication numbers (above), acquisition and processing settings for any quantitative fluorescence comparison, and two deposition items. The data-availability statement says the paper reports no original code, but the calcium analysis used a custom Python script. Deposit the script or correct the statement. On the transcriptomes, two of four PSG datasets have public SRA accessions (D. pealeii SRX14223474, O. bimaculoides SRX1045409), but E. berryi and A. bandense are listed only as "unpublished." A. bandense in particular is included in the phylogeny, so accessions for those two would help.

      Minor comments:

      Figure legends and callouts need a careful consistency pass. Most are small and several are probably production or copy-editing artifacts.. By figure: Figure 1.

      • Panel b: legend lists "venom duct, salivary papilla and buccal mass," but the panel labels Buccal Mass, ASG, PSG, Digestive Gland, and Oesophagus. Reconcile.
      • Panel c (L683-684): the color key is hard to follow. The legend gives "distal-secretory (pink)" and "digestive gland (purple)," but in the image the saturated magenta is the distal-secretory tubules and the digestive gland looks grey-lavender. The inset confirms distal in magenta, proximal in green. The "corresponding color code" does not carry across panels: in e the key uses green for proximal tubule lumen and purple for digestive gland (L690), while in c the magenta marks the distal tubules. Make the color assignments panel-specific.
      • Panel d : "PSGPT" in the image, "PSGT" in the legend. The supplement text uses PSGT, while supplementary Fig. S1 labels it PSGPT. Pick one and use it throughout.
      • Panel a: the tree is titled "venomous animal taxa" but includes non-venomous outgroups (sponges, echinoderms). "Major animal lineages, with venomous coleoids highlighted" would be more accurate.

      Figure 2.

      • The g-i legend has the wrong panel letters: it reads "E. berryi (e), O. bimaculoides (f), and Ascarosepion bandense (g)", but the TEM panels are g, h, and i (in-image labels EB, OB, AB confirm). The letters should be (g), (h), (i).

      Figure 3.

      • Panel r legend (L720): "regions 1-3 highlighted in p" should be "in q." The regions are marked on the CAL-520 projection (q), not the O. bimaculoides ChAT panel (p).
      • Panel h is a chromogenic (DAB) Synorf-1 image with no DAPI, but the d-h legend describes synapsin "along with nuclear staining (DAPI: grey)." That holds for d-g. Note the exception for h.

      Text, references, and supplement.

      • The species epithet is inconsistent: "Ascarosepion bandense" in the main text and Figs 2-3, "Ascarosepion bandensis" in supplementary Fig. S3a, and "Sepia bandensis (Ascarosepion bandense)" in Table S1. The genus is inconsistent in the literature, so just be consistent here
      • Small writing fixes: "predominancy" should be "predominance," "mucopolysaccharids" should be "mucopolysaccharides," "communication sides" should be "sites," and the supplementary Fig. S2 title "two differentiate tubule types" should likely read "two differentiated tubule types."

      Significance

      General assessment and nature of the advance: This is mainly a descriptive and comparative study, and its strengths are breadth and integration. The advance is partly conceptual and substantially technical. I'm not aware of an existing cross-lineage molecular and structural survey of PSG neuromuscular organization across octopus, squid, and cuttlefish. The idea that venom release is under neuromuscular control has been proposed previously, but what is new here is putting it on a molecular and cross-lineage footing, with marker localization, receptor-like signal, and a channel repertoire across the three groups. Synchrotron micro-CT, multiplexed HCR and immunohistochemistry, a comparative cys-loop LGIC phylogeny, and a live ex vivo calcium recording together provide quite a versatile platform, and the paper will be a great resource and a methodological template. Its limitation is the gap between the structural and molecular evidence and the functional language used to describe it, which the major comments describe and can be straightforward to address.

      Context. The relevant prior work includes Young, House, and the more recent histology and transcriptomics in S. officinalis and O. vulgaris, all of which the authors cite. The study also sits within a wider literature on venom-gland innervation in spiders, scorpions, snakes, and centipedes, which the authors use to argue that neuronal control of venom release is a recurring strategy implemented with different molecular components. The most novel proposal, a chemotactile route to PSG activation through the superior buccal lobe that could link the arms and oral sensory systems to venom release, connects to van Giesen et al. (2020) and the group's own micro-CT work. On the current data this remains speculative and should be framed as a hypothesis.

      Audience. Comparative and evolutionary neurobiology, invertebrate zoology, venom biology, and cephalopod biology, with some reach into neurosecretion and neuromuscular-control work. The integrative approach and the resource value should extend its readership beyond the immediate field.

      Reviewer expertise. Comparative and molecular neuroscience of cephalopods, the octopus peripheral nervous system, calcium imaging, multiplexed HCR, and transcriptomics. I am qualified to evaluate the molecular labeling, imaging, and comparative-neurobiology claims. I cannot independently assess the micro-CT reconstruction pipeline or the finer points of the ligand-gated ion channel phylogenetics, and I have weighted my comments on those sections accordingly.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The manuscript entitled « Neurons, Muscles, and Venom: Elucidating a Neural-to-Secretory Pathway in Cephalopod Predation » by Kirchhoff et al. investigates the anatomical and molecular basis of venom release in coleoid cephalopods. The authors used a combination of histology, immunohistochemistry, micro-computed tomography, comparative phylogenetics, and ex vivo calcium imaging to conclude that venom secretion from the posterior salivary gland (PSG) is under neuromuscular control through cholinergic and potentially dopaminergic regulation. They also show that this is a conserved structural feature across octopus, squid, and cuttlefish species. Overall, this manuscript addresses an understudied topic, namely the mechanisms regulating venom secretion, which is poorly understood in most venomous systems despite their ecological and evolutionary significance. As such, this work provides an important anatomical and molecular characterization of cephalopod posterior salivary glands and generates valuable hypotheses regarding the neural regulation of venom secretion. I personally appreciate the quality of the histology work, a methodology largely neglected nowadays in a world thirsty for the use of high-tech, fancy instrumentations. Although I command the authors for this elegant and visually pleasing study, I have some reservations about the major mechanistic conclusions (especially the cholinergic and dopaminergic regulation), which to me rely on indirect evidence, as no experiment directly demonstrates neural control of venom secretion. Therefore this manuscript would benefit from additional functional validation and certainly a more cautious interpretation of the findings.

      Major Comments

      • The principal conclusion of the manuscript is that venom release is under neuronal and neuromuscular regulation, which is highly likely given the authors convincingly demonstrate the presence of actin-positive muscular structures, neuronal markers within the PSG, cholinergic-associated markers, spontaneous calcium activity. Unfortunately, none of these experiments directly demonstrate control of venom secretion ! The presented evidence establishes anatomical proximity between neural and muscular elements but does not demonstrate causality. For instance, no experiments measure venom release following neuronal stimulation or receptor activation/inhibition. Therefore I suggest that the authors substantially temper these conclusions or provide functional experiments demonstrating secretion following pharmacological or electrical stimulation.
      • Furthermore, the conclusion that acetylcholine is a major neurotransmitter regulating the PSG is based on α-bungarotoxin labeling, ChAT expression and phylogenetic identification of nicotinic receptor homologs. My concerns is that α-bungarotoxin binding specificity in cephalopods has not been demonstrated. Although I agree that α-bungarotoxin binding is generally associated with nAChRs binding (not only muscle-type, but also α-7 and α-9 containing neuronal receptors), there are also evidences (weaker, I admit) that it may also interact with GABAA receptors, as well as some VGICs (potassium, sodium). There are also confirmed non-canonical protein interactions, such as with Ly6-family proteins. So first, it must be determined what α-bungarotoxin is binding to in cephalopods. In addition, ChAT expression alone does not prove cholinergic neurotransmission, and to be fair, the receptor phylogeny does not establish receptor expression in the specific cells observed histologically (only that it is found in the venom gland transcriptome). Importantly, from what I could see, no co-localization is shown between, cholinergic neurons, receptor-positive cells, and muscle fibers. The cholinergic circuit remains hypothetical, although it can be defended as a strong hypothesis. To reflect this, the authors should moderate their interpretation and explicitly distinguish between evidence for cholinergic potential and/or demonstrated cholinergic signaling.
      • The authors repeatedly suggest dopaminergic participation in venom release. Here, however, the evidence consists solely of transcriptomic identification of receptors clustering with dopamine-gated channels in phylogenetic analyses. Quid of dopamine synthesis enzymes, dopamine localization, dopaminergic neurons, dopamine-dependent physiological responses ? I believe the statements implying a role for dopamine in venom secretion are premature, so the authors should restrict discussion of dopamine to a candidate pathway requiring future validation.

      I also have some less important or minor comments :

      • The distinction between secretory and smooth-striated tubules is a central component of the proposed secretion model. Yet, the functional interpretation of tubular subtypes remains uncertain, as the authors acknowledge that differentiation was clear in octopus, but incomplete or uncertain in squid, and micro-CT identification of secretory tubules in E. berryi was inconclusive. Given this uncertainty, the proposed model of distal production and proximal transport remains speculative and additional histochemical characterization or molecular markers would strengthen these interpretations.
      • p6 The reference for the statement that « holocrine secretion is proposed for cone snail » is not convincing (Vonk et al. deals with scorpions, and only a schematic drawing is presented to illustrate the different secretion routes for various venomous animals) so the authors should provide a research article as reference, but I am not aware of any published study that explicitly tested this hypothesis and found positive evidence (need to show that whole-cell disintegration contributes substantially to venom release). « Insoluble granules » are seen inside secretory cells and sometimes also retrieved in milked venoms, but not always. If holocrine secretion occurs, they should always be there, if not, merocrine secretion might also be at work.
      • A minor but possibly relevant comment on the fact that most antibodies were originally developed for vertebrate systems. Although some validation controls are shown, stronger evidence for specificity in cephalopod tissues would be desirable, particularly for NeuN, α-actin, and neurofilament (SMI-31).

      Significance

      Overall this manuscript provides an important anatomical and molecular characterization of cephalopod posterior salivary glands and generates valuable hypotheses regarding the neural regulation of venom secretion. The comparative dataset is extensive and likely to be of interest to researchers in cephalopod biology, neurobiology, and venom evolution. However, the major mechanistic conclusions currently exceed the strength of the evidence so that significant revision is therefore required to align the claims with the data.

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      Reply to the reviewers

      1. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees

      1. Reviewer #2

      2.

      The main conclusion of this manuscript, stated in lines 549-554, claims that the difference in pathology between Δnsp2 mutant and WT virus infection cannot be explained by distinct viral replication since both viruses replicate similarly in vitro and in vivo. However, the data presented here is supportive that Δnsp2 mutant replicates to a lower extent when compared to WT virus. This is clearly seen in Figures 1C (at 24 hpi), and Figures 1D and 1E for in vitro experiments where there is a 2-log difference at multiple timepoints.

      1. As described in the results section (line 281-287), the global growth kinetics were similar between viruses across various in vitro models. However, the magnitude of the viral loads differed from virus to virus and across models (Vero and A549-ALI higher loads for WT and A549-ACE2 higher load for ΔNsp2). We agree that this result is central in the understanding of the differences between WT and ∆NSP2 viruses. We have now planned an entire new set of viral replication experiment on A549-hACE2. For this, we will be using freshly titrated virus stocks of WT and two different ΔNsp2 clones. Quantitation of infectious particles will be performed.

      4.

      Authors do not provide a validation of the RNASeq analysis, i.e. authors must select a representative set of up- and down-regulated genes and perform RT-qPCR on the same samples to corroborate if the fold changes observed in RNASeq are also observed via an alternative method.

      1. Using RT-qPCR, we will validate the expression of representative genes involved in immune checkpoint blockade, translation initiation, histone modification and DNA damage gene sets.

      6.

      Reviewer #3

      7.

      Virus localization - IHC/ISH needed (Lines 332-337) To determine whether the observed inflammatory differences relate to differences in viral antigen/RNA distribution which also reflect replication difference (rather than purely host immune modulation by Nsp2), viral IHC or in situ hybridization (ISH) should be performed on lung sections to localize virus within the tissue and correlate with inflammatory zones. Otherwise explain that replication differences may underlie differences in host response.

      1. N protein staining (IHC) with hematoxylin counter staining will be performed to measure the viral antigen distribution and it colocalization within inflammatory zones.

      2. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      1. Reviewer #1

      2.

      Early infection kinetics in ACE2-expressing cells. The authors report transiently reduced viral RNA levels in vivo during the early phase of infection, despite comparable viral loads at later time points. It would therefore be informative to determine whether a similar phenomenon occurs in ACE2-expressing cells. Time-course analyses of viral RNA and/or viral protein expression could help establish whether the mutant primarily affects early infection events rather than overall replicative capacity.

      1. We have conducted a new set of experiments aimed at determining SARS-E RNA expression at 1, 24, 48 and 72HPI. These new results have been added to figure 1 (panels F and G) and the results were described and discussed in the line 288-293 and 577-585 :
      2. “When comparing RNA replication of two different ΔNsp2 mutant clones and the WT virus in A549-hACE2, more expression of virus E gene was observed for clone 1 at low MOI, but no global differences were observed at higher MOI or with the clone 2 (Figure 1F and 1G). Differences between corresponding infection time-points were detected at 72HPI. More robust RNA E expression was observed for clone 1 at MOI of 0.05 and 0.5, when lower E expression was significatively reduced for clone 2 at MOI 0.05. These results indicate that deletion of Nsp2 has minimal impact on SARS-CoV-2 replication under the conditions tested.”
      3. “An Nsp2 deletion mutant was successfully generated that shows similar in vitro replication kinetics relative to the wild type recombinant. Some differences in the magnitude of the viral loads were observed between mutant and WT virus, but this was inconsistent across cell culture systems used. In vitro RNA expression/replication data suggest that this difference may be caused by virus stock batch effects rather than a direct consequence of Nsp2 deletion. Although the WVPRA motif in Nsp2 sequence was reported to be required for efficient Nsp1 and Nsp3 self-cleavage [69], in the context of infection we did not observe any impairment of this process in the absence of Nsp2 under our experimental conditions. Of interest, a recent paper reported that a SARS-CoV-2 Nsp2 deletion mutant displays important viral RNA (Nsp12 and N) expression defects at very early times (3-4h) after infection [70]. We do not observe such defects at 24h, as assessed by E RNA expression measurements. Differences in the cell culture models and assessments of infectious viral progeny prevents a more detailed comparison with the study of Kim et al [70].”

      *Given the reduced pathology and disease progression in the ∆NSP2-SARS-CoV-2 infected mice, have the authors considered assessing whether mice exposed to this mutant SARS-CoV-2 strain have enhanced responses to re-challenge with WT SARS-CoV-2? *

      • We thank the reviewer for this interesting comment.Unfortunately, such experiment will not be doable as our BSL3 facility will soon close for renovation and an undetermined period of time. In an attempt to partially address the reviewer’s comment, we measured SARS-CoV2 neutralizing antibodies in mouse plasma at 7DPI. However, we could not detect any neutralizing activity for any of the groups analyzed at the lowest available plasma dilution.

      Reviewer #2

      1. The main conclusion of this manuscript, stated in lines 549-554, claims that the difference in pathology between Δnsp2 mutant and WT virus infection cannot be explained by distinct viral replication since both viruses replicate similarly in vitro and in vivo. However, the data presented here is supportive that Δnsp2 mutant replicates to a lower extent when compared to WT virus. This is clearly seen in Figures 1C (at 24 hpi), and Figures 1D and 1E for in vitro experiments where there is a 2-log difference at multiple timepoints.

      2. As presented in figure 1, WT and ∆NSP2 mutant virus grow with overall similar kinetics. However, we do observe some variations in viral titers at some time points depending in the cell culture systems used. WT produces more virus in Vero and A549-ALI cells while ∆NSP2 is producing more virus in A549-ACE2 cells. We have conducted additional experiments using two independent ∆NSP2 clones and analyzed the kinetics of E RNA expression at low and high MOI in A549-ACE2 cells. These new data (Fig 1 F, G) show similar overall kinetics of E RNA expression with both clones behaving similar to WT at high MOI and one clone (clone 1) growing better at low MOI. These results suggest that the ∆Nsp2 mutant does not have a growth defect that could explain the difference in pathogenesis. This section of the discussion has been rephrased (line 575-585 and 599-598).

      3. “An Nsp2 deletion mutant was successfully generated that shows similar in vitro replication kinetics relative to the wild type recombinant. Some differences in the magnitude of the viral loads were observed between mutant and WT virus, but this was inconsistent across cell culture systems used. In vitro RNA expression/replication data suggest that this difference may be caused by virus stock batch effects rather than a direct consequence of Nsp2 deletion. Although the WVPRA motif in Nsp2 sequence was reported to be required for efficient Nsp1 and Nsp3 self-cleavage [69], in the context of infection we did not observe any impairment of this process in the absence of Nsp2 under our experimental conditions. Of interest, a recent paper reported that a SARS-CoV-2 Nsp2 deletion mutant displays important viral RNA (Nsp12 and N) expression defects at very early times (3-4h) after infection [70]. We do not observe such defects at 24h, as assessed by E RNA expression measurements. Differences in the cell culture models and assessments of infectious viral progeny prevents a more detailed comparison with the study of Kim et al [70].”
      4. “This difference could not be explained by reduction of viral load associated with the ∆Nsp2 mutation as no significant difference were observed in lung viral titer across the infection course.”

      In Figure 1B, the expression of viral proteins Nsp1 and Nsp3 are normalized to N protein to conclude that they are not influenced by nsp2 deletion. N protein is known to have diminished expression when SARS-CoV-2 mutants replicate deficiently, which is the case of the Δnsp2 mutant under in vitro conditions. Why did the authors choose to normalize against N protein? Why was not a loading control such as beta-actin used?

      • We apologize for the confusion.The wording of the original statement was misleading and have been modified to clarify our data. Nsp1 and Nsp3 have now been normalized to overall protein content (using stain-free blot). A new bar graph showing normalized protein expression was added to figure 1B. Expression of N and S is presented to demonstrate infection level and show that the Nsp1 and Nsp3 expression are not directly impacted by the Nsp2 deletion. Text modification Line 277-279:
      • “Relative to N and S protein expression, comparable levels of Nsp1 and Nsp3 were detected in cells infected with either virus, indicating preserved polyprotein processing.”

      • The infection time-point (48h) used for western blot analysis was added to the figure caption.

      Authors analyze the expression of the envelope (E) gene to assess the viral replication in vivo. GAPDH gene is used as housekeeping gene. I wonder why authors chose these genes for this study, considering that E gene expression values range is small (101 to 10-2). N subgenomic RNA is more appropriate to measure active viral transcription since it is more abundant than E subgenomic RNAs. Also, GAPDH has been reported as not suitable housekeeping gene for SARS-CoV-2 infection (reported in PMID: 36377893).

      • A broadly adopted E sarbeco primers-probe set was used to quantify viral RNA. Using RT-ddPCR, E gene expression was 3-5 log above the limit of detection at 7DPI allowing quantification of active viral transcription. Considering that quantification other viral transcripts is pertinent to a understanding of RNA replication dynamics, quantification of all viral transcripts by RNAseq have been add to the Supplementary figure 3 and described in line 331-339:
      • “The global reduction in E transcript abundance was recapitulated in the RNAseq data (Supplementary Figure 3). In addition, reduced expression of several viral transcripts was observed in ΔNsp2-infected mice at all time points. This effect was particularly evident at 3 DPI (9/12 detected viral genes) and 7 DPI (5/12 detected viral genes), suggesting altered viral transcriptional dynamics despite comparable overall viral shedding. A trend toward improved clearance of the ΔNsp2 virus was also observed when comparing viral transcript abundance. As expected, following Nsp2 deletion, fewer transcripts mapping to the Orf1a region were detected in ΔNsp2-infected mice. This difference was not observed when transcripts aligned to the entire Orf1ab gene were analyzed, likely because Nsp2 constitutes only a small portion of the overall coding sequence.”

      • GAPDH was used as housekeeping gene, because its RNA expression was abundant enough to directly normalize the E expression which requires high dilution for ddPCR quantification. In response to the reviewer’s comment that GAPDH is not suitable as a housekeeping, the cited study refers to clinical samples. In our hands, GAPDH expression in mice remains stable across infection time points.

      There are several statements that are not correct in the Introduction: Lines 37-38: '... SARS-CoV-2 genome encodes a large replicase polyprotein that...' is not true, SARS-CoV-2 genome encodes for two large polyproteins (pp1a and pp1ab).

      • Correction was made to this statement (line 37) :
      • “The SARS-CoV-2 genome encodes two large replicase polyproteins…”

      Line 41: '... leading to the activation of type I interferon...', IFNs as molecules are not activated, the correct phrasing for this sentence would be '... lead to the production / expression of type I interferon' or '... lead to activation of type I interferon signaling cascade...'.

      • The sentence was modified (line 41) :
      • “… leading to the activation of type I interferon, Nuclear Factor-kappa B (NF-κB), and inflammasome signaling pathways.”

      Lines 52-54, this statement is a bit exaggerated. There has been a huge research effort during the past COVID-19 pandemic to understand the replication of coronavirus, especially SARS-CoV-2. Most mechanisms underlying SARS-CoV-2 infection including viral entry, formation or replication organelles, assembly, budding and exocytosis are already well characterize in the scientific literature.

      • The line 52 to 55 have been revised to clarify the knowledge gap targeted by this study :
      • “Although the SARS-CoV-2 replication cycle has been extensively characterized, the molecular determinants underlying the differences in disease severity and immune dysregulation observed between common cold coronaviruses and highly pathogenic SARS-related viruses remain incompletely understood. Several SARS-CoV-2 proteins have been proposed to contribute to viral pathogenesis [4, 23].”

      *Line 295, there seems to be a number missing in '****P

      The inclusion of uncropped Western blot data in the Supplementary Figures is appreciated. However, the approximate positions of the molecular weight markers should also be indicated in the corresponding main figure (Fig. 1B) to facilitate data interpretation.

      • Molecular weight markers have been added to Figure 1B.

      Figures 1C, 1D, 1E could be labelled with the corresponding tested cell line on top of each plot to improve interpretation of the figures.

      • The Figures 1C to 1E were modified to improve the readability.
      • 3A, the p-Value indicated with two asterisks on top of the figure might result confusing for readers. I would indicate it only in the figure caption.*
      • We modified this p-value representation to clarify the figure and keep consistence with statistical test used for the comparison all over the other figure (two-way ANOVA = #).
      • 7B, 7C, 7D additional arrows pointing to the main outlier transcripts could be added (in a similar fashion to ORF1a)*
      • Labeling of Orf1a was removed as the expression of all viral transcripts were added in the Supplementary figure 3.
      • For each volcano plots, label for the top 5 up and down regulated transcripts were added to the Figure 7.

      Supplementary Figure 4A, there is a mistake in the left-panel western blot, authors indicate nsp1 protein to a band that corresponds to nsp2 in the main figure.

      • This error has been corrected.

      Reviewer 3

      1. The absence of Nsp2 in the ΔNsp2 mutant is clear, but the claim that Nsp1 and Nsp3 levels are "comparable when normalized to N protein" is not supported by quantitative data and I suspect differences in replication both in vitro and in vivo explain the difference in host response. Of course, if there is less virus, then host response is different and typically reduced. The authors need to take a more honest approach to evaluation of the data.

      2. We apologize for the confusion. N and S were used as viral load control and not used for normalization. Normalization was done using total cellular protein. The wording of the original statement was misleading and the wording of the statement has been modified to clarify our data analysis. Line 277-279 and 301-303 :

      3. “Relative to N and S protein expression, comparable levels of Nsp1 and Nsp3 were detected in cells infected with either virus, indicating preserved polyprotein processing.”
      4. “Protein expression was normalized relative to total protein content and shown aside the blot images. The experiment was performed twice, and a representative result is shown. Uncropped and Stain-Free blots are presented in Supplementary figure 2A and 2B”

      5. We agree with the that differences in replication could influence the host response, but the objective of figure 1B was to show the direct influence of the Nsp2 deletion on Nsp1 and Nsp3 production.

      6. *Figure 1B - ΔNsp2 and its impact on nsp1 and 3 expressions. Loading control and densitometry (Lines 269-271)à It is not clear whether the absence of nsp2 impacts the nsp1/3 expression. The blot appears to show enhanced Nsp1/Nsp3 in ΔNsp2, and the stain-free total protein signal in Supplementary Figure 4 appears stronger for ΔNsp2. Please provide (a) densitometric quantification of Nsp1/Nsp3 normalized to N, and (b) a standard housekeeping protein blot (eg actin/GAPDH) in the main figure to confirm equal loading. Please also indicate the time point of sample harvest for the WB, so the replication can be correlated figure 1C. *

      7. We have now added a bar graph showing normalized expression of N, Nsp1, Nsp3 and S according to total protein content (figure 1B and supplementary figures 2A-B).Results show similar expression of Nsp1 and Nsp3 relative to N and S between WT and ∆NSP2 virus.

      8. The infection time-point (48h) used for western blot analysis was added to the figure caption.

      9. Figure 1C-E - Replication kinetics and infection dose (Lines 267-277) The claim of "comparable growth kinetics" is not fully supported - WT virus replicates faster and to higher titers in Vero and A549-ALI cultures at early/late time points, with statistically significant differences noted by the authors themselves. Additionally, infection doses were expressed as TCID50 and varied across cell models (Vero, A549-hACE2, A549-ALI), making cross-model comparison difficult and obscuring the actual MOI/copies-per-cell delivered. Please repeat key experiments using a standardized MOI or genome copies/cell across all models with the titer at 1-2h to reflect equal input virus, if different MOI or genome copies/cell dose is needed, please also provide explanation

      . 10. Same MOI of 0.15 was used for Vero and A549-hACE2 growth kinetics. The MOI used was added to the materials and methods section and in the figure 1C and 1D graph titles. 11. For A549-ALI, the cells were differentiated for 3 weeks, during this time cells were growing in a complex 3-dimension structure where not all the cells were directly exposed to virus during infection. As so using MOI to compare the virus replication with other used models didn't seem appropriate to us. For full disclosure, we added the virus dose used in this experiment the figure 1E graph title. 12. To respond to the last point of the comment, we performed an additional growth kinetics experiment on A549-hACE2 using a low and high MOI, results were added in the figure 1F and 1G. The E gene copy number was measured at 1h post-infection and no significant differences were observed between the viruses. Measurement of infectious outputs from these experiments are ongoing and will be presented in the final version of the manuscript.

      1. Cytokine/chemokine data presentation (Lines 332-337) The heatmap in Figure 3A uses a relative 0-10 scale that is difficult to interpret biologically. Please provide the raw concentration data (pg/mL or pg/mg protein) for key mediators, ideally as bar graphs/dot plots, so readers can assess whether the differences between WT and ΔNsp2 are of plausible biological/clinical magnitude. Figure 3A heatmap - visual clarity The color differences between WT and ΔNsp2 groups, and across timepoints, are subtle and difficult to distinguish visually. Consider an alternative visualization (e.g., separate scaling per mediator, or a dot plot) to improve readability

      2. As the goal of the figure 3A was to present modulation of the whole mediator panel in one figure, we chose to conserve the heatmap representation while change the scaling for 0 to 60 to improve visualization.

      3. To answer the reviewer’s comment, we added the raw concentration data for key mediators in the supplementary figures 4A to 4G.

      4. Transcript-level validation of cytokine/chemokine changes (Lines 332-337) In addition to protein-level (ELISA/multiplex) data, qPCR for key cytokine/chemokine transcripts (e.g., Ccl2, Cxcl9, Ifng, Il6, Ccl5) in lung tissue would help determine whether Nsp2 acts at the level of transcription versus translation/secretion.

      5. Gene expressions of key mediator measured by RT-qPCR were added to the supplementary figure 5. Those results were briefly described in line 364-365 :

      6. “Higer levels of transcripts for those genes were also observed (Supplementary Figure 5).”

      In vitro/in vivo correlation for CCL5/IL-6 findings (Lines 338-343) The supplementary A549-hACE2 data show that Nsp2 reduces CCL5 but not IL-6 in response to poly(I:C). Please clarify the rationale for using this specific readout to model the in vivo lung findings. Does Nsp2 expression also modulate CCL2, CXCL9, or IFN-γ in A549 cells? A more complete in vitro dissection - expressing Nsp2 vs. empty vector, stimulating with poly(I:C), and measuring IFN/ISG and chemokine/cytokine transcripts by qPCR alongside ELISA - would better support the proposed mechanism.

      • We chose to use Nsp2/GFP control expression in A549-hACE2 with Poly I:C stimulation because SARS-CoV-2 infection by itself did not induce sufficient production of those mediators to be quantified at protein level in our setting.
      • We chose to limit the number of mediators measured to CCL5 and IL6. Those specific mediators were chosen because CCL5 was the only one upregulated in vivo by the mutant and IL6 was part of those upregulated by the WT virus. In addition, IL6 have been chose as it could be easily quantified upon Poly I:C stimulation in A549.
      • Unlike IFNγ, CCL2 and CXCL9 could be induced at RNA level in A549 upon Poly I:C or SARS-CoV-2 (data not shown), but we did not evaluate the ability of Nsp2 to modulate those mediators at a protein level.
      • We did not observe modulation of IFN (RNA or protein level) or ISGs by Nsp2 in A549-hACE2 with Poly I:C stimulation, but we did not include the results as it was not a key change in mice infection.
      • Quantification of CCL5 and IL6 gene expression by RT-qPCR was added to the supplementary figures 6D and 6E.

      Discrepancy in differential expression results (Lines 475-484) The text states that ΔNsp2 infection shows a predominance of downregulated transcripts relative to WT (Figure 7B-D), yet the supplementary comparison (each condition vs. mock) reportedly shows similar proportions of up/down genes. Please clarify how these two comparisons relate, and whether they are actually measuring the same thing (ΔNsp2 vs WT directly, versus each vs mock separately).

      • This section was edited to clarify the message to take in consideration the reviewer’s comment. Line 511-519 :
      • “Across the course of infection, direct comparison of ΔNsp2- and wild-type-infected mice revealed a predominance of significantly lower transcript levels in the ΔNsp2 condition (Figure 7B–D). This effect was most pronounced at early time points, consistent with the separation observed in the UMAP analysis. Importantly, comparison of each infection condition with mock-infected controls showed that largely the same sets of genes were differentially expressed in both groups, with less than a 9% difference in the proportion of up- and downregulated genes (Supplementary Figure A–B). These findings indicate that deletion of Nsp2 does not substantially alter the transcriptional programs induced by infection but rather attenuates their magnitude. Thus, the differences observed between wild-type and ΔNsp2 infections primarily reflect weaker induction of infection-responsive genes rather than active repression in the absence of Nsp2.”

      Mouse experiment design - tissue collection details (Line 300) Please clarify, for each timepoint (3, 5, 7 DPI), which tissues/samples were collected and which assays were performed on each. The Methods describe blood collection by cardiac puncture and multiple lung lobe allocations, but Figure 2A's schematic appears to show only lung collection for titer measurements - please reconcile the figure with the text.

      • The figure 2A have been modified to improve clarity of the experimental design.

      Figure 4C - terminology clarification (Line 367) The phrase "a distinct global leukocyte profile was already evident at 5 DPI" is not self-explanatory. Please define what is meant by "global leukocyte profile" and explain how Figure 4C/4B should be read/interpreted in the main text.

      • Using “global leukocyte profile” we refer to changes in the proportion of each leucocyte populations and represented in a global manner in the stacked bar graph. Lines 399-401 were reworded for easier data interpretation by readers:
      • “Distinct global leukocyte profiles, characterized by altered proportions of immune cell populations, were already evident at 3 DPI, indicating that the two viruses elicited divergent pulmonary immune responses.”

      3. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      1. Reviewer #1

      2.

      Cell-intrinsic inflammatory responses. Given the marked reductions in CCL2 and IL-6 observed in ∆NSP2-SARS-CoV-2 infected mice, it would be valuable to determine whether infected ACE2-expressing cells intrinsically generate altered inflammatory signals. Measurement of inflammatory mediators following infection could help connect the cellular phenotype to the reduced pulmonary inflammation observed in vivo.

      1. Secretion of IL-6 and some chemokines in response of SARS-CoV-2 infection in A549-A2 were attempted but since levels were near the detection limits, these could not be quantitated.

      4.

      Reviewer #2

      5.

      For in vivo experiments, there are some specific timepoints (Fig. 2F - 3 DPI; Fig. 2E - 7 DPI) were Δnsp2 mutant viral load is lower compared to the wild-type virus. The main conclusions in the Discussion must be modified according to this fact.

      1. Figure 2E : The tendency for better viral clearance was highlighted in the result section (line 328-330). As no significant difference were observed and the trend at 7DPI is to the limit of detection, those results were not discussed further.

      7.

      • 3A depicts IFN alpha expression as upregulated in mock mice, shouldn't this be white considering it is baseline expression?*
      1. We thank the reviewer for the remark. The color of the heatmap did not represent fold change but relative production. Therefore, mock condition with the high level of IFN⍺ were display in red. As we want to keep 1 color gradient and a visualization of all mediators in on graphic, we will keep this data representation style.

      9.

      Reviewer #3

      10.

      Histological staining choice - Carstairs vs H&E (Lines 332-337) The Carstairs-stained sections of WT and ΔNsp2 lungs appear visually similar, without obvious bronchointerstitial pneumonia. Carstairs staining is typically used to highlight fibrin/connective tissue, whereas H&E is the standard for assessing general tissue architecture and inflammatory cell infiltration. Please justify the choice of Carstairs staining for this analysis and describe how this choice may have limited the ability to detect or differentiate inflammatory changes between groups.

      1. We thank the reviewer for this interrogation. In our past COVID study (PMID : 38365933, 40100623 and 40075368 ), we implement Carstairs staining to evaluate lung immunopathology. We chose this method instead of classic H&E staining because it offers more information to characterize the pathology with specific procedure to detect fibrosis, platelet accumulation and hemorrhagic (via red blood cell) in addition with the nucleus and global tissue’s structure. However, this choice of method did not have limited the ability to detect or differentiate inflammatory.

      12.

      Additionally: (a) what are the predominant differentially expressed transcripts, and do they correspond to the GSEA pathways highlighted in Figure 8 (e.g., DNA damage, epigenetic regulation)? (b) Given that fewer ORF1a-derived transcripts are detected in ΔNsp2-infected lungs, could differences in viral transcriptional/replication dynamics confound the host transcriptomic comparison? Please explain in the text. (c) Please specify how ORF1a-derived transcripts were quantified, given that ORF1a encodes Nsp1-11.

      1. We thank the reviewer for the comment, here is the explanation regarding the transcriptomic analysis carried out in the study :
      2. (A) We chose the GSEA approach instead of gene ontology (GO) on differential expressed gene (DEG), because this type of analysis takes into consideration how all genes of a pathways were regulated without using the significance threshold for each individual gene. As such, it reflects more how groups of genes were modulated then what are the top DEG. Taking this in consideration, highlighting predominant differentially expressed transcripts of discussed pathways does not seem relevant to us.
      3. (B) Using our analysis pipeline (DEseq2), each gene expression was compared individually between the condition, thus change in one gene did not impact others. Moreover, only host genes were used for GSEA so viral genes expression could not impact this part of the transcriptomic analysis.
      4. (C) Localization of viral gene/portion (Orf1a, Orf1ab, S, Orf3a, E, M, Orf6 Orf7a, Orf7b, Orf8, N and Orf10) was annotated on the viral genome then sequenced reads pseudo-maps (Kallisto quant). Kallisto uses an Expectation-Maximization (EM)algorithm to probabilistically assign multi-mapping reads to the correct transcripts (e.g. Nsp4 region with Orf1a and Orf1ab). In the end, Nsp2 deletion will have a higher impact on the number of reads (normalized sequencing deep) assigned to Orf1a as Nsp2 covers a greater proportion of this “gene” than in the whole Orf1ab gene.

      17.

      Figure 7 and 8 - figure consolidation (Lines 475-484) Consider combining Figures 7 and 8 into a single main figure, given their closely related content (differential expression and pathway enrichment from the same comparisons).

      1. We thank the reviewer for the comment, but we chose to keep the figure 7 and 8 separated to keep a suitable font size and helping readers to analyze easily the figures.

      19.

      Figure 8 - presentation format (Lines 485-502)Figure 8 would benefit from a presentation similar to Figure 9B (dot plot with size = gene count, color = padj), allowing readers to assess both the magnitude (NES) and statistical confidence (padj) of each pathway - information not currently visible in the heatmap.

      1. We thank the reviewer for the comment. As the padj value associated with all the pathways presented range between 10-9 and 10-8, we preferer to not overload the figure with noncrucial information.
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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The authors investigated the role of SARS-CoV-2 Nsp2 by generating a recombinant virus lacking Nsp2 (ΔNsp2) and comparing it to wild-type (WT) recombinant virus. While ΔNsp2 replicated comparably to WT in vitro (Vero, A549-hACE2, ALI cultures) and in vivo (lung viral titers), ΔNsp2-infected K18-hACE2 mice showed markedly improved survival and reduced clinical disease severity. This was associated with reduced pulmonary and systemic inflammation, a shift from a myeloid-dominated, Th1-polarized response (WT) toward a more balanced antiviral response with enhanced lymphocyte/NK recruitment and altered antigen-presenting cell populations (ΔNsp2). RNA-seq revealed distinct early transcriptional programs, with ΔNsp2 infection showing reduced signatures related to epigenetic regulation/histone modification, RNA processing, and acute lung injury. CLIP-seq and proximity labeling further suggest that Nsp2 directly interacts with host RNA and components of the translational machinery, supporting a model where Nsp2 drives immunopathology by modulating host RNA processing and translation.

      Major Comments

      (1) The absence of Nsp2 in the ΔNsp2 mutant is clear, but the claim that Nsp1 and Nsp3 levels are "comparable when normalized to N protein" is not supported by quantitative data and I suspect differences in replication both in vitro and in vivo explain the difference in host response. Of course, if there is less virus, then host response is different and typically reduced. The authors need to take a more honest approach to evaluation of the data.

      (2) Figure 1B - ΔNsp2 and its impact on nsp1 and 3 expression. Loading control and densitometry (Lines 269-271) It is not clear whether the absence of nsp2 impacts the nsp1/3 expression. The blot appears to show enhanced Nsp1/Nsp3 in ΔNsp2, and the stain-free total protein signal in Supplementary Figure 4 appears stronger for ΔNsp2. Please provide (a) densitometric quantification of Nsp1/Nsp3 normalized to N, and (b) a standard housekeeping protein blot (eg actin/GAPDH) in the main figure to confirm equal loading. Please also indicate the time point of sample harvest for the WB, so the replication can be correlated figure 1C.

      (3) Figure 1C-E - Replication kinetics and infection dose (Lines 267-277) The claim of "comparable growth kinetics" is not fully supported - WT virus replicates faster and to higher titers in Vero and A549-ALI cultures at early/late time points, with statistically significant differences noted by the authors themselves. Additionally, infection doses were expressed as TCID50 and varied across cell models (Vero, A549-hACE2, A549-ALI), making cross-model comparison difficult and obscuring the actual MOI/copies-per-cell delivered. Please repeat key experiments using a standardized MOI or genome copies/cell across all models with the titer at 1-2h to reflect equal input virus, if different MOI or genome copies/cell dose is needed, please also provide explanation.

      (4) Histological staining choice - Carstairs vs H&E (Lines 332-337) The Carstairs-stained sections of WT and ΔNsp2 lungs appear visually similar, without obvious bronchointerstitial pneumonia. Carstairs staining is typically used to highlight fibrin/connective tissue, whereas H&E is the standard for assessing general tissue architecture and inflammatory cell infiltration. Please justify the choice of Carstairs staining for this analysis, and describe how this choice may have limited the ability to detect or differentiate inflammatory changes between groups.

      (5) Virus localization - IHC/ISH needed (Lines 332-337) To determine whether the observed inflammatory differences relate to differences in viral antigen/RNA distribution which also reflect replication difference (rather than purely host immune modulation by Nsp2), viral IHC or in situ hybridization (ISH) should be performed on lung sections to localize virus within the tissue and correlate with inflammatory zones. Otherwise explain that replication differences may underlie differences in host response.

      (6)Cytokine/chemokine data presentation (Lines 332-337) The heatmap in Figure 3A uses a relative 0-10 scale that is difficult to interpret biologically. Please provide the raw concentration data (pg/mL or pg/mg protein) for key mediators, ideally as bar graphs/dot plots, so readers can assess whether the differences between WT and ΔNsp2 are of plausible biological/clinical magnitude.

      (7) Transcript-level validation of cytokine/chemokine changes (Lines 332-337) In addition to protein-level (ELISA/multiplex) data, qPCR for key cytokine/chemokine transcripts (e.g., Ccl2, Cxcl9, Ifng, Il6, Ccl5) in lung tissue would help determine whether Nsp2 acts at the level of transcription versus translation/secretion.

      (8) In vitro/in vivo correlation for CCL5/IL-6 findings (Lines 338-343) The supplementary A549-hACE2 data show that Nsp2 reduces CCL5 but not IL-6 in response to poly(I:C). Please clarify the rationale for using this specific readout to model the in vivo lung findings. Does Nsp2 expression also modulate CCL2, CXCL9, or IFN-γ in A549 cells? A more complete in vitro dissection - expressing Nsp2 vs. empty vector, stimulating with poly(I:C), and measuring IFN/ISG and chemokine/cytokine transcripts by qPCR alongside ELISA - would better support the proposed mechanism.

      (9) Discrepancy in differential expression results (Lines 475-484) The text states that ΔNsp2 infection shows a predominance of downregulated transcripts relative to WT (Figure 7B-D), yet the supplementary comparison (each condition vs. mock) reportedly shows similar proportions of up/down genes. Please clarify how these two comparisons relate, and whether they are actually measuring the same thing (ΔNsp2 vs WT directly, versus each vs mock separately). Additionally: (a) what are the predominant differentially expressed transcripts, and do they correspond to the GSEA pathways highlighted in Figure 8 (e.g., DNA damage, epigenetic regulation)? (b) Given that fewer ORF1a-derived transcripts are detected in ΔNsp2-infected lungs, could differences in viral transcriptional/replication dynamics confound the host transcriptomic comparison? Please explain in the text. (c) Please specify how ORF1a-derived transcripts were quantified, given that ORF1a encodes Nsp1-11.

      Minor Comments

      (1) Figure 3A heatmap - visual clarity The color differences between WT and ΔNsp2 groups, and across timepoints, are subtle and difficult to distinguish visually. Consider an alternative visualization (e.g., separate scaling per mediator, or a dot plot) to improve readability.

      (2) Mouse experiment design - tissue collection details (Line 300) Please clarify, for each timepoint (3, 5, 7 DPI), which tissues/samples were collected and which assays were performed on each. The Methods describe blood collection by cardiac puncture and multiple lung lobe allocations, but Figure 2A's schematic appears to show only lung collection for titer measurements - please reconcile the figure with the text.

      (3) Figure 4C - terminology clarification (Line 367) The phrase "a distinct global leukocyte profile was already evident at 5 DPI" is not self-explanatory. Please define what is meant by "global leukocyte profile" and explain how Figure 4C/4B should be read/interpreted in the main text.

      (4) Figure 7 and 8 - figure consolidation (Lines 475-484) Consider combining Figures 7 and 8 into a single main figure, given their closely related content (differential expression and pathway enrichment from the same comparisons).

      (5) Figure 8 - presentation format (Lines 485-502) Figure 8 would benefit from a presentation similar to Figure 9B (dot plot with size = gene count, color = padj), allowing readers to assess both the magnitude (NES) and statistical confidence (padj) of each pathway - information not currently visible in the heatmap.

      Significance

      This is the first study to use both a mouse model and reverse genetics-derived viruses to investigate the role of Nsp2 in viral pathogenesis and host responses. The study integrates multiple levels of analysis, including in vitro and in vivo replication kinetics, comprehensive disease outcome measurements (survival, clinical scores, and weight loss), inflammatory mediator quantification by ELISA, immune cell phenotyping, and omics approaches (RNA-seq and CLIP-seq) to characterize global virus-host interactions. The preprint is accessible to researchers with a biological science background. The experimental design is logical, and the methodologies are easy to follow. However, while CLIP-seq/BioID data suggest Nsp2 interacts with translational machinery and host RNAs, the direct causal link isn't established in the study. The histopathology assessment methodology, inflammatory mediator heatmap analysis are visually confusing. RNA-seq interpretation is possibly confounded by differences in viral replication between wild type and mutant.

      We are molecular virologists expert in studying host responses to infection with significant experience studying coronaviruses

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Lacasse et al. use reverse genetics to generate a SARS-CoV-2 Δnsp2 mutant virus to analyze the effect of non-structural protein 2 (nsp2) in viral replication and pathogenesis both in vitro and in vivo. This study presents a considerable amount of work to corroborate the role of nsp2 in viral replication, which has not been evaluated before. The manuscript is properly written and the language style is correct. However, the main weakness of this manuscript is an incorrect interpretation of the results. I expand on this issue and other relatively minor issues below.

      Major

      • The main conclusion of this manuscript, stated in lines 549-554, claims that the difference in pathology between Δnsp2 mutant and WT virus infection cannot be explained by distinct viral replication since both viruses replicate similarly in vitro and in vivo. However, the data presented here is supportive that Δnsp2 mutant replicates to a lower extent when compared to WT virus. This is clearly seen in Figures 1C (at 24 hpi), and Figures 1D and 1E for in vitro experiments where there is a 2-log difference at multiple timepoints. For in vivo experiments, there are some specific timepoints (Fig. 2F - 3 DPI; Fig. 2E - 7 DPI) were Δnsp2 mutant viral load is lower compared to the wild-type virus. The main conclusions in the Discussion must be modified according to this fact.
      • In Figure 1B, the expression of viral proteins Nsp1 and Nsp3 are normalized to N protein to conclude that they are not influenced by nsp2 deletion. N protein is known to have diminished expression when SARS-CoV-2 mutants replicate deficiently, which is the case of the Δnsp2 mutant under in vitro conditions. Why did the authors choose to normalize against N protein? Why was not a loading control such as beta-actin used?
      • Authors analyze the expression of the envelope (E) gene to assess the viral replication in vivo. GAPDH gene is used as housekeeping gene. I wonder why authors chose these genes for this study, considering that E gene expression values range is small (101 to 10-2). N subgenomic RNA is more appropriate to measure active viral transcription since it is more abundant than E subgenomic RNAs. Also, GAPDH has been reported as not suitable housekeeping gene for SARS-CoV-2 infection (reported in PMID: 36377893).
      • Authors do not provide a validation of the RNASeq analysis, i.e. authors must select a representative set of up- and down-regulated genes and perform RT-qPCR on the same samples to corroborate if the fold changes observed in RNASeq are also observed via an alternative method.

      Minor

      • There are several statements that are not correct in the Introduction: Lines 37-38: '... SARS-CoV-2 genome encodes a large replicase polyprotein that...' is not true, SARS-CoV-2 genome encodes for two large polyproteins (pp1a and pp1ab). Line 41: '... leading to the activation of type I interferon...', IFNs as molecules are not activated, the correct phrasing for this sentence would be '... lead to the production / expression of type I interferon' or '... lead to activation of type I interferon signaling cascade...'. Lines 52-54, this statement is a bit exaggerated. There has been a huge research effort during the past COVID-19 pandemic to understand the replication of coronavirus, especially SARS-CoV-2. Most mechanisms underlying SARS-CoV-2 infection including viral entry, formation or replication organelles, assembly, budding and exocytosis are already well characterize in the scientific literature.
      • Line 295, there seems to be a number missing in '****P < 0.000'
      • Figures 1C, 1D, 1E could be labelled with the corresponding tested cell line on top of each plot to improve interpretation of the figures.
      • The inclusion of uncropped Western blot data in the Supplementary Figures is appreciated. However, the approximate positions of the molecular weight markers should also be indicated in the corresponding main figure (Fig. 1B) to facilitate data interpretation.
      • Fig. 3A depicts IFN alpha expression as upregulated in mock mice, shouldn't this be white considering it is baseline expression?
      • Fig. 3A, the p-Value indicated with two asterisks on top of the figure might result confusing for readers. I would indicate it only in the figure caption.
      • Figs. 7B, 7C, 7D additional arrows pointing to the main outlier transcripts could be added (in a similar fashion to ORF1a)
      • Supplementary Figure 4A, there is a mistake in the left-panel western blot, authors indicate nsp1 protein to a band that corresponds to nsp2 in the main figure.

      Significance

      Strengths: the study is supported by a well-designed experimental framework, appropriate statistical analyses, and an adequate sample size. The manuscript is clearly written and employs a suitable scientific style and language. Furthermore, the breadth of experiments performed and the volume of data presented are substantial.

      Limitations: the main limitations include drawing erroneous conclusions due to an incorrect interpretation of the data, the absence of appropriate controls in certain experiments, and the lack of independent validation of the RNA-seq findings.

      Advance: this work addresses an important gap in our understanding of the role of nsp2 in coronavirus replication. To the best of my knowledge, this is the first study to investigate this question using a comprehensive reverse genetics approach combined with both in vitro and in vivo experimentation.

      Audience: the manuscript will be of primary interest to specialized research groups working in the field of coronavirus virology.

      Expertise: I am a researcher with over seven years of postdoctoral experience in molecular virology, with a particular focus on coronaviruses in two recognized international institutions. My expertise includes the development and application of coronavirus reverse genetics systems to generate deletion mutants for investigating gene function, as well as the design and generation of replicon systems for antiviral research.

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      Referee #1

      Evidence, reproducibility and clarity

      Lacasse et al present a intriguing study assessing the role of NSP2 as a virulence factor in SARS-CoV-2, they present a well designed body of work showing the loss of this non-structural protein attenuates pathogenesis in a K-18-hAce2 mouse model. The in vivo data are compelling and support the conclusion that loss of NSP2 is associated with altered host inflammatory responses rather than major defects in viral replication. The observations of reduced CCL2, IL-6, CD45+ leukocyte infiltration, and disease severity in ∆NSP2 virus, despite comparable viral titers, are particularly interesting. Furthermore, the preservation of IFN-γ and CXCL9 responses suggests that antiviral immunity remains largely intact.

      However, the mechanistic link between the in vitro phenotype and the in vivo immune phenotype remains incompletely defined. The manuscript would be strengthened by additional experiments addressing the following points:

      1. Early infection kinetics in ACE2-expressing cells. The authors report transiently reduced viral RNA levels in vivo during the early phase of infection, despite comparable viral loads at later time points. It would therefore be informative to determine whether a similar phenomenon occurs in ACE2-expressing cells. Time-course analyses of viral RNA and/or viral protein expression could help establish whether the mutant primarily affects early infection events rather than overall replicative capacity.
      2. Cell-intrinsic inflammatory responses. Given the marked reductions in CCL2 and IL-6 observed in ∆NSP2-SARS-CoV-2 infected mice, it would be valuable to determine whether infected ACE2-expressing cells intrinsically generate altered inflammatory signals. Measurement of inflammatory mediators following infection could help connect the cellular phenotype to the reduced pulmonary inflammation observed in vivo.
      3. Given the reduced pathology and disease progression in the ∆NSP2-SARS-CoV-2 infected mice, have the authors considered assessing whether mice exposed to this mutant SARS-CoV-2 strain have enhanced responses to re-challenge with WT SARS-CoV-2?

      Significance

      This study provides critical insights into the role of NSP2 in modulating host inflammatory responses during SARS-CoV-2 infection. By demonstrating that attenuation of the ∆NSP2 virus is linked to altered inflammatory signalling rather than impaired viral replication, it highlights a distinct mechanism influencing disease severity. The preservation of key antiviral responses such as IFN-γ and CXCL9 further underscores the selective impact on host immunity. These findings advance understanding of viral-host interactions and suggest potential avenues for therapeutic intervention targeting inflammation without compromising antiviral defence.