Wow these look crazy! Very prominent phenotype!

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):  

Summary:

The manuscript by Cupollilo et al describes the development, characterization, and application of a novel activity labeling system; fast labelling of engram neurons (FLEN). Several such systems already exist but this study adds additional capability by leveraging an activity marker that is destabilized (and thus temporally active) as well as being driven by the full-length promoter of cFos. The authors demonstrate the activity-dependent induction and time course of expression, first in cultured neurons and then in vivo in hippocampal CA3 neurons after one trial of contextual fear conditioning. In a series of ex vivo experiments, the authors perform patch clamp analysis of labeled neurons to determine if these putative engram neurons differ from non-labelled neurons using both the FLEN system as well as the previously characterized RAM system. Interestingly the early labelled neurons at 3 h post CFC (FLEN+) demonstrated no differences in excitability whereas the RAMlabelled neurons at 24h after CFC had increased excitability. Examination of synaptic properties demonstrated an increase in sEPCS and mEPSC frequencies as well as those for sIPSCs and mIPSCs which was not due to a change in the mossy fiber input to these neurons.

Strengths:

Overall the data is of high quality and the study introduces a new tool while also reassessing some principles of circuit plasticity in the CA3 that have been the focus of prior studies.

Weaknesses:

No major weaknesses were noted.

Reviewer #2 (Public review): 

Summary: 

Cupollilo et al. investigate the properties of hippocampal CA3 neurons that express the immediate early gene cFos in response to a single foot shock. They compare ex-vivo the electrophysiological properties of these "engram neurons" labeled with two different cFos promoter-driven green markers: Their new tool FLEN labels neurons 2-6 h after activity, while RAM contains additional enhancers and peaks considerably later (>24 h). Since the fraction of labeled CA3 cells is comparable with both constructs, it is assumed (but not tested) that they label the same population of activated neurons at different time points. Both FLEN+ and RAM+ neurons in CA3 receive more synaptic inputs compared to non-expressing control neurons, which could be a causal factor for cFos activation, or a very early consequence thereof. Frequency facilitation and E/I ratio of mossy fiber inputs were also tested, but are not different in both cFos+ groups of neurons. One day after foot shock, RAM+ neurons are more excitable than RAM- neurons, suggesting a slow increase in excitability as a major consequence of cFos activation.

Strengths: 

The study is conducted to high standards and contributes significantly to our understanding of memory formation and consolidation in the hippocampus. Modifications of intrinsic neuronal properties seem to be more salient than overall changes in the total number of (excitatory and inhibitory) inputs, although a switch in the source of the synaptic inputs would not have been detected by the methods employed in this study

Weaknesses: 

With regard to the new viral tool, a direct comparison between the new tool FLEN and existing cFos reporters is missing. 

Reviewer #1 (Recommendations for the authors):

I have only minor suggestions for the authors to consider. 

(1) In the in vitro characterization, the percentage of labelled neurons seems very low after a powerful and prolonged activation. It was somewhat surprising and raised the question of how accurately the FLEN construct reflects endogenous cFOS activity. Could the authors speak to this?

The reviewer is correct that the level of FLEN positive neurons, as compared to mCherry positive neurons, is low as compared to studies using viral infection with RAM vectors in neuronal cultures (Sorensen et al, 2016, Sun et al, 2020), which is around 70-80% following chemical stimulation. The authors do not provide evidence however for a comparison with endogenous c-Fos activity in cell cultures. The reason for a discrepancy in the effect of chemical stimulation of cultured neurons is not clear, but may depend on culture conditions which may vary between labs. 

FLEN was constructed using a mouse c-Fos promoter (-355 to +109) (Cen et al, 2003). To answer the reviewer’s question we performed an additional experiment in cultured neurons in which we found that 77.1 % of FLEN positive neurons were also c-fos positive neurons (using immunocytochemistry).

(2) The authors compare the two labelling strategies and interpret their data with the presumption that both label a similar set of active neurons. This is particularly relevant when they suggest there might be a progressive increase in the excitability of active neurons with time. This is certainly a possibility, but the authors should also consider other possibilities that the two markers might label different populations of neurons. For example, if they require different thresholds for activation, it is possible that one is more sensitive to activity than the other. As these are unknown variables the authors should temper the interpretation accordingly.

Indeed, the reviewer is correct that this limitation should be discussed. We have added this as a point of discussion in the text (line 355-358). In the article describing the RAM strategy (Sorensen et al, 2016) the authors use RAM to label DG neurons activated during an experience in a context A (Figure 4). Exploiting the fact that engram cells are re-activated when the animal is re-exposed to the same environment of training (memory recall), they performed c-Fos staining 90 minutes following either context A or context B re-exposure. The RAM-c-Fos overlap percentage was higher in A-A rather than A-B (A-A was a bit more than 20%). This means that RAM has captured a group of cells during training that, at least in part, were re-activated during recall. This could in part support the assumption that RAM and c-Fos share a certain overlap. Of course, this was done in DG, while we worked in CA3. In addition, both strategies label in their great majority c-Fos+ neurons (see above answer to point #1). This can not completely rule out the possibility that FLEN and RAM label partly distinct population of activated cells. 

(3) An increase in the frequency of synaptic events is observed in neurons labelled with both markers. The authors propose that this may be due to an increase in synaptic contacts based on prior studies. However, as this is the first functional assessment why not consider changes in release probability as a mechanism for this finding? 

We have added this as a possibility in the text (line 362-363).

(4) It would be useful to include plots of the average frequency of m/sEPSCs and m/sIPSCs in Figures 4 and 5. These figures could also be combined into a single figure.

We agree with the reviewer that figure 4 and 5 could be merged into a single figure. In the revised version, figure 5A becomes panel C in figure 4. Text and figure descriptions were adjusted accordingly.

Reviewer #2 (Recommendations for the authors): 

(1) Abstract, line 24: "In contrast, FLEN+ CA3 neurons show an increased number of excitatory inputs." RAM+ neurons also show an increased number of excitatory inputs, so this is not "in contrast". Also, not just excitatory, but also inhibitory synaptic inputs are more numerous in cFos+ neurons. Please improve the summary of your findings.

“In contrast” referred to the fact that FLEN+ neurons do not show differences in excitability as compared to FLEN- neurons, as mentioned in the previous sentence. We now provide a more explicit sentence to explain this point: “On the other hand, like RAM+ neurons, FLEN+ CA3 neurons show an increased number of excitatory inputs.”

(2) Novel tool: Destabilized cFos reporters were introduced 23 years ago and are also part of the TetTag mouse. I am not sure that changing the green fluorescent protein to a different version merits a new acronym (FLEN). To convince the readers that this is more than a branding exercise, the authors should compare the properties (brightness, folding time, stability) of FLEN to e.g. the d2EGFP reporter introduced by Bi et al. 2002 (J Biotechnol. 93(3):231) and show significant improvements.

We thank the reviewer for this comment which compelled us to evaluate the features of other tools used to label neurons activated following contextual fear conditioing. The key properties of FLEN as compared to other tools used to label engrams is that: (i) it is a viral tool, as opposed to transgenic mice, (ii) a c-fos promoter drives the expression of a brightly fluorescent protein allowing their identification ex vivo for functional analysis, (iii) the fluorescent protein is rapidly destabilized, providing the possibility to label neurons only a few hours after their activation by a behavioural task.

We did not find any viral tools providing the possibility to label c-fos activated neurons for functional assesment. We have not been able to find references for the use of the d2EGFP reporter introduced by Bi et al. 2002 in a behavioural context. One of the major difference and improvement is certainly the brightness of ZsGreen. In cell cultures, ZsGreen1 showed a 8.6-fold increase in fluorescence intensity as compared with EGFP (Bell et al, 2007).

Amongst tools with comparable properties, eSARE was developed based on a synthetic Arc promoter driving the expression of a destabilized GFP (dEGFP) (Kawashima et al 2013). We initially used ESARE–dGFP but unfortunately, in our experimental conditions we found that the signal to noise ratio was not satisfactory (number of cells label in the home cage vs. following contextual fear conditining).

We developed a viral tool to avoid the use of transgenic reporter lines which require laborious breeding and is experimentally less flexible. Nevertheless, many transgenic mice based on the expression of fluorescent proteins under the control of IEG promoters have been developed and used. Some of these mice show a time course of expression of the transgene which is comparable to FLEN. For instance, in organotypic slices from Tet-Tag mice, the time course of expression of EGFP slices follows with a small delay endogenous cFOS expression, and starts decaying after 4 hours (Lamothe-Molina et al, 2022). However, the fluorescence was too weak to visualize neurons in the slice (Christine Gee, personal communication), and imaging is perfomed after immunocytochemistry against GFP. 

Therefore, we feel that the name given to the FLEN strategy is legitimate. The features of the FLEN strategy were summarized in the discussion (Lines 318-322).

(3) Line 214: "...FLEN+ CA3 PNs do not show differences in [...] patterns of bursting activity as compared to control neurons." It looks quite different to me (Figure 3E). Just because low n precludes meaningful statistical analysis, I would not conclude there is no difference.

We agree with the reviewer that the data in Figure 3E are not conclusive due to small sample size, which limits the reliability of statistical comparison. Additionally, the classification of bursting neurons is highly dependent on the specific criteria used, which vary considerably across the literature. To avoid overinterpretation or misleading conclusions, we decided to remove the panel E of Figure 3 showing the fraction of bursting neurons. Nevertheless, we draw the attention to the more robust and interpretable results: RAM⁺ neurons exhibit an increase in firing frequency and a distinct action potential discharge pattern, data which we believe are informative of altered excitability.

(4) Line 304: Remove the time stamp.

This was done.

(5) Line 334: "...results may be explained by an overall increased activity of CA1 neurons..." I don't understand - isn't CA1 downstream of CA3? 

The reviewer is correct that the sentence was misleading. We removed the reference to CA1, as it was more of a general principle about neuronal activity.

(6) Line 381: "resolutive", better use "sensitive". 

This was changed.

(7) Figure S3: Fear-conditioned animals were 3 days off Dox, controls only 2 days. As RAM expression accumulates over time off Dox, this is not a fair comparison.

We thank the reviewer for pointing out the incorrect reporting of the experimental design in Figure S3 panel A (bottom), which could lead to misinterpretation of results. In fact, the two groups of mice (CFC vs. HC) underwent all experimental steps in parallel. Specifically, both groups were maintained on and off Doxycycline for the same duration and received viral injection on the same day. 48 hours after Dox withdrawal, the CFC group was trained for contextual conditioning, while the HC group remained in the home cage in the holding room. All animals were thus sacrificed 72 hours after Dox removal. We have corrected the figure to accurately reflect this timeline.

(8) Please provide sequence information for c-cFos-ZsGreen1-DR. Which regulatory elements of the cFos promoter are included, is the 5' NTR included? This information is very important.

The information is now provided in the Methods section.

(9) Please provide the temperature during pharmacological treatments (TTX etc.) before fixation.

The pharmacological treatment was performed in the incubator at 37°C, this is now indicated in the methods.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Shigella flexneri is a bacterial pathogen that is an important globally significant cause of diarrhea. Shigella pathogenesis remains poorly understood. In their manuscript, Saavedra-Sanchez et al report their discovery that a secreted E3 ligase effector of Shigella, called IpaH1.4, mediates the degradation of a host E3 ligase called RNF213. RNF213 was previously described to mediate ubiquitylation of intracellular bacteria, an initial step in their targeting of xenophagosomes. Thus, Shigella IpaH1.4 appears to be an important factor in permitting evasion of RNF213-mediated host defense.

Strengths:

The work is focused, convincing, well-performed, and important. The manuscript is well-written.

We would like to thank the reviewer for their time evaluating our manuscript and the positive assessment of the novelty and importance of our study. We provide a comprehensive response to each of the reviewer’s specific recommendations below and highlight any changes made to the manuscript in response to those recommendations.

Reviewer #1 (Recommendations for the authors):

(1) In the abstract (and similarly on p.10), the authors claim to have shown "IpaH1.4 protein as a direct inhibitor of mammalian RNF213". However, they do not show the interaction is direct. This, in my opinion, would require demonstrating an interaction between purified recombinant proteins. I presume that the authors are relying on their UBAIT data to support the direct interaction, but this is a fairly artificial scenario that might be prone to indirect substrates. I would therefore prefer that the 'direct' statement be modified (or better supported with additional data). Similarly, on p.7, the section heading states "S. flexneri virulence factors IpaH1.4 and IpaH2.5 are sufficient to induce RNF213 degradation". The corresponding experiment is to show sufficiency in a 293T cell, but this leaves open the participation of additional 293T-expressed factors. So I would remove "are sufficient to", or alternatively add "...in 293T cells".

We agree with the reviewer and made the recommended changes to the text in the abstract, in the results section on page 7, and in the Discussion on page 11. During the revision of our manuscript two additional studies were published that provide convincing biochemical evidence for the direct interaction between IpaH1.4 and RNF213 (PMID: 40205224; PMID: 40164614). These studies address the reviewer’s concern extensively and are now briefly discussed and cited in our revised MS.

(2) In the abstract the authors state "Linear (M1-) and lysine-linked ubiquitin is conjugated to bacteria by RNF213 independent of the linear ubiquitin chain assembly complex (LUBAC)." However, it is not shown that RNF213 is able to directly perform M1-ubiquitylation. It is shown that RNF213 is required for M1-linked ubiquitylation in IpaH1.4 or MxiE mutants, this is different than showing conjugation is done by RNF213 itself. This should be reworded.

We agree and edited the text accordingly

(3) Introduction: one of the main points of the paper is that RNF213 conjugates linear ubiquitin to the surface of bacteria in a manner independent of the previously characterized linear ubiquitin conjugation (LUBAC) complex. This is indeed an interesting result, but the introduction does not put this discovery in much context. I would suggest adding some discussion of what was known, if anything, about the type of Ub chain formed by RNF213, and specifically whether linear Ub had previously been observed or not.

We now provide context in the Introduction on page 3 and briefly discuss previous work that had implicated LUBAC in the ubiquitylation of cytosolic bacteria. We emphasize that LUBAC specifically generates linear (M1-linked) ubiquitin chains, while the types of ubiquitin linkages deposited on bacteria through RNF213-dependent pathways had remained unidentified.

(4) Figure 3C: is the difference in 7KR-Ub between WT and HOIP KO cells significant? If so, the authors may wish to acknowledge the possibility that HOIP partially contributes to M1-Ub of MxiE mutant Shigella

The frequencies at which bacteria are decorated with 7KR-Ub is not statistically different between WT and HOIP KO cells. We have included this information in the panel description of Figure 3.

(5) On page 11, the authors state that "...we observed that LUBAC is dispensable for M1-linked ubiquitylation of cytosolic S. flexneri ∆ipaH1.4. We found that lysine-less internally tagged ubiquitin or an M1-specific antibody bound to S. flexneri ∆ipaH1.4 in cells lacking LUBAC (HOIL-1KO or HOIPKO) but failed to bind bacteria in RNF213-deficient cells". In fact, what is shown is that M1-ubiquitylation in ∆ipaH1.4 infection is RNF213-dependent (5E), but the work with lysine mutants, HOIP or HOIL-1 KOs are all with ∆mxiE, not ∆ipaH1.4 (3B) in this version of the manuscript. Ideally, the data with ∆ipaH1.4 could be added, but alternatively, the conclusion could be re-worded.

We now include the data demonstrating that staining of ∆ipaH1.4 with an M1-specific antibody is unchanged from WT cells in HOIL-1 KO and HOIP KO cells. These data are shown in supplementary data (Fig. S3E) and referred to on page 9 of the revised manuscript.

(6) The UBAIT experiment should be explained in a bit more detail in the text. The approach is not necessarily familiar to all readers, and the rationale for using Salmonella-infected ceca/colons is not well explained (and seems odd). Some appropriate caution about interpreting these data might also be welcome. Did HOIP or HOIL show up in the UBAIT? This perhaps also deserves some discussion.

As expected, HOIP (listed under its official gene name Rnf31 in the table of Fig.S2B) was identified as a candidate IpaH1.4 interaction partner as the third most abundant hit from the UBAIT screen. Remarkably, Rnf213 was the hit with the highest abundance in the IpaH1.4 UBAIT screen. To address the reviewer’s comments, we now explain the UBAIT approach in more detail and provide the rational for using intestinal protein lysates from Salmonella infected mice. The text on page 8 reads as follows: “To investigate potential physical interactions between IpaH1.4 and IpaH2.5, we reanalyzed a previously generated dataset that employed a method known as ubiquitin-activated interaction traps (UBAITs) (32). As shown in Fig. S2A, the human ubiquitin gene was fused to the 3′ end of IpaH2.5, producing a C-terminal IpaH2.5-ubiquitin fusion protein. When incubated with ATP, ubiquitin-activating enzyme E1, and ubiquitin-conjugating enzyme E2, the IpaH2.5-ubiquitin "bait" protein is capable of binding to and ubiquitylating target substrates. This ubiquitylation creates an iso-peptide bond between the IpaH2.5 bait and its substrate, thereby enabling purification via a Strep affinity tag incorporated into the fusion construct (32). IpaH2.5-ubiquitin bait and IpaH3-ubiquitin control proteins were incubated with lysates from murine intestinal tissue. To detect interaction partners in a physiologically relevant setting, we used intestinal lysates derived from mice infected with Salmonella, which in contrast to Shigella causes pronounced inflammation in WT mice and therefore better simulates human Shigellosis in an animal model. Using UBAIT we identified HOIP (Rnf31) as a likely IpaH2.5 binding partner (Fig. S2B), thus confirming previous observations (28) and validating the effectiveness our approach. Strikingly, we identified mouse Rnf213 as the most abundant interaction partner of the IpaH2.5-ubiquitin bait protein (Fig. S2B). Collectively, our data and concurrent reports showing direct interactions between IpaH1.4 and human RNF213 (36, 37) indicate that the virulence factors IpaH1.4 and IpaH2.5 directly bind and degrade mouse as well as human RNF213.”

(7) It would be helpful if the authors discussed their results in the context of the prior work showing IpaH1.4/2.5 mediate the degradation of HOIP. Do the authors see HOIP degradation? If indeed HOIP and RNF213 are both degraded by IpaH1.4 and IpaH2.5, are there conserved domains between RNF213 and HOIP being targeted? Or is only one the direct target? A HOIP-RNF213 interaction has previously been shown (https://doi.org/10.1038/s41467-024-47289-2). Since they interact, is it possible one is degraded indirectly? To help clarify this, a simple experiment would be to test if RNF213 degraded in HOIP KO cells (or vice-versa)?

We appreciate the reviewer’s suggestions. We conducted the proposed experiments and found that WT S. flexneri infections result in RNF213 degradation in both WT and HOIP KO cells. Similarly, we found that HOIP degradation was independent of RNF213. We have included these data in Figs. 5A and S3B of our revised submission. A study published during revisions of our paper demonstrates that the LRR of IpaH1.4 binds to the RING domains of both RNF213 and LUBAC (PMID: 40205224). We refer to this work in our revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors find that the bacterial pathogen Shigella flexneri uses the T3SS effector IpaH1.4 to induce degradation of the IFNg-induced protein RNF213. They show that in the absence of IpaH1.4, cytosolic Shigella is bound by RNF213. Furthermore, RNF213 conjugates linear and lysine-linked ubiquitin to Shigella independently of LUBAC. Intriguingly, they find that Shigella lacking ipaH1.4 or mxiE, which regulates the expression of some T3SS effectors, are not killed even when ubiquitylated by RNF213 and that these mutants are still able to replicate within the cytosol, suggesting that Shigella encodes additional effectors to escape from host defenses mediated by RNF213-driven ubiquitylation.

Strengths:

The authors take a variety of approaches, including host and bacterial genetics, gain-of-function and loss-of-function assays, cell biology, and biochemistry. Overall, the experiments are elegantly designed, rigorous, and convincing.

Weaknesses:

The authors find that ipaH1.4 mutant S. flexneri no longer degrades RNF213 and recruits RNF213 to the bacterial surface. The authors should perform genetic complementation of this mutant with WT ipaH1.4 and the catalytically inactive ipaH1.4 to confirm that ipaH1.4 catalytic activity is indeed responsible for the observed phenotype.

We would like to thank the reviewer for their time evaluating our manuscript and the positive assessment of our work, especially its scientific rigor. We conducted the experiment suggested by the reviewer and included the new data in the revised manuscript. As expected, complementation of the ∆ipaH1.4 with WT IpaH1.4 but not with the catalytically dead C338S mutant restored the ability of Shigella to efficiently escape from recognition by RNF213 (Figs. 5C-D).

Reviewer #2 (Recommendations for the authors):

The authors should perform genetic complementation of the ipaH1.4 mutant with WT ipaH1.4 and the catalytically inactive ipaH1.4 to confirm that ipaH1.4 catalytic activity is indeed responsible for the observed phenotype.

We performed the suggested experiment and show in Figs. 5C-D that complementation of the ∆ipaH1.4 mutant with WT IpaH1.4 but not with the catalytically dead C338S mutant restored the ability of Shigella to efficiently escape from recognition by RNF213. These data demonstrate that the catalytic activity of IpaH1.4 is required for evasion of RNF213 binding to the bacteria.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to investigate whether and how Shigella avoids cell-autonomous immunity initiated through M1-linked ubiquitin and the immune sensor and E3 ligase RNF213. The key findings are that the Shigella flexneri T3SS effector, IpaH1.4 induces degradation of RNF213. Without IpaH1.4, the bacteria are marked with RNF213 and ubiquitin following stimulation with IFNg. Interestingly, this is not sufficient to initiate the destruction of the bacteria, leading the authors to conclude that Shigella deploys additional virulence factors to avoid this host immune response. The second key finding of this paper is the suggestion that M1 chains decorate the mxiE/ipaH Shigella mutant independent of LUBAC, which is, by and large, considered the only enzyme capable of generating M1-linked ubiquitin chains.

Strengths:

The data is for the most part well controlled and clearly presented with appropriate methodology. The authors convincingly demonstrate that IpaH1.4 is the effector responsible for the degradation of RNF213 via the proteasome, although the site of modification is not identified.

Weaknesses:

(1)The work builds on prior work from the same laboratory that suggests that M1 ubiquitin chains can be formed independently of LUBAC (in the prior publication this related to Chlamydia inclusions). In this study, two pieces of evidence support this statement -fluorescence microscopy-based images and accompanying quantification in Hoip and Hoil knockout cells for association of M1-ub, using an antibody, to Shigella mutants and the use of an internally tagged Ub-K7R mutant, which is unable to be incorporated into ubiquitin chains via its lysine residues. Given that clones of the M1-specific antibody are not always specific for M1 chains, and because it remains formally possible that the Int-K7R Ub can be added to the end of the chain as a chain terminator or as mono-ub, the authors should strengthen these findings relating to the claim that another E3 ligase can generate M1 chains de novo.

(2) The main weakness relating to the infection work is that no bacterial protein loading control is assayed in the western blots of infected cells, leaving the reader unable to determine if changes in RNF213 protein levels are the result of the absent bacterial protein (e.g. IpaH1.4) or altered infection levels.

(3)The importance of IFNgamma priming for RNF213 association to the mxiE or ipaH1.4 strain could have been investigated further as it is unclear if RNF213 coating is enhanced due to increased protein expression of RNF213 or another factor. This is of interest as IFNgamma priming does not seem to be needed for RNF213 to detect and coat cytosolic Salmonella.<br /> Overall, the findings are important for the host-pathogen field, cell-autonomous/innate immune signaling fields, and microbial pathogenesis fields. If further evidence for LUBAC independent M1 ubiquitylation is achieved this would represent a significant finding.

We would like to thank the reviewer for their time evaluating our manuscript and the positive assessment of our work and its significance. We provide a comprehensive response to the main three critiques listed under ‘weaknesses’ and also have responded to each of the reviewer’s specific recommendations below. We highlight any changes made to the manuscript in response to those recommendations.

(1) As the reviewer correctly pointed out, 7KR ubiquitin cannot only be used for linear ubiquitylation but can also function as a donor ubiquitin and can be attached as mono-ubiquitin to a substrate or to an existing ubiquitin chain as a chain terminator. To distinguish between 7KR INT-Ub signals originating from linear versus mono-ubiquitylation, we followed the reviewer’s advice and generated a N-terminally tagged 7KR INT-Ub variant. The N-terminal tag prevents linear ubiquitylation but still allows 7KR INT-Ub to be attached as a mono-ubiquitin. We found that the addition of this N-terminal tag significantly reduced but not completely abolished the number of Δ_mxiE_ bacteria decorated with 7KR INT-Ub. These data are shown in a new Fig. S1 and indicate that 7KR lacking the N-terminal tag is attached to bacteria both in the form of linear (M1-linked) ubiquitin and as donor ubiquitin, possibly as a chain terminator. While we cannot rule out that the anti-M1 antibodies used here cross-react with other ubiquitin linkages, we reason that the 7KR data strongly argues that linear ubiquitin is part of the ubiquitin coat encasing IpaH1.4-deficient cytosolic Shigella. Collectively, our data show that both linear and lysine-linked (especially K27 and K63) ubiquitin chains are part of the RNF213-dependent ubiquitin coat on the surface of IpaH1.4 mutants. And furthermore, our data strongly indicate that this ubiquitylation of IpaH1.4 mutants is independent of LUBAC.

(2) We used GFP-expressing strains of S. flexneri for our infection studies and were therefore able to use GFP expression as a loading control. We have incorporated these data into our revised figures. These new data (Figs. 4A, 5A, and S3B) show that bacterial infection levels were comparable between WT and mutant infections and that therefore the degradation of RNF213 (or HOIP – see new data in Fig. S3B) is not due to differences in infection efficiency.

(3) We agree with the reviewer that the mechanism by which RNF213 binds to bacteria is an important unanswered question. Similarly, whether other ISGs have auxiliary functions in this process or whether binding efficiencies vary between different bacterial species are important questions in the field. However, these questions go far beyond the scope of this study and were therefore not addressed in our revisions.

Reviewer #3 (Recommendations for the authors):

(1) An N-terminally tagged K7R-ub should be used as a control to test whether the signal found around the mutant shigella is being added via the N terminal Met into chains. As it is known that certain batches of the M1-specific antibodies are in fact not specific and able to detect other chain types, the authors should test the specificity of the antibody used in this study (eg against different di-Ub linkage types) and include this data in the manuscript.

We agree with the reviewer in principle. The anti-linear ubiquitin (anti-M1) monoclonal antibody, clone 1E3, prominently used in this study was tested by the manufacturer (Sigma) by Western blotting analysis and according to the manufacturer “this antibody detected ubiquitin in linear Ub, but not Ub K11, Ub K48, Ub K63.” However, this analysis did not include all possible Ub linkage types and thus the reviewer is correct that the anti-M1 antibody could theoretically also detect some other linkage types. To address this concern, we added new data during revisions demonstrating that 7KR INT-Ub targeting to S. flexneri is largely dependent on the N-terminus (M1) of ubiquitin. Our combined observations therefore overwhelmingly support the conclusion that linear (M1-linked) as well as K-linked ubiquitin is being attached to the surface of IpH1.4 S. flexneri bacteria in an RNF213-dependent and LUBAC-independent manner.

(2) The M1 signal detected on bacteria with the antibody is still present in either Hoip or Hoil KO’s but due to the potential non-specificity of the antibody, the authors should test whether K7R ub is detected on bacteria in the Hoil ko (in addition to Hoip KO). This would strengthen the authors’ data on LUBAC-independent M1 and is important because Hoil can catalyse non-canonical ubiquitylation.

The specific linear ubiquitin-ligating activity of LUBAC is enacted by HOIP. We show that linear ubiquitylation of susceptible S. flexneri mutants as assessed by anti-M1 ubiquitin staining or 7KR INT-Ub recruitment occurs in HOIPKO cells at WT levels (Figs. 3B, 3C, S3E [new data]). In our view , these data unequivocally show that the observed linear ubiquitylation of cytosolic S. flexneri ipaH1.4 and mxiE mutants is independent of LUBAC.

(3) For Figure 4A, do mxiE bacteria show similar invasion - authors should include a bacterial protein control to show levels of bacteria in WT and mxiE infected conditions. A similar control should be included in Figure 5A.

We used GFP-expressing strains of S. flexneri for our infection studies and were therefore able to use GFP expression as a loading control. We have incorporated these data into our revised figures. These new data (Figs. 4A, 5A, and S3B) show that bacterial infection levels were comparable between WT and mutant infections and that therefore the degradation of RNF213 (or HOIP – see new data in Fig. S3B) is not due to differences in infection efficiency.

(4) Can the authors speculate why IFNg priming is needed for the coating of Shigella mxiE mutant but not in the case of Salmonella or Burkholderia? Is this just amounts of RNF213 or something else?

In our studies we did not directly compare ubiquitylation rates of cytosolic Shigella, Burkholderia, and Salmonella bacteria with each other under the same experimental conditions. However, such a direct comparison is needed to determine whether IFNgamma priming is required for RNF213-dependent bacterial ubiquitylation of some but not other pathogens. Two papers published during the revisions of our manuscript (PMID: 40164614, PMID: 40205224) reports robust RNF213 targeting to IpaH1.4 Shigella mutants in unprimed cells HeLa cells (whereas we used A549 and HT29 cells). Therefore, differences in reagents, cell lines, and/or other experimental conditions may determine whether IFNgamma priming is necessary to observe substantial RNF213 translocation to cytosolic bacteria.

(5) Typos - there are several, but this is hard to annotate with line numbers so the authors should proofread again carefully.

We proofread the manuscript and corrected the small number of typos we identified

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Raices et al., provides novel insights into the role and interactions between SPO-11 accessory proteins in C. elegans. The authors propose a model of meiotic DSBs regulation, critical to our understanding of DSB formation and ultimately crossover regulation and accurate chromosome segregation. The work also emphasizes the commonalities and species-specific aspects of DSB regulation.

Strengths:

This study capitalizes on the strengths of the C. elegans system to uncover genetic interactions between a large number of SPO-11 accessory proteins. In combination with physical interactions, the authors synthesize their findings into a model, which will serve as the basis for future work, to determine mechanisms of DSB regulation.

Weaknesses:

The methodology, although standard, lacks quantification. This includes the mass spectrometry data , along with the cytology. The work would also benefit from clarifying the role of the DSB machinery on the X chromosome versus the autosomes.

• We have uploaded the MS data and added a summary table with the number of peptides and coverage.

• We have added statistics to the comparisons of DAPI body counts.

• We have provided additional images of the change in HIM-5 localization

• We have quantified the overlap (or lack thereof) between XND-1 and HIM-17 and the DNA axis

Reviewer #2 (Public Review):

Summary:

Meiotic recombination initiates with the formation of DNA double-strand break (DSB) formation, catalyzed by the conserved topoisomerase-like enzyme Spo11. Spo11 requires accessory factors that are poorly conserved across eukaryotes. Previous genetic studies have identified several proteins required for DSB formation in C. elegans to varying degrees; however, how these proteins interact with each other to recruit the DSB-forming machinery to chromosome axes remains unclear.

In this study, Raices et al. characterized the biochemical and genetic interactions among proteins that are known to promote DSB formation during C. elegans meiosis. The authors examined pairwise interactions using yeast two-hybrid (Y2H) and co-immunoprecipitation and revealed an interaction between a chromatin-associated protein HIM-17 and a transcription factor XND-1. They further confirmed the previously known interaction between DSB-1 and SPO-11 and showed that DSB-1 also interacts with a nematodespecific HIM-5, which is essential for DSB formation on the X chromosome. They also assessed genetic interactions among these proteins, categorizing them into four epistasis groups by comparing phenotypes in double vs. single mutants. Combining these results, the authors proposed a model of how these proteins interact with chromatin loops and are recruited to chromosome axes, offering insights into the process in C. elegans compared to other organisms.

Weaknesses:

This work relies heavily on Y2H, which is notorious for having high rates of false positives and false negatives. Although the interactions between HIM-17 and XND-1 and between DSB-1 and HIM-5 were validated by co-IP, the significance of these interactions was not tested, and cataloging Y2H interactions does not yield much more insight.

We appreciate that the reviewer recognized the value of our IP data, but we beg to differ that we rely too heavily on the Y2H. We also provide genetic analysis on bivalent formation to support the physical interaction data. We do acknowledge that there are caveats with Y2H, however, including that a subset of the interactions can only be examined with proteins in one orientation due to auto-activation. While we acknowledge that it would be nice to have IP data for all of the proteins using CRISPR-tagged, functional alleles, these strains are not all feasible (e.g. no functional rec-1 tag has been made) and are beyond the scope of the current work.

Moreover, most experiments lack rigor, which raises serious concerns about whether the data convincingly supports the conclusions of this paper. For instance, the XND-1 antibody appears to detect a band in the control IP; however, there was no mention of the specificity of this antibody.

We previously showed the specificity of this antibody in its original publication showing lack of staining in the xnd-1 mutant by IF (Wagner et al., 2010). To further address this, however, we have now included a new supplementary figure (Figure S1) demonstrating the specificity of the XND-1 antibody by Western blot. The antibody detects a distinct band in extracts from wild-type (N2) worms, but this band is absent in two independent xnd-1 mutant strains. This confirms that the antibody specifically recognizes XND-1, supporting the validity of the IP results shown in the main figures.

Additionally, epistasis analysis of various genetic mutants is based on the quantification of DAPI bodies in diakinesis oocytes, but the comparisons were made without statistical analyses.

We have added statistical analysis to all datasets where quantification was possible, strengthening the rigor and interpretation of our findings.

For cytological data, a single representative nucleus was shown without quantification and rigorous analysis. The rationale for some experiments is also questionable (e.g. the rescue by dsb-2 mutants by him-5 transgenes in Figure 2), making the interpretation of the data unclear. Overall, while this paper claims to present "the first comprehensive model of DSB regulation in a metazoan", cataloging Y2H and genetic interactions did not yield any new insights into DSB formation without rigorous testing of their significance in vivo. The model proposed in Figure 4 is also highly speculative.

Regarding the cytology, we provide new images and quantification of HIM-17 and XND-1 overlap with the DNA axes. We also added full germ line images showing HIM-5 localization in wild type and dsb-1 mutants, to provide a more complete and representative view of the observed phenotype. To further support our findings, we’ve also included images demonstrating that this phenotype is consistently observed with both in live worm with the the him-5::GFP transgene and in fixed worms with an endogenously tagged version of HIM-5.

Reviewer #3 (Public Review):

During meiosis in sexually reproducing organisms, double-strand breaks are induced by a topoisomerase-related enzyme, Spo11, which is essential for homologous recombination, which in turn is required for accurate chromosome segregation. Additional factors control the number and genome-wide distribution of breaks, but the mechanisms that determine both the frequency and preferred location of meiotic DSBs remain only partially understood in any organism.

The manuscript presents a variety of different analyses that include variable subsets of putative DSB factors. It would be much easier to follow if the analyses had been more systematically applied. It is perplexing that several factors known to be essential for DSB formation (e.g., cohesins, HORMA proteins) are excluded from this analysis, while it includes several others that probably do not directly contribute to DSB formation (XND-1, HIM-17, CEP-1, and PARG-1).

We respectfully disagree with the reviewer’s statement regarding the selection of factors included in our analysis. In this work, our focus was specifically on SPO-11 accessory factors — proteins that directly interact with or regulate SPO-11 activity during doublestrand break formation. Cohesins and chromosome axis proteins (such as the HORMA domain proteins) are essential for establishing the correct chromosome architecture that supports DSB formation, but there is no evidence that they are direct accessory factors of SPO-11. Therefore, they were intentionally excluded from this study to maintain a clear and focused scope on proteins that more directly modulate SPO-11 function.

Conversely, XND-1, HIM-17, CEP-1, and PARG-1 have all been implicated in regulating aspects of SPO-11-mediated DSB formation or its immediate environment. Although their contributions mayinvolve broader chromatin or DNA damage response regulation, prior literature supports their inclusion as relevant modulators of SPO-11 activity, justifying their analysis within the context of this work.

The strongest claims seem to be that "HIM-5 is the determinant of X-chromosome-specific crossovers" and "HIM-5 coordinates the actions of the different accessory factors subgroups." Prior work had already shown that mutations in him-5 preferentially reduce meiotic DSBs on the X chromosome. While it is possible that HIM-5 plays a direct role in DSB induction on the X chromosome, the evidence presented here does not strongly support this conclusion. It is also difficult to reconcile this idea with evidence from prior studies that him-5 mutations predominantly prevent DSB formation on the sex chromosomes, while the protein localizes to autosomes.

HIM-5 is not the only protein that is autosomally enriched but preferentially affects the X chromosome: MES-4 and MRG-1 are both autosomally-enriched but influence silencing of the X chromosome. While HIM-5 appears autosomally-enriched, it does not appear to be autosomal-exclusive. While we would ideally perform ChIP to determine its localization on chromatin, this method for assaying DSB sites is likely insufficient to identify DSB sites which differ in each nucleus and for which there are no known hotspots in the worm.

him-5 mutants confer an ~50% reduction in total number of breaks and a very profound change in break dynamics (seen by RAD-51 foci (Meneely et al., 2012)). Since the autosomes receives sufficient breaks in this context to attain a crossover in >98% of nuclei, this indicates that the autosomes are much less profoundly impacted by loss of DSB functions than is the X chromosome. Indeed, prior data from co-author, Monica Colaiacovo, showed that fewer breaks occur on the X (Gao, 2015) likely resulting from differences in the chromatin composition of the X and autosome resulting from X chromosome silencing.

The conclusion that HIM-5 must be required for breaks on the X comes from the examination of DSB levels and their localization in different mutants that impair but do not completely abrogate breaks. In any situation where HIM-5 protein expression is affected (xnd-1, him-17, and him-5 null alleles), breaks on the X are reduced/ eliminated. By contrast, in dsb-2 mutants, where HIM-5 expression is unaffected, both X and autosomal breaks are impacted equally. As discussed above, in the absence of HIM-5 function, there are ~15 breaks/ nucleus. The Ppie1::him-5 transgene is expressed to lower levels than Phim-5::him-5, but in the best case, the ectopic expression of this protein should give a maximum of ~15 breaks (the total # of breaks is thought to be ~30/nucleus). By these estimates, Ppie-1::him-5; him-17 and him-5 null mutants have the same number of breaks. Yet, in the former case, breaks occur on the X; whereas in the latter they do not. The best explanation for this discrepancy is that HIM-5 is sufficient to recruits the DSB machinery to the X chromosome.

The one experiment that seems to elicit the conclusion that HIM-5 expression is sufficient for breaks on the X chromosome is flawed (see below). The conclusion that HIM-5 "coordinates the activities of the different accessory sub-groups" is not supported by data presented here or elsewhere.

We have reorganized the discussion to more directly address the reviewers’ concerns. We raise the possibility that HIM-5 has an important role in bringing together the SPO-11 and its interacting components (DSB-1/2/3) with the other DSB inducing factors, including those factors that regulating DSB timing (XND-1), coordination with the cell cycle (REC-1), association with the chromosome axis (PARG-1, MRE-11), and coupling to downstream resection and repair (MRE-11, CEP-1).  

This raises a natural question: if HIM-5 has such a central role, why are the phenotypes of HIM-5 so mild? We propose that while the loss of DSBs on the X appears mild, more profound effects are seen in the total number, timing, and placement of the DSBs across the genome- all of which are diminished or altered in the absence of HIM-5. The phenotypes of him-5 loss reminiscent of those observed in Prdm9-/- in mice where breaks are relocated to transcriptional start sites and show significant delay in formation. As with PRDM9, the comparatively subtle phenotypes of HIM-5 loss do not diminish its critical role in promoting proper DSB formation in most mammals.

Like most other studies that have examined DSB formation in C. elegans, this work relies on indirect assays, here limited to the cytological appearance of RAD-51 foci and bivalent chromosomes, as evidence of break formation or lack thereof. Unfortunately, neither of these assays has the power to reveal the genome-wide distribution or number of breaks. These assays have additional caveats, due to the fact that RAD-51 association with recombination intermediates and successful crossover formation both require multiple steps downstream of DSB induction, some of which are likely impaired in some of the mutants analyzed here. This severely limits the conclusions that can be drawn. Given that the goal of the work is to understand the effects of individual factors on DSB induction, direct physical assays for DSBs should be applied; many such assays have been developed and used successfully in other organisms.

We appreciate the reviewer’s thoughtful comments. We agree that RAD-51 foci are an indirect readout of DSB formation and that their dynamics can be influenced by defects in downstream repair processes. However, in C. elegans, the available methods for directly detecting DSBs are limited. Unlike other organisms, C. elegans lacks γH2AX, eliminating the possibility of using γH2AX as a DSB marker. TUNEL assays, while conceptually appealing, have proven unreliable and poorly reproducible in the germline context. Similarly, RPA foci do not consistently correlate with the number of DSBs and are influenced by additional processing steps.

Given these limitations, RAD-51 foci remain the most widely accepted surrogate for monitoring DSB formation in C. elegans. While we fully acknowledge the caveats associated with this approach — particularly the potential effects of downstream repair defects — RAD-51 analysis continues to provide valuable insight into DSB dynamics and regulation, especially when interpreted in combination with other phenotypic assessments.

Throughout the manuscript, the writing conflates the roles played by different factors that affect DSB formation in very different ways. XND-1 and HIM-17 have previously been shown to be transcription factors that promote the expression of many germline genes, including genes encoding proteins that directly promote DSBs. Mutations in either xnd-1 or him-17 result in dysregulation of germline gene expression and pleiotropic defects in meiosis and fertility, including changes in chromatin structure, dysregulation of meiotic progression, and (for xnd-1) progressive loss of germline immortality. It is thus misleading to refer to HIM-17 and XND-1 as DSB "accessory factors" or to lump their activities with those of other proteins that are likely to play more direct roles in DSB induction.

It is clear that we will not reach agreement about the direct vs indirect roles here of chromatin remodelers/transcription factors in break formation. In yeast, there is a precedent for SPP1 and in mouse for Prdm9, both of which could be described as transcription factors as well, as having roles in break formation by creating an open chromatin environment for the break machinery. We envision that these proteins function in the same fashion. The changes in histone acetylation in the xnd-1 mutants supports such a claim.

We do not know what the reviewer is referring to in statement that “XND-1 and HIM-17 have previously been shown to be transcription factors that promote the expression of many germline genes.” While the Carelli et al paper indeed shows a role for HIM-17 in expression of many germline genes, there is only one reference to XND-1 in this manuscript (Figure S3A) which shows that half of XND-1 binding sites overlap with the co-opted germline promoters. There is no transcriptional data at all on xnd-1 mutants, save our studies (referenced herein) that XND-1 regulates him-5 expression.

For example, statements such as the following sentence in the Introduction should be omitted or explained more clearly: "xnd-1 is also unique among the accessory factors in influencing the timing of DSBs; in the absence of xnd-1, there is precocious and rapid accumulation of DSBs as monitored by the accumulation of the HR strand-exchange protein RAD-51.

We are not sure what is confusing here. The distribution of RAD-51 foci is significantly altered in xnd-1 mutants and peak levels of breaks are achieved as nuclei leave the transition zone (Wagner et al., 2010; McClendon et al., 2016). There is no other mutation that causes this type of change in RAD-51 distribution.

"The evidence that HIM-17 promotes the expression of him-5 presented here corroborates data from other publications, notably the recent work of Carelli et al. (2022), but this conclusion should not be presented as novel here.

We have clarified this in the text. We note that this paper showed alterations in him-5 levels by RNA-Seq but they did not validate these results with quantitative RT-PCR. Thus, our studies do provide an important validation of their prior results.

The other factors also fall into several different functional classes, some of which are relatively well understood, based largely on studies in other organisms. The roles of RAD50 and MRE-11 in DSB induction have been investigated in yeast and other organisms as well as in several prior studies in C. elegans. DSB-1, DSB-2, and DSB-3 are homologs of relatively well-studied meiotic proteins in other organisms (Rec114 and Mei4) that directly promote the activity of Spo11, although the mechanism by which they do so is still unclear.

Whilst we agree that we understand some of the functions of the homologs, there are clearly examples in other processes of conserved proteins adopting unique regulatory function. We should not presume evolutionary conservation until proven. Indeed the comparison between the Mer2 proteins becomes particularly relevant here. For example, the RMM complex in plants does not contain PRD3, although this protein is thought to have function in DSB formation and repair (Lambing et al, 2022; Vrielynck et al., 2021; Thangavel et al., 2023). In Sordaria, as well, the Mer2 homolog has distinct functions (Tesse et al., 2017).  

Mutations in PARG-1 (a Poly-ADP ribose glycohydrolase) likely affect the regulation of polyADP-ribose addition and removal at sites of DSBs, which in turn are thought to regulate chromatin structure and recruitment of repair factors; however, there is no convincing evidence that PARG-1 directly affects break formation.

Our prior collaborative studies on PARG-1 showed that is has a non-catalytic function that promote DSBs that is independent of accumulation of PAR (Janisiw et al., 2020; Trivedi et al., 2022)

CEP-1 is a homolog of p53 and is involved in the DNA damage response in the germline, but again is unlikely to directly contribute to DSB induction.

We respectfully disagree with the reviewer’s statement. While CEP-1 is indeed a homolog of p53 and plays a major role in the DNA damage response, prior work from Brent Derry’s lab and from our group (Mateo et al., 2016) demonstrated that specific cep-1 separationof-function alleles affect DSB induction and/or repair pathway choice independently of canonical DNA damage checkpoint activation. In particular, defects in DSB formation observed in certain cep-1 mutants can be rescued by exogenous irradiation, supporting a direct or closely linked role in promoting DSB formation rather than merely responding to damage. Thus, based on these functional data, we considered CEP-1 a relevant factor to include in our analysis. We have now clarified this rationale in the revised manuscript.

HIM-5 and REC-1 do not have apparent homologs in other organisms and play poorly understood roles in promoting DSB induction. A mechanistic understanding of their functions would be of value to the field, but the current work does not shed light on this. A previous paper (Chung et al. G&D 2015) concluded that HIM-5 and REC-1 are paralogs arising from a recent gene duplication, based on genetic evidence for a partially overlapping role in DSB induction, as well as an argument based on the genomic location of these genes in different species; however, these proteins lack any detectable sequence homology and their predicted structures are also dissimilar (both are largely unstructured but REC-1 contains a predicted helical bundle lacking in HIM-5). Moreover, the data presented here do not reveal overlapping sets of genetic or physical interactions for the two genes/proteins. Thus, this earlier conclusion was likely incorrect, and this idea should not be restated uncritically here or used as a basis to interpret phenotypes.

Actually, there is quite good bioinformatic analysis that the rec-1 and him-5 loci evolved from a gene duplication and that each share features of the ancestral protein (Chung et al., 2015). We are sorry if the reviewer casts aspersions on the prior literature and analyses. The homology between these genes with the ancestral protein is near the same degree as dsb-1, dsb-2, or dsb-3 to their ancestral homologs (<17%).

DSB-1 was previously reported to be strictly required for all DSB and CO formation in C. elegans. Here the authors test whether the expression of HIM-5 from the pie-1 promoter can rescue DSB formation in dsb-1 mutants, and claim to see some rescue, based on an increase in the number of nuclei with one apparent bivalent (Figure 2C). This result seems to be the basis for the claim that HIM-5 coordinates the activities of other DSB proteins. However, this assay is not informative, and the conclusion is almost certainly incorrect. Notably, a substantial number of nuclei in the dsb-1 mutant (without Ppie-1::him-5) are reported as displaying a single bivalent (11 DAPI staining bodies) despite prior evidence that DSBs are absent in dsb-1 mutants; this suggests that the way the assay was performed resulted in false positives (bivalents that are not actually bivalents), likely due to inclusion of nuclei in which univalents could not be unambiguously resolved in the microscope. A slightly higher level of nuclei with a single unresolved pair of chromosomes in the dsb-1; Ppie-1::him-5 strain is thus not convincing evidence for rescue of DSBs/CO formation, and no evidence is presented that these putative COs are X-specific. The authors should provide additional experimental evidence - e.g., detection of RAD-51 and/or COSA-1 foci or genetic evidence of recombination - or remove this claim. The evidence that expression of Ppie-1::him-5 may partially rescue DSB abundance in dsb-2 mutants is hard to interpret since it is currently unknown why C. elegans expresses 2 paralogs of Rec114 (DSB-1 and DSB-2), and the age-dependent reduction of DSBs in dsb-2 mutants is not understood.

We have removed this claim in part because we have been unable to create the triple mutants strains to analyze COSA-1 foci.

To the point about 11 vs 12 DAPI bodies: the literature is actually replete with examples of 11 DAPI bodies vs 12 in mutants with no breaks:

Hinman al., 2021: null allele of dsb-3 has an average of 11.6 +/- 0.6 breaks;

Stamper et al, 2013, show just over 60% of dsb-1 nuclei with 12 DAPI bodies and 5-10% with 10 DAPI bodies. (Figure 1);

In addition, we also previously showed (Machovina et al., 2016) that a subset of meiotic nuclei have a single RAD-51 focus and can achieve a crossover. RAD-51 foci in spo-11 were also reported in Colaiacovo et al., 2003.

Several of the factors analyzed here, including XND-1, HIM-17, HIM-5, DSB-1, DSB-2, and DSB-3, have been shown to localize broadly to chromatin in meiotic cells. Coimmunoprecipitation of pairs of these factors, even following benzonase digestion, is not strong evidence to support a direct physical interaction between proteins.

Similarly, the super-resolution analysis of XND-1 and HIM-17 (Figure 1EF) does not reveal whether these proteins physically interact with each other, and does not add to our understanding of these proteins functions, since they are already known to bind to many of the same promoters. Promoters are also likely to be located in chromatin loops away from the chromosome axis, so in this respect, the localization data are also confirmatory rather than novel.

While the binding to promoters would be expected to be on DNA loops, that has not been definitively shown in the worm germ line. The supplemental data of the Carelli paper suggests that there are ~250 binding sites for each protein at these coopted promoters. This could not account for crossover map seen in C. elegans.

The reviewer states correct that we do not reveal that these proteins interact, but we have shown that the two proteins co-IP and have a Y2H interaction. This interaction is supporedt by a recent publication (Blazickova et al., 2025) corroborating this conclusion and identifies XND-1 in HIM-17 co-IPs also in the presence of benzonase. We do now show, however, by immuno-localization that the two proteins appear to be adjacent, but nonoverlapping. As now described in the text, AlphaFold 3 modeling and structural analysis suggests that the two proteins do interact directly and that the tagged 5’ end of HIM-17 used in our studies is likely to be at least 200nm from the putative XND-1 binding interface, a distance that is consistent with our confocal images showing frequent juxtaposition of the two proteins.

The phenotypic analysis of double mutant combinations does not seem informative. A major problem is that these different strains were only assayed for bivalent formation, which (as mentioned above) requires several steps downstream of DSB induction. Additionally, the basis for many of the single mutant phenotypes is not well understood, making it particularly challenging to interpret the effects of double mutants. Further, some of the interactions described as "synergistic" appear to be additive, not synergistic. While additive effects can be used as evidence that two genes work in different pathways, this can also be very misleading, especially when the function of individual proteins is unknown. I find that the classification of genes into "epistastasis groups" based on this analysis does not shed light on their functions and indeed seems in some cases to contradict what is known about their functions. ‘

As described above, each of the proteins analyzed is thought to have a direct role in regulating meiotic DSB formation and single mutant phenotypes are consistent with this interpretation. In almost all-if not all- of these cases, IR induced breaks suppress univalent phenotypes (or uncover a downstream repair defect (e.g. in mre-11)) supporting this conclusion. We have changed the terminology from “epistasis groups” since this is not strict epistasis, but rather, “functional groups”.  

The yeast two-hybrid (Y2H) data are only presented as a single colony. While it is understandable to use a 'representative' colony, it is ideal to include a dilution series for the various interactions, which is how Y2H data are typically shown.

The Y2H data are presented as spots on a plate and are from three to four individual transformants per interaction tested, and are not individual colonies. The experiment was repeated in triplicate from different transformations. We have now made this clearer in the materials and methods section. This approach has been successfully used to examine protein interactions in our prior manuscripts of yeast and human proteins [Gaines et al (2015) Nat. Comms 6:7834; Kondrashova et al (2017) Cancer Discovery 7:984; Garcin et al (2019) PLoS Genetics 15:e1008355; Bonilla et al (2021) eLife 1: e68080) Prakash et al (2022) PNAS 119: e2202727119, etc]

Additional (relatively minor) concerns about these data:

(1) Several interactions reported here seem to be detected in only one direction - e.g., MRE-11-AD/HIM-5-BD, REC-1-AD/XND-1-BD, and XND-1-AD/HIM-17-BD - while no interactions are seen with the reciprocal pairs of fusion proteins. I'm not sure if some of this is due to pasting "positive" colony images into the wrong position in the grid, but this should be addressed.

The asymmetry in the interactions observed is due to the well-known phenomenon in yeast two-hybrid (Y2H) assays where certain plasmids exhibit self-activation when fused in one orientation, making interpretation of reciprocal interactions challenging. In our experiment, some of the plasmids indeed showed self-activation in one direction, which likely accounts for the lack of interaction seen with the reciprocal pairs of fusion proteins. We have clarified this point in the Methods.

(2) DSB-3 was only assayed in pairwise combinations with a subset of other proteins; this should be explained; it is also unclear why the interaction grids are not symmetrical about the diagonal.

We have now completed the analysis by adding the interactions of DSB-3 with the remaining proteins that were missing from the initial set.

(3) I don't understand why the graphic summaries of Y2H data are split among 3 different figures (1, 2, and 3).

We chose to split the graphic summaries of the Y2H data across Figures 1, 2, and 3 because we felt this organization better aligns with the flow of the results presented in each figure. Each set of interactions is shown in the context of the specific experiments and findings discussed in those sections, which we believe helps provide a clearer and more logical presentation of the data.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Figure 1: B) The IP is difficult to interpret - there is a band of the corresponding size to XND-1 in the control lane calling into question the specificity of the IP/Western.

We added a supplemental figure with the specificity of the antibody showing that there is a background non-specific band.

C) More information about the mass spectrometry should be included. No indication of the number of times a peptide was identified, or the overall coverage of the identified proteins.

Done

This is important as in the results section (line 114) the authors indicate that there was "strong" interaction yet there is no way to assess this.

D) Why wasn't hatching measured in the him-5p::him-5; him-17(ok424) strain?

Great question. I guess we need to do this while back out for review. If anyone has suggestions of what to say here. Clearly we overlooked this point but do have the strain.

E) Quantification of the cytology should be included.

We have now quantified overlap between XND-1 and HIM-17

Figure 2: C) Statistics should be included.

Done

E) Quantification should be included for the cytology. I recommend changing the eals15 to HIM-5.

We included better images showing whole gonads instead of one or two nuclei. We were not sure what the reviewers want us to quantify here since the relocalization of the protein to the cytoplasm is very clear.

I have a general issue with the use of the term epistasis - this is used to order gene function based on different mutant phenotypes, usually with null alleles. While I think the authors have valid points with how they group the different SPO-11 accessory proteins, I do not think they should use the word epistasis, but rather genetic interactions.

We appreciate the reviewers thoughts on this matter and have removed the term epistasis and use functional groups or genetic interactions throughout the text.

Figure 4 and the nature of the X chromosome: First, I think it would help the non-C. elegans reader to include a little more information on the X chromosome with respect to its differences compared to the autosomes. I also think that, if possible, it would be beneficial to include a model of the X in Figure 4.

We have added more about X/autosome differences in the intro and during the discussion of HIM-5 function and have added a figure showing difference in the behavior of the X/autosomes during DSB/crossover formation.

Minor points:

Abstract: Given the findings of Silva and Smolikove on SPO-11 breaks, I recommend removing "early" from line 28 in the Abstract.

Done

Introduction (line 93): I think "biochemical studies" is a stretch here - I recommend "interaction studies".

Done

Results: (lines 160-161): mutations are not required for breaks. Line 172, there is a problem with the sentence.

Corrected

Reviewer #2 (Recommendations For The Authors):

Major comments:

(1) Figure 1B- The signal for XND-1 seems to appear both in the control and him-17::HA IP. Do the authors have tested the specificity of the XND-1 antibody?

We included a supplementary figure demonstrating the specificity of the XND-1 antibody by Western blot. This was also previously published (Wagner et al., 2010)

(2) Figure 1D - can the authors provide an explanation why the him-5p::him-5 transgene that drives a higher expression than pie-1p::him-5 fails to suppress the Him phenotype seen in him-17? What are the HIM-5 levels like in these two strains compared to N2 and him-17 null mutants? Can this information provide explanation for the differential effect of the him-5 transgenes?

We previously reported that him-5p::him-5 drives higher expression than pie-1p::him-5 (McClendon et al, 2016).

The reason that him-5p::him-5 does not rescue, despite higher wild type expression is that HIM-17 directly regulates expression of him-5. Since HIM-17 does not regulate the pie-1 promoter, the pie-1p::him-5 construct can at least partially suppress the him-17 mutation.

We have (hopefully) explained this better in the text.  

(3) Line 102- the subheading "HIM-5 is the essential factor for meiotic breaks in the Xchromosome" may not be appropriate for this section. This is what has previously been known. However, the results in Figure 1 demonstrate that a him-5 transgene can partially rescue the him-17 and ¬xnd-1 phenotype, but not that it is essential for meiotic DSB formation on X chromosomes.

We think some of the concern here is sematic and have changed the phraseology to say that HIM-5 is SUFFICIENT for DSBs on the X… which had not previously been shown.

Vis-à-vis the X chromosome, in all genetic backgrounds examined, the absence of HIM-5 consistently results in a complete lack of DSBs on the X. For instance, in dsb-2 mutants— where HIM-5 is still expressed—DSBs are reduced genome-wide, but the X chromosome occasionally retains breaks. In contrast, even a weak allele of him-17 results specifically in the loss of X chromosome breaks, underscoring a unique requirement for HIM-5 in promoting DSBs on the X. While Figure 1 shows that a him-5 transgene can partially rescue him-17 and xnd-1 phenotypes, the consistent observation that X breaks are absent without HIM-5 supports its classification as sufficient for DSB formation on the X chromosome.

(4) Figure 1E - please consider enlarging the images and showing multiple examples.

Done.

I also suggest that the authors perform a more rigorous analysis to support the conclusion that XND-1 and HIM-17 localize away from the axis by quantifying multiple images and doing line-scan analysis.

Provided. New images are provided in both, the main and supplemental figures, and quantification is included. There is no detectable overlap of the two protein with one another or the DNA axes (see quantification of overlap in Fig. 1).

(5) Line 162 - This is the first mention of DSB-1, DSB-2, and DSB-3 in the paper. DSB-1 and DSB-2 are Rec114 homologs in C. elegans (Tesse et al., 2017), while DSB-3 is a homolog of Mei4 (Hinman et al., 2021). These proteins should be properly introduced in the introduction with appropriate citations.

Done. We appreciate the reviewer pointing out that this was the first reference to these genes.

(6) Line 169 - the rationale for this experiment is unclear. Why did the Y2H interaction between HIM-5 and DSB-1 prompt the authors to test the rescue of dsb-1 or dsb-2 phenotypes by the ectopic expression of him-5? Do the authors have evidence that HIM-5 level is reduced in dsb-1 or dsb-2 mutants?

We have reorganized this section to better explain the motivation for looking at these interactions. We did see a difference in the localization in HIM-5 in the dsb-1 mutant animals and we did have a sense that HIM-5 was critical for breaks on the X. We reasoned that it could have independent functions in promoting breaks that were not yet appreciated so wanted to do this experiment.

(7) Line 172 - "very slightly reduced". This claim requires statistical analysis.

We added statistical analysis, but we also removed this claim.

(8) Figures 2C and 2D - Can the authors provide an explanation why the pie-1p::him-5 transgene fails to suppress the phenotypes in dsb-1, while the him-5p::him-5 trasgene can? Again, the rationale for these experiments is unclear. Because of this, the interpretation is also unclear.

The difference in rescue between the pie-1p::him-5 and him-5p::him-5 transgenes likely reflects differences in expression levels. As previously shown (McClendon et al., 2016), the him-5p::him-5 construct results in significantly higher expression of HIM-5 protein compared to pie-1p::him-5. This elevated expression likely explains its ability to partially rescue the dsb-1 phenotype. In contrast, the lower expression driven by the pie-1 promoter is insufficient to compensate for the absence of dsb-1 function. We have clarified the rationale and interpretation of these experiments in the revised manuscript to better reflect this point.

(9) Lines 184-185 - the data for endogenously tagged HIM-5::3xHA are not shown anywhere in the paper. This must be shown.

We have added this in the supplemental figures.

(10) Figure 2D and 2E - what does the localization of pie-1p::him-5::GFP (eaIs15) and him5p::him-5::GFP (eaIs4) look like in wild-type and dsb-1 mutants? Are the cytoplasmic aggregates caused by increased levels of HIM-5 expression? Can the differential behavior of him-5 transgenes provide explanation for differential rescues?

We now show both live and fixed images of Phim-5::him-5::gfp transgenes, as well as the localization of the endogenously HA-tagged HIM-5 locus (Figure 2 and S3). In all cases, the protein is initially nuclear and then absent from meiotic nuclei with similar timing. The Ppie1::him-5 transgene was very difficult to image due to low expression (even in wild type) so it not shown here. We presume it is the slightly elevated level of expression of the Phim5::him-5::gfp that can explain the differential rescue.

(11) Lines 221-222, where are the results shown? Please refer to Figure S3.

Done

(12) Figure S3 - these need statistical analyses.

Done

(13) Lines 230-231 - what about the rec-1; parg-1; cep-1 triple mutant?

This is an excellent suggestion and not one we have not yet pursued. Given the lack of strong phenotypes in all combination of double mutants, we prioritized other experiments . However, we agree that examining the rec-1; parg-1; cep-1 triple mutant would provide a valuable test of whether these factors act in the same pathway, and we appreciate the reviewer highlighting this potential future direction.

(14) Line 298 - I suggest the authors take a look at the Alphafold prediction of DSB-1/DSB-2/DSB-3 and the comparison to human and budding yeast Rec114/Mei4 complex in Guo et al., 2022 eLife, which could provide insights into the Y2H results.

We thank the reviewer for these comments and have indeed used these interactions and predicted homologies to zero in a region of interaction between these proteins that resembles what is seen in humans and yeast with a dimer of REC114 like proteins wraps stabilizing a central Mei4 helix . This is now shown in Figure 3H, I. Satisfyingly, this modeling predicts that a trimer comprised of 2 DSB-1 proteins with DSB-3 is more stable than a DSB1-DSB-2-DSB-3 trimer. This might explain why DSB-2 is not required in young adults and only becomes essential as DSB-1 levels drop in older animals (Rosu et al., 2013)

(15) Can the authors introduce mutations within the DSB-1 interfaces that disrupt the interaction to either SPO-11 or DSB-2?

We have begun to address this question by introducing targeted mutations within DSB-1. As shown in Figure 3E and 3F, mutations in the C-terminal region of DSB-1—which includes a core of four α-helices—disrupt its interaction with DSB-2 and DSB-3, but not with SPO-11. These findings suggest that the C-terminus mediates interactions specifically with DSB2 and DSB-3

(16) Line 323 - The him-5 phenotypes are too weak to support the idea that it serves as the linchpin for the whole DSB complex. Do the authors have an explanation for why him-5 mutants exhibit X-chromosome-specific DSB defects?

In response to the reviewer, above, and in the text, we have included a more detailed explanation of why we think HIM-5 has a key role in coordinating meiotic break formation. Although, identified for its role on the X, the phenotypes associated with DSB formation in the mutant are really quite pleiotropic and severe.

(17) Line 436 - C. elegans lacks DSB hotspots.

Removed

Minor comments:

(1) Figure 1A - please show the raw data for the yeast two-hybrid.

We show representative yeast colonies in Figure S3.

(2) It looks like the labeling for Figure 1B and 1C are switched.

Fixed.

(3) Figure 1B - what does the red box indicate? Please explain it in the legend.

It indicates the XND-1 band. We added that information in the legend.

(4) Figure 1C - in the legend, it was noted that the results are from GFP pulldowns of HIM17::GFP. However, the method for Figure 1B and the method section noted that HIM-17 was tagged with 3xHA, and the pull-down was performed using anti-HA affinity matrix. Please reconcile this discrepancy.

That’s because they were done in two different sets of experiments. For the IPs we used a HIM-17::HA strain and for the MS, a HIM-17::GFP strain.

(5) Also in Figure 1C - please call Table S2 in the main text when discussing the mass spec results. Also, it is not clear what HIM-17 and GFP indicate in the table. What makes CKU80 different from the other proteins listed under GFP? Please explain more clearly in the legend.

We have move the table to supplemental data where we have included all of the peptide counts and gene coverage. We have included in the revised method rationale for inclusion in this table which explains why CKU-80 differs.

(6) Line 527 - it is unclear what experiment was done for HIM-17. Please revise it to indicate that this is for "HIM-17 immunoprecipitation". Also please indicate the strain used for HIM17 pull-down (AV280?).

(7) Line 113- please be specific about how the HIM-17 IP was performed. Which epitope and strains are used for pull-downs?

This indeed was AV280. This has been added to the text and methods.

(8) Figure 1D- What does ND mean? In the text, it was stated that there was only a minor suppression of hatching rates. The hatching rate for him-5p::him-5; him-17 must have been measured, and the data must be presented.

ND does mean not determined. We have removed the statement about “minor suppression”. We only tested the overall population dynamics in the Phim-5::him-5;him17(ok424) and the DAPI body counts. The failure to suppress the latter suggests there would be no enect on hatching rates, although we did not test this directly. Since we had done this for the Ppie-1::him-5;him-17 strain, we provided this information to further support the claims of genetic rescue by ectopic expression.

(9) Line 151 - please specify that STED was used.

We have removed the STED images, and just show the confocal images with Lightning Processing.

(10) Figure 1E- the authors suggested that HIM-17 and XND-1 mainly localize to autosomes but not the X chromosome. However, there is not enough evidence that the chromosome excluded from HIM-17 staining is indeed an X chromosome.

(11) Figure 1E (Line 154) - what are the active chromatin markers examined? Where are the data?

We have previously shown that the chromosome lacking XND-1 staining is the X (Wagner et al., 2010). The X is heterochromatic and chromatin marks associated with active transcription, including H3K4me3 and HTZ-1 (a variant H2A), preferentially localize to autosomes, effectively anti-marking the X chromosome. As shown in the new Figure 1E, a single chromosome has very little XND-1 and HIM-17 associated proteins. This is the X chromosome.

(12) Line 172 - It should be a comma instead of the period after "In dsb-1 mutants".

Fixed

(13) Figure S3H-K - I suggest the authors indicate the alleles of mre-11 (null vs. iow1) on the graph, similarly to him-5(e1490) to avoid confusion.

Done

(14) Lines 294 and 600 - Guo et al. 2022 is now published in eLife. The authors must cite the published paper, not the preprint.

Fixed

(15) Line 407 - the reference Carelli et al., 2022 is missing.

Added

(16) Line 766 - please remove "is" before nuclear.

Done

Reviewer #3 (Recommendations For The Authors):

Major issues:

In my view, the most interesting mechanistic finding in the paper is the evidence that HIM-5 may not bind to chromatin in the absence of DSB-1. If validated, this would suggest that HIM-5 is likely to be directly involved in a process that promotes break formation, in contrast to factors such as HIM-17 and XND-1. It does not, however, support the idea that HIM-5 is at the top of a hierarchy of DSB factors, as it is interpreted here. More importantly, the data supporting this claim are unconvincing; only a single image of an unfixed gonad from an animal expressing HIM-5::GFP is shown. Immunofluorescence should be performed and the results must be quantified.

We have provided additional images of the HIM-5 relocalization to show that we observed this in both fixed and live worms with two different tagged strains. The exclusion from the nucleus is seen in all scenarios. Whether the protein now accumulates exclusively in the cytoplasm/ is destabilized is challenging to address with the fixed images due to the arbitrariness of defining “background” staining.

More generally, this type of analysis, looking at the interdependence of different factors for their association with chromosomes, is much more informative than the genetic interaction data presented in the paper, which does not seem to provide any mechanistic insights into the functions of the factors analyzed. The paper could potentially be greatly improved through a more extensive, systematic analysis of the interdependence of DSBpromoting factors for their localization to chromosomes.

We have at least added this for XND-1 and HIM-17 and show they are not interdependent for chromosome association. We also provide for the first time data on the localization of HIM-5 in the dsb-1 mutant. Many of the other interactions have already been shown in the literature and/or were not warranted base on the lack of genetic interaction we present here.

Minor issues:

The title is vague and inconclusive. A more concrete title summarizing the major findings would help readers to assess whether the work is of interest.

We have discussed the title extensively with all authors and all would like to keep the current title.

The authors claim that the expression of HIM-5 from a different promoter (Ppie-1::him-5) but not its endogenous promoter (Phim-5::him-5) can partially rescue the DSB defect in him-17 mutants. To support this claim, they should really quantify the germline expression of HIM-5 in wild-type, him-17, him-17; Ppie-1::him-5, and Phim-5::him-5; him-17.

We had previously reported the expression in the N2 background of both transgenes (McClendon et al., 2016)

Panel O appears to be missing from Figure S3.

Fixed

The evidence for chromosome fusions in cep-1; mre-11 mutants shown in S4D is not convincing and the claim should be removed unless stronger evidence can be obtained.

A clearer image has been added

The basis of the following statement is unclear: "Furthermore, rec-1;him-5 double mutants give an age-dependent severe loss of DSBs (like dsb-2 mutants) suggesting that the ancestral function of the protein may have a more profound effect on break formation." The manuscript does not seem to include data regarding age-dependent loss of DSBs and no other publication is cited to support this claim. The interpretation is also perplexing; I think that it may be predicated on the idea that REC-1 and HIM-5 are paralogs, but as stated above, this claim is not well supported and is likely specious.

We have added the reference. This was shown in Chung et al., 2013 – the paper that presented the cloning of the rec-1 locus.

Reviewer #3 (Public review):

Summary:

Knoerzer-Suckow et al. explore the mechanisms of organelle inheritance during endodyogeny in Toxoplasma gondii using an innovative dual-labeling approach to track the distribution of maternal organelles into daughter parasites. They can clearly distinguish between maternal and daughter-derived organelles using their dual-labeling Halo Tag approach. They reveal that different organelles are trafficked to daughter parasites in three broad patterns, which they have binned into groups. Their findings reveal a role for MyoF in the inheritance of micronemes and rhoptries, and notably, they observe that the inner membrane complex (IMC) is not recycled. Instead, the IMC undergoes a pronounced relocalization to the posterior of the maternal cell, where it is likely targeted for degradation.

Strengths:

The data surrounding their MyoF knockdown experiments, IMC degradation, and trafficking of MIC2 after auxin washout are compelling. These data add to the knowledge of how organelle inheritance occurs in T. gondii, increasing the field's understanding of endodyogeny.

Weaknesses:

(1) The evidence provided to support the claim that microneme and rhoptry inheritance specifically traffics through the residual body does not sufficiently substantiate the claim. The temporal resolution of the imaging is inadequate to precisely trace the path of microneme and rhoptry inheritance. From the data shown in the manuscript, it can be concluded that at least some of the micronemes and rhoptries might be recycled through the residual body, but it is unclear whether many or most of these organelles do so.

(2) The absence of specific markers for the residual body brings into question whether microneme inheritance occurs through a discrete residual body or simply via the basal end of the maternal parasite. The authors need a robust way to visualize and define the residual body to claim that micronemes and rhoptries are specifically transported through this structure.

Reviewer #1 (Public review):

Summary:

The extent to which P. falciparum liver stage parasites export proteins into the host cell is unclear. Most blood-stage exported proteins tested in liver stages were not exported. An exception is LISP2, which is exported in P. berghei but not P. falciparum liver stages. While the machinery for export is present in liver stages, efforts to demonstrate export have so far been mostly unsuccessful. Parasite proteins exported during the liver stage could be presented by MHC and thereby become the target of immune control, an incentive to study liver stage export and identify proteins exported during this stage. However, particularly for P. falciparum, it is very difficult to study liver stages.

This work studies LSA3 in P. falciparum blood and liver stages. The authors show that this protein is exported into the host cell in blood stages, but in liver stages, no or only very little export was detected. A disruption of LSA3 reduced liver stage load in a humanized mouse model, indicating this protein contributes to efficient development of the parasites in the liver.

The paper also studies the localization of LSA3 in blood stages and uses a known inhibitor to show that it is processed by plasmepsin 5, a protease important for protein trafficking. The work also shows that LSA3 is not needed for passage through the mosquito.

Strengths:

The main strength of this work is the use of the humanized mouse model to study liver stages of P. falciparum, which is technically challenging and requires specialized facilities. The biochemical analysis of LSA3 localization and processing by plasmepsin 5 is thorough and mostly overcame adverse issues such as a cross-reactive antibody and the negative influence of the GFP-tag on LSA3 trafficking. The mosquito stage analysis is also notable, as these kinds of studies are difficult with P. falciparum. However, there was no evidence for a function of LSA3 in mosquito stages.

Weaknesses:

The cross-reactivity of the antibody, together with the co-infection strategy, prevents reliable assessment of LSA3 localization in liver stages. Despite this, it seems LSA3 is not exported in liver stages, and the paper does not bring us closer to the original goal of finding an exported liver stage protein.

While the localization analysis in blood stages is well done and thorough, the advance is somewhat limited. LSA3 may be in structures like J dots, but this hypothesis was not tested. Although parasites with a disrupted LSA3 were generated, the function of this protein was not explored. Given that a previous publication found some inhibitory effect of LSA3 antibodies on blood stage growth, a comparison of the growth of the LSA3 disruption clones with the parent would have been very welcome and easy to do. At this point, LSA3 is one more of many proteins exported in blood stages for which the function remains unclear.

It might be possible to refine some of the conclusions. The impact on liver stage development is interesting, but which phase of the liver stage is affected, and the phenotype remains largely unknown. The co-infection (WT together with LSA3 mutant) has the advantage of a direct comparison of the mutant with the control in the same liver, but complicates phenotypic analysis if the LSA3 antibody is also cross-reactive in liver stages. This issue adds a question mark to the shown localization and precludes phenotypic comparisons. The authors write that they do not know if the cross-reactive protein is expressed at that stage. But this should be immediately evident from the mixed WT/mutant infection. If all cells are positive for LSA3, there is a cross-reaction. If about half of the cells are negative, there isn't. In the latter case, the localization shown in the paper is indeed LSA3, and morphological differences between WT and LSA3 disruption could be assessed without additional experiments.

Significance:

The conclusion from the paper that "our study presents just the second PEXEL protein so far identified as important for normal P. falciparum liver-stage development and confirms the hypothesized potential of exported proteins as malaria vaccine candidates" is partially misleading. Neither LISP2 nor LSA3 seems to be exported in P. falciparum liver stages, and we can't confirm the potential of vaccines with proteins exported in this stage. LSA3 is still important and may still be the target of the immune response, but based on this work, probably not due to export in liver stages.

Isn't it cool to have this extra layer of discussion? I could tag my annotation, share a link with further resources, and more.

Isn't it cool to have this extra layer of discussion? I could tag my annotation, share a link with further resources, and more.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

Summary: 

The study by Klug et al. investigated the pathway specificity of corticostriatal projections, focusing on two cortical regions. Using a G-deleted rabies system in D1-Cre and A2a-Cre mice to retrogradely deliver channelrhodopsin to cortical inputs, the authors found that M1 and MCC inputs to direct and indirect pathway spiny projection neurons (SPNs) are both partially segregated and asymmetrically overlapping. In general, corticostriatal inputs that target indirect pathway SPNs are likely to also target direct pathway SPNs, while inputs targeting direct pathway SPNs are less likely to also target indirect pathway SPNs. Such asymmetric overlap of corticostriatal inputs has important implications for how the cortex itself may determine striatal output. Indeed, the authors provide behavioral evidence that optogenetic activation of M1 or MCC cortical neurons that send axons to either direct or indirect pathway SPNs can have opposite effects on locomotion and different effects on action sequence execution. The conclusions of this study add to our understanding of how cortical activity may influence striatal output and offer important new clues about basal ganglia function. 

The conceptual conclusions of the manuscript are supported by the data, but the details of the magnitude of afferent overlap and causal role of asymmetric corticostriatal inputs on some behavioral outcomes may be a bit overstated given technical limitations of the experiments. 

For example, after virally labeling either direct pathway (D1) or indirect pathway (D2) SPNs to optogenetically tag pathway-specific cortical inputs, the authors report that a much larger number of "non-starter" D2-SPNs from D2-SPN labeled mice responded to optogenetic stimulation in slices than "non-starter" D1 SPNs from D1-SPN labeled mice did. Without knowing the relative number of D1 or D2 SPN starters used to label cortical inputs, it is difficult to interpret the exact meaning of the lower number of responsive D2-SPNs in D1 labeled mice (where only ~63% of D1-SPNs themselves respond) compared to the relatively higher number of responsive D1-SPNs (and D2-SPNs) in D2 labeled mice. While relative differences in connectivity certainly suggest that some amount of asymmetric overlap of inputs exists, differences in infection efficiency and ensuing differences in detection sensitivity in slice experiments make determining the degree of asymmetry problematic. 

It is also unclear if retrograde labeling of D1-SPN- vs D2-SPN- targeting afferents labels the same densities of cortical neurons. This gets to the point of specificity in some of the behavioral experiments. If the target-based labeling strategies used to introduce channelrhodopsin into specific SPN afferents label significantly different numbers of cortical neurons, might the difference in the relative numbers of optogenetically activated cortical neurons itself lead to behavioral differences? 

We thank the reviewer for the comments and for raising additional interpretations of our results. We agree that determining the relative number of D1- versus D2-SPN starter cells would allow a more accurate estimate of connectivity. However, due to current technical limitations, achieving this level of precision remains challenging. As the reviewer also noted, differences in the number of cortical neurons targeting D1- versus D2-SPNs could introduce additional complexity to the functional effects observed in the behavioral experiments. Moreover, functional heterogeneity is likely to exist not only among cortical neurons projecting to striatal D1- or D2-SPNs, but also within the striatal D1- and D2-SPN populations themselves. Addressing these questions at the single-neuron level will require more refined viral tools in combination with improved recording and manipulation techniques. Despite these limitations, our results suggest that a subpopulation of cortical neurons selectively targets striatal D1-SPNs, supporting a functional dichotomy of pathway-specific corticostriatal subcircuits in the control of behavior.   

Reviewer #2 (Public review): 

Summary: 

Klug et al. use monosynaptic rabies tracing of inputs to D1- vs D2-SPNs in the striatum to study how separate populations of cortical neurons project to D1- and D2-SPNs. They use rabies to express ChR2, then patch D1-or D2-SPNs to measure synaptic input. They report that cortical neurons labeled as D1-SPN-projecting preferentially project to D1-SPNs over D2-SPNs. In contrast, cortical neurons labeled as D2-SPN-projecting project equally to D1- and D2-SPNs. They go on to conduct pathway-specific behavioral stimulation experiments. They compare direct optogenetic stimulation of D1- or D2-SPNs to stimulation of MCC inputs to DMS and M1 inputs to DLS. In three different behavioral assays (open field, intra-cranial self-stimulation, and a fixed ratio 8 task), they show that stimulating MCC or M1 cortical inputs to D1-SPNs is similar to D1-SPN stimulation, but that stimulating MCC or M1 cortical inputs to D2-SPNs does not recapitulate the effects of D2-SPN stimulation (presumably because both D1- and D2-SPNs are being activated by these cortical inputs). 

Strengths: 

Showing these same effects in three distinct behaviors is strong. Overall, the functional verification of the consequences of the anatomy is very nice to see. It is a good choice to patch only from mCherry-negative non-starter cells in the striatum. This study adds to our understanding of the logic of corticostriatal connections, suggesting a previously unappreciated structure. 

Weaknesses: 

One limitation is that all inputs to SPNs are expressing ChR2, so they cannot distinguish between different cortical subregions during patching experiments. Their results could arise because the same innervation patterns are repeated in many cortical subregions or because some subregions have preferential D1-SPN input while others do not. 

Thank you for raising this thoughtful concern. It is indeed not feasible to restrict ChR2 expression to a specific cortical region using the first-generation rabies-ChR2 system alone. A more refined approach would involve injecting Cre-dependent TVA and RG into the striatum of D1- or A2A-Cre mice, followed by rabies-Flp infection. Subsequently, a Flp-dependent ChR2 virus could be injected into the MCC or M1 to selectively label D1- or D2-projecting cortical neurons. This strategy would allow for more precise targeting and address many of the current limitations.

However, a significant challenge lies in the cytotoxicity associated with rabies virus infection. Neuronal health begins to deteriorate substantially around 10 days post-infection, which provides an insufficient window for robust Flp-dependent ChR2 expression. We have tested several new rabies virus variants with extended survival times (Chatterjee et al., 2018; Jin et al., 2024), but unfortunately, they did not perform effectively or suitably in the corticostriatal systems we examined.

In our experimental design, the aim is to delineate the connectivity probabilities to D1 or D2-SPNs from cortical neurons. Our hypothesis considered includes the possibility that similar innervation patterns could occur across multiple cortical subregions, or that some subregions might show preferential input to D1-SPNs while others do not, or a combination of both scenarios. This leads us to perform a series behavior test that using optogenetic activation of the D1- or D2-projecting cortical populations to see which could be the case.

In the cortical areas we examined, MCC and M1, during behavioral testing, there is consistency with our electrophysiological results. Specifically, when we stimulated the D1-projecting cortical neurons either in MCC or in M1, mice exhibited facilitated local motion in open field test, which is the same to the activation of D1 SPNs in the striatum along (MCC: Fig 3C & D vs. I; M1: Fig 3F & G vs. L). Conversely, stimulation of D2-projecting MCC or M1 cortical neurons resulted in behavioral effects that appeared to combine characteristics of both D1- and D2-SPNs activation in the striatum (MCC: Fig 3C & D vs. J; M1: Fig 3F & G vs. M). The similar results were observed in the ICSS test. Our interpretation of these results is that the activation of D1-projecting neurons in the cortex induces behavior changes akin to D1 neuron activation, while activation of D2-projecting neurons in the cortex leads to a combined effect of both D1 and D2 neuron activation. This suggests that at least some cortical regions, the ones we tested, follow the hypothesis we proposed.

There are also some caveats with respect to the efficacy of rabies tracing. Although they only patch non-starter cells in the striatum, only 63% of D1-SPNs receive input from D1-SPN-projecting cortical neurons. It's hard to say whether this is "high" or "low," but one question is how far from the starter cell region they are patching. Without this spatial indication of where the cells that are being patched are relative to the starter population, it is difficult to interpret if the cells being patched are receiving cortical inputs from the same neurons that are projecting to the starter population. The authors indicate they are patching from mCherry-negative neurons within the region of the mCherry-positive neurons, but since the mCherry population will include both true starter cells and monosynaptically connected cells, this is not perfectly precise. Convergence of cortical inputs onto SPNs may vary with distance from the starter cell region quite dramatically, as other mapping studies of corticostriatal inputs have shown specialized local input regions can be defined based on cortical input patterns (Hintiryan et al., Nat Neurosci, 2016, Hunnicutt et al., eLife 2016, Peters et al., Nature, 2021). 

This is a valid concern regarding anatomical studies. Investigating cortico-striatal connectivity at the single-cell level remains technically challenging due to current methodological limitations. At present, we rely on rabies virus-mediated trans-synaptic retrograde tracing to identify D1- or D2-projecting cortical populations. This anatomical approach is coupled with ex vivo slice electrophysiology to assess the functional connectivity between these projection-defined cortical neurons and striatal SPNs. This enables us to quantify connection ratios, for example, the proportion of D1-projecting cortical neurons that functionally synapse onto non-starter D1-SPNs.

To ensure the robustness of our conclusions, it is essential that both the starter cells and the recorded non-starter SPNs receive comparable topographical input from the cortex and other brain regions. Therefore, we carefully designed our experiments so that all recorded cells were located within the injection site, were mCherry-negative (i.e., non-starter cells), and were surrounded by ChR2-mCherry-positive neurons. This configuration ensured that the distance between recorded and starter cells did not exceed 100 µm, maintaining close anatomical proximity and thereby preserving the likelihood of shared cortical innervation within the examined circuitry.

These methodological details are also described in the section on ex vivo brain slice electrophysiology, specifically in the Methods section, lines 453–459:

“D1-SPNs (eGFP-positive in D1-eGFP mice, or eGFP-negative in D2-eGFP mice) or D2-SPNs (eGFP-positive in D2-eGFP mice, or eGFP-negative in D1-eGFP mice) that were ChR2-mCherry-negative, but in the injection site and surrounded by cells expressing ChR2-mCherry were targeted for recording. This configuration ensured that the distance between recorded and starter cells did not exceed 100 µm, maintaining close anatomical proximity and thereby preserving the likelihood of shared cortical innervation within the examined circuitry.”

This experimental strategy was implemented to control for potential spatial biases and to enhance the interpretability of our connectivity measurements.

A caveat for the optogenetic behavioral experiments is that these optogenetic experiments did not include fluorophore-only controls, although a different control (with light delivered in M1) is provided in Supplementary Figure 3. Another point of confusion is that other studies (Cui et al, J Neurosci, 2021) have reported that stimulation of D1-SPNs in DLS inhibits rather than promotes movement. This study may have given different results due to subtly different experimental parameters, including fiber optic placement and NA.

We appreciate the reviewer’s thoughtful evaluation and comments. We have added a short discussion of Cui et al.’s study on optogenetic stimulation of D1-SPNs in the DLS (lines 341-343), which reports findings that contrast with ours and those of other studies.

Reviewer #3 (Public review): 

Review of resubmission: The authors provided a response to the reviews from myself and other reviewers. While some points were made satisfactorily, particularly in clarification of the innervation of cortex to striatum and the effects of input stimulation, many of my points remain unaddressed. In several cases, the authors chose to explain their rationale rather than address the issues at hand. A number of these issues (in fact, the majority) could be addressed simply by toning done the confidence in conclusions, so it was disappointing to see that the authors by and large did not do this. I repeat my concerns below and note whether I find them to have been satisfactorily addressed or not. 

In the manuscript by Klug and colleagues, the investigators use a rabies virus-based methodology to explore potential differences in connectivity from cortical inputs to the dorsal striatum. They report that the connectivity from cortical inputs onto D1 and D2 MSNs differs in terms of their projections onto the opposing cell type, and use these data to infer that there are differences in cross-talk between cortical cells that project to D1 vs. D2 MSNs. Overall, this manuscript adds to the overall body of work indicating that there are differential functions of different striatal pathways which likely arise at least in part by differences in connectivity that have been difficult to resolve due to difficulty in isolating pathways within striatal connectivity, and several interesting and provocative observations were reported. Several different methodologies are used, with partially convergent results, to support their main points. 

However, I have significant technical concerns about the manuscript as presented that make it difficult for me to interpret the results of the experiments. My comments are below. 

Major: 

There is generally a large caveat to the rabies studies performed here, which is that both TVA and the ChR2-expressing rabies virus have the same fluorophore. It is thus essentially impossible to determine how many starter cells there are, what the efficiency of tracing is, and which part of the striatum is being sampled in any given experiment. This is a major caveat given the spatial topography of the cortico-striatal projections. Furthermore, the authors make a point in the introduction about previous studies not having explored absolute numbers of inputs, yet this is not at all controlled in this study. It could be that their rabies virus simply replicates better in D1-MSNs than D2-MSNs. No quantifications are done, and these possibilities do not appear to have been considered. Without a greater standardization of the rabies experiments across conditions, it is difficult to interpret the results. 

This is still an issue. The authors point out why they chose various vectors. I can understand why the authors chose the fluorophores etc. that they did, yet the issues I raised previously are still valid. The discussion should mention that this is a potential issue. It does not necessarily invalidate results, but it is an issue. Furthermore, it is possible (in all systems) that rabies replicates better/more efficiently in some cells than others. This is one possible interpretation that has not really been explored in any study. I don't suggest the authors attempt to do that, but it should be raised as a potential interpretation. If the rabies results could mean several different things, the authors owe it to the readership to state all possible interpretations of data.

We thank the reviewer for the comments and suggestions. Because the same fluorophore (mCherry) was used in both TVA- and ChR2-expressing viruses, it was not possible to distinguish true starter SPNs from TVA-only SPNs or monosynaptically labeled SPNs. This limitation makes it difficult to precisely assess the efficiency of rabies labeling and retrograde tracing in our experimental setup. Moreover, differences in rabies replication efficiency between D1- and D2-SPNs could potentially lead to an apparent lower connection probability from D1-projecting cortical neurons to D2-SPNs than from D2-projecting cortical neurons to D1-SPNs. We have added this clarification to the Discussion (lines 280-297).

The authors claim using a few current clamp optical stimulation experiments that the cortical cells are healthy, but this result was far from comprehensive. For example, membrane resistance, capacitance, general excitability curves, etc are not reported. In Figure S2, some of the conditions look quite different (e.g., S2B, input D2-record D2, the method used yields quite different results that the authors write off as not different). Furthermore, these experiments do not consider the likely sickness and death that occurs in starter cells, as has been reported elsewhere. Health of cells in the circuit is overall a substantial concern that alone could invalidate a large portion, if not all, of the behavioral results. This is a major confound given those neurons are thought to play critical roles in the behaviors being studied. This is a major reason why first-generation rabies viruses have not been used in combination with behavior, but this significant caveat does not appear to have been considered, and controls e.g., uninfected animals, infected with AAV helpers, etc, were not included. 

This issue remains unaddressed. I did not request clarity about experimental design, but rather, raised issues about the potential effects of toxicity. I believe this to be a valid concern that needs to be discussed in the manuscript, especially given what look visually like potential differences in S2. 

We understand and appreciate the reviewer’s concern regarding the potential cytotoxicity of rabies virus infection. Although we performed the in vivo optogenetic behavioral experiments during a period when rabies-infected cells are generally considered relatively healthy, some deficits in starter cells may still occur and could contribute to the observed effects of optogenetic cortical stimulation. We have added this clarification to the Discussion (lines 298-306).

The overall purity (e.g., EnvA pseudotyping efficiency) of the RABV prep is not shown. If there was a virus that was not well EnvA-pseudotyped and thus could directly infect cortical (or other) inputs, it would degrade specificity. This issue has not been addressed. Viral strain is irrelevant. The quality of the specific preparations used is what matters.

While most of the study focuses on the cortical inputs, in slice recordings, inputs from the thalamus are not considered, yet likely contribute to the observed results. Related to this, in in vivo optogenetic experiments, technically, if the thalamic or other inputs to the dorsal striatum project to the cortex, their method will not only target cortical neurons but also terminals of other excitatory inputs. If this cannot be ruled it, stating that the authors are able to selectively activate the cortical inputs to one or the other population should be toned down. 

The authors added text to the discussion to address this point. While it largely does what is intended, based on the one study cited, I disagree with the authors' conclusions that it is "clear" that potential contamination from other sites does not play a role. The simplest interpretation is the one the authors state, and there is some supporting evidence to back up that assertion, but to me that falls short of making the point "clear" that there are no other interpretations. 

The statements about specificity of connectivity are not well founded. It may be that in the specific case where they are assessing outside of the area of injections, their conclusions may hold (e.g., excitatory inputs onto D2s have more inputs onto D1s than vice versa). However, how this relates to the actual site of injection is not clear. At face value, if such a connectivity exists, it would suggest that D1-MSNs receive substantially more overall excitatory inputs than D2s. It is thus possible that this observation would not hold over other spatial intervals. This was not explored and thus the conclusions are over-generalized. e.g., the distance from the area of red cells in the striatum to recordings was not quantified, what constituted a high level of cortical labeling was not quantified, etc. Without more rigorous quantification of what was being done, it is difficult to interpret the results. 

Again, the goal here would be to make a statement about this in the discussion to clarify limitations of the study. I don't expect the authors to re-do all of these experiments, but since they are discussing the corticostriatal circuits, which have multiple subdomains, this remains a relevant point. It has not been addressed. 

The results in Figure 3 are not well controlled. The authors show contrasting effects of optogenetic stimulation of D1-MSNs and D2-MSNs in the DMS and DLS, results which are largely consistent with the canon of basal ganglia function. However, when stimulating cortical inputs, stimulating the inputs from D1-MSNs gives the expected results (increased locomotion) while stimulating putative inputs to D2-MSNs had no effect. This is not the same as showing a decrease in locomotion - showing no effect here is not possible to interpret. 

I think that the caveat of showing no clear effects of inputs to D2 stimulation should be pointed out. Yes, I understand that the viruses appeared to express etc., but again it remains possible that the results are driven by a lack of e.g., sufficient ChR2 expression. Aside from a full quantification of the number of cells expressing ChR2, overlap in fiber placement and ChR2 expression (which I don't suggest), this remains a possibility and should be pointed out, as it remains a possibility. 

In the light of their circuit model, the result showing that inputs to D2-MSNs drive ICSS is confusing. How can the authors account for the fact that these cells are not locomotor-activating, stimulation of their putative downstream cells (D2-MSNs) does not drive ICSS, yet the cortical inputs drive ICSS? Is the idea that these inputs somehow also drive D1s? If this is the case, how do D2s get activated, if all of the cortical inputs tested net activate D1s and not D2s? Same with the results in Figure 4 - the inputs and putative downstream cells do not have the same effects. Given potential caveats of differences in viral efficiency, spatial location of injections, and cellular toxicity, I cannot interpret these experiments. 

The explanation the authors provide in their rebuttal makes sense, however this should be included in the discussion of the manuscript, as it is interesting and relevant. 

We thank the reviewer for the valuable comments and suggestions. In line with the reviewer’s recommendation, we have incorporated these explanations into the Discussion (lines 242–279) to help interpret the complex behavioral outcomes of optogenetic stimulation of cortical neurons projecting to D1- or D2-SPNs.

Reviewer #2 (Recommendations for the authors): 

I appreciate the authors' responses, which helped clarify some experimental choices. I appreciate that the experiment in Fig S3 serves as a reasonable light control for optogenetics experiments. The careful comparison with methods in Cui et al (2021) is useful, although not added to the main manuscript. Some of the other citations here don't really address the controversy, e.g. Kravitz at al is in DMS, but perhaps fully addressing this issue is outside the scope of the current manuscript and awaits further experiments. I also appreciate the clarification for recording locations that "This configuration ensured that the distance between recorded and starter cells did not exceed 100 µm, maintaining close anatomical proximity and thereby preserving the likelihood of shared cortical innervation within the examined circuitry." However, the statement in the reviewer response does not seem to be added to the manuscript's methods, which I think would be helpful. The criteria for choosing recorded cells are still a bit fuzzy without a map of recording locations and histology. There is also a problem that mCherry-positive cells could be starter cells or could be monosynaptically traced cells, so it is hard to know the area of the starter cell population in these experiments for sure. My evaluation of the manuscript remains largely the same as the original. However, I have adjusted my public review a bit to incorporate the authors' responses. I still think this paper has valuable information, suggesting an interesting and previously unappreciated structure of corticostriatal inputs that I hope this group and others will continue to investigate and incorporate into models of basal ganglia function.

We thank the reviewer for the valuable suggestions. We have now included a comparison with Cui et al. in the Discussion. In addition, we have added the criteria for selecting recorded cells to the Methods section: ‘This configuration ensured that the distance between recorded and starter cells did not exceed 100 µm, maintaining close anatomical proximity and thereby preserving the likelihood of shared cortical innervation within the examined circuitry.’

Reviewer #3 (Public review):

Summary:

The manuscript by Zhang et al. demonstrates that MORC2 undergoes liquid-liquid phase separation (LLPS) to form nuclear condensates critical for transcriptional repression. Using a combination of in vitro LLPS assays, cellular studies, NMR spectroscopy, and crystallography, the authors show that a dimeric scaffold formed by CC3 drives phase separation, while multivalent interactions between an intrinsically disordered region (IDR) and a newly defined IDR-binding domain (IBD) further promote condensate formation. Notably, LLPS enhances MORC2 ATPase activity in a DNA-dependent manner and contributes to transcriptional regulation, establishing a functional link between phase separation, DNA binding, and transcriptional control. Overall, the manuscript is well-organized and logically structured, offering mechanistic insights into MORC2 function, and most conclusions are supported by the presented data. Nevertheless, some of the claims are not sufficiently supported by the current data and would benefit from additional evidence to strengthen the conclusions.

The following suggestions may help strengthen the manuscript:

Major comments:

(1) The central model proposes that multivalent interactions between the IDR and IBD promote MORC2 LLPS. However, the characterization of these interactions is currently limited. It is recommended that the authors perform more systematic analyses to investigate the contribution of these interactions to LLPS, for example, by in vitro assays assessing how the IDR or IBD individually influence MORC2 phase separation.

(2) The authors mention that DNA binding can promote MORC2 LLPS. It is recommended that they generate a phase diagram to systematically assess how DNA influences phase separation.

(3) The authors use the N39A mutant as a negative control to study the effect of DNA binding on ATP hydrolysis. Given that N39A is defective in DNA binding, it could also be employed to directly test whether DNA binding influences MORC2 phase separation.

(4) Many of the cellular and in vitro LLPS experiments employ EGFP fusions. The authors should evaluate whether the EGFP tag influences MORC2 phase separation behavior.

reply to u/GrandRevolutionary99 at https://reddit.com/r/stationery/comments/1nrkuqf/i_need_help_to_create_my_own_letterhead_for_my/

Typewriter enthusiasts often use 100% cotton or high linen content papers with weights in the 32 pound range for 8.5x11. This gives you some nice tactile feel, but will also feed into most typewriters, even with a solid backing sheet. If you want to do thicker card stocks, then you might opt for a bigger standard typewriter which generally have larger diameter platens and more easily handle much thicker paper (they were meant for doing carbon packs up up to 10 sheets or more.)

When it comes to the look of your letters, you can generally choose between silk (clean, crisp imprints), nylon (almost as clean as silk, but with more "grain"), and cotton typewriter ribbon (which leaves a very grainy/old timey and "typewriter-y" imprint). Comparisons here.

I've got a small fleet of typewriters and prefer to use the pica sizes for personal correspondence. I also tend toward the cursive or Vogue typefaces for those as well.

In the US, a lot of stationers have pre-cut paper and envelopes for 6-3/8" x 8-1/2" paper which is a good size sheet for quick notes. My typewriter pen pal Tom Hanks' most recent letter to me was on a custom page of 7.125 x 10.25" and had space for design at the top and bottom with some reasonable space in the middle. If you do custom designs, be sure to order a box or two of plain stock to use as second, third, etc. pages behind your first page if you tend to write over your first page.

Naturally custom designing your own can be fun as well, but get a few samples of the size and weight you want and try them out before ordering in quantity.

Lenore Fenton can give you tips on making carbon copies of your letters if you want to keep them for your own files while sending out the originals: https://www.youtube.com/watch?v=JUJfCfqgsX0

Searching r/typewriters for stationery, letterhead, paper, etc. might give you some ideas as well.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review)

The weaknesses are in the clarity and resolution of the data that forms the basis of the model. In addition to whole embryo morphology that is used as evidence for convergent extension (CE) defects, two forms of data are presented, co-expression and IP, as well as a strong reliance on IF of exogenously expressed proteins. Thus, it is critical that both forms of evidence be very strong and clear, and this is where there are deficiencies; 1) For vast majority of experiments general morphology and LWR was used as evidence of effects on convergent extension movements rather than Keller explants or actual cell movements in the embryo. 2) The study would benefit from high or super resolution microscopy, since in many cases the differences in protein localization are not very pronounced. 3) The IP and Western analysis data often show subtle differences, and not apparent in some cases. 4) It is not clear how many biological repeats were performed or how and whether statistical analyses were performed. 

(1) To more objectively assess the convergent extension phenotypes, we developed a Fiji macro to automatically quantify the LWR in various injected Xenopus embryos, as detailed in the Methods section. We acknowledge that a limitation in the current manuscript is how to link our mechanistic model at the molecular level with the actual cellular behavior during convergent extension, and we plan to perform cell biological studies in the future to elucidate the link;

(2) We have repeated some of the imaging experiments in DMZ explants using a Zeiss LSM 900 confocal equipped with Airyscan2 detector that can increase the resolution to ~100 nm. The new data are in Suppl. Fig. 4, 9, 11, 16;

(3) We have repeated all IP and western blots at least three times and provided quantification and statistical analyses;

(4) We have added the information on biological repeats and statistical analyses in all figures and figure legends.

Reviewer #2 (Public Review):

The protein localization experiments in animal cap assays are for the most part convincing, but with the caveat that the authors assume that the proteins are acting within the same cell. As Fzd and Vangl2 are thought to localize to opposite cell ends in many contexts, can the authors be sure that the effects they observe are not due to trans interactions? 

In our previous publication, we provided evidence that Vangl is necessary and sufficient to recruit Dvl to the plasma membrane within the same cell (Figure 3 in 10.1093/hmg/ddx095). In a more recent publication ( 10.1038/s41467-025-57658-0 ), we further elucidated a mechanism through which Dvl oligomerization switches its binding from Vangl to Fz, and determined that Dvl binding to Vangl and Fz are differentially mediated by its PDZ and DEP domain, respectively. In the current manuscript, we also performed co-IP experiment under various conditions to demonstrate binding between Dvl and Vangl. We feel that these evidences together provide a strong argument for our model where Vangl2 acts within the same cell to sequester Dvl from Fz.

In regards to the Dvl patches induced by Wnt11 (Fig. 3 and Suppl. Fig. 9), we performed separate injection of EGFP- and mSc-tagged Dvl into adjacent blastomeres, and demonstrated that the Wnt11-induced patches arise from symmetrical accumulation of Dvl at contact of two neighboring cells (Suppl. Fig. 9a-c’). This scenario is different from epithelial PCP where Fz/Dvl and Vangl/Pk are asymmetrically accumulated at the contact between two adjacent cells.

The authors propose a model whereby Vangl2 acts as an adaptor between Dvl and Ror, to first prevent ectopic activation of signaling, and then to relay Dvl to Fzd upon Wnt stimulation. This is based on the observation that Ror2 can be co-IPed with Vangl2 but not Dvl; and secondly that the distribution of Ror2 in membrane patches after Wnt11 stimulation is broader than that of Fzd7/Dvl, while Vangl2 localizes to the edges of these patches. The data for both these points is not wholly convincing. The co-IP of Ror2 and Vangl2 is very weak, and the input of Dvl into the same experiment is very low, so any direct interaction could have been missed. Secondly, the broader distribution of Ror2 in membrane patches is very subtle, and further analysis would be needed to firm up this conclusion. 

(1) We repeated the co-IP experiment with Myc-tagged Vangl or Dvl. Using the same anti-Myc antibody and experimental condition (including the expression level of Vangl, Dvl and Ror2), we still found that Ror2 could be pulled down by Vangl but not Dvl (Suppl. Fig. 15b). Whereas this data confirms our previous conclusion, we acknowledge that a negative data does not fully exclude the possibility for direct biding between Ror and Dvl.

(2) We re-analyzed the signal intensity of Dvl and Ror in Wnt11-induced patches. By quantifying the intensity ratio between Ror and Dvl along the patches, we found an increase over two folds at the border of the patches (Fig. 7j, bottom panel). We interpret this data to suggest that Ror is accumulated to a higher level than Dvl at the patch borders.     

A final caveat to these experiments is that in the animal cap assays, loss of function and gain of function both cause convergence and extension defects, so any genetic interactions need to be treated with caution i.e. two injected factors enhancing a phenotype does not imply they act in the same direction in a pathway, in particular as there are both cis/trans and positive/negative feedbacks between the PCP proteins. 

We agree with the reviewer that a difficulty in studying PCP/ non-canonical signaling is that both loss and gain of function of any its components can cause convergence and extension defects. Genetic interactions, especially synergistic interactions, should be interpreted with caution. But we do want to point out that, in a number of case, we were also able to demonstrate epistasis. For instance, we found that Dvl2 over-expression induced CE defects can be rescued by Pk over-expression (Fig. 1e and f), whereas Vangl/ Pk co-injection induced severe CE defects can be reciprocally rescued by Dvl2 over-expression (Fig. 1g). Likewise, we showed that Fz2/ Dvl2 co-injection induced CE defects can be rescued by wild-type Vangl2 but not Vangl2 RH mutant (Suppl. Fig. 6b), and Ror2 can rescue Vangl2 overexpression induced CE defect (Suppl. Fig. 14). Collectively, these functional interaction data consistently demonstrate an antagonism between Dvl/ Fz/ Ror2 and Vangl2/ Pk, which is correlated with our imaging and biochemical studies.

As you can see from the reviews, the referees generally agree that your paper is a potentially valuable contribution to the field. Your observations are important because of the novel model based on the inhibitory feedback regulation between planar cell polarity (PCP) protein complexes. However, the reviewers also stated that the model is only partly supported by data because of insufficient clarity and missing controls in several experiments supporting the proposed model. The paper would be significantly improved if your conclusions are backed up by additional experimentation. Specifically, the referees wanted to see the reproducibility of the results shown in Figures 3, 4, 8, S3, S7, S12. 

We hope that you are able to revise the paper along the lines suggested by the referees to increase the impact of your study on the current understanding of PCP signaling mechanisms. 

We thank the reviewers for careful reading of our manuscript and for their constructive critiques and suggestions. We have repeated the animal cap studies in original Figures 3, 4, 8 and S3 with DMZ explants, and the new data are in Supplementary Fig. 9, 11, 16 and 4, respectively. We also repeated the biochemical studies in original Figure S 7and 12, and the new data are in Supplementary Fig. 8 and 15.

Reviewer #1 (Recommendations For The Authors):

Major points:(1) The author conducted an analysis of the subcellular localization of PCP core proteins, including Vangl2, Pk, Fz, and Dvl, within animal cap explants (ectodermal explants). To validate the model proposing that 'non-canonical Wnt induces Dvl to transition from Vangl to Fz, while PK inhibits this transition, and they function synergistically with Vangl to suppress Dvl during Convergent Extension (CE),' it is crucial to assess the subcellular localization of PCP core proteins in dorsal marginal zone (DMZ) cells, which are known to undergo CE. Notably, the overexpression of Wnt11 alone, as employed by the author, does not induce animal cap elongation. Therefore, the use of animal cap explants may not be sufficient to substantiate the model during Convergent Extension (CE). Indeed, previous knowledge indicates that Vangl2 and Pk localize to the anterior region in DMZ explants. However, the results presented in this manuscript appear to differ from this established understanding. Consequently, to provide more robust support for the proposed model, it is advisable to replicate the key experiments (Figures 3, 4, 8, and Figure S3) using DMZ explants. 

We repeated the experiments in Figure 3, 4, 8 and Figure S3 with DMZ explant and the new data are in new Supplementary Fig. 9, 11, 16 and 4, respectively.In regards to “previous knowledge indicates that Vangl2 and Pk localize to the anterior region in DMZ explants”, we are aware Vangl/ Pk localization to the anterior cell cortex in neural epithelium from the studies by the Sokol and Wallingford labs, but are not aware of similar reports in DMZ explants. When we examined the localization of small amount of injected EGFP-mPk2 (0.1 ng mRNA) in DMZ explants, we saw a somewhat uniform distribution on the plasma membrane (Suppl. Fig. 4). In addition, in a related recent publication, we examined endogenous XVangl2 protein localization in activin induced animal cap explants that do undergo CE. What we observed was that whereas low level injected Dvl2 and Fz form clusters on the plasma member, endogenous XVangl2 remains uniformly distributed on the plasma membrane (Suppl. Fig. 3S-Z in 10.1038/s41467-025-57658-0 ). These observations may suggest potential differences of PCP protein localization during neural vs. mesodermal convergence and extension.

(2) The author suggests that 'Vangl2 and Pk together synergistically disrupt Fz7-Dvl2 patches.' As shown in Figure 4 (panels J' to I'), it is evident that the co-expression of Pk and Vangl2 increases Fz7 endocytosis. Nevertheless, a significant amount of Fz7 still co-localizes with Dvl2. To strengthen the author's hypothesis, additional clear assay is required such as Fluorescence resonance energy transfer (FRET) assay. 

We appreciate this valuable advice. Since none of the tagged Fz/ Dvl/ Vangl proteins we had were suitable for FRET, we made proteins tagged with mClover and mRuby2, which were reported as optimized FRET pairs. But in our hands mRuby2 seems to require very long time (~2 days) to mature and become detectable at room temperature, and is not suitable for our Xenopus experiments. We are in the process of establishing a luciferase based NanoBiT system to detect Fz-Dvl and Dvl-Vangl interactions in live cells and cell lysates, and will use it in future studies to investigate their interaction dynamics.

For the current manuscript, we reason that a substantial reduction of Fz7-Dvl2 clusters with Vangl2/ Pk co-injection would still support our idea that Vangl2 and Pk act synergistically to sequester Dvl from Fz to prevent their clustering in response to non-canonical Wnt ligands.

(3) The IP data is less clear and evident. A couple of examples are: a) Fig 2g where the authors report that the Vangl2 R177H variant reduced Vangl2 interaction with Pk and recruitment of Pk to the plasma membrane, but it appears that the variant interacts slightly better than WT Vangl2 with Pk. In Fig. S7a, the authors state that Pk overexpression can indeed significantly reduce Wnt11-induced dissociation of EGFP-Vangl2 and Flag-Dvl2 in the DMZ. However, there is a minimal impact when compared to the Wnt11 absent control. Based on the results presented in Fig S12a the authors indicate that Wnt11 reduces the association between Vangl2 and Dvl2, which can be discerned, but loss of Ror2 does not change this in any obvious way - but the authors indicate it does. In S12b, the authors have suggested that Ror and Dvl do not form a direct binding interaction. However, the interpretation of Figure S12b is not entirely convincing due to several issues. Notably, the expression levels of each protein appear inconsistent, the bands are not sufficiently clear, and there is the detection of three different tag proteins on a single blot. To strengthen the validity of these findings, it is advisable to repeat this experiment with improved quality. 

We repeated all the co-IP and western blot analyses pointed out by the reviewer, and performed quantification and statistical analyses.

Fig 2g had a mistake in the labeling and is replaced with new Figure 2g;

Fig. S7a is replaced by new data in Supplementary Figure 8a and b;

Fig. S12a and 12b are replaced by new data in Supplementary Figure 15a, a’ and b, respectively. In 15a and a’, we noticed a consistent decrease of Dvl2-Vangl2 co-IP in Xror2 morphant. The reason for this is not yet clear and will need further study in the future.

Minor points: (1) In all the whole embryo injection assays examining morphology, no Western analysis is performed to show roughly equivalent and appropriate levels of the various proteins are being expressed. Differences will affect the data. 

Although we did not do western analyses to examine the protein levels in various functional interaction assays, we did examine how co-expression of Vangl2, mPk2 or Dvl2 may impact each other’s protein levels in Supplementary Fig. 2, which did not reveal any significant change when co-injected in different combination.

(2) The author's prior publication (Bimodal regulation of Dishevelled function by Vangl2 during morphogenesis, Hum Mol Genet. 2017) presented clear evidence of Vangl2 overexpression inducing Dvl2 membrane localization. However, Figure S4 in the current manuscript did not provide clear evidence of membrane localization. To strengthen the hypothesis that Vangl2-RH mutant also induces Dvl2 membrane localization, further comprehensive imaging analysis is needed. 

We re-analyzed the imaging data and replaced old Figure S4 with a new Supplementary Fig. 5.

(3) In Supplementary Figure 9, the authors propose that the overexpression of Vangl2/Pk induces Fz7 endocytosis, as indicated by its co-localization with FM4-64. However, it raises a question: how does the Fz7-GFP protein internalize into the cells without endocytosis, as seen in Figures S9a-c'? To enhance readers' understanding, a discussion addressing this point should be included. 

We think that this might be a technical issue. As detailed in the Method section, we only incubated the embryos transiently with FM4-64 for 30 minutes, and the embryos were subsequently washed and dissected in 0.1X MMR without the dye. Therefore, only the Fz7-GFP protein endocytosed during the 30 minute-incubation would be labeled by FM-64, whereas that endocytosed before or after the incubation would not. Alternatively, the very few Fz7-GFP puncta occasionally observed in the absence of Vangl2/Pk overexpression could be vesicles trafficking to the plasma membrane.

(4) Statistical analyses are absent for several results, including those in Figure 2f, Figure S4d, and Figure S7b. 

We repeated these experiments and included statistical analyses. The new data are in Figure 2f, Supplementary Fig. 5d and Supplementary Fig. 8b.

(5) This manuscript lacks any results regarding Ck1. Therefore, it is advisable to consider removing the discussion or mention of CK1. 

We agree, and tune down the discussion on CK1 and removed CK1 from our model in Fig. 9.

Reviewer #2 (Recommendations For The Authors):

(1) In all the convergence and extension assays, the authors should report n numbers (i.e. number of animals), what statistical test is used, and what the error bars show. Ideally dot-plots would be used instead of bar charts as they give a better insight into the data distribution. It might be useful to give a section on the statistical analyses used in the M&M, including e.g. any power calculations carried out, as now required by many journals. 

We have follow the advice to use dot-plots for all the quantification analyses in the manuscript. We include in the figure legends the statistical test used and what the error bars show. The number of embryos analyzed were included in each panel in the figures. We also provided more details in the Methods section on how the LWR quantification was carried out.

(2) I think Figure 2g is wrongly labelled? FLAG bands are in all three lanes in the western blot, but not labelled as such in the schematic. 

We corrected the schematic labeling in Figure 2g, and thank the reviewer for catching this mistake.

(3) In Figure S7, the authors show that co-IP of Dvl and Vangl2 is reduced by Wnt11 and the effects of Wnt are blocked by Pk. Does Pk have any effect in the absence of Wnt? 

We examined the effect of Pk over-expression on Dvl2-Vangl2 co-IP as advised, and did not see a significant impact in the absence of Wnt11 co-injection. The data is included in the new Supplementary Figure 8a. We interpret the data to suggest that “at least under the condition of our co-IP experiment, Pk may not directly impact the steady-state binding between Vangl and Dvl”.

(4) In Figure 3, the authors show (as published previously) that Wnt11 induces patches of Dvl at the plasma membrane. It would be useful to see Dvl in the absence of Wnt and Vangl2/Dvl in the absence of Wnt. 

Dvl is widely known as a cytoplasmic protein and its localization has been published by many labs over the past 20-30 years. In our recent publication (10.1038/s41467-025-57658-0 ), we also re-examined Dvl localization when injected at various dosages. So we did not feel it was necessary to show its localization in the absence of Wnt11 again, but included a reference to our prior publication. In regards to Vangl/Dvl distribution in the absence of Wnt11, the readers can see Suppl. Fig. 5b as an example, in addition to our previous publications referenced in the manuscript.

(5) In the review figures, the difference in Fz7-GFP patch formation in d' and e' (vs e.g. a') is not very clear. Could the images be improved or (better) quantified in some way? 

We assume that “review figures” refer to Figure 3 or 4? If so, we felt that Fz7-GFP patch formation was clear in Fig. 3d’, e’ or Fig. 4d’, e’. Nevertheless, we repeated these experiments in DMZ explants as advised by Reviewer 1, and additional examples of Fz7-EGFP patch formation can be seen in the new Suppl. Fig. 9d-f’ and Suppl. Fig. 11d-f’.

(6) In Figure 6d, I'm concerned that the loss of flag-Dvl2 might occur via dephosphorylation in the IP reaction. Also the M&M don't include methodological details about buffers and whether phosphatase inhibitors were used. A compelling control would be anti-FLAG pulldown showing retention of phosphorylation. Also Figure 6f shows a reduced ratio of fast-to-slow migrating bands of Dvl with Vangl2/Pk - unless I have misunderstood, is this ratio the wrong way round? 

We added co-IP buffer and protease inhibitor information in Methods.

We agree that the concern about dephosphorylation during IP reaction is valid, and that direct pull down of Dvl to show the phosphorylated form is a compelling control. We therefore note that in Suppl. Fig. 8a and 15b, direct pull down of Flag-Dvl or Myc-Dvl (with anti-Flag or anti-Myc) did show the slower migrating, phosphorylated form. Additional examples in which Vangl only co-IP the faster migrating unphosphorylated Dvl include Suppl. Fig. 15a, and in a related paper we published recently (Fig. 3R and R’ in 10.1038/s41467-025-57658-0 ).

Finally, we did wrongly label Figure 6f in the last submission, and the ratio should have been “slow/fast”. We have made the correction, and appreaicte the reviewer for the meticulousness in perusing our manuscript.

(7) In Figure 7, what does Ror2 look like in the absence of Wnt11? 

We included new Figure 7a-c to show that without Wnt11 co-injection, Ror2 is uniformly distributed on the plasma membrane.

(8) Also in Figure 7, Ror2 patches are said to be slightly wider than Dvl2 patches "reminiscent of Vangl2" - I wouldn't describe them as being similar. Vangl2 shows a distinct dip in the center of the Dvl patches, Ror2 does not show a dip, and is only (at best) in a slightly wider patch, and I would want to see further examples to be convinced that the localization domain is reproducibly wider. The merge of many samples in 7d may actually be making the distribution harder to see and if the Xror2 and Dvl2 intensities were normalized I'm not sure how different the curves would appear. (i.e. the Xror2 curve looks like a flattened version of the Dvl2 curve). 

We have added an additional panel in the new Figure 7j to compare the intensity ratio of Ror/ Dvl2 along the patches, and this analysis reveals an over two folds increase of the ratio at the border region. This quantification may make a more convincing argument that at the patch border region, Dvl is diminished whereas Ror2 accumulate with Vangl2. 

(9) In Figure S12a, the authors suggest Wnt11 induced dissociation of Dvl from Vangl2 (by co-IP), and this is reduced after Ror2 MO. This would be more convincing with replicates and quantitation. 

We have repeated this experiment with Vangl2 pull down and added quantification. The data is in the new Suppl. Fig. 15a.

(10) In Figure S12b, the authors suggest Ror2 can co-IP Vangl2 but not Dvl. This is not very convincing, as the Dvl input band is very weak, and the Vangl2 co-IP band is very weak. 

We repeated the co-IP experiment with Myc-tagged Vangl or Dvl. Using the same anti-Myc antibody and experimental condition (including the expression level of Vangl, Dvl and Ror2), we still found that Ror2 could be pulled down by Vangl but not Dvl (Suppl. Fig. 15b).

(11) "Prickle" spelled "Prickel" in the abstract (and abbreviated to "PK" not "Pk" at one place in the abstract and several places in text) 

We have corrected these typos.

(12) Quite a lot of interesting observations are in supplemental figures. Normally it might be expected that extra data supporting a conclusion would be in supplemental, but here some of the supplemental data feels like it is more than simply additional evidence. For instance supplemental Figures 2 and 3 feel more than just supplemental (and Supplemental Figure 3 if merged with Figure 2 would make it easier for the reader). Moreover, for example, the description of the results in Figure 2 is punctuated by references to supplemental Figures 4 and 5 that contain key data to support the conclusions, which means the reader has to flick backwards and forwards from place to place in the manuscript to follow the argument. It is of course up to the authors, but in some cases putting supplemental data back into the main figures (for which there is no size or number limit) would increase clarity. 

These are excellent points; in the resubmitted manuscript we have a total of 24 data figures, and we used 8 as main figures since we felt that they provide the most relevant and conclusive evidence to our model. We will consult the copy editors at eLife on how to arrange the rest as main vs. supporting figures when requesting publication as version of record.

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Reply to the reviewers

We thank the reviewers for their thoughtful comments and overall very supportive feedback.

Reviewer #1 writes: "The study is very thorough and the experiments contain the appropriate controls. (...) The findings of the study can have relevance for human conditions involving disrupted mitochondrial dynamics, caused for example by mutations in mitofusins." Reviewer #2 writes: "The dataset is rich and the time-resolved approach strong." Reviewer #3 writes: "I admire the philosophy of the research, acknowledging an attempt to control for the many possible confounding influences. (...) This is a powerful and thoughtful study that provides a collection of new mechanistic insights into the link between physical and genetic properties of mitochondria in yeast."

We address all points below. We have not yet updated our text and figures since we expect substantial additions from new experiments. But we have included Figure R1 with some additional analyses of existing data at the bottom of the manuscript.

Reviewer1

1.1 Statistical comparisons are missing throughout the manuscript (with the exception of Fig. 2c). Appropriate statistical tests, along with p-values, should be used and reported where different gorups are compared, for example (but not limited to) Fig. 3d and most panels of Fig. 4.

We initially decided not to add too many extra labels to the already very busy plots, given that the magnitude of change mostly speaks for itself. However, we will try to find meaningful statistical tests together with a sensible graphical representation for all of the figures. For one example see Figure R1A.

1.2. I do not agree with the use of Atp6 protein as a direct read-out of mtDNA content. While Atp6 protein levels will decrease with decreasing mtDNA content, the inverse is not necessarily true: decreased Atp6 protein levels do not necessarily indicate decreased mtDNA levels, because they could alternatively or additionally be caused by decreased transcription and/or translation. Therefore, please do not equate Atp6 protein levels to mtDNA levels, and instead rephrase the text referencing the Atp6 experiments in the Results and Discussion sections to measure "mtDNA expression" or "mt-encoded protein" or similar. For example, on p. 14 line 431 should read "mtDNA expression" rather than "decreased synthesis of mtDNA", and line 440 on the same page "mean mtDNA levels" should be "mtDNA expression" or similar.

All three reviewers agree that using Atp6-NG as a direct proxy for mtDNA requires more validation, or at least rephrasing of the text. We agree that this is the most important point to address. We had previously tried using the mtDNA LacO array (Osman et al. 2015) to directly assess the amount of nucleoids per cell. However, the altered mitochondrial morphology of the Fzo1 depleted cells combined with the LacI-GFP which is still in mitochondria even when mtDNA is gone, increases the noise level to a point that we cannot interpret the signal. However, as this manuscript was in the submission process, the Schmoller lab (co-authors #2 and #7) adapted the HI-NESS system to label mtDNA in live yeast cells(Deng et al. 2025). This system promises much better signal to noise and we expect we can address all concerns regarding the actual count of nucleoids per cell. Should this unexpectedly fail for technical reasons, we will try to calibrate the Atp6-levels with DAPI staining at defined time points and will rephrase the text as the reviewer suggests.

1.3. In Fig. 3, the authors use the fluorescence intensity of a mitochondrially-targeted mCardinal as a read-out of mitochondrial mass. Please provide evidence that this is not affected by MMP, either with relevant references or by control experiments (e.g. comparing it to N-acridine orange or other MMP-independent dyes or methods).

Whether or not the import of any mitochondrial protein is dependent on the MMP depends largely on the signal sequence. The preSu9-signaling sequence was previously characterized as largely independent of the MMP compared to other presequences (Martin, Mahlke, and Pfanner 1991), which is why Vowinckel (Vowinckel et al. 2015) and others (Di Bartolomeo et al. 2020; Perić et al. 2016; Ebert et al. 2025) have previously used this as a neutral reference to the strongly MMP-dependent pre-Cox4 signal to estimate MMP. As one control in our own data, we consider that the population-averaged mitochondrial fluorescent signal Figure S3C stays constant in the first few hours, in agreement with the total averaged mitochondrial proteome (Fig R1E). As additional controls, we plan to compare the signal to an MMP independent dye as the reviewer suggests.

1.4. In Fig. 2e-f, the authors use a promoter reporter with Neongreen to answer whether the reduced levels of the nuclear-encoded mitochondrial proteins Mrps5 and Qcr7 are due to decreased expression or to protein degradation, and find no evidence of degradation of the Neongreen reporter protein. However, subcellular localization might affect the availability of the protein to proteases. Although not absolutely required, it would be relevant to know if the Neongreen fusion protein is found in the same subcellular compartment as Mrps5 and Qcr7 at 0h and 9h after Fzo1 depletion.

Here, it seems we need to explain the set-up and interpretation of the data better. The key point we are trying to make with the promoter-Neongreen construct is that the regulation is not mainly at the level of transcription. We are showing that the reduction in the levels of the actual protein (orange bars) is not (mainly) explained by a reduction in expression, since the promoter is similarly active at 0 and at 9 hours (grey bars). If expression from the promoter were strongly reduced, the Neongreen would be diluted with growth and would also decrease, but this is not the case. The fluorophore itself is just floating around in the cytosol and is not subject to the same post-translational regulation as Mrps5 and Qcr7, so there is no reason to expect degradation.

1.5. Fzo1 depletion leads to a very rapid drop in MMP during the first hour of depletion. In the Discussion, can the authors speculate on the possible mechanism of this rapid MMP drop that occurs well before mtDNA or mt-encoded proteins are decreased in level?

This is indeed an interesting point. We think there are likely three reasons causing this initial drop: Firstly, due to the fragmentation the mixing of mitochondrial content is disturbed and smaller fragments may have suboptimal stoichiometry of components (see also (Khan et al. 2024) who look at this in detail including the Fzo1 deletion); secondly, already fairly early, some mitochondrial fragments may not contain any mtDNA and therefore will be unable to synthesize ETC proteins; thirdly, altered morphological features like changes in the surface-to-volume ratios may play a role. Sadly, mechanistically following up on this is not possible with the tools in our hands and therefore outside of the scope of this manuscript. But we are happy to include these speculations in our discussion.

1.6. In Fig. 2a, the mtDNA copy number of Fzo1-depleted cells is ca 1.3-fold of the control cells at the 0h timepoint. Why might this be? Is it an impact of one of the inducers? If so, we might be looking at the combination of two different processes when measuring copy number: one that is an induction caused by the inducer(s), and the other a consequence of Fzo1 depletion itself.

We believe that this 30% increase is within the noise of the experiment rather than an effect of the induction. Since we normalize to t=0 uninduced, the first black data point does not have error bars, emphasizing this difference. None of the protein data suggests that there is an increase in mtDNA encoded proteins (see e.g. 2B, or Atp6 fluorescence data). In the planned HI-NESS experiment, we will see in our single cell data whether there is an actual increase in mtDNA upon TIR induction. Additionally, we will run a qPCR to carefully determine mtDNA levels of untreated wild-type cells, tetracycline treated wild-type cells and tetracycline induced TIR expressing cells to exclude effects of tetracycline as well as the expression of TIR on mtDNA.

Minor comments:

1.7. p. 3, line 71: "ten thousands of dividing cells.." should be "tens of thousands of dividing cells".

Thank you, will correct.

1.8.-p.4, line 116: please be even more clear with what the "depleted" cells and controls are treated with: are depleted cells treated with both inducers, and controls with neither?

We will make this more clear. Depleted cells are treated with both inducers, the control cells are not. However, in Figure 1A and in S1 we do controls to show that inducing TIR per se or adding aTC per se does not change growth rate or mitochondrial morphology. We will make this more clear.

1.9. -p.5, lines 147-148: the authors write "the rate with which the abundance of Cox2 and Var1 proteins decreases was similar to the rate of mtDNA loss" though the actual rate is not shown. Please calculate and show rates for these processes side by side to make comparison possible, or alternatively rephrase the statement.

Indeed this was not phrased well. We will call it dynamics rather than rates.

1.10. -Fig. 2d: changing the y-axis numbering to match those in panels a and b would facilitate comparisons.

Makes sense, we will change this.

1.11. Fig. 2e: it is recommended to label the western blot panels to indicate what protein is being imaged in each (Neongree,, Mrps5, Qcr7).

We will adapt the labelling to make it more clear.

1.12. -p.9, line 262: I suggest referencing Fig. 4e at the end of the first sentence for clarity.

We will modify the sentence as suggested.

1.13. -In the sections related to Fig. 3a and Fig. 5a as well as the connected supplemental data, the authors discuss both the median and the mean of mitochondrial mass and Atp6 protein, respectively. For purposes of clarity, I suggest decreasing the focus on the mean (that is provided only in the supplemental data) and focusing the text mainly on the median. The two show differing trends and it is very good that both are shown, but the clarity of the text can be improved by focusing more on the median where possible.

We will check the phrasing and simplify.

1.14. -p. 14, line 435: the statement that mt mass is maintained over the first 9h of depletion is only true for the mean mt mass, not for the median. Please make this clear or rephrase.

We will check phrasing, make it more clear and also point out the extended proteomics data (see Fig R1), which corresponds to the mean of the populations

1.15.-p.14, line 452: "mitofusions" should be "mitofusins".

Thanks for catching this.

Reviewer 2:

2.1. While inducible TIR is used to reduce background, the manuscript should rigorously exclude auxin/TIR off-targets (growth, mitochondrial phenotypes, gene expression). Please include full matched controls: (plus minus)auxin, (plus minus)TIR, epitope tag alone, and a degron control on an unrelated mitochondrial membrane protein.

We agree that rigorous controls are crucial for the interpretation of the results. However, we think we have already included most of the controls the reviewer is asking for, but we might have not pointed this out clearly enough. For example, in Fig 1A, we could make it more clear by adding more labels in which samples we added aTC, which is only described in the figure legend.

Here is a list of all the controls:

• Each depletion experiment is always matched with an experiment of the same strain without induction. So the genetic background as well as effects such as light exposure, time spent in the microfluidics systems, etc are controlled for.
• Figure S1D shows that the growth rate is wildtype like in a strain containing either the AID tag or the TIR protein AND upon addition of both chemicals. It also shows that the final genetic background (AID-tag and TIR) also grows like wildtype if the inducers are not added. This conclusively shows that neither the tags/constructs nor the chemicals per se affect growth rate
• In Figure S1C we show the mitochondrial morphology of the same controls. We will make sure to label them more consistently to match panel D, and include an actual wildtype and a FLAG-AID-Fzo1 strain without TIR treated with both aTC and 5-Ph-IAA as direct comparison
• In figure 1A we compare the Fzo1 protein levels of a strain with and without TIR. We show that in absence of TIR, adding either aTC or Auxin does not change Fzo1 levels and that the levels are comparable in the strain that is able to deplete Fzo1 directly before addition of 5-Ph-IAA (after 2 h of induction of TIR through addition of tetracycline)
• Additionally, in Figure S2C we show that two hours after adding aTC, the entire proteome does not change significantly apart from a strong induction of TIR. We can also make this more clear in the figure legend.
• Additionally, we will run a qPCR to carefully determine mtDNA levels of untreated wild-type cells, tetracycline treated wild-type cells and tetracycline induced TIR expressing cells to exclude effects of tetracycline as well as the expression of TIR on mtDNA. (also in response to 1.6.) In summary, we think we have controlled sufficiently for all confounding parameters and most importantly showed that addition of either aTC or Auxin as well as the FLAG-AID tag per se does not disturb mitochondria or cell growth. We do not see what a degron control on an unrelated protein will tell us. Depending on the nature of the protein, it may or may not have a phenotype that may or may not be related to morphology changes etc.

2.2. The Mitoloc preSu9 vs Cox4 import ratio is only a proxy of mitochondrial membrane potential (ΔΨm) and itself depends on mitochondrial mass, protein expression, matrix ATP, and import saturation. The authors need to calibrate ΔΨm with orthogonal dyes (TMRE/TMRM) and pharmacologic titrations (FCCP/antimycin/oligomycin) to generate a response curve; show that Mitoloc tracks dye-based ΔΨm across the relevant range and corrects for mass/photobleaching. Report single-cell ΔΨm vs mass residuals.

We completely agree that the MitoLoc system is only a rough proxy for the actual membrane potential. That is why we make no quantitative claims on the absolute value or absolute difference between groups of cells. We also make very clear in Fig 3B what we are actually measuring and can emphasize again in the text that this is only a proxy. We agree that it is a good idea to compare MitoLoc values to TMRE staining as the reviewer suggests, we will do these experiments in depleted and control cells at different timepoints. Please note though that also dye staining has its caveats, especially in dynamic live cell experiments. TMRM for example is not compatible with the acidic pH 5 medium that is typically used for yeast and subjecting cells to washing steps and higher pH may change both morphology of mitochondria and the MMP, especially in cells that are already “stressed”. We prefer not to complete elaborate pharmacological titration experiments because firstly, this was extensively done in the original MitoLoc paper by the Ralser lab ((Vowinckel et al. 2015), cited 120 times); secondly, the value of the MMP is not the most critical claim of the manuscript. See also 3.12. Please note that in Figure S4D we had already plotted MMP vs mitochondrial concentration.

2.3. To use Atp6-mNeon as a proxy for mtDNA is an assumption. Interpreting Atp6 intensity as "functional mtDNA" could be confounded by translation, turnover, or assembly. Please (i) report mtDNA copy number time courses (you have qPCR), nucleoid counts (DAPI/PicoGreen or TFAM/Abf2 tagging), and (ii) assess translation (e.g., 35S-labeling or puromycin proxies) and turnover (proteasome/AAA protease inhibition, mitophagy mutants -some data are alluded to- plus mRNA levels for mtDNA-encoded genes). This will support the "reduced synthesis" versus "increased degradation" conclusion.

We agree with all three reviewers that Atp6 is only a proxy for mtDNA (Jakubke et al. 2021; Roussou et al. 2024) and the correlation should be checked more carefully. We will use the very recently established Hi-NESS system to follow nucleoids/ mtDNA during depletion experiments. See detailed reply to 1.2.

(ii) in Figure 2C we inhibit mitochondrial translation and show that in this case control and depleted cells have the same level of Cox2, at least suggesting that degradation is not the key mechanism controlling the levels of mtDNA encoded proteins. We cannot do proteasome inhibitor assays since the nature of the AID-TIR systems requires an active proteasome. In figure S5C we show that the Atp6 depletion is similar in an atg32 deletion. This does not completely exclude a contribution of mitophagy to the observed phenotype, but does confirm that mitophagy is not the primary reason for cells becoming petite.

2.4. The promoter-NeonGreen reporters argue against transcriptional down-regulation of nuclear OXPHOS. Please add mRNA (RT-qPCR/RNA-seq) for representative genes and a pulse-chase or degradation-pathway dependency (e.g., proteasome/mitophagy/autophagy mutants) to firmly assign active degradation. The authors need to normalize proteomics to mitochondrial mass (e.g., citrate synthase/porin) to separate organelle abundance from protein turnover.

While we are happy to perform qPCR experiments for selected genes, a full RNA-seq experiment seems outside the scope of this study. As explained above, a proteasome inhibitor experiment is not possible in this set-up. Bulk mitophagy/autophagy seems unlikely to be the cause of the decrease of the nuclear-encoded OXPHOS proteins, since most other mitochondrial proteins do not decrease on average on population level in the first hours. This data is now plotted as additional figure (see below) and will be included in the supplementary of the revised manuscript (Fig R1E).

2.5. Using preSu9-mCardinal intensity as "mitochondrial concentration" is sensitive to expression, import competence, and morphology/segmentation. The authors should provide validation that this metric tracks 3D volume across fragmentation states (e.g., correlation with mito-GFP volumetrics; detergent-free CS activity; TOMM20/Por1 immunoblot per cell).

We agree that this is an important point and the co-authors discussed this point quite intensively. In figure S3A and B we show (using confocal data) that there is a very strong correlation between the total fluorescence signal and the 3D volume reconstruction. However, the slope of the correlation is different between tubular and fragmented mitochondria (compare panels A and B) and see figure legend. Since we are dealing with diffraction-limited objects it is likely that the 3D reconstruction is sensitive to morphology, especially if mitochondria are “clumping”. We therefore think that the total fluorescence signal is actually a better estimate of mitochondrial mass per cell than the 3D volume reconstruction (especially for our data obtained with a conventional epifluorescence microscope). The mean of the total mitochondrial fluorescence also better matches the population average mitochondrial proteome (Fig R1E). To consolidate this assumption, we will additionally compare our data to a strain with Tom70-Neongreen and to MMP independent dyes.

Notably, since the morphology is similarly altered in mothers and buds this is of minor impact for our main point – the unequal distribution between mother and buds.

2.6. The unequal mother-daughter distribution is compelling, but causality remains inferred. Test whether modulating inheritance machinery (actin cables/Myo2, Num1, Mmr1) or altering fission (Dnm1 inhibition) modifies segregation defects and rescues mtDNA/Atp6 decline. Complementation with Fzo1 re-expression at defined times would help order the phenotype cascade.

We agree that rescue experiments would be very useful. We have some preliminary data for tether experiments, for example with Num1. The general problem is that the fragmented mitochondria clump together. We have not found a method to restore an equal distribution between mother and daughter cells. We will try to optimize the assay, but are not overly confident it will work. Mmr1 deletion aggravates the Fzo1 phenotype, likely also because the distribution becomes even more heterogeneous, but we have not rigorously analyzed this.

We like the idea of the Fzo1 re-expression and will run such experiments. This will be especially powerful in combination with the new HI-NESS mtDNA reporter. We may be able to track exactly when cells reach the point-of-no return and become petite. This will also help connecting our mathematical model more directly to the data.

2.7. The model is useful but should include parameter sensitivity (segregation variance, synthesis slopes, initial nucleoid number) and prospective validation (e.g., predict rescue upon partial restoration of synthesis or inheritance, then test experimentally).

We will refine our model to include the to-be-measured nucleoids/mtDNA values. We will include a parameter sensitivity analysis with the updated model.

Reviewer 3:

3.1. About the use of Atp6 as a good proxy for mtDNA content. This is assumed from l285 onwards, based on a previous publication. As the link is fairly central to part of the paper's arguments, and the system in this study is being perturbed in several different ways, a stronger argument or demonstration that this link remains intact (and unchanged, as it is used in comparisons) would seem important.

We agree, see 1.2.

3.2. About confounding variables and processes. The study does an admirable job of being transparent and attempting to control for the many different influences involved in the physical-genetic link. But some remain less clearly unpacked, including some I think could be quite important. For example, there is a lot of focus on mito concentration -- but given the phenotypes are changing the sizes of cells, do concentration changes come from volume changes, mito changes, or both? In "ruling out" mitophagy -- a potentially important (and intuitive) influence, the argument is not presented as directly as it could be and it's not completely clear that it can in fact be ruled out in this way. There are a couple of other instances which I've put in the smaller points below.

Thank you for acknowledging our efforts to show transparent and well-controlled experiments! We address each of the specific points below.

3.3. full genus name when it first appears

We will add the full name.

3.4. I may be wrong here, but I thought the petite phenotype more classically arises from mtDNA deletion mutations, not loss? The way this is phrased implies that mtDNA loss is [always] the cause. Whether I'm wrong on that point or not, the petite phenotype should be described and referenced.

We can expand the text and cite additional relevant papers. The term “petite” refers to any strain that is respiratory incompetent and leads to small colonies (not necessarily small cells!) (Seel et al. 2023). This can be mutations or gene loss (fragments) on the mtDNA (these are called cytoplasmic petite), or chemically induced loss of mtDNA (e.g. EtBr), or mutations of nuclear genes required for respiration (these are termed nuclear petite; some nuclear petites show loss of mtDNA in addition to the mutation in the nuclear genome) (Contamine and Picard 2000).

3.5. para starting l59 -- should mention for context that mitochondria in (healthy, wildtype) yeast are generally much more fused than in other organisms

ok.

3.6. Fig 1C -- very odd choice of y-axis range! either start at zero or ensure that the data fill as much vertical space of the plot as possible

True, this was probably some formatting relic. We will adapt the axis to fill the full space. Most of our axes start at 0, but that doesn’t make so much sense here, since we consider the solidity in the control as “baseline”.

3.7. "wild-type like more tubular mitochondria" reads rather awkwardly. "more tubular mitochondria (as in the wild-type)"?

Thank you, sounds better.

3.8. l106 -- imaging artefacts? are mitos fragmenting because of photo stress? -- this is mentioned in l577-8 in the Methods, but the data from the growth rate and MMP comparison isn't given -- an SI figure would be helpful here. It would be reassuring to know that mito morphology wasn't changing in response to phototoxicity too.

In the methods we just briefly point out that we have done all our “due diligence” controls to check that we do not generate phototoxicity, something that we highlight in the cited review. We do not explicitly have a figure for this, but figure S1A shows that the solidity of the mitochondrial network in control cells stays the same over 9 hours, even though these cells are exposed to the same cultivation and imaging regime as the depleted cells. We will also add a picture of control cells after 9 h. In S1B we show that control cells containing TIR but no AID tag treated with both chemicals imaged over 9 hours also show the same solidity (~mitochondrial morphology) as untreated control. Also, the doubling times of cells grown in our imaging system (Fig R1B) are very similar to the shake flask (Fig R1A). All in all, we are very confident that our imaging settings did not impact our reported phenotypes.

3.9. para l146 -- so this suggests mtDNA-encoded proteins have a very rapid turnover, O(hours) -- is this known/reasonable?

Reference (Christiano et al. 2014) suggests that respiratory chain proteins are shorter lived than the average yeast protein. However, based on Figure 2C we think the dynamics mostly speak for a dilution by growth.

3.10. section l189 -- it's hard to reason fully about these statistics of mitochondrial concentration given that the petite phenotype is fundamentally affecting overall cell volume. can we have details on the cell size distribution in parallel with these results? to put it another way -- how does mitochondrial *amount* per cell change?

This is a good point. We report mostly on mitochondrial “concentrations” because we think this is what the cell actually cares about (mitochondrial activity in relationship to cytosolic activity). But we will include additional graphs on mitochondrial amount as well as size distributions (Fig R1C, related to Fig 4F). We can already point out that the size distribution of the population does not change much in the first hours. The “petite” phenotype refers to small colonies on growth medium with limited supply of a fermentable carbon source, not to smaller size of single cells.

3.11. l199 the mean in Fig S3C certainly does change -- it increases, clearly relative both to control and to its initial value. rather than sweeping this under the carpet we should look in more detail to understand it (a consequence of the increased skew of the distribution)?

This relates somewhat to the previous point. The increase in average concentration is not due to an increased amount in the population, but due to the fact that it is the small buds that get a very high amount of the mitochondria which “exaggerates” the asymmetric/heterogenous distribution. This will be clarified by the figures we mention in the point above.

3.12. para line 206 -- this doesn't make it clear whether your MMP signal is integrated over all mitochondria in the cell, or normalised by mitochondrial content? this matters quite a lot for the interpretation if the distributions of mitochondrial content are changing. reading on, this is even more important for para line 222. Reading further on, there is an equation on l612 that gives a definition, but it doesn't really clarify (apologies if I'm misunderstanding).

For each cell, we basically calculate the relative mitochondrial enrichment of the MMP sensitive vs the MMP insensitive pre-sequence.

So, MMP= (total intensity of mitochondrial pre-Cox4 Neongreen/ total intensity of mitochondrial pre-Su9 Cardinal) / (total cytosolic pre-Cox4 Neongreen/ total cytosolic pre-Su9 Cardinal).

We calculate this value for each cell, but we do not have the optical resolution to calculate it for individual mitochondrial fragments.

Both constructs are driven by the same strong promoter, so transcription of the fluorophore should never limit the uptake. Also, in Figure 3D we compare control and depleted cells with similar total mitochondrial concentration, so the difference must be due to a different import of the two fluorophores, see also Fig S4D. The calculated “MMP” value is of course only a crude proxy for the actual membrane potential in millivolts and we do not want to make any claims on absolute values or quantitative differences. But essentially what we are interested in is “mitochondrial health/activity” and we think the system is good at reporting this. See also 2.2.

3.13. l230 -- a point of personal interest -- low mito concentrations are connected to low "function" (MMP) and give extended division times -- this is interestingly exactly the model needed to reproduce observations in HeLa cells (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002416). That model went on to predict several aspects of downstream cellular behaviour -- it would be very interesting to see how compatible that picture (parameterised using HeLa observations) is with yeast!

Thank you for pointing out your interesting paper, which we will include in our discussion. Another recent preprint about fission yeast (Chacko et al. 2025) also fits into this picture. Since you were kind enough to disclose your identity, we would be happy to discuss this further with you in person if we can maybe follow-up on this.

3.14. l239 "less mitochondria" -- a bit tricky but I'd say "fewer mitochondria" or "less mitochondrial content"

Thanks, we will think about how to best rephrase this, probably less mitochondrial content.

3.15. Section l234 So here (and in Fig 4) the focus is on overall distributions of mitochondrial concentration in different cells (mother-to-be, mother, bud; gen 1, gen >1). But we've just seen that one effect of fzo1 is to broader the distribution of mitochondrial concentration across cells. Can't we look in more depth at the implications of this heterogeneity? For example in Fig 4F (which is cool) we look at the distribution of all fzo1 mothers-to-be, mothers, and buds. But this loses information about the provenance. For example, do mothers-to-be with extremely low mito concentrations just push everything to the bud, while mothers-to-be with high mito concentrations distribute things more evenly? It would seem very easy and very interesting to somehow subset the distribution of mothers-to-be by concentration and see how different subsets behave

This is a good point. When analyzing the data, we pretty much plotted everything against everything and then chose the graphs that we think will best guide the reader through the story-line. We can make additional supplementary plots where we show the starting concentrations/amounts of the mother in relationship to the resulting split ratio at the end of the cycle (Fig R1D).

3.16. l285 -- experimental design -- do we know that Atp6 will continue to be a good proxy for functional mtDNA in the face of the perturbations provided by Fzo1 depletion? Especially if there is impact on the expression of mitoribosomes, the relationship between mtDNA and Atp6 may look rather different in the mutant?

This is actually our top-priority experiment now. We will use the HI-NESS system and possibly DAPI staining to make a more direct link to mtDNA/ nucleoid numbers, see 1.2.

3.17. l290 -- ruled out mitophagy. This message could be much clearer. Comparing Fig S5C and Fig 3A side-by-side is a needlessly difficult task -- put Fig 3A into Fig S5. Then we see that when mitophagy is compromised, the distribution of mitochondrial concentration has a lower median and much lower upper quartile than in the mitophagy-equipped Fzo1 mutant? What is going on here? For a paper motivated by disentangling coupled mechanisms, this should be made clearer!

Thanks for pointing this out. We can of course easily include the control in the corresponding figure. Compromising mitophagy is likely to generally affect mitochondrial health and turnover a little bit, independent of what is going on with Fzo1. The second evidence that speaks against large-scale mitophagy is the proteomics data: On population level the dynamics of the respiratory chain proteins are very different from those of other (nuclear encoded) mitochondrial proteins. We will add additional supplementary figures to make this more clear, see Fig R1E. Most mitochondrial proteins in the proteomics experiment stay constant in the first few hours, consistent with the imaging data showing that the mean mitochondrial content of the population does not change initially. This again highlights that it is the unequal distribution which is the problem and not massive degradation of mitochondria.

3.18. With the Atp6 signal, how do we know that fluorescence from different cells is comparable? Buds will be smaller than mother cells for example, potentially leading to less occlusion of the fluorescent signal by other content in the cytoplasm

This is of course a general problem that anyone faces doing quantitative fluorescence microscopy. From the technical side, we have done the best we could by taking a reasonable amount of z-slices and by choosing fluorophores that are in a range with little cellular background fluorescence (e.g. Neongreen is much better than GFP). From a practical standpoint, we are always comparing to the control, which is subject to the same technical limitations as the depleted cells and the cell sizes are very similar. So, even if we are systematically overestimating the Atp6 concentration in the bud by a few %, the difference to the control would still be qualitatively true. We therefore do not think that any of our conclusions are affected by this.

3.19. l343 -- maintenance of mtDNA -- here the point about l285 (is the Atp6-mtDNA relationship the same in the Fzo1 mutant) is particularly important, as we're directly tying findings about the protein product to implications about the mtDNA

We will carefully address this, see above.

3.20. l367 -- on a first read this description of the model feels like lots of choices have been made without being fully justified. Why a log-normal distribution (when the fit to the data looks rather flawed); why the choice of 5 groups for nucleoid number (why not 3? or 8?); the process used for parameter fitting is very unclear (after reading the methods I think some of these values are read directly from the data, but the shapes of the distributions remain unexplained). l705 -- presumably the ratio was drawn from a log-normal distribution and then the corresponding nucleoid numbers were rounded to integers? the ratio itself wasn't rounded? (also l367) How were the log-normal distributions fitted to experiments (Figs. S7A,B)? Just by eye?

We will update our model based on measured nucleoid counts and then explain more stringently the choices we make/ parameters we select.

3.21. l711 by random selection -- just at random? ("selection" could be confusing) Overall, it feels like the model may be too complicated for what it needs to show. Either (a) the model should show qualitatively that unequal inheritance and reduced production leads to rapid loss -- which a much simpler model, probably just involving a couple of lines of algebra, could show. Or (b) the model should quantitatively reproduce the particular numerical observations from the experiments -- it's not totally clear that it does this (do the cell-cycle-based decay timescales in Fig 7 correspond to the hour-based decay timescales in other plots, for example). At the moment the model is at a (b) level of detail but it's only clear that it's reporting the (a) level of results.

If the HI-NESS and Fzo1 re-addition experiments work as explained above, all parameters will have direct experimental data, and we should get much closer to (a).

3.22. A lot of the discussion repeats the results; depending on editorial preferences some of this text could probably be pared back to focus on the literature connections and context.

We will think about streamlining the discussion once some of the additional material alluded to above has been added.

3.23. Data availability -- it looks like much of the data required to reproduce the results is not going to be made available. Images and proteomic data are promised, but the data associated with mitochondrial concentration and other features are not mentioned. For FAIR purposes all the data (including statistics from analysis of the images) should be published.

We maybe didn’t phrase this clearly. All data will be made available. Where technically feasible, this will be directly accessible in a repository, otherwise by request to the corresponding author.

On our OMERO server, we have deposited many TB of raw images as well as all the intermediate steps such as segmentation masks, and the csv files with all the extracted data for each cell (including background corrections etc). Additionally, we can include csvs with the data grouped in a way that we used to generate all the box blots etc. As of now, the OMERO data is unfortunately only available by requesting a personal guest login from our bioinformatics facility, but we were promised that with the next technical update there will be a public link available. The proteomics data and the model are already fully accessible. The raw western blot images with corresponding ponceau staining will be included with the final publication either as additional supplementary material or in whatever format matches the journal requirements.

3.24 l660 -- can an overview of the EM protocol be given, to avoid having to buy the Mayer 2024 article?

The cited paper is open access. But we can also include more details in our method section.

References:

Chacko, L. A., H. Nakaoka, R. Morris, W. Marshall, and V. Ananthanarayanan. 2025. 'Mitochondrial function regulates cell growth kinetics to actively maintain mitochondrial homeostasis', bioRxiv.

Christiano, R., N. Nagaraj, F. Frohlich, and T. C. Walther. 2014. 'Global proteome turnover analyses of the Yeasts S. cerevisiae and S. pombe', Cell Rep, 9: 1959-65.

Contamine, V., and M. Picard. 2000. 'Maintenance and integrity of the mitochondrial genome: a plethora of nuclear genes in the budding yeast', Microbiol Mol Biol Rev, 64: 281-315.

Deng, Jingti, Lucy Swift, Mashiat Zaman, Fatemeh Shahhosseini, Abhishek Sharma, Daniela Bureik, Francesco Padovani, Alissa Benedikt, Amit Jaiswal, Craig Brideau, Savraj Grewal, Kurt M. Schmoller, Pina Colarusso, and Timothy E. Shutt. 2025. 'A novel genetic fluorescent reporter to visualize mitochondrial nucleoids', bioRxiv: 2023.10.23.563667.

Di Bartolomeo, F., C. Malina, K. Campbell, M. Mormino, J. Fuchs, E. Vorontsov, C. M. Gustafsson, and J. Nielsen. 2020. 'Absolute yeast mitochondrial proteome quantification reveals trade-off between biosynthesis and energy generation during diauxic shift', Proc Natl Acad Sci U S A, 117: 7524-35.

Ebert, A. C., N. L. Hepowit, T. A. Martinez, H. Vollmer, H. L. Singkhek, K. D. Frazier, S. A. Kantejeva, M. R. Patel, and J. A. MacGurn. 2025. 'Sphingolipid metabolism drives mitochondria remodeling during aging and oxidative stress', bioRxiv.

Jakubke, C., R. Roussou, A. Maiser, C. Schug, F. Thoma, R. Bunk, D. Horl, H. Leonhardt, P. Walter, T. Klecker, and C. Osman. 2021. 'Cristae-dependent quality control of the mitochondrial genome', Sci Adv, 7: eabi8886.

Khan, Abdul Haseeb, Xuefang Gu, Rutvik J. Patel, Prabha Chuphal, Matheus P. Viana, Aidan I. Brown, Brian M. Zid, and Tatsuhisa Tsuboi. 2024. 'Mitochondrial protein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence', Nature Communications, 15: 8274.

Martin, J., K. Mahlke, and N. Pfanner. 1991. 'Role of an energized inner membrane in mitochondrial protein import. Delta psi drives the movement of presequences', J Biol Chem, 266: 18051-7.

Osman, C., T. R. Noriega, V. Okreglak, J. C. Fung, and P. Walter. 2015. 'Integrity of the yeast mitochondrial genome, but not its distribution and inheritance, relies on mitochondrial fission and fusion', Proc Natl Acad Sci U S A, 112: E947-56.

Perić, Matea, Peter Bou Dib, Sven Dennerlein, Marina Musa, Marina Rudan, Anita Lovrić, Andrea Nikolić, Ana Šarić, Sandra Sobočanec, Željka Mačak, Nuno Raimundo, and Anita Kriško. 2016. 'Crosstalk between cellular compartments protects against proteotoxicity and extends lifespan', Scientific Reports, 6: 28751.

Roussou, Rodaria, Dirk Metzler, Francesco Padovani, Felix Thoma, Rebecca Schwarz, Boris Shraiman, Kurt M. Schmoller, and Christof Osman. 2024. 'Real-time assessment of mitochondrial DNA heteroplasmy dynamics at the single-cell level', The EMBO Journal, 43: 5340-59-59.

Seel, A., F. Padovani, M. Mayer, A. Finster, D. Bureik, F. Thoma, C. Osman, T. Klecker, and K. M. Schmoller. 2023. 'Regulation with cell size ensures mitochondrial DNA homeostasis during cell growth', Nat Struct Mol Biol, 30: 1549-60.

Vowinckel, J., J. Hartl, R. Butler, and M. Ralser. 2015. 'MitoLoc: A method for the simultaneous quantification of mitochondrial network morphology and membrane potential in single cells', Mitochondrion, 24: 77-86.

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Referee #2

Evidence, reproducibility and clarity

Dengler and colleagues use an AID-based acute depletion of Fzo1 in budding yeast, coupling microfluidics live imaging, single-cell quantification (>30k cells), proteomics, an mtDNA-encoded Atp6 reporter, and simple modeling to argue that fusion loss causes (i) rapid fragmentation and ΔΨm decline, (ii) progressive mtDNA/RC depletion, and (iii) unequal mother-daughter mitochondrial inheritance; together with a failure of compensatory synthesis, these changes drive petite formation. The time-resolved design is valuable, but several readouts are indirect, and some claims (particularly those regarding membrane potential, synthesis "failure," and causality) appear over-interpreted without additional controls.

Major points

1. While inducible TIR is used to reduce background, the manuscript should rigorously exclude auxin/TIR off-targets (growth, mitochondrial phenotypes, gene expression). Please include full matched controls: {plus minus}auxin, {plus minus}TIR, epitope tag alone, and a degron control on an unrelated mitochondrial membrane protein.
2. The Mitoloc preSu9 vs Cox4 import ratio is only a proxy of mitochondrial membrane potential (ΔΨm) and itself depends on mitochondrial mass, protein expression, matrix ATP, and import saturation. The authors need to calibrate ΔΨm with orthogonal dyes (TMRE/TMRM) and pharmacologic titrations (FCCP/antimycin/oligomycin) to generate a response curve; show that Mitoloc tracks dye-based ΔΨm across the relevant range and corrects for mass/photobleaching. Report single-cell ΔΨm vs mass residuals.
3. To use Atp6-mNeon as a proxy for mtDNA is an assumption. Interpreting Atp6 intensity as "functional mtDNA" could be confounded by translation, turnover, or assembly. Please (i) report mtDNA copy number time courses (you have qPCR), nucleoid counts (DAPI/PicoGreen or TFAM/Abf2 tagging), and (ii) assess translation (e.g., 35S-labeling or puromycin proxies) and turnover (proteasome/AAA protease inhibition, mitophagy mutants -some data are alluded to- plus mRNA levels for mtDNA-encoded genes). This will support the "reduced synthesis" versus "increased degradation" conclusion.
4. The promoter-NeonGreen reporters argue against transcriptional down-regulation of nuclear OXPHOS. Please add mRNA (RT-qPCR/RNA-seq) for representative genes and a pulse-chase or degradation-pathway dependency (e.g., proteasome/mitophagy/autophagy mutants) to firmly assign active degradation. The authors need to normalize proteomics to mitochondrial mass (e.g., citrate synthase/porin) to separate organelle abundance from protein turnover.
5. Using preSu9-mCardinal intensity as "mitochondrial concentration" is sensitive to expression, import competence, and morphology/segmentation. The authors should provide validation that this metric tracks 3D volume across fragmentation states (e.g., correlation with mito-GFP volumetrics; detergent-free CS activity; TOMM20/Por1 immunoblot per cell).
6. The unequal mother-daughter distribution is compelling, but causality remains inferred. Test whether modulating inheritance machinery (actin cables/Myo2, Num1, Mmr1) or altering fission (Dnm1 inhibition) modifies segregation defects and rescues mtDNA/Atp6 decline. Complementation with Fzo1 re-expression at defined times would help order the phenotype cascade.
7. The model is useful but should include parameter sensitivity (segregation variance, synthesis slopes, initial nucleoid number) and prospective validation (e.g., predict rescue upon partial restoration of synthesis or inheritance, then test experimentally).

Significance

The dataset is rich and the time-resolved approach strong, but key conclusions rely on indirect proxies and need orthogonal validation and at least one causal rescue experiment to avoid over-interpretation.

La tecnología pensada para comunidades debe práctica y no solo teórica, y para lograrlo se pueden usar dos estrategias: la envolvente, que ofrece una herramienta integral como Grafoscopio, o la incrustada, que mejora las herramientas que la gente ya utiliza, como se muestra con Cardumem. La idea es encontrar que entre estas dos formas se alinee para que la tecnología llegue a las necesidades reales de una comunidad y no solo el entorno académico u operativo de la programación.

This is a really interesting method using a peptide tag to target proteins to extracellular vesicles for ease of isolation in E. coli! I can think of lots of benefits and applications!

I'm guessing that you measured this in your initial paper, but might be worth mentioning here as well. Have you shown that the VNp tag doesn't affect enzyme activity, stability, folding?

Very cool!

Reviewer #3 (Public review):

Summary

This manuscript, from the developers of the novel DREADD-selective agonist DCZ (Nagai et al., 2020), utilizes a unique dataset where multiple PET scans in a large number of monkeys, including baseline scans before AAV injection, 30-120 days post-injection, and then periodically over the course of the prolonged experiments, were performed to access short- and long-term dynamics of DREADD expression in vivo, and to associate DREADD expression with the efficacy of manipulating the neuronal activity or behavior. The goal was to provide critical insights into practicality and design of multi-year studies using chemogenetics, and to elucidate factors affecting expression stability.

Strengths are systematic quantitative assessment of the effects of both excitatory and inhibitory DREADDs, quantification of both the short-term and longer-term dynamics, a wide range of functional assessment approaches (behavior, electrophysiology, imaging), and assessment of factors affecting DREADD expression levels, such as serotype, promoter, titer (concentration), tag, and DREADD type.

These finding will undoubtedly have a very significant impact on the rapidly growing, but still highly challenging field of primate chemogenetic manipulations. As such, the work represents an invaluable resource for the community.

reply to u/todddiskin at https://www.reddit.com/r/typewriters/comments/1nlodr0/how_do_you_use_your_machines/

Some various recent uses:

• I've got writing projects sitting in two different machines.
• I use one on my primary desk for typing up notes on index cards, recipes, my commonplace "book", letters, and other personal correspondence.
• I use a few of my portables on the porch in the mornings/evenings for journaling.
• One machine in the hallway is for impromptu ideas and poetry and an occasional bit of typewriter art.
• One machine near the kitchen is always gamed up for adding to the ever-growing shopping list.
• I'll often get one out for scoring baseball games.
• Participating in One Typed Page and One Typed Quote
• Typing up notes in zoom calls - I've got a camera mount over a Royal KMG that has its own Zoom account so people can watch the notes typed in real time.
• Labels for folders, index card dividers, and sticky labels.
• Addressing envelopes.
• Writing out checks.
• Typecasting
• Hiding a flask or two of bourbon (the Fold-A-Matic Remingtons are great for this)
• Supplementing the nose of my bourbon and whisky collection.

At the end of the day though, unless you're Paul Sheldon, typewriters are unitaskers and are designed to do one thing well: put text on paper. All the rest are just variations on the theme. 😁🤪☠️

see also: https://www.reddit.com/r/typewriters/search/?q=typewriter+uses

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

Desveaux et al. describe human mAbs targeting protein from the Pseudomonas aeruginosa T3SS, discovered by employing single cell B cell sorting from cystic fibrosis patients. The mAbs were directed at the proteins PscF and PcrV. They particularly focused on two mAbs binding the T3SS with the potential of blocking activity. The supplemented biochemical analysis was crystal structures of P3D6 Fab complex. They also compared the blocking activity with mAbs that were described in previous studies, using an assay that evaluated the toxin injection. They conducted mechanistic structure analysis and found that these mAbs might act through different mechanisms by preventing PcrV oligomerization and disrupting PcrVs scaffolding function.

Strengths:

The antibiotic resistance crisis requires the development of new solutions to treat infections caused by MDR bacteria. The development of antibacterial mAbs holds great potential. In that context, this report is important as it paves the way for the development of additional mAbs targeting various pathogens that harbor the T3SS. In this report, the authors present a comparative study of their discovered mAbs vs. a commercial mAb currently in clinical testing resulting in valuable data with applicative implications. The authors investigated the mechanism of action of the mAbs using advanced methods and assays for the characterization of antibody and antigen interaction, underlining the effort to determine the discovered mAbs suitability for downstream application.

Weaknesses:

Although the information presented in this manuscript is important, previous reports regarding other T3SS structures complexed with antibodies, reduce the novelty of this report. Nevertheless, we provide several comments that may help to improve the report. The structural analysis of the presented mAbs is incomplete and unfortunately, the authors did not address any developability assessment. With such vital information missing, it is unclear if the proposed antibodies are suited for diagnostic or therapeutic usage. This vastly reduces the importance of the possibly great potential of the authors' findings. Moreover, the structural information does not include the interacting regions on the mAb which may impede the optimization of the mAb if it is required to improve its affinity.

As described in the manuscript (Fig. 6), our mAbs are markedly less effective in every in vitro T3SS inhibition assay than the mAbs recently described by Simonis et al. They are therefore very unlikely to outperform these mAbs in in vivo animal models of P. aeruginosa infection. Considering the high cost of animal experiments and ethical concerns-and in accordance with the Reduction principal of the 3Rs guidelines-we chose not to pursue in vivo experiments. Instead, we focused on leveraging the new isolated mAbs to investigate the mechanisms of action and structural features of anti-PcrV mAbs.

Following the reviewer's suggestion, we have now added mAb interaction features into the structural data presented in the manuscript. However, based on the efficiency data, the structural analysis and the mechanistic insights presented, we do not consider further therapeutic use and optimization of our mAbs to be warranted.

Reviewer #2 (Public review):

Summary:

Desveaux et al. performed Elisa and translocation assays to identify among 34 cystic fibrosis patients which ones produced antibodies against P. aeruginosa type three secretion system (T3SS). The authors were especially interested in antibodies against PcrV and PcsF, two key components of the T3SS. The authors leveraged their binding assays and flow cytometry to isolate individual B cells from the two most promising sera, and then obtained monoclonal antibodies for the proteins of interest. Among the tested monoclonal antibodies, P3D6 and P5B3 emerged as the best candidates due to their inhibitory effect on the ExoS-Bla translocation marker (with 24% and 94% inhibition, respectively). The authors then showed that P5B3 binds to the five most common variants of PcrV, while P3D6 seems to recognize only one variant. Furthermore, the authors showed that P3D6 inhibits translocon formation, measured as cell death of J774 macrophages. To get insights into the P3D6PcrV interaction, the authors defined the crystal structure of the P3D6-PcrV complex. Finally, the authors compared their new antibodies with two previous ones (i.e., MEDI3902 and 30-B8).

Strengths:

(1) The article is well written.

(2) The authors used complementary assays to evaluate the protective effect of candidate monoclonal antibodies.

(3) The authors offered crystal structure with insights into the P3D6 antibody-T3SS interaction (e.g., interactions with monomer vs pentamers).

(4) The authors put their results in context by comparing their antibodies with respect to previous ones.

Weaknesses:

The authors used a similar workflow to the one previously reported in Simonis et al. 2023 (antibodies from cystic fibrosis patients that included B cell isolation, antibody-PcrV interaction modeling, etc.) but the authors do not clearly explain how their work and findings differentiate from previous work.   

We employed a similar mAb isolation pipeline to that used by Simonis et al., beginning with the screening of a cohort of cystic fibrosis patients chronically infected with P. aeruginosa. As in Simonis et al., we isolated specific B cells using a recombinant PcrV bait, followed by single-cell PCR amplification of immunoglobulin genes. The main differences in methodology between the two studies are as follows: i) the use of individuals from different cohorts, and therefore having different Ab repertoires; ii) the nature of the screening assays, although in both cases the screening was focused on the inhibition of T3SS function; iii) the PcrV labeling strategy, with Simonis et al. employing direct labeling, whereas we used a biotinylated tag combined with streptavidin;

The number of specific mAbs obtained and produced was higher in Simonis et al. (47 versus 9 in our study). They sorted B cells from three individuals compared to two in our work and possibly started with a larger amount of PBMCs per donor, which may account for the higher number of specific B cells and mAbs isolated. Considering that the strategies were overall very similar, the greater number of mAbs isolated in Simonis et al. likely explains, to a large extent, why they identified mAbs targeting different epitopes compared to ours, including highly potent mAbs that we did not recover. 

Our modeling study, unlike that of Simonis et al., which relied on an AlphaFold prediction of the multimeric structure of P. aeruginosa PcrV, was based on the experimentally determined structure of the homologous Salmonella SipD pentamer, as described in the manuscript. Furthermore, we compared our mAb P3D6 not only with 30-B8 from Simonis et al., but also with MEDI3902. Finally, in contrast to the approach of Simonis et al., we used functional assays to investigate the differences in mechanisms of action among these mAbs, which target three distinct epitopes.

(2) Although new antibodies against P. aeruginosa T3SS expand the potential space of antibodybased therapies, it is unclear if P3D6 or P5B3 are better than previous antibodies. In fact, in the discussion section authors suggested that the 30-B8 antibody seems to be the most effective of the tested antibodies.  

As explained above and shown in the Results section (Figure 6), the 30-B8 mAb is markedly more effective at inhibiting T3SS activity in both in vitro assays used.

(3) The authors should explain better which of the two antibodies they have discovered would be better suited for follow-up studies. It is confusing that the authors focused the last sections of the manuscript on P3D6 despite P3D6 having a much lower ExoS-Bla inhibition effect than P5B3 and the limitation in the PcrV variant that P3D6 seems to recognize. A better description of this comparison and the criteria to select among candidate antibodies would help readers identify the main messages of the paper. 

The P3D6 mAb shows stronger inhibitory activity than P5B3 in the two assays used, as shown in Supplementary Figure 1. An error in the table in Figure 2B was corrected and this table now reflects the results presented in Supplementary Figure 1. 

The final sections of the manuscript focus on P3D6, which is more potent than P5B3, and for which we successfully determined a co-crystal structure with PcrV*. All parallel attempts to obtain a structure of P5B3 in complex with PcrV* failed. The P3D6-PcrV* structure was used to analyze epitope recognition and mechanisms of action in comparison to previously described mAbs. As previously mentioned, we do not consider further studies aimed at therapeutic development and optimization of our mAbs to be justified given the current data. Therefore, we believe that the main message of the paper is adequately captured in the title.

(4) This work could strongly benefit from two additional experiments:

(a) In vivo experiments: experiments in animal models could offer a more comprehensive picture of the potential of the identified monoclonal antibodies. Additionally, this could help to answer a naïve question: why do the patients that have the antibodies still have chronic P. aeruginosa infections? 

As explained above, the mAbs we isolated are significantly less potent than those described by Simonis et al., and are therefore unlikely to outperform the best anti-PcrV candidates in vivo. In light of the data, and considering ethical concerns related to animal use in research and budgetary constraints, we decided not to proceed with in vivo experiments.

There are a number of reasons that may explain why patients with anti-PcrV Abs blocking the T3SS can still be chronically infected with Pa. First these Abs may be at limiting concentration, particularly in sites where Pa replicates, and thus unable to clear infection. in addition, it has been described that the T3SS is downregulated in chronic infection in cystic fibrosis patients. This suggests that a therapeutic intervention with T3SS inhibiting Abs may be more efficient if done early in cystic fibrosis patients to prevent colonization when Pa possesses an active T3SS. Finally, T3SS is not the only virulence mechanism employed by P. aeruginosa during infection. Indeed, multiple protein adhesins and polysaccharides are important factors facilitating the formation of bacterial biofilms that are crucial for establishing chronic persistent infection. In this regard, a combination of Abs targeting different factors on the P. aeruginosa surface may be needed to treat chronic infections.  

(b) Multi-antibody T3SS assays (i.e., a combination of two or more monoclonal antibodies evaluated with the same assays used for characterization of single ones). This could explore the synergistic effects of combinatorial therapies that could address some of the limitations of individual antibodies. 

Given the high potency of the Simonis mAbs and the mechanisms of action highlighted by our analysis, it is unlikely that our mAbs would synergize with those described by Simonis. Additionally, since our two mAbs cross-compete for binding, synergy between them is also improbable.

Reviewer #1 (Recommendations for the authors):

Line 166: How was the serum-IgG purified? (e.g., protein A, protein G). 

Protein A purification was used, as now mentioned in the manuscript. Purified Igs were thus predominantly IgG1, IgG2 and IgG4, as indicated.

(2) Line 196: When mentioning affinities, it is preferable to present in molar units. 

To facilitate comparisons, Ab concentrations were presented in µg/mL as in Simonis et al.

(3) Line 206: The author states that P3D6 displays significantly reduced ExoS-Bla injection (Figure 2B), but according to the presented table, ExoS-Bla inhibition was higher for P5B3. Additionally, when using "significantly", what was the statistical test that was used to evaluate the significance? Please clarify.

We thank the reviewer for pointing out this inconsistency. Indeed, the names of P3D6 and P5B3 were exchanged when building the table related to Figure 2B. The corrected version of this figure is now presented in the new version of the manuscript. An ANOVA was performed to evaluate the significance of the observed difference (adjusted p-values < 0.001) and it is now mentioned in the figure caption.  

(4) Line 215: "P3B3" typo.

This was corrected.

(5) Figure 3B: Could the author explain the higher level of ExoS-Bla injection when using VRCO1 antibody compared to no antibody.  

A slightly higher level of the median is observed in the case of three variants out of five. However, this difference is not statistically significant (p-value > 0.05).

(6) Supplement Figure 1: the presented grey area is not clear (is it the 95%CI?) and how was the IC50 calculated? With what model was it projected? Are the values for IC50 beyond the 100µg/mL mark a projection? It seems that projecting such greater values (such as the IC50 of over 400µg/mL for variant 5) is prone to high error probability.

The grey area represents the 95% confidence interval (95% CI) and it is now mentioned in the figure caption. The IC50 and 95% CI were both inferred by the dose-response drc R package based on a three-parameters log-logistic model and it is now explained in the Materials & Methods section. The p-values for IC50 beyond the 100µg/mL were below 0.05 but we agree that such extrapolation should be considered with precaution (see below our response to comment number 7).

(7) Line 227: The author describes that P5B3 has similar IC50 values towards variants 1-4, but the  IC50 towards variant 5 is substantially higher with 400µg/mL, albeit the only difference between variant 4 and 5 is the switch position 225 Arg -> Lys which are very similar in their properties. Please provide an explanation. 

As explained in our response to comment number 6, we agree that the comparison of IC50 that are estimated to be close or higher than the highest experimental concentration is somehow speculative. Indeed, we performed further statistical analysis that showed no significant difference between the IC50 toward the five PcrV variants of mAb P5B3. In contrast, the difference between the IC50 of mAbs P5B3 and P3D6 toward variant 1 is statistically significant. This is now explained in the manuscript.

(8) Line 233: Pore assembly: It is not clear how the data was normalized. The authors mention the methods normalization against the wildtype strain in the absence of antibodies, but did not elaborate clearly if the mutant strain has the same base cytotoxicity as the wild type. It would be helpful to show the level of cytotoxicity of the wild type compared to the mutant in the absence of antibodies to understand the baseline of cytotoxicity of both strains.  

In these experiments we did not use the wild-type strain. As explained, the only strain that allows the measurement of pore formation by translocators PopB/PopD is the one lacking all effectors. All the experiments were done with this strain, and all the measurements were normalized accordingly. 

(9) Figure 4: The explanation is redundant as it is clearly stated in the results. It would be better for the caption to describe the figure and leave interpretation to the results section. Overall, this comment is relevant to all figure captions, as it will reduce redundancy. My suggestion is to keep the figure caption as a road map to understand what is shown in the figure. For example, the Figure 4 caption should include that the concentration is presented in logarithmic scale, what is the dashed line, what is the grey area (what interval does it represent?), what each circle represents, and what is the regression model used? 

Figure captions have been improved as suggested. 

(10) Line 432: The authors apparently misquoted the original article describing the chimeric form PcrV* by describing the fusion of amino acids 1-17 and 136-249. I quote the original article by Tabor et al. "[...] we generated a truncated PcrV fragment (PcrVfrag) comprising PcrV amino acids 1-17 fused to amino acids 149-236 [...]". Additionally, how does the absence of amino acid 21 in the variant affect the conclusion? 

Our construct was inspired by the one described in Tabor et al. but was not identical. We have therefore replaced "was constructed based on a construct by Tabor et al." for "whose design was inspired by the construct described in Tabor et al."

Amino acid 21 is only absent in the construct used for crystallization experiments; all other experiments looking at Ab activity were performed with bacteria bearing full-length PcrV. The difference in P3D6 activity between variants V1 and V2-appears to be explained by the nature of the residue at position 225, according to the structural data, as explained now in more detail in the manuscript. Accordingly, the difference in efficiency of P3D6 against the V1 and V2  variants is explained by the residue at position 225, as both variants have the same residue at position 21. However, while the nature of the residue at position 225 appears to explain the absence of efficiency of the Ab for the variants studied, an impact of residue 21 could not be totally ruled out in putative variants with a Ser at 225 but different amino acids at 21.

(11) Line 569: Missing word - ESRF stands for European Synchrotron Radiation Facility. 

This has been corrected.

(12) Line 268-269 (Figure 5A): The description of the alpha helices in relation to the figure is incomplete. Helices 2,3 and 5 are not indicated. 

Indeed, since the structure is well-known and in the interest of visibility and simplicity, we only included the most relevant secondary structure features.

(13) Line 271-272: It would be good to elaborate on the exact binding platform between LC and HC of the Fab and the residues on the PcrV side. For example, the author could apply the structure to PDBePISA (EMBL-EBI) which will provide details about the interface between the PcrV and the antibody. It is very interesting to learn what regions of the antibody are in charge of the binding, such as: is the H-CDR3 the major contributor of the binding or are other CDRs more involved? Additionally, in line 275 they state that the substitution of Ser 225 with Arg or Lys is consistent with the P3D6 insufficient binding. What contributed to this result on the antibodies side? 

In order to address this question, we are now providing a LigPlot figure (supplementary Figure 3) in which specific interactions between PcrV* and the Fab are shown.

(14) Line 291: It is unclear from what data the authors concluded that anti-PscF targets 3 distinct regions of PscF. 

The data are shown in Supplementary Table 2, as mentioned in the manuscript. We have now modified the order of the anti-PcrV mAbs in the table to better illustrate the three identified epitope clusters (Sup table 2). Similarly, the anti-PscF mAbs appear to group into three clusters as P3G9 and P5E10 only compete with themselves, while mabs P3D6 and P5B3 compete with themselves and each other.

(15) Line 315: It is preferable to introduce results in the results section instead of the discussion. 

While preparing the manuscript, we initially included these results as a separate paragraph in the Results section, but ultimately chose the current format to improve flow and avoid redundancy.

(16) Supplement Figure 2: What was the regression model used to evaluate IC50, and what is presented in the graph? What is the dashed line (see comment for Figure 4 above)? 

The regression is based on a three-parameters log-logistic model and the light-colors area correspond to the 95% IC. The dashed lines visually represents 100% of ExoS-Bla injection. These information are now mentioned in the figure caption.

(17) Figure 6B: It would be better to show an additional rotation of the PcrV bound by Fab 30-B8 that corresponds to the same as the one represented with Fab MEDI3092. This would clear up the differences in binding regions. Same for Fab P3D6. 

Figure 6 already depicts two orientations. Despite the fact that we agree that additional orientations could be of interest, we believe that this would add unnecessary complexity to the figure, and would prefer to maintain the figure as is, if possible.

(18) Line 356-358: The author proposes an experiment to support the suggested mechanism of P3D6, it would follow up with a bio-chemical analysis showing the prevention of PcrV oligomerization in its presence. 

We understand the reviewers’ comment regarding the potential use of biochemical approaches to test our hypothesis. However, this not currently feasible as we have been unable to achieve in vitro oligomerization of PcrV alone, possibly due to the absence of other T3SS components, such as the polymerized PscF needle.

(19) Line 456: Missing details about how the ELISA was conducted including temperature, how the antigen was absorbed, plate type, etc. 

Experimental details have been added.

(20) Line 460: Missing substrate used for alkaline phosphatase. 

The nature of the substrate was added to the methods.

search.tag~write-to-think

🧊/ ♖/ hyperpost/ 🎭/ gyuri/ do/ web/ search/ via/ hypothes.is/ tag/ 📅/ 20/ 25/ 09/ 3/ hypothes.is-tag-write-to-think.html

indy0

dweb.link

ipfs.io

You see a shirt on a clothing rack with an original price tag of $100 and a sale price tag of $60. Even if you wouldn't normally spend more than $40 on a shirt, the initial, higher price of $100 serves as an anchor, making the sale price of $60 seem like a great deal in comparison.

https://www.mimeographrevival.com/posts/tag/diy/

To do list: <br /> read through various articles on these topics

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

“Alternative possibilities are discussed regarding the prior and likelihood of the model. Given that the second case study inspired the introduction of the zero-inflation likelihood, it is not clear how applicable the general methodology is to various datasets. If every unique dataset requires a tailored prior or likelihood to produce the best results, the methodology will not easily replace more traditional statistical analyses that can be applied in a straightforward manner. Furthermore, the differences between the results produced by the two Bayesian models in case study 2 are not discussed. In specific regions, the models provide conflicting results (e.g., regions MH, VPMpc, RCH, SCH, etc.), which are not addressed by the authors. A third case study would have provided further evidence for the generalizability of the methodology.”

We hope in this paper to propose a ‘standard workflow’ for these data; this standard workflow uses the horseshoe prior and we propose that this is the approach used to describe cell count data instead of the better established, but to our thinking, inefficient, t-testing approach.

The horseshoe prior is robust and allows a partially-pooled model to used while weighing-up the contribution of different data points. This is an analogue of excluding outliers and, in any analysis it is normal to investigate further if there are points being excluded as outliers. Often this reveals a particular challenge with the data, in the case of the data here, there are a lot of zeros, indicating that some samples should be excluded because the preparation failed to tag cells rather than because there were no cells to tag. This idea behind the ZIP example is to show that the Bayesian method can allow for this sort of further investigation and, indeed, as the reviewer notes this sort of extended analysis is often bespoke, tailored to the data.

We have clearly failed to explain that the ‘standard workflow’ we propose replace the more traditional methods is the first one we describe, with the horseshoe prior; this produces better results on both datasets than the traditional approach. However, we also feel it is useful to show how a more tailored follow-on can be useful; we need to make it clear that this is intended as an illustration of an ‘optional extra’ rather than a part of the more straightforward ‘standard workflow’.

To make this clearer we have made altered the text in several locations:

• end of Introduction: added clarifying sentence “Here, our aim is to introduce a ‘standard’ Bayesian model for cell count data. We illustrate the application of this model to two datasets, one related to neural activation and the other to developmental lineage. For the second dataset, we also demonstrate a second example extension Bayesian model.”

• Section Hierarchical modeling: “Our goal in both cases is to quantify group differences in the data. We present a ‘standard’ hierarchical model. This model reflects the experimental features common to cell count experiments and reflects the hierarchical structure of cell count data; the standard model is designed to deal robustly and efficiently with noise. On some occasions, to reflect a specific hypotheses, the structure of a particular experiment or an observed source of noise, this model can be further refined or changed to target the analysis. We will give an example of this for our second dataset.”

• Section Horseshoe prior: “The alternative is via a flexible prior such as the horseshoe Carvalho et al., 2010; Piironen and Vehtari, 2017. This more generic option may be suitable as a default ‘standard’ approach in the typical case where outliers are poorly understood.”

• Discussion: word ‘standard’ added to sentence: “Our standard workflow uses a horseshoe prior, along with the partial pooling, this allows our model to deal effectively with outliers.”

• Discussion: modified sentence “The horseshoe prior model workflow we have exhibited here is intended as a standard approach.”

Indeed, because the horseshoe prior deals robustly with outliers, whereas the ZIP is intended to model the outliers, any substantial difference between the two should be examined carefully. The referee is right to point out that we have not explained this in any detail and has helpfully listed a few brain regions were there are differences. This is useful, particularly since the examples listed illustrate in a useful way the opportunities and hazards this sort of data presents. To address this, we have added a new version of Figure 6 to the revised manuscript

Previously Figure 6 showed two example brain regions: MPN and TMd. We have now added MH and SCH to the figure, and new text commenting on the insights the plots provide, both in the Results and Discussion.

Reviewer #2 (Public review):

“A clearer link between the experimental data and model-structure terminology would be a benefit to the non-expert reader.”

This is a very good point and we are acutely aware through our own work how difficult it can be moving between fields with different research goals, different scientific cultures and different technical vocabularies. Just as it can be difficult translating from one language to another without losing nuance and meaning, it can be a real challenge finding technical terms that are useful for the non-expert reader while retaining the precision the application requires! In the long run, we hope that, just as some of the very specialized vocabulary that surrounds frequentist statistics has become familiar to to the working experimental scientists, the precise terminology involved in Bayesian modelling will become familiar and transparent. However, in advance of that day, we have included a glossary of terms at the end of the main text, and have made numerous small tweaks to make sure that link between data and model terminology is clearer and better explained.

Reviewer #1 (Recommendations fro the authors):

(1) “I would strongly recommend that the authors include more case studies in the manuscript, and address the qualitative differences between the different versions of the model.”

We agree that our method will only become established when it is applied to more datasets, we hope to contribute to further analysis and we know other people are already using the approach on their own data. We do, however, feel that adding more datasets to this paper will make it longer and more complex; the plan, instead, is to use the method on novel datasets to test specific hypotheses, so that the results will include novel scientific findings as well as adding another illustration of the Bayesian approach applied to data that is already well studied.

(2) “Figure 6 is not discussed in the main text.”

We had discussed the results presented in Figure 6 in the second paragraph of the section “Case study two – Ontogeny of inhibitory interneurons of the mouse thalamus”, however the reviewer is right in that we did not directly refer to the Figure – this was an oversight. In any case, in the revised manuscript we present a new version of Figure 6 (in response to above comment), which is now explicitly cited in the text.

Revised Figure 6: Example data and inferences highlighting model discrepancies. On the left under ‘data’: boxplots with medians and interquartile ranges for the raw data for four example brain regions. The shape of each point pairs left and right hemisphere readings in each of the five animals. On the right under ‘inference’: HDIs and confidence intervals are plotted. Purple is the Bayesian horseshoe model, pink is the Bayesian ZIP model, and orange is the sample mean. The Bayesian estimates are not strongly influenced by the zero-valued observations (MPN, SCH, TMd) or large-valued outliers (MH) and have means close to the data median. This explains the advantage of the Bayesian results over the confidence interval.

Reviewer #2 (Recommendations from the authors):

(1) “This is a generally well-written methodology paper that also provides the underlying code as a resource. As a reviewer outside both cell-count modelling and hierarchical-Bayesian approaches (though with a general interest in the topics) I found the method a little difficult to follow and would have liked to have been left with a better understanding of how the method is applied to the data. For example, in Figure 1 we are introduced to brain region count, animal count, and “items”. Then in the next line: pooling, model, structure, population and etc in subsequent lines. It is not clear what the subscripts (the pools?) are referring to: are they different regions R or animals N? These terms need to be better linked to the data and/or trimmed. Having said that, the later results look like a solid contribution to the field with a significant reduction in uncertainty from the Bayesian approach over the frequentist one. A future version of the manuscript, therefore, would benefit from greater precision of language as well as an economy and greater focus of terms linking the method to the biology. This is particularly the case around the exposition parts in Figure 1, Figure 2, and the “Hierarchical modelling” section.”

This is another important point. We have now made numerous small changes to tighten up the text in the paper, in response to both this point and the next point.

(2) “Language throughout could be sharpened. Subjectivity like “surprising outliers” could be removed and quirky grammar like “often small, ten is a typical” improved. There are also typos “an rate” etc that should be tidied up.”

As per previous response, we have made numerous tweaks and small improvements and feel that the paper is stronger in this respect.

(3) “Figure 1 caption. “It is a spectrum that depends” Is spectrum the right word here? Also, “thicker stroke” what does this refer to? Wasn’t immediately clear. In A, why is the whole animal within the R bracket that signifies brain regions, and then the brain regions are within the N bracket that signifies whole animals? Apart from the teal colouring, what are the other coloured regions in the image referring to? Improving this first figure would greatly help a reader unfamiliar with the context of the approach.”

We have replaced the word “spectrum” with “continuum”. We have replaced “ Observed quantities have been highlighted with a thicker stroke in the graphical model.” with “The observed data quantities, y_i to y_n, are highlighted with a thick line in the model diagrams”. We have added the following text to describe the red and green lines in panel A: “green and red lines indicate regions labeled as damaged”.

(4) “On P2 there is no discussion of priors when running through the advantage of the Bayesian approach. Is this a choice or an oversight? Priors do have a role in the later analysis.”

A short additional paragraph has been added to the introduction outlining the advantage of having a prior, but also noting that the obligation to pick a prior can be intimidating and that suggesting priors is one of the contributions of our paper: “A Bayesian model also includes a set of probability distributions, referred to as the prior, which represent those beliefs it is reasonable to hold about the statistical model parameters before actually doing the experiment. The prior can be thought of as an advantage, it allows us to include in our analysis our understanding of the data based on previous experiments. The prior also makes explicit in a Bayesian model assumptions that are often implicit in other approaches. However, having to design priors is often considered a challenge and here we hope to make this more straightforward by suggesting priors that are suitable for this class of data.”

(5) “On P4 more explanation would help greatly. Formulas like 23*10*4 or 50*6+50*4 are presented without explanation. What are the various numbers being multiplied? Regions, animals? Again, a clearer link between biological data and model structure would be advantageous.”

We have now modified this line to clearly state the numbers’ sources: “The index i runs over the full set of samples, which in this case comprises 23 brain regions ×10 animals ×4 groups ≈920 datapoints in the first study, and 50 brain regions × 6 HET animals + 50 brain regions × 4 KO animals ≈500 datapoints in the second.”

(6) “P6 and Results. Is it possible to show examples of the data set sampled from? Perhaps an image or two for the two experiments. Both Figures 4 and 5 as they currently are could be made slightly smaller to provide space for a small explanatory sub-panel. This would help ground the results.”

This is a good idea. We have now added heatmap visualisations of both entire datasets to revised versions of Figures 4 and 5 (assuming that this is what the reviewer was suggesting).

Reviewer #1 (Public review):

Summary:

Ever since the surprising discovery of the membrane-associated Periodic Skeleton (MPS) in axons, a significant body of published work has been aimed at trying to understand its assembly mechanism and function. Despite this, we still lack a mechanistic understanding of how this amazing structure is assembled in neuronal cells. In this article, the authors report a "gap-and-patch" pattern of labelled spectrin in iPSC-derived human motor neurons grown in culture. The mid-sections of these axons exhibit patches with reasonably well-organized MPS that are separated by gaps lacking any detectable MPS and having low spectrin content. Further, they report that the intensity modulation of spectrin is correlated with intensity modulations of tubulin as well. However, neurofilament fluorescence does not show any correlation. Using DIC imaging, the authors show that often the axonal diameter remains uniform across segments, showing a patch-gap pattern. Gaps are seen more abundantly in the midsection of the axon, with the proximal section showing continuous MPS and the distal segment showing continuous spectrin fluorescence but no organized MPS. The authors show that spectrin degradation by caspase/calpain is not responsible for gap formation, and the patches are nascent MPS domains. The gap and patch pattern increases with days in culture and can be enhanced by treating the cells using the general kinase inhibitor staurosporine. Treatment with the actin depolymerizing agent Latrunculin A reduces gap formation. The reasons for the last two observations are not well understood/explained.

Strengths:

The claims made in the paper are supported by extensive imaging work and quantification of MPS. Overall, the paper is well written and the findings are interesting. Although much of the reported data are from axons treated with staurosporine, this may be a convenient system to investigate the dynamics of MPS assembly, which is still an open question.

Weaknesses:

Much of the analysis is on staurosporine-treated cells, and the effects of this treatment can be broad. The increase in patch-gap pattern with days in culture is intriguing, and the reason for this needs to be checked carefully. It would have been nice to have live cell data on the evolution of the patch and gap pattern using a GFP tag on spectrin. The evolution of individual patches and possible coalescence of patches can be observed even with confocal microscopy if live cell super-resolution observation is difficult.

Some more comments:

(1) Axons can undergo transient beading or regularly spaced varicosity formation during media change if changes in osmolarity or chemical composition occur. Such shape modulations can induce cytoskeletal modulations as well (the authors report modulations in microtubule fluorescence). The authors mention axonal enlargements in some instances. Although they present DIC images to argue that the axons showing gaps are often tubular, possible beading artefacts need to be checked. Beading can be transient and can be checked by doing media changes while observing the axons on a microscope.

(2) Why do microtubules appear patchy? One would imagine the microtubule lengths to be greater than the patch size and hence to be more uniform.

(3) Why do axons with gaps increase with days in culture? If patches are nascent MPS that progressively grow, one would have expected fewer gaps with increasing days in culture. Is this indicative of some sort of degeneration of axons?

(4) It is surprising that Latrunculin A reduces gap formation induced by staurosporine (also seems to increase MPS correlation) while it decreases actin filament content. How can this be understood? If the idea is to block actin dynamics, have the authors tried using Jasplakinolide to stabilize the filaments?

(5) The authors speculate that the patches are formed by the condensation of free spectrins, which then leaves the immediate neighborhood depleted of these proteins. This is an interesting hypothesis, and exploring this in live cells using spectrin-GFP constructs will greatly strengthen the article. Will the patch-gap regions evolve into continuous MPS? If so, do these patches expand with time as new spectrin and actin are recruited and merge with neighboring patches, or can the entire patch "diffuse" and coalesce with neighboring patches, thus expanding the MPS region?

Author response:

Reviewer #1 (Public review)

Summary:

Ever since the surprising discovery of the membrane-associated Periodic Skeleton (MPS) in axons, a significant body of published work has been aimed at trying to understand its assembly mechanism and function. Despite this, we still lack a mechanistic understanding of how this amazing structure is assembled in neuronal cells. In this article, the authors report a "gap-and-patch" pattern of labelled spectrin in iPSC-derived human motor neurons grown in culture. The mid-sections of these axons exhibit patches with reasonably well-organized MPS that are separated by gaps lacking any detectable MPS and having low spectrin content. Further, they report that the intensity modulation of spectrin is correlated with intensity modulations of tubulin as well. However, neurofilament fluorescence does not show any correlation. Using DIC imaging, the authors show that often the axonal diameter remains uniform across segments, showing a patch-gap pattern. Gaps are seen more abundantly in the midsection of the axon, with the proximal section showing continuous MPS and the distal segment showing continuous spectrin fluorescence but no organized MPS. The authors show that spectrin degradation by caspase/calpain is not responsible for gap formation, and the patches are nascent MPS domains. The gap and patch pattern increases with days in culture and can be enhanced by treating the cells using the general kinase inhibitor staurosporine. Treatment with the actin depolymerizing agent Latrunculin A reduces gap formation. The reasons for the last two observations are not well understood/explained.

We thank the reviewer for the detailed and accurate description of the data shown and its relevance to further our understanding of MPS assembly mechanism and function.

Strengths:

The claims made in the paper are supported by extensive imaging work and quantification of MPS. Overall, the paper is well written and the findings are interesting. Although much of the reported data are from axons treated with staurosporine, this may be a convenient system to investigate the dynamics of MPS assembly, which is still an open question.

We thank the reviewer for the positive comments on the manuscript, the techniques used and the proposed model.

Weaknesses:

Much of the analysis is on staurosporine-treated cells, and the effects of this treatment can be broad. The increase in patch-gap pattern with days in culture is intriguing, and the reason for this needs to be checked carefully. It would have been nice to have live cell data on the evolution of the patch and gap pattern using a GFP tag on spectrin. The evolution of individual patches and possible coalescence of patches can be observed even with confocal microscopy if live cell super-resolution observation is difficult.

We will consider the inclusion of live imaging experiments using the expressión of C-terminus-tagged human beta2-spectrin in the revised version of the manuscript. We are familiar with live-imaging and FRAP experiments and we will explore how to develop these experiments to generate data for inclusion in a revised submission.

Some more comments:

(1) Axons can undergo transient beading or regularly spaced varicosity formation during media change if changes in osmolarity or chemical composition occur. Such shape modulations can induce cytoskeletal modulations as well (the authors report modulations in microtubule fluorescence). The authors mention axonal enlargements in some instances. Although they present DIC images to argue that the axons showing gaps are often tubular, possible beading artefacts need to be checked. Beading can be transient and can be checked by doing media changes while observing the axons on a microscope.

We don´t discard the presence of “nano beads” in these axons. It was recently suggested that the normal morphology of axons is indeed resembling “pearls-on-a-string” (Griswold et al., 2025), with “nano beads” separated by thin tubular "connectors" (also referred to as NSV, for non-synaptic varicosities). However, it is unlikely that the gap-patch pattern of beta2-spectrin can be attributed to such a morphology, given we used formaldehyde as fixative, and Griswold and colleagues show that the use of aldehyde-based fixatives do not preserve NSVs. We are able to see scattered axonal enlargements (“micro beads”), as we described in distal portions in Fig. 1C(C2) and E. However, the number, appearance and staining of these are not compatible with the gap-patch pattern in beta2-spectrin. Moreover, we would have expected to see these NSVs in our extensive STED imaging, yet we did not. We will discuss this further in the resubmission.

(2) Why do microtubules appear patchy? One would imagine the microtubule lengths to be greater than the patch size and hence to be more uniform.

Our stainings are for tubulin protein isoforms beta-III and alpha-II. That is, they would label microtubules, but free tubulin as well. The slight decrease in intensity for tubulin within gaps is indeed something to investigate, but we don´t interpret this as “patchy microtubules”. If the Reviewer refers to Fig. 2C-D, it is actually difficult to anticipate the slight decrease in intensity by the naked eye. To further support this, we will consider including stainings and quantitative analyses for microtubules in the resubmission. We are familiar with the use of permeabilizing conditions during fixation (in protocols known as “cytoskeletal fixation” to label microtubules (and not free tubulin).

(3) Why do axons with gaps increase with days in culture? If patches are nascent MPS that progressively grow, one would have expected fewer gaps with increasing days in culture. Is this indicative of some sort of degeneration of axons?

We agree with the apparent discrepancy. However, one has to take into account that these axons are still elongating even at 2 weeks in culture. Hence, at any time point, there is a new axonal compartment recently added, and hence, with low beta2-spectrin and no MPS. Also, the dynamical evolution of the MPS has to take into account beta2-spectrin supply. If supply is somehow lower than a given threshold, it is expected that there will be more gaps, given the new, more distant parts of the axons have a lower supply of beta2-spectrin . To explore this formally, we are working on simulations of these multifactorial dynamic systems to better understand this, that together with key experimental observations would enhance our understanding into overall MPS assembly in growing axons. However, findings for this project will be the subject of another manuscript.

(4) It is surprising that Latrunculin A reduces gap formation induced by staurosporine (also seems to increase MPS correlation) while it decreases actin filament content. How can this be understood? If the idea is to block actin dynamics, have the authors tried using Jasplakinolide to stabilize the filaments?

The results with the co-treatment with Latrunculin A and Staurosporine are indeed intriguing, and provide clear evidence that the gap-and-patch pattern arises from local assembly of the MPS, requiring new actin filaments. However, the fact that F-actin within the pre-formed MPS seems unaffected is not surprising. There are many different populations of F-actin in axons (i.e. MPS rings, longitudinal filaments, actin patches, actin trails). Latrunculin A affects filaments indirectly. The target of Latrunculin A is not actin filaments, but free monomers. It ultimately affects actin filaments as they end up losing monomers, and devoid of new monomers, filaments get shorter and eventually disappear. The drastic decrease in F-actin in our axons reflects that. The fact that F-actin in the MPS is preserved only speaks to the fact that these filaments are stable -if they are not losing monomers in the time frame of the treatment, the filament remains unaffected. We will support this with more observations and imaging and with a more extensive discussion summarizing the literature on the matter in the resubmission.

On the other hand, the use of F-actin stabilizing drugs (like Jasplakinolide) would have a different effect. We will study how an experiment with these drugs could be informative of the process under investigation for the resubmission

(5) The authors speculate that the patches are formed by the condensation of free spectrins, which then leaves the immediate neighborhood depleted of these proteins. This is an interesting hypothesis, and exploring this in live cells using spectrin-GFP constructs will greatly strengthen the article. Will the patch-gap regions evolve into continuous MPS? If so, do these patches expand with time as new spectrin and actin are recruited and merge with neighboring patches, or can the entire patch "diffuse" and coalesce with neighboring patches, thus expanding the MPS region?

We agree with the reviewer's interpretation. A virtue of our experimental model and our interpretations of the observations in fixed cells is that it gives rise to informative questions such as the ones posed by the reviewer. As stated above, we will consider the inclusion of live imaging experiments using the expressión of C-terminus tagged human beta2-spectrin in the revised version of the manuscript. We are familiar with live-imaging and FRAP experiments and we think we can provide the evidence suggested.

Reviewer #2 (Public review):

Summary:

In this manuscript, Gazal et al. describe the presence of unique gaps and patches of BetaII-spectrin in medial sections of long human motor neuron axons. BII-spectrin, along with Alpha-spectrin, forms horizontal linkers between 180nm spaced F-actin rings in axons. These F-actin rings, along with the spectrin linkers, form membrane periodic structures (MPS) which are critical for the maintenance of the integrity, size, and function of axons. The primary goal of the authors was to address whether long motor axons, particularly those carrying familial mutations associated with the neurodegenerative disorder ALS, show defects in gaps and patches of BetaII-spectrin, ultimately leading to degradation of these neurons.

We thank the reviewer for the detailed and accurate description of the data shown.

Strengths:

The experiments are well-designed, and the authors have used the right methods and cutting-edge techniques to address the questions in this manuscript. The use of human motor neurons and the use of motor neurons with different familial ALS mutations is a strength. The use of isogenic controls is a positive. The induction of gaps and patches by the kinase inhibitor staurosporine and their rescue by Latrunculin A is novel and well-executed. The use of biochemical assays to explore the role of calpains is appropriate and well-designed. The use of STED imaging to define the periodicity of MPS in the gaps and patches of spectrin is a strength.

We thank the reviewer for the positive comments on the manuscript, the techniques used and the proposed model.

Weaknesses:

The primary weakness is the lack of rigorous evaluation to validate the proposed model of spectrin capture from the gaps into adjacent patches by the use of photobleaching and live imaging. Another point is the lack of investigation into how gaps and patches change in axons carrying the familial ALS mutations as they age, since 2 weeks is not a time point when neurodegeneration is expected to start.

We will consider the inclusion of live imaging experiments using the expressión of tagged human beta2-spectrin in the revised version of the manuscript. We are familiar with live-imaging and FRAP experiments and we believe we can provide the evidence suggested. We don't discard the notion that axons carrying familial ALS mutations will show defects in MPS formation and/or stability when observed at longer culture times, or under culture conditions that promote neuronal aging (Guix et al., 2021). Thus, we will continue to work with these cells, but the goal of that project lies well beyond the primary message of the present manuscript, and we anticipate that the revised version will not include new data on this matter. 

Reviewer #3 (Public review):

Summary:

Gazal et al present convincing evidence supporting a new model of MPS formation where a gap-and-patch MPS pattern coalesces laterally to give rise to a lattice covering the entire axon shaft.

Strengths:

(1) This is a very interesting study that supports a change in paradigm in the model of MPS lattice formation.

(2) Knowledge on MPS organization is mainly derived from studies using rat hippocampal neurons. In the current manuscript, Gazal et al use human IPS-derived motor neurons, a highly relevant neuron type, to further the current knowledge on MPS biology.

(3) The quality of the images provided, specifically of those involving super-resolution, is of a high standard. This adequately supports the conclusions of the authors.

We thank the reviewer for the positive comments on the manuscript, the techniques used and the proposed model.

Weaknesses:

(1) The main concern raised by the manuscript is the assumption that staudosporine-induced gap and patch formation recapitulates the physiological assembly of gaps and patches of betaII-spectrin.

We will further explore the inclusion of more measurements of other parameters and variables towards establishing whether these gaps-and-patches patterns are equivalent structures in control and staurosporine-treated cells. 

(2) One technical challenge that limits a more compelling support of the new model of MPS formation is that fixed neurons are imaged, which precludes the observation of patch coalescence.

As stated before regarding similar comments by other reviewers, we will consider the inclusion of live imaging experiments in the revised version of the manuscript.

Nicolas Unsain, PhD, and Thomas Durcan, PhD.

References

Griswold, J.M., Bonilla-Quintana, M., Pepper, R. et al. Membrane mechanics dictate axonal pearls-on-a-string morphology and function. Nat Neurosci 28, 49–61 (2025). https://doi.org/10.1038/s41593-024-01813-1

Guix F.X., Marrero Capitán A., Casadomé-Perales A., Palomares-Pérez .I, López Del Castillo I., Miguel V., Goedeke L., Martín M.G., Lamas S., Peinado H., Fernández-Hernando C., Dotti C.G. Increased exosome secretion in neurons aging in vitro by NPC1-mediated endosomal cholesterol buildup. Life Sci Alliance. 2021 Jun 28;4(8):e202101055. doi: 10.26508/lsa.202101055. Print 2021 Aug.

The phrase "get things done" actually appears in this advertisement for the Memindex Jr. in 1930!

Author response:

The following is the authors’ response to the original reviews.

Joint Public Review:

Weaknesses:

The lack of pleiotropy is an unconfirmable assumption of MR, and the addition of those models is therefore quite important, as this is a primary weakness of the MR approach. Given that concern, I read the sensitivity analyses using pleiotropy-robust models as the main result, and in that case, they can't test their hypotheses as these models do not show a BMI instrumental variable association. The other weakness, which might be remedied, is that the power of the tests here is not described. When a hypothesis is tested with an under-powered model, the apparent lack of association could be due to inadequate sample size rather than a true null. Typically, when a statistically significant association is reported, power concerns are discounted as long as the study is not so small as to create spurious findings. That is the case with their primary BMI instrumental variable model - they find an association so we can presume it was adequately powered. But the primary models they share are not the pleiotropy-robust methods MR-Egger, weighted median, and weighted mode. The tests for these models are null, and that could mean a couple of things: (1) the original primary significant association between the BMI genetic instrument was due to pleiotropy, and they therefore don't have a robust model to explore the effects of the tobacco genetic instrument. (2) The power for the sensitivity analysis models (the pleiotropy-robust methods) is inadequate, and the authors share no discussion about the relative power of the different MR approaches. If they do have adequate power, then again, there is no need to explore the tobacco instrument.

Reviewing Editor Comments:

We suggest that the authors add power estimates to assess whether the sample size is sufficient, given the strength and variability of the genetic instruments. It would also be helpful to present effect estimates for the tobacco instruments alone, to clarify their independent contribution and improve the interpretation of the joint models. In addition, the role of pleiotropy should be addressed more clearly, including which model is considered primary. Stratified analyses by smoking status are encouraged, as prior studies indicate that BMI-HNC associations may differ between smokers and non-smokers. Finally, the comparison with previous studies should be revised, as most reported null findings without accounting for tobacco instruments. If this study finds an association, it should not be framed as a replication

We would like to highlight that post-hoc power calculations are often considered redundant since the statistical power estimated for an observed association is directly related to its p-value[1]. In other words, the uncertainty of the association is already reflected in its 95% confidence interval. However, we understand power calculations may still be of interest to the reader, so we have incorporated them in the revised manuscript. We have edited the text as follows (lines 151-155):“Consequently, we used the total R² values to examine the statistical power in our study[42]. However, we acknowledge that the value of post-hoc power calculations is limited, since the statistical power estimated for an observed association is already reflected in the 95% confidence interval presented alongside the point estimate[43].” We have also added supplementary figures 1 and 2.

We can see that when using the latest HEADSpAcE data we were able to detect BMI-HNC ORs as small as 1.16 with 80% power, while the GAME-ON dataset only permitted the detection of ORs as small as 1.26 using the same BMI instruments (Figure B). We have explained these figures in the results section as follows (lines 257-263): “Using the BMI genetic instruments (total R²= 4.8%) and an α of 0.05, we had 80% statistical power to detect an OR as small as 1.16 for HNC risk (Supplementary Figure 1). For WHR (total R²= 3.1%) and WC (total R²= 4.4%), we could detect odds ratios (ORs) as small as 1.20 and 1.17, respectively. This is an improvement in terms of statistical power compared to the GAME-ON analysis published by Gormley et al.[28], for which there was 80% power to detect an OR as small as 1.26 using the same BMI genetic instruments (Supplementary Figure 2).”

The reason we use inverse variance weighted (IVW) Mendelian randomization (MR) to obtain our main results rather than the pleiotropy-robust methods mentioned by the reviewer/editors (i.e., MR-Egger, weighted median and weighted mode) is that the former has greater statistical power than the latter[2]. Hence, instead of focussing on the statistical significance of the pleiotropy-robust analyses, we consider it is of more value to compare the consistency of the effect sizes and direction of the effect estimates across methods. Any evidence of such consistency increases our confidence in our main findings, since each method relies on different assumptions. As we cannot be sure about the presence and nature of horizontal pleiotropy, it is useful to compare results across methods even though they are not equally powered. It is true that our results for the genetically predicted effects of body mass index (BMI) on the risk of head and neck cancer (HNC) differ across methods. This is precisely what led us to question the validity of our main finding (suggesting a positive effect of BMI on HNC risk). We have now clarified this in the methods section of the revised manuscript as advised. Lines 165-171:

“Because the IVW method assumes all genetic variants are valid instruments[44], which is unlikely the case, three pleiotropy-robust two-sample MR methods (i.e., MR-Egger[45], weighted median[46] and weighted mode[47]) were used in sensitivity analyses. When the magnitude and direction of effect estimates are consistent across methods that rely on different assumptions, the main findings are more convincing. As we cannot be sure about the presence and nature of horizontal pleiotropy, it is useful to compare results across methods even if they are not equally powered.”

We understand that the reviewer/editors are concerned that we do not have a robust model to explore the role of tobacco consumption in the link between BMI and HNC. However, we have a different perspective on the matter. If indeed, the main IVW finding for BMI and HNC is due to pleiotropy (since some of the pleiotropy-robust methods suggest conflicting results), then the IVW multivariable MR method is a way to explore the potential source of this bias[3]. We were particularly interested in exploring the role of smoking in the observed association because smoking and adiposity are known to influence each other [4-9] and share a genetic basis[10, 11].

We agree that it would be useful to present the univariable MR effect estimates for smoking behaviour and HNC risk along those obtained using multivariable MR. We have now included the univariable MR estimates for both smoking behaviour variables as a note under Supplementary Table 11 and in the manuscript (lines 316-318): “In univariable IVW MR, both CSI and SI were linked to an increased risk of HNC (CSI OR=4.47 per 1-SD higher CSI, 95%CI 3.31–6.03, p<0.001; SI OR=2.07 per 1-SD higher SI 95%CI 1.60–2.68, p<0.001) (Additional File 2: note in Supplementary Table 11).”

We understand the appeal of conducting stratified MR analyses by smoking status. However, we anticipate such analyses would hinder the interpretation of our findings as they can induce collider bias which could spuriously lead to different effect estimates across strata[12, 13].

We thank the reviewer/editors for their comment regarding the way we frame of our findings. We have now edited the discussion section to highlight our study results are different to those obtained in studies that do not account for smoking behaviour. Lines 398-401: “With a much larger sample (N=31,523, including 12,264 cases), our IVW MR analysis suggested BMI may play a role in HNC risk, in contrast to previous studies. However, our sensitivity analyses implied that causality was uncertain.”

Reviewer #1 (Recommendations for the authors):

The authors do share a table of the percent variance explained of the different genetic instruments, which vary widely, and that table is very welcome because we can get some sense of their utility. The problem is that they don't translate that into a power estimate for the case-control study size that they use. They say that it is the biggest to date, which is good, but without some formal power estimate, it is not particularly reassuring. A framework for MR study power estimates was reported in PMID: 19174578, but that was using very simple MR constructs in use in 2009, and it isn't clear to me if that framework can be used here. That power paper suggests that weak genetic instruments need very large sample sizes, far larger than what is used in the current manuscript. I am unable to estimate the true strength of the instruments used here, and so I am unsure of whether power is an issue or not.

We have now included power calculations in our manuscript to address the reviewer’s concerns. Nevertheless, as mentioned above, post-hoc power calculations are of limited value, as statistical power is already reflected in the uncertainty around the point estimates (the 95% confidence intervals). Hence, it is important to avoid drawing conclusions regarding the likelihood of true effects or false negatives based on these calculations.

Although the hypothesis here is that smoking accounts for the apparent BMI association previously reported for HNC, it would have been preferable to see the estimates for their 2 genetic instruments for tobacco alone. The current results only show the BMI instruments alone and then with the tobacco instruments. I would like to see what the risk estimates are for the tobacco instrument alone, so that I can judge for myself what happens in the joint models. As presented, one can only do that for the BMI instruments.

We thank the reviewer for this comment. The univariable IVW MR estimate of smoking initiation was OR=2.07 (95%CI 1.60 to 2.68, p<0.001), while the one for comprehensive smoking index was OR=4.47 (95%CI 3.31 to 6.03, p<0.001). We have included this information in the manuscript as requested (please see response to reviewing editor above).

On line 319, they write that "We did not find evidence against bias due to correlated pleiotropy..." I find this difficult to parse, but I think it means that they should believe that correlated pleiotropy remains a problem. So again, they seem to see their primary model as compromised, and so do I. This limitation is again stated by the authors on lines 351-352.

We apologise if the wording of the sentence was not easy to understand. When using the CAUSE method, we did not find evidence to reject the null hypothesis that the sharing (correlated pleiotropy) model fits the data at least as well as the causal model. In other words, our CAUSE finding and the inconsistencies observed across our other sensitivity analyses led us to believe that our main IVW MR estimate for BMI-HNC was likely biased by correlated pleiotropy. We believe it is important to explore the source of this bias, which is why we used multivariable MR to investigate the direct effect of BMI on HNC risk while accounting for smoking behaviour.

In the following paragraphs (lines 358-369), the authors state that their findings are consistent with prior reports, but that doesn't seem to be the case if we take their primary BMI instrument as representing the outcome of this manuscript. Here, they find an association between the BMI instrument and HNC risk, but in each of the other papers they present the primary finding was null without the extensive model changes or the aim of accounting for tobacco with another instrument. I don't see that as replication.

This is a good point. We have now edited the discussion of our manuscript to avoid giving the impression that our findings replicate those from studies that do not account for smoking behaviour in their analyses. We have edited lines 384-401 as follows:

“Previous MR studies suggest adiposity does not influence HNC risk[27-29]. Gormley et al.[28] did not find a genetically predicted effect of adiposity on combined oral and oropharyngeal cancer when investigating either BMI (OR=0.89 per 1-SD, 95% CI 0.72–1.09, p=0.26), WHR (OR=0.98 per 1-SD, 95% CI 0.74–1.29, p=0.88) or waist circumference (OR=0.73 per 1-SD, 95% CI 0.52–1.02, p=0.07) as risk factors. Similarly, a large two-sample MR study by Vithayathil et al.[29] including 367,561 UK Biobank participants (of which 1,983 were HNC cases) found no link between BMI and HNC risk (OR=0.98 per 1-SD higher BMI, 95% CI 0.93–1.02, p=0.35). Larsson et al.[27] meta-analysed Vithayathil et al.’s[29] findings with results obtained using FinnGen data to increase the sample size even further (N=586,353, including 2,109 cases), but still did not find a genetically predicted effect of BMI on HNC risk (OR=0.96 per 1-SD higher BMI, 95% CI 0.77–1.19, p=0.69). With a much larger sample (N=31,523, including 12,264 cases), our IVW MR analysis suggested BMI may play a role in HNC risk, in contrast to previous studies. However, our sensitivity analyses implied that causality was uncertain.”

We also deleted part of a sentence in the discussion section, so lines 416-418 now look as follows: “An important strength of our study was that the HEADSpAcE consortium GWAS used had a large sample size which conferred more statistical power to detect effects of adiposity on HNC risk compared to previous MR analyses[27-29].”

On lines 384-386 they note a strength is that this is the largest study to date, but I would reiterate that larger and more powerful does not equate to adequately powered.

This is true. We have included power calculations in the manuscript as requested.

It's well known that different HNC subsites have different etiologies, as they mention on lines 391-392, and it is implicit in their use of data on HPV positive and negative oropharyngeal cancer. They say that they did not find evidence for heterogeneity in this study, but that would only be true for the null BMI instrument. The effect sizes for their smoking instruments are strikingly different between the subsites.

We agree and are sorry for the confusion we may have caused by the way we worded our findings. We have edited the text to clarify that the lack of subsite heterogeneity only applied to our results for BMI/WHC/WC-HNC risk. Lines 418-424 now read as follows:

“Furthermore, the availability of data on more HNC subsites, including oropharyngeal cancers by HPV status, allowed us to investigate the relationship between adiposity and HNC risk in more detail than previous MR studies which limited their subsite analyses to oral cavity and overall oropharyngeal cancers[28, 68]. This is relevant because distinct HNC subsites are known to have different aetiologies[69], although we did not find evidence of heterogeneity across subsites in our analyses investigating the genetically predicted effects of BMI, WHR and WC on HNC risk.”

Finally, the literature on mutational patterns gives us strong reason to believe that HNC caused by tobacco are biologically distinct from tumors not caused by tobacco. The authors report in the introduction that traditional observational studies of BMI and HNC have reported different findings in smokers versus never smokers, so I would assume there is a possibility that the BMI instrument could have different associations with tumors of the tobacco-induced phenotype and tumors with a non-tobacco induced phenotype. I would assume that authors have access to the data on self-reported tobacco use behavior, even if they can't separate these tumors by molecular types. Stratifying their analysis by tobacco users or not might reveal different results with the BMI instrument.

We appreciate the reviewer’s comment. We agree that it would have been interesting to present stratified analyses by smoking status along our main findings. However, we decided against this because of the risk of inducing collider bias in our MR analyses i.e., where stratifying on smoking status may induce spurious associations between the adiposity instruments and confounding factors. Multivariable MR is considered a better way of investigating the direct effects of an exposure (adiposity) on an outcome (HNC) accounting for a third variable (smoking)[14], which is why we opted for this method instead.

References:

(1) Heinsberg LW, Weeks DE: Post hoc power is not informative. Genet Epidemiol 2022, 46(7):390-394.

(2) Burgess S, Butterworth A, Thompson SG: Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013, 37(7):658-665.

(3) Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C et al: Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2019, 4:186.

(4) Morris RW, Taylor AE, Fluharty ME, Bjorngaard JH, Asvold BO, Elvestad Gabrielsen M, Campbell A, Marioni R, Kumari M, Korhonen T et al: Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium. BMJ Open 2015, 5(8):e008808.

(5) Taylor AE, Morris RW, Fluharty ME, Bjorngaard JH, Asvold BO, Gabrielsen ME, Campbell A, Marioni R, Kumari M, Hallfors J et al: Stratification by smoking status reveals an association of CHRNA5-A3-B4 genotype with body mass index in never smokers. PLoS Genet 2014, 10(12):e1004799.

(6) Taylor AE, Richmond RC, Palviainen T, Loukola A, Wootton RE, Kaprio J, Relton CL, Davey Smith G, Munafo MR: The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study. Hum Mol Genet 2019, 28(8):1322-1330.

(7) Asvold BO, Bjorngaard JH, Carslake D, Gabrielsen ME, Skorpen F, Smith GD, Romundstad PR: Causal associations of tobacco smoking with cardiovascular risk factors: a Mendelian randomization analysis of the HUNT Study in Norway. Int J Epidemiol 2014, 43(5):1458-1470.

(8) Carreras-Torres R, Johansson M, Haycock PC, Relton CL, Davey Smith G, Brennan P, Martin RM: Role of obesity in smoking behaviour: Mendelian randomisation study in UK Biobank. BMJ 2018, 361:k1767.

(9) Freathy RM, Kazeem GR, Morris RW, Johnson PC, Paternoster L, Ebrahim S, Hattersley AT, Hill A, Hingorani AD, Holst C et al: Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index. Int J Epidemiol 2011, 40(6):1617-1628.

(10) Thorgeirsson TE, Gudbjartsson DF, Sulem P, Besenbacher S, Styrkarsdottir U, Thorleifsson G, Walters GB, Consortium TAG, Oxford GSKC, consortium E et al: A common biological basis of obesity and nicotine addiction. Transl Psychiatry 2013, 3(10):e308.

(11) Wills AG, Hopfer C: Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults. Addict Behav 2019, 89:98-103.

(12) Coscia C, Gill D, Benitez R, Perez T, Malats N, Burgess S: Avoiding collider bias in Mendelian randomization when performing stratified analyses. Eur J Epidemiol 2022, 37(7):671-682.

(13) Hamilton FW, Hughes DA, Lu T, Kutalik Z, Gkatzionis A, Tilling K, Hartwig FP, Davey Smith G: Non-linear Mendelian randomization: evaluation of effect modification in the residual and doubly-ranked methods with simulated and empirical examples. Eur J Epidemiol 2025.

(14) Sanderson E, Davey Smith G, Windmeijer F, Bowden J: An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol 2019, 48(3):713-727.

Reviewer #2 (Public review):

This paper remarkably reveals the identification of plasma membrane repair proteins, revealing spatiotemporal cellular responses to plasma membrane damage. The study highlights a combination of sodium dodecyl sulfate (SDS) and lase for identifying and characterizing proteins involved in plasma membrane (PM) repair in Saccharomyces cerevisiae. From 80 PM, repair proteins that were identified, 72 of them were novel proteins. The use of both proteomic and microscopy approaches provided a spatiotemporal coordination of exocytosis and clathrin-mediated endocytosis (CME) during repair. Interestingly, the authors were able to demonstrate that exocytosis dominates early and CME later, with CME also playing an essential role in trafficking transmembrane-domain (TMD) containing repair proteins between the bud tip and the damage site.

Weaknesses/limitations:

(1) Why are the authors saying that Pkc1 is the best characterized repair protein? What is the evidence?

(2) It is unclear why the authors decided on the C-terminal GFP-tagged library to continue with the laser damage assay, exclusively the C-terminal GFP-tagged library. Potentially, this could have missed N-terminal tag-dependent localizations and functions and may have excluded functionally important repair proteins.

(3) The use of SDS and laser damage may bias toward proteins responsive to these specific stresses, potentially missing proteins involved in other forms of plasma membrane injuries, such as mechanical, osmotic, etc.). SDS stress is known to indirectly induce oxidative stress and heat-shock responses.

(4) It is unclear what the scale bars of Figures 3, 5, and 6 are. These should be included in the figure legend.

(5) Figure 4 should be organized to compare WT vs. mutant, which would emphasize the magnitude of impairment.

(6) It would be interesting to expand on possible mechanisms for CME-mediated sorting and retargeting of TMD proteins, including a speculative model.

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-2025-03094

Corresponding author(s): Saurabh S. Kulkarni

1. General Statements

We thank the reviewers for their strong praise of the manuscript, highlighting its rigor, depth, and conceptual importance. They consistently described the study as a beautiful, fascinating, and conceptually strong piece of work that addresses a timely question in multiciliated cells. They also noted the high quality of the data, careful quantification, and the use of multiple genetic and pharmacological approaches, all of which improve the reproducibility and credibility of the findings. Importantly, they emphasized the novelty of discovering a direct mechanistic link between Piezo1-mediated mechanotransduction and Foxj1-driven transcriptional control of multiciliation, representing a significant breakthrough for both the cilia field and mechanobiology more broadly. Collectively, these strengths highlight the manuscript’s wide impact and make it highly suitable for publication in a high-impact journal.

2. Description of the planned revisions

Reviewer #1:

There are two experiments that would significantly strengthen these claims.

• First if their model is correct then even short term treatment with Yoda1 should induce the pathway and effect centriole numbers. While I appreciate the challenge of long term Yoda1 treatment its not clear to me why it would be needed if short term treatment is setting off the transcriptional cascade. Yoda is used throughout the paper to induce all the pathways but we don't know if it actually induces the phenotype. I think this should be addressed with either short term treatments or a dose response to find a dose that does not lead to skin pealing. It is hard to ignore this obvious deficiency.
• Second, the model predicts that all of this is to regulate Foxj1 levels to regulate the subtle balance between cell size and centriole number. If this is correct, then the overexpression of Foxj1 should have a profound effect on centriole number in multiciliated cells. This is such an easy experiment that would validate many of the claims. RESPONSE:

We recognize that the reviewer is asking us to test the sufficiency of the pathway with these comments: “If their model is correct, then they should be able to activate the pathway in one way or another to stimulate centriole number. This is a significant limitation to their overall model.” And “If this is correct, then the overexpression of Foxj1 should have a profound effect on centriole number in multiciliated cells.”

To address reviewers’ suggestions, we will perform the following experiments.

1. A brief exposure (15 and 30 mins) to Yoda1 and wait for 3 hours to examine changes in centriole amplification. This will avoid skin peeling from long-term exposure.
2. A brief exposure to Yoda1 (15 mins) followed by a 30-minute wait period, and the cycle repeats a total of 4 times for a total of 3 hours to examine centriole amplification.
3. The above two experiments will also be done in a constitutively active-Yap background to increase the probability that synergistic activation can lead to centriole amplification.
4. Although Foxj1 is essential for multiciliogenesis, it is not sufficient to induce multiciliogenesis, as shown by multiple previous studies. Therefore, we do not expect overexpression of Foxj1 to have a profound effect on centriole number. While we will conduct the experiments because we truly want to address the suggestions and gain insight into the answers ourselves, we respectfully ask the Reviewer to consider the following responses to their concern.

Yoda1 sufficiency: We agree that testing whether acute Yoda1 treatment can induce centriole amplification is an important question. We will conduct experiments with short-pulse and cyclic Yoda1 exposure, including in a constitutively active-YAP background (listed above), to address this possibility. However, several challenges complicate interpretation: (i) PIEZO1 adapts and desensitizes upon activation, (ii) transient signaling may be sufficient to cause secondary signaling but insufficient to drive stable transcriptional programs required for amplification, and (iii) centriole number is inherently variable, making modest effects difficult to resolve. However, we must recognize that failure to observe sufficiency under these conditions would not invalidate the model for two reasons: 1) absence of evidence is not evidence of absence, and thus, we may not have found the right experimental design. 2) PIEZO1–YAP is a necessary input but not sufficient on its own, as elaborated below. For both reasons, we are very careful about the interpretation of results in the manuscript, which shows that this pathway is necessary for centriole amplification using loss-of-function approaches.

Foxj1 overexpression: Foxj1 is a well-established regulator essential for motile and multiciliogenesis across species (Xenopus, zebrafish, mouse). Loss of Foxj1 reduces cilia number in MCCs, but its activation alone does not have a profound effect on ciliogenesis/cilia number in MCCs. This is because Foxj1 is a part of a larger network essential for multiciliogenesis. This parallels the behavior of other transcriptional regulators, such as Myb, where loss of function impairs centriole amplification, but overexpression does not drive the formation of supernumerary centrioles. Both studies are seminal discoveries in the field of ciliogenesis, but they did not demonstrate the sufficiency of these molecules/pathways. Thus, our results, demonstrating that Foxj1 is necessary to induce tension-dependent centriole amplification, are significant, as the reviewer mentioned. The lack of Foxj1 sufficiency to induce centriole amplification is not a deficiency of the study, but rather evidence that Foxj1 is a part of a larger network essential for tension-dependent centriole amplification.

Necessity versus sufficiency: We respectfully emphasize that sufficiency is not a prerequisite for demonstrating the significance of a pathway. Mechanochemical signaling is inherently complex, involving many mechanosensitive proteins and pathways. In our case, mechanical stretch increases centriole amplification, with PIEZO1–YAP signaling identified as a key mediator. However, we do not claim that PIEZO1–YAP alone is sufficient. Other pathways, including cadherin-mediated junctions, F-actin–myosin contractility, integrin–focal adhesion signaling, and nuclear mechanotransduction, likely contribute and may regulate unique downstream effectors that collectively promote centriole amplification. Therefore, PIEZO1–YAP should be regarded as one essential component within a larger network.

__TIMELINE: __We will perform these additional proposed experiments. Since the first author, a postdoctoral researcher on this manuscript, has started a new job and will be coming in on weekends to complete the experiments, we estimate it will take approximately 2-3 months to finish them.

Reviewer #2:

1. Considering the Yap-piezo mechanism of action, the authors' logic for the selection of myb, foxj, plk4 and ccno as transcriptional targets is clear, but the HCR-derived signal and the differences seen in the yap morphants are not very strong, notwithstanding the statistical significance. There appear to be distinct subgroups within the treated populations (in Figure S6B, although these data seem quite different in Fig. 7H, so a comment on the technical differences might be helpful), so that the extent to which Yap1 regulates (Myb-)Foxj1 expression in MCCs is not clearly demonstrated by this experiment. Related to this point, it is unclear why 20-25% of the yap1/ piezo1 MO-treated embryos do not show a decline in FOXj1 in Fig. 6, given the qualitative nature of the scoring. Assuming the KD penetrance would vary on a cell-to-cell basis, rather than an embryo-to-embryo basis, this may suggest that there are additional relevant targets (some of which are discussed by the authors). Single-cell analysis might be a way to address this; however, this is not a trivial experiment, it might be sufficient to include a caveat in the text. Furthermore, the conclusion that Foxj1 regulates centriole amplification in a tension-dependent manner is well-supported by the data.

RESPONSE: We appreciate the reviewer’s thoughtful observation. Differences in the expression of Foxj1 from experiment to experiment are possible due to a combination of factors, including heterogeneity in MCC development across embryos, slightly different embryonic stages, differences in embryo quality between fertilizations, and variability in morpholino delivery and knockdown penetrance, which can occur both across embryos and on a cell-to-cell basis within an embryo. We also note that technical aspects of HCR RNA-FISH, such as proteinase K treatment and washing steps, can affect signal intensity, potentially contributing to the appearance of distinct subgroups within treated populations.

We agree that single-cell analysis would be a powerful way to dissect these differences, but as the reviewer notes, this is not a trivial experiment and is beyond the scope of the present study. We have therefore added clarifications in the text and discussion to acknowledge these sources of variability and to highlight the possibility of parallel pathways regulating foxj1 expression.

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Controls for the knockdowns by the various MOs should be provided.

RESPONSE: We appreciate the reviewer’s comment. The piezo1 MO has been previously established in Kulkarni et al. (2021). Additionally, the current manuscript includes MO control experiments for both erk2 and yap1, through KD at the 1-cell stage using the MO oligonucleotide, followed by mosaic-rescue with the respective WT RNA constructs (mCherry-ERK2 and yap1-GFP) and a nuclear tracer molecule such as H2B-RFP (Fig. 5, E-H, Fig. S5, C&D, Fig. 3, D-F). The mosaic-rescue is a robust experiment that provides an internal control within the same embryo, thereby avoiding differences that may arise due to embryo-to-embryo variability, embryo quality, or differences in fertilization batches. This approach also serves as a valuable tool for detecting cell-autonomous effects, providing a clear readout against uninjected neighboring cells, as the injected cells are labeled with a tracer. We will perform a similar mosaic-rescue experiment for the foxj1 MO.

TIMELINE: We will conduct mosaic-rescue experiments for the foxj1 MO. We will need 1 month to complete the experiment.

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__Minor comments:

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Autocorrection of ERK1/2 or MEK1/2 pathways to 1/2 should be avoided. – We are unclear on this comment. Can reviewer please clarify what they mean.

Reviewer # 3

Major concerns

1- The presented data do not yet establish a specific, direct pathway linking mechanotransduction to centriole number, because the molecular players tested (PIEZO1, Ca²⁺, PKC, ERK, YAP, Foxj1) are highly pleiotropic. As such, the observed centriole number phenotypes, and some of the major conclusions, could be indirect. It is therefore critical to test the specificity and causality of the proposed pathway. This could be done with the authors' own strategies and/or with the following potential approaches:

• Genetic dependency and sufficiency tests: It could be shown that Yoda1 has no effect in PIEZO1 loss-of-function MCCs, and that wild-type PIEZO1, but not conductance-ad PIEZO1 pore mutants restores Yoda1 responsiveness across centriole number, pERK, and YAP readouts. For example, PIEZO1 C terminus was shown to govern Ca²⁺ influx and ERK1/2 activation. Comparing full length PIEZO1 with a C terminal deletion in MCC restricted rescue; loss of rescue of centriole amplification and ERK/YAP activation with the C terminal deletion can provide a genetics anchored specificity test beyond broad inhibitors.

RESPONSE:

• To address the reviewer’s concern, we will test whether Yoda1 affects ERK and Yap activation when Piezo1 is depleted. We appreciate the reviewer’s thoughtful suggestion to employ genetic rescue experiments with Piezo1 mutants. Unfortunately, these are not technically feasible in Xenopus, as the Piezo1 coding sequence is exceptionally large (~7.5 kb)____, and repeated attempts by our group to generate and express stable, translatable transcripts have been unsuccessful. To address genetic dependency and specificity despite these technical barriers, we have employed a combination of orthogonal strategies that together provide strong genetic and mechanistic evidence:

• Mosaic loss-of-function experiments (Fig. 1) demonstrate that Piezo1 regulates centriole number in a cell-autonomous manner, ruling out global epithelial or indirect tissue-wide effects.

• Pharmacological activation/inhibition with Piezo1-specific agonist (Yoda1) and inhibitors (GSMTx4, gadolinium) produced consistent phenotypes, including activation of downstream ERK and YAP readouts. Notably, Yoda1 is a Piezo-specific agonist, not a broad pharmacological agent.
• Downstream pathway dissection (calcium chelation, PKC inhibition, ERK2 depletion, and YAP1 knockdown/rescue) consistently converges on the same phenotypes, reduced centriole amplification and altered Foxj1 expression, providing multiple independent lines of evidence that the Piezo1–Ca²⁺–PKC–ERK–YAP axis specifically controls centriole number.

• Positive feedback regulation of Piezo1 expression by YAP/Foxj1 (Fig. 7) further strengthens the argument for a pathway-specific role rather than pleiotropic, indirect effects. Taken together, while full-length Piezo1 rescue experiments are technically not possible in Xenopus due to gene size constraints, our data employ state-of-the-art genetic, pharmacological, and orthogonal functional assays to rigorously test pathway specificity. These complementary approaches provide compelling evidence for the causal role of Piezo1-mediated mechanotransduction in centriole number control in MCCs.

• Downstream bypass/rescue experiments: In PIEZO1 loss-of-function or BAPTA conditions, can enforcing MEK/ERK activation or YAP rescue centriole number defect? Conversely, can MEK inhibitors block Yoda1-induced effects.

RESPONSE: We appreciate the reviewer’s insightful questions.

• We will express CA Yap in the Piezo1 KD background to assess if we can rescue centriole number. We also note that the converse experiment has already been performed in our study: 1) PKC inhibition abolishes Yoda1-induced ERK phosphorylation and nuclear localization (Fig. 2), 2) both MEK inhibition and ERK2 depletion block Yoda1-induced Yap activation and nuclear entry (Figs. 4, S2). Thus, we have directly demonstrated that MEK inhibition prevents Yoda1-induced effects, satisfying this aspect of the reviewer’s concern.

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2- Image quantification and analysis must be described in greater detail in the Methods section, as they are central to the major conclusions of the manuscript. For example, the authors should explain how nuclear, cytoplasmic, and centriole segmentation were performed, and how relative protein levels in the nucleus versus the cytoplasm (e.g., YAP, volume- or area-based) were quantified. Specifically, the thresholds and segmentation criteria applied to different cellular structures under various conditions, as well as the use of Imaris and other software, should be clearly detailed.

RESPONSE: We will describe the methods in greater detail.

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3- PIEZO1 mRNA was shown to incrase in a Foxj1 linked feedback loop. Does this increase translate into an increase in total protein levels?

RESPONSE: If the reviewer is referring to Figure 7B, that is the Piezo1 antibody, so yes, the Piezo1 protein levels have increased.

If the reviewer is referring to Figure 7C and D, we show that loss of Foxj1 leads to a reduction in Piezo1 mRNA expression.

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4- Is the proposed signaling cascade active in mammalian multiciliated cells (e.g., airway epithelium). If possible, testing this by using one of the major players of the pathway as a readout such as as ERK phosphorylation, YAP nuclear localization in mammalian MCCs will reveal whether regulation of centriole number through this pathway is conserved and would strengthen the generality.

RESPONSE: We agree with the reviewer that testing conservation of this pathway in mammalian MCCs is of great interest. Indeed, another group is currently investigating the role of Yap in the mammalian airway epithelium; in their temporally controlled Yap knockout model (the global Yap KO being embryonic lethal), they observed that Yap loss led to a reduction in centriole number. To avoid overlap and direct competition with this ongoing work, we chose to focus our efforts on Xenopus.

Importantly, Xenopus has become a widely recognized and powerful system for MCC biology, enabling mechanistic dissection of centriole amplification and ciliogenesis. Several key discoveries in the field, including the identification of MCIDAS as a master regulator of MCC fate, were first made in Xenopus before being validated in mammals. Similarly, our study provides a mechanistic framework in Xenopus that can inform and guide ongoing studies in the mammalian airway.

********************************************

5- Throughout the results section, there are multiple times where authors raised specific hypothesis about their data (e.g. foxj1 regulation of number control, apical actin/YAP). However, they have not tested them. These hypothesis are very exciting and if possible, testing experimentally, would strengthen the conclusions associated with them.

RESPONSE: We are not sure what the reviewer means here by “authors raised specific hypothesis about their data (e.g., foxj1 regulation of number control, apical actin/YAP). However, they have not tested them”,

BECAUSE:

• Foxj1 regulation of centriole number: We demonstrate a clear reduction in centriole number upon Foxj1 depletion, and importantly, we extend this finding by showing that the reduction is tension-dependent (Fig. 6). We will perform a rescue assay to demonstrate the specificity.
• Foxj1 and YAP: We never claimed that Foxj1 regulates YAP expression, and this is not part of our proposed model. Instead, our data show that Piezo1–ERK–YAP signaling regulates Foxj1
• Foxj1 and apical actin: Foxj1 regulation of apical F-actin has already been established in prior work, and in our study, we clearly observe reduced apical actin intensity in Foxj1-depleted MCCs (Fig. 6). To further strengthen this conclusion, we will provide a quantitative analysis of apical actin intensity in Foxj1 morphants. ********************************************

__TIMELINE: __We will perform these additional proposed experiments. Since the first author, a postdoc on this manuscript, has started a new job and will be coming in on weekends to finish the experiments, we estimate it will take approximately 2-3 months to complete them.

Minor comments

MCC vs non MCC identification (Fig. 1): Clarify how non MCCs were distinguished from MCCs (e.g. markers/criteria). – Can the reviewer please clarify which panel or panels? Or provide more specific text that needs to be changed.

Add the Kintner group reference linking motile cilia number and centriole number in Xenopus MCCs.– Can the reviewer clarify where and which reference? Thank you.

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 2

Major comments:

1. It should be clarified whether the immunoblots and the related quantitations in Figs. 2 and S2 are all from separate blots/ exposures. If so, they are not useful as controls, and these blots should be repeated with the relevant samples analyzed in parallel. Size markers and labels should be included (2B, 2G; S2B and S2G). An increase in total ERK would alter the interpretation of the increase in nuclear pERK in the IF experiments. RESPONSE: We thank the reviewer for raising this important point regarding clarification of the immunoblots. All experimental groups were analyzed in parallel with their corresponding controls. Because the primary antibodies for pERK and ERK were both raised in rabbit, we optimized our workflow to prevent protein loss during stripping and to ensure accurate visualization. Specifically, lysates from each experimental group were loaded in duplicate on the same gel, separated by a molecular weight ladder that served as a reference point. After transfer, the blot was cut along the ladder, and the two halves were processed in parallel: one probed with anti-pERK and the other with anti-ERK. This strategy ensured that all samples from a single experiment (e.g., Control and Yoda1-treated groups) were analyzed under identical conditions, with staining and imaging performed together at the same exposure. To enhance clarity, we have provided this data as __uncut, full-length __as Supplemental Figure 7 (Figure S7) in the revised revision.

********************************************

Minor comments:

1. Reference list should be checked for completeness; some citations lack journal/ volume/ page/ year details. – We have corrected the references.
2. An 'overexposed' version of the image selected for centrioles in Figure 5F might be included with the Chibby-BFP at the same level as in the other figures. At present, the Yap KD cell in the image appears to have normal centrioles; this is potentially confusing, even though the authors clearly explain the matter in the text. – __We have added a new panel to Fig. 5F to avoid confusion.

__ 3. It might be clearer to present injected/ uninjected in the same orientation in Fig. 6A and B. – __Unfortunately, that is not possible because the injected and uninjected sides are left and right, and they cannot be in the same orientation.

__ 4. Figure 7B lacks the schematic described in the figure legend. – We have removed the Schematic sentence from the figure legend. That was an error on our side. Thank you for catching it.

Reviewer 3

1. Abstract: "how MCCs regulate centriole/cilia numbers remains a major knowledge gap" overstates the field; please soften to reflect recent advances (mechanics/apical area scaling; PIEZO1 implication). – We changed the text to “incompletely understood”.
2. GsMTx4 rationale: State that GsMTx4 is a spider venom peptide that inhibits cationic mechanosensitive channels (including PIEZO1) and justify its use alongside Yoda1.– GsMTx4 was used in the previous manuscript, and its use was justified there. Here, we are only comparing the results. However, we have added a sentence describing what GSMTx4 is. We have also included a sentence explaining the use of Yoda1. “GsMTx4, a spider venom peptide used in our previous study, inhibits cationic mechanosensitive channels, including Piezo1.”

“For this experiment, we used the Piezo1 channel-specific chemical agonist, Yoda1, to increase the sensitivity of Piezo1 and upregulate calcium entry into cells”

Timeline statement: "Centriole amplification to migration and apical docking takes ~4-5 h (personal observation)" is not appropriate; either cite time lapse literature or include your own time lapse data.– We have added a reference that showed imaging for 2 hours, but it was not enough to capture the entire process from intercalation to maturation, so we also kept “personal observation” still in the manuscript. We are unaware of any study that has done time-lapse imaging for 4 hours to capture the entire process of centriole amplification.

Redundancy: The description of Yoda1 as a channel specific agonist is repeated; keep only once.- Removed

"WT yap1 GFP construct previously used by Dr. Lance Davidson ..." should move construct description to Methods and keep only the citation in Results.– We moved it to Methods.

"(Unpublished data; Dr. Mahjoub)" should be removed unless data are shown.- Removed

Replace "as shown previously in our eLife paper" with "as we previously showed or shown previously (Kulkarni et al., 2021)".– We have made the change.

The two hypotheses for how Foxj1 could regulate number under tension (actin remodeling vs. transcriptional control of amplification genes) belong in the Discussion unless tested. Moreover, the part on the discussion on yap sequestration by apical actin and the two possibilities presented also should go do discussion. – We have moved both to the discussion section.

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.

Reviewer 3

1- The hypothesis about the centriole pool of Piezo as the mechnosensor for centriole number regulation is very exciting and novel. Can localization controlled variants be used to test whether a centriole associated pool directly senses tension for number control (for example, centrosome targeted PIEZO1 via a PACT tag). Alternatively, broad cellular Ca sensors (GcaMP) or centrosome proximal Ca sensors (e.g., PACT GCaMP) can be used detect local calcium microdomains during tethering or Yoda1 treatment.

RESPONSE: We appreciate the reviewer's curiosity and excitement; however, these experiments will not alter the conclusion of this paper and will be part of the next study, which aims to delve deeper into how different pools of Piezo1 at centrioles versus cell junctions function in MCCs. To that point, we had thought about these experiments. As mentioned earlier, the Piezo1 coding sequence is exceptionally large (~7.5 kb)____, and repeated attempts by our group to generate and express stable, translatable transcripts have been unsuccessful. Thus, the idea of centrosome-targeted PIEZO1 via a PACT is very exciting; however, it is not technically feasible. Beyond size, PIEZO1 is a trimeric, large plasma-membrane mechanosensitive channel that requires proper ER processing and bilayer incorporation. PACT localizes cargo to the centriole/pericentriolar material, not a membrane compartment; thus, a PACT-anchored PIEZO1 would be membrane-mismatched and almost certainly nonfunctional even if expressed/

Second, Centrosome-proximal GCaMP (PACT-GCaMP) would show correlation, not causation. This experiment does not address the question “centriole pool of Piezo as the mechanosensor for centriole number regulation”. It will only show if the Ca2+ influx is happening at the basal bodies, but not whether and how that Ca2+ is essential for centriole amplification. For this purpose, we will need to find a way to block Ca2+ influx specifically at basal bodies, rather than junctions, which will require extensive controls.

We do not claim that any specific Piezo1 or Ca2+ pool is critical for controlling centriole number and thus the suggested experiment would not alter the manuscript's conclusions. We therefore view the above as exciting future directions rather than prerequisites.

********************************************

2- Because the proposed pathway is tension-sensing and YAP pathway is tightly linked to the actin cytoskeleton, the role of actin cysoskeleton in the proposed pathway should be tested directly. The authors mention different hypothesis around actin but has not tested them in the manuscript. For example, actin-depedent sequestration of Yap at the apical surface is intriguing. Does actin polymerization induced by drugs release Yap from the apical surface?

RESPONSE: We would like to thank the reviewer for their suggestion. As per the reviewers' suggestion, we have moved this section to discussion, stating that “In the future, we plan to address this question by examining how Yap is sequestered by apical actin.”.

However, we appreciate the reviewer’s enthusiasm and would like to share some experiments we are thinking/planning of to test the hypothesis.

We plan to examine if the actin polymerization or contractility is responsible for Yap sequestration/release from the apical surface with the following experiments: 1) if the Yap is displaced by Jasplakinolide treatment, which stabilizes filamentous actin, 2) use of ROCK inhibitor to decrease contractility in the absence or presence of Yoda1, 3) Use genetic constructs such as Shroom3 to increase ROCK-mediated contractility to observe changes in Yap localization and dynamics.

Although these experiments are interesting, they do not alter the conclusion of the current manuscript, and they represent future directions for our research.

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Referee #3

Evidence, reproducibility and clarity

This manuscript investigates how mechanical tension is transduced into centriole amplification in Xenopus multiciliated cells (MCCs). Building on prior work that centriole number scales with MCC apical area and that this scaling depends on PIEZO1, the study proposes that MCCs repurpose a canonical mechanochemical axis-PIEZO1 → Ca²⁺/PKC → ERK1/2 → YAP → Foxj1-to regulate centriole number rather than mitosis. The authors use tethered vs. untetheredanimal cap explants to modulate tissue tension, combine pharmacologic perturbations with genetic loss of function and rescue, quantititative image analysis and present a model in which tension gated PIEZO1 activates ERK/YAP, influences Foxj1, and tunes centriole number in MCCs.

The manuscript tackles an important and timely problem with clear disease relevance. It major advance is their presented model that posits that post mitotic MCCs repurpose a canonical mechanotransduction module to regulate organelle number rather than proliferation. It is a conceptually strong study addressing an important problem with a clean mechanical paradigm. However, to support the central claim that centriole number control is a specific, direct consequence of the PIEZO1-Ca²⁺-ERK/YAP pathway within MCCs, the revision should establish specificity and causality and provide experimental data for some of the major conclusions as detailed below. Addressing these points are critical to support the mechanistic conclusions and impact.

Major concerns:

1) The presented data do not yet establish a specific, direct pathway linking mechanotransduction to centriole number, because the molecular players tested (PIEZO1, Ca²⁺, PKC, ERK, YAP, Foxj1) are highly pleiotropic. As such, the observed centriole number phenotypes, and some of the major conclusions, could be indirect. It is therefore critical to test the specificity and causality of the proposed pathway. This could be done with the authors' own strategies and/or with the following potential approaches:

• Genetic dependency and sufficiency tests: It could be shown that Yoda1 has no effect in PIEZO1 loss-of-function MCCs, and that wild-type PIEZO1, but not conductance-dead PIEZO1 pore mutants restores Yoda1 responsiveness across centriole number, pERK, and YAP readouts. For example, PIEZO1 C terminus was shown to govern Ca²⁺ influx and ERK1/2 activation. Comparing full length PIEZO1 with a C terminal deletion in MCC restricted rescue; loss of rescue of centriole amplification and ERK/YAP activation with the C terminal deletion can provide a genetics anchored specificity test beyond broad inhibitors.

• Downstream bypass/rescue experiments: In PIEZO1 loss-of-function or BAPTA conditions, can enforcing MEK/ERK activation or YAP rescue centriole number defect? Conversely, can MEK inhibitors block Yoda1-induced effects.

2) The hypothesis about the centriole pool of Piezo as the mechnosensor for centriole number regulation is very exciting and novel. Can localization controlled variants be used to test whether a centriole associated pool directly senses tension for number control (for example, centrosome targeted PIEZO1 via a PACT tag). Alternatively, broad cellular Ca sensors (GcaMP) or centrosome proximal Ca sensors (e.g., PACT GCaMP) can be used detect local calcium microdomains during tethering or Yoda1 treatment.

3) Because the proposed pathway is tension-sensing and YAP pathway is tightly linked to the actin cytoskeleton, the role of actin cysoskeleton in the proposed pathway should be tested directly. The authors mention different hypothesis around actin but has not tested them in the manuscript. For example, actin-depedent sequestration of Yap at the apical surface is intriguing. Does actin polymerization induced by drugs release Yap from the apical surface?

4) Image quantification and analysis must be described in greater detail in the Methods section, as they are central to the major conclusions of the manuscript. For example, the authors should explain how nuclear, cytoplasmic, and centriole segmentation were performed, and how relative protein levels in the nucleus versus the cytoplasm (e.g., YAP, volume- or area-based) were quantified. Specifically, the thresholds and segmentation criteria applied to different cellular structures under various conditions, as well as the use of Imaris and other software, should be clearly detailed.

5) PIEZO1 mRNA was shown to incrase in a Foxj1 linked feedback loop. Does this increase translate into an increase in total protein levels?

6) Is the proposed signaling cascade active in mammalian multiciliated cells (e.g., airway epithelium). If possible, testing this by using one of the major players of the pathway as a readout such as as ERK phosphorylation, YAP nuclear localization in mammalian MCCs will reveal whether regulation of centriole number through this pathway is conserved and would strengthen the generality.

7) Throughout the results section, there are multiple times where authors raised specific hypothesis about their data (e.g. foxj1 regulation of number control, apical actin/YAP). However, they have not tested them. These hypothesis are very exciting and if possible, testing experimentally, would strengthen the conclusions associated with them.

Minor concerns:

1) Abstract: "how MCCs regulate centriole/cilia numbers remains a major knowledge gap" overstates the field; please soften to reflect recent advances (mechanics/apical area scaling; PIEZO1 implication).

2) MCC vs non MCC identification (Fig. 1): Clarify how non MCCs were distinguished from MCCs (e.g. markers/criteria).

3) GsMTx4 rationale: State that GsMTx4 is a spider venom peptide that inhibits cationic mechanosensitive channels (including PIEZO1) and justify its use alongside Yoda1.

4) Timeline statement: "Centriole amplification to migration and apical docking takes ~4-5 h (personal observation)" is not appropriate; either cite time lapse literature or include your own time lapse data.

5) Redundancy: The description of Yoda1 as a channel specific agonist is repeated; keep only once.

6) "WT yap1 GFP construct previously used by Dr. Lance Davidson ..." should move construct description to Methods and keep only the citation in Results.

7) "(Unpublished data; Dr. Mahjoub)" should be removed unless data are shown.

8) Add the Kintner group reference linking motile cilia number and centriole number in Xenopus MCCs.

9) Replace "as shown previously in our eLife paper" with "as we previously showed or shown previously (Kulkarni et al., 2021)".

10) The two hypotheses for how Foxj1 could regulate number under tension (actin remodeling vs. transcriptional control of amplification genes) belong in the Discussion unless tested. Moreover, the part on the discussion on yap sequestration by apical actin and the two possibilities presented also should go do discussion.

Significance

This manuscirpt dissects Piezo1-mediated mechanotransduction to regulation of centriole number in Xenopus multiciliated cells (MCCs) via Ca²⁺, ERK/YAP, and Foxj1. While Piezo1 and its downstream effectors have been implicated broadly in mechanosensation, cellular tension responses, and transcriptional regulation, their specific role in centriole nubmer control in MCCs has been unknown By integrating pharmacological manipulation, genetic perturbation, and functional readouts, the authors demonstrate that this pathway directly influences centriole number.

The findings extend published knowledge in two main ways:

(1) they connect a mechanosensitive ion channel to the transcriptional program governing Foxj1 expression and multiciliation, a mechanistic link not previously defined, and

(2) they highlight the pleiotropic yet coordinated nature of Piezo1 signaling in organelle biogenesis. This work will be of broad interest to cell and developmental biologists studying ciliogenesis, epithelial differentiation, and mechanotransduction, as well as to biomedical researchers interested in multicilaited cells and ciliopathies. By situating a well-studied mechanosensor within the context of MCC biology, the study opens new directions for understanding how tissue-level forces shape organelle number control and function.

At the same time, the impact of the study is weakened by concerns regarding the causability and specificity of the pathway, since the signaling components examined are highly pleiotropic and it remains challenging to separate direct effects on centriole number from broader cellular consequences. The causal relationships among Piezo1 activity, downstream signaling, and Foxj1 expression require stronger substantiation, and the extent to which this pathway operates in mammalian multiciliated cells remains an open question. Addressing these limitations would strengthen the robustness, generality, and translational relevance of the conclusions.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigated how the type-I interferon response (ISG) and antigen presentation (AP) pathways are repressed in luminal breast cancer cells and how this repression can be overcome. They found that a STING agonist can reactivate these pathways in breast cancer cells, but it also does so in normal cells, suggesting that this is not a good way to create a therapeutic window. Depletion of ADAR and inhibition of KDM5 also activate ISG and AP genes. The activation of ISG and AP genes is dependent on cGAS/STING and the JAK kinase. Interestingly, although both ADAR depletion and KDM5 inhibition activate ISG and AP genes, their effects on cell fitness are different. Furthermore, KDM5 inhibitor selectively activates ISG and AP genes in tumor cells but not normal cells, arguing that it may create a larger therapeutic window than the STING agonist. These results also suggest that KDM5 inhibition may activate ISG and AP genes in a way different from ADAR loss, and this process may affect tumor cell fitness independently of the activation of ISG and AP genes.

The authors further showed that KDM5 inhibition increases R-loops and DNA damage in tumor cells, and XPF, a nuclease that cuts R-loops, is required for the activation of ISG and AP genes. Using H3K4me3 CUT&RUN, they found that KMD5 inhibition results in increased H3K4me3 not only at genes, but also at repetitive elements including SINE, LINE, LTR, telomeres, and centromeres. Using S9.6 CUT&TAG, they confirmed that R-loops are increased at SINE, LINE, and LTR repeated with increased H3K4me3. Together, the results of this study suggest that KMD5 inhibition leads to H3K4me3 and R-loop accumulation in repetitive elements, which induces DNA damage and cGAS/STING activation and subsequently activates AP genes. This provides an exciting approach to stimulate the anti-tumor immunity against breast tumors.

KDM5 inhibition activates interferon and antigen presentation genes through R-loops.

Strengths:

A new approach to make breast tumors "hot" for anti-tumor immunity.

Weaknesses:

Future in vivo studies are needed to show the effects of KDM5 inhibitors on the immunotherapy responses of breast tumors.

Comments on revised version:

The authors have adequately addressed my comments.

<<tag>> cognate

surfactant A&D = innate immunity; tag pathogens for phagocytosis, enhance release of cytokines by macrophages surfactant B = helps arrange phospholipids into lamellar bodies; assist entry of phospholipids into surgace monolayer as alveolar expand during inspiration

Reviewer #1 (Public review):

Summary:

The authors present a nanobody-based pulse-labeling system to track yeast NPCs. Transient expression of a nanobody targeting Nup84 (fused to NeonGreen or an affinity tag) permits selective visualization and biochemical capture of NPCs. Short induction effectively labels NPCs, and the resulting purifications match those from conventional Nup84 tagging. Crucially, when induction is repressed, dilution of the labeled pool through successive cell cycles allows the visualization of "old" NPCs (and potentially individual NPCs), providing a powerful view of NPC lifespan and turnover without permanently modifying a core scaffold protein.

Strengths:

(1) A brief expression pulse labels NPCs, and subsequent repression allows dilution-based tracking of older (and possibly single) NPCs over multiple cell cycles.

(2) The affinity-purified complexes closely match known Nup84-associated proteins, indicating specificity and supporting utility for proteomics.

Weaknesses:

(1) Reliance on GAL induction introduces metabolic shifts (raffinose → galactose → glucose) that could subtly alter cell physiology or the kinetics of NPC assembly. Alternative induction systems (e.g., β-estradiol-responsive GAL4-ER-VP16) could be discussed as a way to avoid carbon-source changes.

(2) While proteomics is solid, a comprehensive supplementary table listing all identified proteins (with enrichment and statistics) would enhance transparency.

(3) Importantly, the authors note that the method is particularly useful "in conditions where direct tagging of Nup84 interferes with its function, while sub-stoichiometric nanobody binding does not." After this sentence, it would be valuable to add concrete examples, such as experiments examining NPC integrity in aging or stress conditions where epitope tags can exacerbate phenotypes. These examples will help readers identify situations in which this approach offers clear advantages.

I think ChatGPT is used so vastly because it's free. I remember Chegg, and never using it because of the price tag.

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Reply to the reviewers

Manuscript number: RC-2025-02879 Corresponding author(s): Matteo Allegretti; Alia dos Santos

1. General Statements

In this study, we investigated the effects of paclitaxel on both healthy and cancerous cells, focusing on alterations in nuclear architecture. Our novel findings show that:

• Paclitaxel-induced microtubule reorganisation during interphase alters the perinuclear distribution of actin and vimentin. The formation of extensive microtubule bundles, in paclitaxel or following GFP-Tau overexpression, coincides with nuclear shape deformation, loss of regulation of nuclear envelope spacing, and alteration of the nuclear lamina.

• Paclitaxel treatment reduces Lamin A/C protein levels via a SUN2-dependent mechanism. SUN2, which links the lamina to the cytoskeleton, undergoes ubiquitination and consequent degradation following paclitaxel exposure.

• Lamin A/C expression, frequently dysregulated in cancer cells, is a key determinant of cellular sensitivity to, and recovery from, paclitaxel treatment.

Collectively, our data support a model in which paclitaxel disrupts nuclear architecture through two mechanisms: (i) aberrant nuclear-cytoskeletal coupling during interphase, and (ii) multimicronucleation following defective mitotic exit. This represents an additional mode of action for paclitaxel beyond its well-established mechanism of mitotic arrest.

We thank the reviewers for their time and constructive feedback. We have carefully considered all comments and have carried out a full revision. The updated manuscript now includes additional data showing:

• Overexpression of microtubule-associated protein Tau causes similar nuclear aberration phenotypes to paclitaxel. This supports our hypothesis that increased microtubule bundling directly leads to nuclear disruption in paclitaxel during interphase.

• Paclitaxel's effects on nuclear shape and Lamin A/C and SUN2 expression levels occur independently of cell division.

• Reduced levels of Lamin A/C and SUN2 upon paclitaxel treatment occur at the protein level via ubiquitination of SUN2.

• The effects of paclitaxel on the nucleus are conserved in breast cancer cells.

Full Revision

We have also edited our text and added further detail to clarify points raised by the reviewers. We believe that our revised manuscript is overall more complete, solid and compelling thanks to the reviewers' comments.

1. Point-by-point description of the revisions

Reviewer #1 Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown.

We thank the reviewer for this valuable comment. To elucidate the mechanism behind the decrease in Lamin A/C and SUN2 levels, we have now performed several additional experiments. First, we performed RT-qPCR to quantify mRNA levels of these genes, relative to the housekeeping gene GAPDH (Supplementary Figure 3B and O). The levels of SUN2 and LMNA mRNA remained the same between control and paclitaxel-treated cells, indicating that this effect instead occurs at the protein level. We have also tested post-translational modifications as a potential regulatory mechanism for Lamin A/C and SUN2. In addition to the phosphorylation of Ser404 which we had already tested (Supplementary Figure 3C), we have now included additional Phos-tag gel and Western blotting data showing that the overall phosphorylation status of Lamin A/C is not affected by paclitaxel (Supplementary Figure 3E and F). We also pulled-down Lamin A/C from cell lysates and then Western blotted for polyubiquitin and acetyl-lysine, which showed that the ubiquitination and acetylation states of Lamin A/C are also not affected by paclitaxel (Supplementary Figure 3G-I). However, Western blots for polyubiquitin of SUN2 pulled down from cell lysates showed that paclitaxel treatment results in significant SUN2 ubiquitination (Figure 3M and N). Therefore, we propose that the downregulation of SUN2 following paclitaxel treatment occurs by ubiquitin-mediated proteolysis.

The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

We addressed this important point by using an alternative method to stabilise/bundle microtubules in interphase, namely by overexpressing GFP-Tau, as suggested by reviewer 2. Following GFP- Tau overexpression, large microtubule bundles were observed throughout the cytoplasm (Figure 4A), and this resulted in a significant decrease in nuclear solidity (Figure 4B). Furthermore, in cells where microtubule bundles extensively contacted the nucleus, the nuclear lamina became unevenly distributed and appeared patchy (Figure 4C). This supports our hypothesis that the aberrations to nuclear shape and Lamin A/C localisation in paclitaxel-treated cells are due to the presence of microtubules bundles surrounding the nucleus.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

We thank the reviewer for raising this important point. We have addressed this by conducting additional repeats for the cell confluency measurements to increase the statistical power of our experiments (Figure 5A). Our data now show that GFP-lamin A/C had a statistically significant effect on rescuing cell growth at both 1 nM and 5 nM paclitaxel, while Lamin A/C knockdown exacerbated the inhibition of cell growth at 5 nM paclitaxel but not 1 nM paclitaxel (Figure 5A). In addition, we note that the duration of paclitaxel treatment before assessing viability was specified in the figure legend: "Bar graph comparing cell viability between wild-type (red), GFP-Lamin A/C overexpression (green), and Lamin A/C knockdown (blue) cells following 20 h incubation in 0, 1, 5, or 10 nM paclitaxel." We also repeated cell viability analysis after 48 h incubation in paclitaxel instead of 20 h to allow for a longer time for differences to take effect (Figure 5B).

We also added figures showing the cytoskeletal reorganisation at both 1 and 10 nM in addition to 0 and 5 nM (Supplementary Figure 1A) showing that microtubule bundling and condensation of actin into puncta correlated with increased paclitaxel concentration. Vimentin colocalised well with microtubules at all concentrations.

We have also included in our results section further clarification for the use of 5nM paclitaxel in this study. The new section reads as follows: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

We thank the reviewer for pointing out the need for clarification in the wording of our manuscript. We have reworded the title and relevant sections of our abstract, introduction, and discussion to make it clearer that the effects of paclitaxel on the nucleus are due to a combination of aberrant nuclear cytoskeletal coupling during interphase and multimicronucleation following mitotic slippage. We have also added additional data in support of the effect of paclitaxel on nuclear architecture during interphase. For this, we used serum-starved cells (which divide only very slowly such that the majority of cells do not pass through mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data confirmed that paclitaxel's effects on nuclear solidity, and Lamin A/C and SUN2 proteins levels can occur independently of cell division (Figure 2C; Figure 3H-J). Finally, when we overexpressed GFP-Tau (as discussed above) we observed similar aberrations to nuclear solidity and Lamin A/C localisation. This indicates that these effects occur due to microtubule bundling in interphase, especially as in our study GFP-Tau did not lead to multimicronucleation or appear to affect mitosis (Figure 4).

Below are the main changes to the text regarding the interphase effect of paclitaxel:

• Title: "Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling"

• Abstract: "Overall, our data supports nuclear architecture disruption, caused by both aberrant nuclear-cytoskeletal coupling during interphase and exit from defective mitosis, as an additional mechanism for paclitaxel beyond mitotic arrest."

• Introduction: "Here we propose that cancer cells have increased vulnerability to paclitaxel both during interphase and following aberrant mitosis due to pre-existing defects in their NE and nuclear lamina."

• Discussion: "Overall, our work builds on previous studies investigating loss of nuclear integrity as an anti-cancer mechanism of paclitaxel separate from mitotic arrest14,20,21. We propose that cancer cells show increased sensitivity to nuclear deformation induced by aberrant nuclear-cytoskeletal coupling and multimicronucleation following mitotic slippage. Therefore, we conclude that paclitaxel functions in interphase as well as mitosis, elucidating how slowly growing tumours are targeted."

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

As mentioned above, we have now included additional information in our Results section to clarify our paclitaxel dose range: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

We appreciate the reviewer's summary and thank them for their time. We believe our comprehensive revisions have addressed all comments, strengthening the manuscript and making it more robust and compelling.

Reviewer #2 Evidence, reproducibility and clarity This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state- of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

We thank the reviewer for the summary and for highlighting our efforts in using the latest imaging technical advances.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

We thank the reviewer for highlighting the lack of clarity in our wording. We have revised the title, abstract and relevant sections of the introduction and discussion to clarify our message that the effects of paclitaxel on the nucleus arise from a combination of aberrant nuclear-cytoskeletal coupling during interphase and multimicronucleation following exit from defective mitosis. We have also included additional data where we used slow-dividing, serum-starved cells (under these conditions, the majority of cells do not undergo mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data show that even in these cells there is a clear effect of paclitaxel on nuclear solidity, and Lamin A/C and SUN2 protein levels, further supporting our hypothesis that these phenotypes can occur independently of cell division (Figure 2C; Figure 3H-J). Furthermore, we performed additional experiments where we used overexpression of GFP-Tau as an alternative method of stabilising microtubules in interphase and observed similar aberrations to nuclear solidity and Lamin A/C localisation. As GFP-Tau overexpression did not lead to micronucleation or appear to affect mitosis, these data support the hypothesis that nuclear aberrations occur due to microtubule bundling in interphase (Figure 4). We discuss these experiments in more detail below. Finally, we have reworded the introduction to better introduce alternative hypotheses and mechanisms for paclitaxel's activity.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

We would like to note that the paper from Xiang-Xi Xu's lab (Smith et al, 2021) was cited in our original manuscript (reference 14 in both the original and revised manuscripts). We have now also included additional review articles from the Xiang-Xi Xu lab (PMID:36368286 20 and PMID: 35048083 21). Furthermore, we have clarified the wording in both the introduction and discussion to better reflect the current understanding of paclitaxel's mechanism and alternative hypotheses.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

We thank the reviewer for suggesting this control experiment using the microtubule stabiliser Tau. We have now included these experiments in the revised version of the manuscript (Figure 4). The overexpression of GFP-Tau supports our hypothesis that cytoskeletal reorganisation in paclitaxel exerts mechanical stress on the nucleus during interphase, resulting in nuclear deformation and aberrations to the nuclear lamina. In particular, GFP-Tau overexpression resulted in large microtubule bundles throughout the cytoplasm (Figure 4A). Notably, in cells where these bundles extensively contacted the nucleus, we observed a significant decrease in nuclear solidity (Figure 4B) accompanied by changes in nuclear lamina organisation, including a patchy lamina phenotype, similar to that induced by paclitaxel (Figure 4C).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

We thank the reviewer for raising important points regarding the specificity of paclitaxel's effects. We agree that microtubule stabilisation can induce myriad cellular changes, including alterations to focal adhesions and other cytoskeletal components. Our focus on Lamin A/C and nuclear morphology is grounded both in the established clinical relevance of nuclear mechanics in cancer and builds on mechanistic work from other groups.

Lamin A/C expression is commonly altered in cancer, and nuclear morphology is frequently used in cancer diagnosis35. Lamin A/C also plays a crucial role in regulating nuclear mechanics32 and, importantly, determines cell sensitivity to paclitaxel14. However, the mechanism by which Lamin A/C determines sensitivity of cancer cells to paclitaxel is unclear.

Our data are consistent with Lamin A/C being a determinant of paclitaxel survival sensitivity. We also provide evidence that paclitaxel itself reduces Lamin A/C protein levels and disrupts its organisation at the nuclear envelope. We directly link these effects to microtubule bundling around the nucleus and degradation of force-sensing LINC component SUN2, highlighting the importance of nuclear architecture and mechanics to overall cellular function. Furthermore, we show that recovery from paclitaxel treatment depends on Lamin A/C expression levels. This has clinical relevance, as unlike cancer cells, healthy tissue with non-aberrant lamina would be able to selectively recover from paclitaxel treatment.

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

We agree with the reviewer that Supplementary Figure 6 does not add significant value to the manuscript and thank the reviewer for pointing this out. We have removed it from the manuscript accordingly.

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

We thank the reviewer for pointing out the missing information. For our cryo-ET experiments, we collected data from 9 lamellae from paclitaxel-treated cells and 6 lamellae from control cells, with each lamella derived from a single cell. This information has now been added to the figure legend (Figure 2F).

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

We appreciate the reviewer's concerns and have performed major revisions to strengthen the significance of our study. Specifically, we conducted two key sets of experiments to validate our original conclusions: serum starvation to control for the effects of cell division, and overexpression of the microtubule stabiliser Tau to demonstrate that paclitaxel can affect the nucleus via its microtubule bundling activity in interphase.

By elucidating the mechanistic link between microtubule stabilisation and nuclear-cytoskeletal coupling, our findings contribute to our understanding of paclitaxel's multifaceted actions in cancer cells.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

Reviewer #3 Evidence, reproducibility and clarity The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

We thank the reviewer for the positive feedback.

Although similar ideas are published, which may be suitable to be cited? • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review. • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

We thank the reviewer for bringing to our attention these important review articles. In our initial manuscript, we only cited the original paper (14, also reference 14 in the original manuscript). We have now included citations to the suggested publications (20,21).

We would also like to emphasise how our manuscript distinguishes itself from the work of Smith et al.14,20,21:

• Cell-type focus: In their study 14, Smith et al. examined the effect of paclitaxel on malignant ovarian cancer cells and proposed that paclitaxel's effects on the nucleus are limited to cancer cells. However, our data extends these findings by demonstrating paclitaxel's effects in both cancerous and non-cancerous backgrounds.

• Cytoskeletal reorganisation: Smith et al. show reorganisation of microtubules in paclitaxel-treated cells14. Our data show re-organisation of other cytoskeletal components, including F-actin and vimentin.

• Multimicronucleation: Smith et al. propose that paclitaxel-induced multimicronucleation occurs independently of cell division14. Although we observe progressive nuclear abnormalities during interphase over the course of paclitaxel treatment, our data do not support this conclusion; we find that multimicronucleation occurs only following mitosis.

• Direct link between microtubule bundling and nuclear aberrations: We show that nuclear aberrations caused by paclitaxel during interphase (distinct from multimicronucleation) are directly linked to microtubule bundling around the nucleus, suggesting they result from mechanical disruption and altered force propagation.

• Lamin A/C regulation: Consistent with Smith et al.14, we show that Lamin A/C depletion leads to increased sensitivity to paclitaxel treatment. However, we further demonstrate that paclitaxel itself leads to reduced levels of Lamin A/C and that this effect occurs independently of mitosis and is mediated via force-sensing LINC component SUN2. Upon SUN2 knockdown, Lamin A/C levels are no longer affected by paclitaxel treatment.

• Recovery: Finally, our work reveals that cells expressing low levels of Lamin A/C recover less efficiently after paclitaxel removal. This might help explain how cancer cells could be more susceptible to paclitaxel.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

We thank the reviewer for this comment. Our initial study aimed to understand the effects of paclitaxel on nuclear architecture in non-aberrant backgrounds. To show that the observed effects of paclitaxel are also applicable to cancer cells, we have now repeated our main experiments using MDA-MB-231 human breast cancer cells (Supplementary Figure 1B; Supplementary Figure 3P-T). Similar to our findings in human fibroblasts, paclitaxel treatment of MDA-MB-231 led to cytoskeletal reorganisation (Supplementary Figure 1B), a decrease in nuclear solidity (Supplementary Figure 3P), aberrant (patchy) localisation of Lamin A/C (Supplementary Figure 3Q), and a reduction in Lamin A/C and SUN2 levels (Supplementary Figure 3R-T).

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

We have added further detail to the figure legend for clarification: "Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Different colours distinguish individual α-tubulin clusters, representing individual microtubule filaments or filament bundles."

Generally, the figures need additional description to be clear.

We have added further clarification and detail to our figure legends.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

We changed this sentence to: "Figure 3 - Paclitaxel treatment results in aberrant organisation of the nuclear lamina and decreased Lamin A/C levels via SUN2."

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?.

We thank the reviewer for pointing this out and we agree that the ratio of Lamin A to Lamin C can vary with culture conditions. To confirm that paclitaxel treatment reduces total Lamin A/C levels regardless of this ratio, we repeated the Western blot analysis in three additional biological replicates using cells in which Lamin C levels exceeded Lamin A levels. These experiments confirmed a comparable decrease in total Lamin A/C levels. Figure 3B and 3C have been updated accordingly.

Also, the effect on Lamin A/C and SUN2 levels are not significant of robust.

Decreased Lamin A/C and SUN2 levels following paclitaxel treatment were consistently seen across three or more biological repeats (Figure 3B-C), and this could be replicated in a different cell type (MDA-MB-231) (Supplementary Figure 3R-T). Furthermore, Western blotting results are consistent with the patchy Lamin A/C distribution observed using confocal and STORM following paclitaxel treatment (Figure 3A; Supplementary Figure 3A), where Lamin A/C appears to be absent from discrete areas of the lamina.

Any mechanisms are speculated for the reason for the reduction?

We have now included additional data which aims to shed light on the mechanism behind the decrease in Lamin A/C and SUN2 levels following paclitaxel treatment. We found that SUN2 is selectively degraded during paclitaxel treatment. Immunoprecipitation of SUN2 followed by Western blotting against Polyubiquitin C showed increased SUN2 ubiquitination in paclitaxel (Figure 3M and N). Furthermore, in our original manuscript, we showed that Lamina A/C levels remained unaltered during paclitaxel treatment in cells where SUN2 had been knocked down. We propose that changes in microtubule organisation affect force propagation to Lamin A/C specifically via SUN2 and that this leads to Lamina A/C removal and depletion. Future work will be needed to fully understand this mechanism.

In addition to the findings described above, we report no significant changes in mRNA levels for LMNA or SUN2 in paclitaxel (Supplementary Figure 3B and O). Phos-tag gels followed by Western blotting analysis for Lamin A/C also did not detect changes to the overall phosphorylation status of Lamin A/C due to paclitaxel treatment. This is in agreement with our initial data showing no changes to Lamin A/C Ser 404 phosphorylation levels (Supplementary Figure 3E and F). Finally, Lamin A/C immunoprecipitation experiments followed by Western blotting for Polyubiquitin C and acetyl-lysine showed no significant changes in the ubiquitination and acetylation state of Lamin A/C in paclitaxel-treated cells (Supplementary Figure 3G-I).

Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

The nuclear lamina and LINC complex proteins play a critical role in regulating nuclear integrity, stiffness and mechanical responsiveness to external forces28,31-33,54,75, as well as in maintaining the nuclear intermembrane distance69,74. In particular, SUN-domain proteins physically bridge the nuclear lamina to the cytoskeleton through interactions with Nesprins, thereby preserving the perinuclear space distance30,69,74. Mutations in Lamins have been shown to disrupt chromatin organization, alter gene expression, and compromise nuclear structural integrity, and experiments with LMNA knockout cells reveal that nuclear mechanical fragility is closely coupled to nuclear deformation47. Furthermore, nuclear-cytoskeletal coupling is essential during processes such as cell migration, where cells undergo stretching and compression of the nucleus; weakening or loss of the lamina in such cases compromises cell movement47,73. In our work, we show that alterations to nuclear Lamin A/C and SUN2 by paclitaxel treatment coincide with nuclear deformations (Figure 2A-D, F, G; Figure 3A-D, F, G; Supplementary Figure 3A, P-T) and that these deformations are reversible following paclitaxel removal (Supplementary Figure 4B-D). Our experiments also demonstrate that Lamin A/C expression levels significantly influence cell growth, cell viability, and cell recovery in paclitaxel (Figure 5). Therefore, drawing on current literature and our results, we propose that, during interphase, paclitaxel induces severe nuclear aberrations through the combined effects of: i) increased cytoskeletal forces on the NE caused by microtubule bundling; ii) loss of ~50% Lamin A/C and SUN2; iii) reorganisation of nucleo-cytoskeletal components.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

We thank the reviewer for the feedback and valuable suggestions. In response, we have included experiments using human breast cancer cell line MDA-MB-231 to further corroborate our findings and interpretations. We believe these additions have improved the clarity, robustness and impact of our manuscript, and we are grateful for the reviewer's contributions to its improvement.

Author response:

We thank the reviewers for their thoughtful and thorough consideration of the work. We appreciate the positive reception they give the work, and plan to address several of the comments with further experiments. To outline that work (and ensure that we are on the right track to addressing those concerns), we summarize the core concerns that prompt new experiments:

(1) Does the YFP tag on the ACRs interfere with simultaneous GCaMP imaging of RubyACR-expressing cells and could bleaching of the YFP complicate interpretation of the experiments here?

We will test whether 920 nm (2p) and 650 nm (1p) excitation cause YFP bleaching that interferes with interpretation of inhibitory calcium (i.e. GCaMP) signals. Because the YFP tag enhances opsin sensitivity, we prioritized these tagged RubyACRs for initial characterization. FLAG-tagged ACRs are in progress, but will take time to fully characterize. Considering that the RubyACR-EYFP versions work very well, and in many cases people will want the YFP tag, either for visualizing expression or to maximize sensitivity, we feel the current work is a valuable contribution on its own. Indeed several labs have already requested these lines.

(2) Are the ACRs activated by two-photon illumination?

We will examine GCaMP signals at increasing 2p intensities to determine whether imaging unintentionally activates RubyACRs, as well as whether 2p illumination could be used for intentional opsin activation.

(3) How toxic is the expression of these opsins?

We will update the quantification of toxicity in Table 1 to include all the drivers we used in this study. In fact the toxicity we observed was primarily with the vGlut driver, which was why that was the only information in the table. The other drivers we used did not appreciably reduce survival rate, but showing the one case where it did have a big effect left a strong and understandably inaccurate impression that toxicity was a big pitfall. We note that the widely used CSChrimson has similar % survival to the RubyACRs when expressed with these vGlut drivers.

We also plan to examine whether ACR expression leads to cell-autonomous perturbations. We will determine whether expression leads to some frequency of neuronal cell death, and we will evaluate whether any morphological effects occur.

We will also clarify in the Discussion that potential toxicity may be driver-specific (as it is here) and should be evaluated case-by-case by investigators using the tool.

(4) Use functional imaging to confirm inhibition of the neurons used only for behavioral experiments (pIP10 & PPL1-γ1pedc)

We will perform these imaging experiments. One caveat is that inhibition may not be readily detectable with GCaMP, as the resting calcium levels in pIP10 and PPL1-γ1pedc neurons may already be quite low. This differs from the non-spiking Mi1 neurons, where inhibition was clearly observed with GCaMP. For this reason, we consider the behavioral results stronger evidence of efficacy, but we agree that imaging could provide useful supporting evidence, recognizing that a negative result would be difficult to interpret.

(5) Confirm that the GtACR1 will inhibit locomotion in the flybowl when activated with green light, its spectral peak.

We will perform this benchmark experiment. Please note that our intention with this study was to find an effective red-light activated opto-inhibitor because these wavelengths are much less perturbing to behavior. In that respect, regardless of GtACR1’s performance with green light, the RubyACRs clearly provide important new tools for Drosophila behavioral neuroscience.

Author response:

Reviewer #1 (Public review):

Summary:

Review of the manuscript titled " Mycobacterial Metallophosphatase MmpE acts as a nucleomodulin to regulate host gene expression and promotes intracellular survival".

The study provides an insightful characterization of the mycobacterial secreted effector protein MmpE, which translocates to the host nucleus and exhibits phosphatase activity. The study characterizes the nuclear localization signal sequences and residues critical for the phosphatase activity, both of which are required for intracellular survival.

Strengths:

(1) The study addresses the role of nucleomodulins, an understudied aspect in mycobacterial infections.

(2) The authors employ a combination of biochemical and computational analyses along with in vitro and in vivo validations to characterize the role of MmpE.

Weaknesses:

(1) While the study establishes that the phosphatase activity of MmpE operates independently of its NLS, there is a clear gap in understanding how this phosphatase activity supports mycobacterial infection. The investigation lacks experimental data on specific substrates of MmpE or pathways influenced by this virulence factor.

We thank the reviewer for this insightful comment and agree that identification of the substrate of MmpE is important to fully understand its role in mycobacterial infection.

MmpE is a putative purple acid phosphatase (PAP) and a member of the metallophosphoesterase (MPE) superfamily. Enzymes in this family are known for their catalytic promiscuity and broad substrate specificity, acting on phosphomonoesters, phosphodiesters, and phosphotriesters (Matange et al., Biochem J., 2015). In bacteria, several characterized MPEs have been shown to hydrolyze substrates such as cyclic nucleotides (e.g., cAMP) (Keppetipola et al., J Biol Chem, 2008; Shenoy et al., J Mol Biol, 2007), nucleotide derivatives (e.g., AMP, UDP-glucose) (Innokentev et al., mBio, 2025), and pyrophosphate-containing compounds (e.g., Ap4A, UDP-DAGn) (Matange et al., Biochem J., 2015). Although the binding motif of MmpE has been identified, determining its physiological substrates remains challenging due to the low abundance and instability of potential metabolites, as well as the limited sensitivity and coverage of current metabolomic technologies in mycobacteria.

(2) The study does not explore whether the phosphatase activity of MmpE is dependent on the NLS within macrophages, which would provide critical insights into its biological relevance in host cells. Conducting experiments with double knockout/mutant strains and comparing their intracellular survival with single mutants could elucidate these dependencies and further validate the significance of MmpE's dual functions.

We thank the reviewer for the comment. In our study, we demonstrate that both the nuclear localization and phosphatase activity of MmpE are required for full virulence (Figure 3D–E). Importantly, deletion of the NLS motifs did not impair MmpE’s phosphatase activity in vitro (Figure 2F), indicating that its enzymatic function is structurally independent of its nuclear localization. These findings suggest that MmpE functions as a bifunctional protein, with distinct and non-overlapping roles for its nuclear trafficking and phosphatase activity. We have expanded on this point in the Discussion section “MmpE Functions as a Bifunctional Protein with Nuclear Localization and Phosphatase Activity”.

(3) The study does not provide direct experimental validation of the MmpE deletion on lysosomal trafficking of the bacteria.

We thank the reviewer for the comment. The role of Rv2577/MmpE in phagosome maturation has been demonstrated in M. tuberculosis, where its deletion increases colocalization with lysosomal markers such as LAMP-2 and LAMP-3 (Forrellad et al., Front Microbiol, 2020). In our study, we found that mmpE deletion in M. bovis BCG led to upregulation of lysosomal genes, including TFEB, LAMP1, LAMP2, and v-ATPase subunits, compared to the wild-type strain. These results suggest that MmpE may regulate lysosomal trafficking by interfering with phagosome–lysosome fusion.

To further validate MmpE’s role in phagosome maturation, we will perform fluorescence colocalization assays in THP-1 macrophages infected with BCG/wt, ∆mmpE, complemented, and NLS-mutant strains. Co-staining with LAMP1 and LysoTracker will allow us to assess whether the ∆mmpE mutant is more efficiently trafficked to lysosomes.

(4) The role of MmpE as a mycobacterial effector would be more relevant using virulent mycobacterial strains such as H37Rv.

We thank the reviewer for the comment. Previously, the role of Rv2577/MmpE as a virulence factor has been demonstrated in M. tuberculosis CDC 1551, where its deletion significantly reduced bacterial replication in mouse lungs at 30 days post-infection (Forrellad et al., Front Microbiol, 2020). However, that study did not explore the underlying mechanism of MmpE function. In our work, we found that MmpE enhances M. bovis BCG survival in both macrophages (THP-1 and RAW264.7) and mice (Figure 2A-B, Figure 6A), consistent with its proposed role in virulence. To investigate the molecular mechanism by which MmpE promotes intracellular survival, we used M. bovis BCG as a biosafe surrogate and this model is widely accepted for studying mycobacterial pathogenesis (Wang et al., Nat Immunol, 2025; Wang et al., Nat Commun, 2017; Péan et al., Nat Commun, 2017).

Reviewer #2 (Public review):

Summary:

In this paper, the authors have characterized Rv2577 as a Fe3+/Zn2+ -dependent metallophosphatase and a nucleomodulin protein. The authors have also identified His348 and Asn359 as critical residues for Fe3+ coordination. The authors show that the proteins encode for two nuclease localization signals. Using C-terminal Flag expression constructs, the authors have shown that the MmpE protein is secretory. The authors have prepared genetic deletion strains and show that MmpE is essential for intracellular survival of M. bovis BCG in THP-1 macrophages, RAW264.7 macrophages, and a mouse model of infection. The authors have also performed RNA-seq analysis to compare the transcriptional profiles of macrophages infected with wild-type and MmpE mutant strains. The relative levels of ~ 175 transcripts were altered in MmpE mutant-infected macrophages and the majority of these were associated with various immune and inflammatory signalling pathways. Using these deletion strains, the authors proposed that MmpE inhibits inflammatory gene expression by binding to the promoter region of a vitamin D receptor. The authors also showed that MmpE arrests phagosome maturation by regulating the expression of several lysosome-associated genes such as TFEB, LAMP1, LAMP2, etc. These findings reveal a sophisticated mechanism by which a bacterial effector protein manipulates gene transcription and promotes intracellular survival.

Strength:

The authors have used a combination of cell biology, microbiology, and transcriptomics to elucidate the mechanisms by which Rv2577 contributes to intracellular survival.

Weakness:

The authors should thoroughly check the mice data and show individual replicate values in bar graphs.

We kindly appreciate the reviewer for the advice. We will update the relevant mice data in the revised manuscript.

Reviewer #3 (Public review):

Summary:

In this manuscript titled "Mycobacterial Metallophosphatase MmpE Acts as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival", Chen et al describe biochemical characterisation, localisation and potential functions of the gene using a genetic approach in M. bovis BCG and perform macrophage and mice infections to understand the roles of this potentially secreted protein in the host cell nucleus. The findings demonstrate the role of a secreted phosphatase of M. bovis BCG in shaping the transcriptional profile of infected macrophages, potentially through nuclear localisation and direct binding to transcriptional start sites, thereby regulating the inflammatory response to infection.

Strengths:

The authors demonstrate using a transient transfection method that MmpE when expressed as a GFP-tagged protein in HEK293T cells, exhibits nuclear localisation. The authors identify two NLS motifs that together are required for nuclear localisation of the protein. A deletion of the gene in M. bovis BCG results in poorer survival compared to the wild-type parent strain, which is also killed by macrophages. Relative to the WT strain-infected macrophages, macrophages infected with the ∆mmpE strain exhibited differential gene expression. Overexpression of the gene in HEK293T led to occupancy of the transcription start site of several genes, including the Vitamin D Receptor. Expression of VDR in THP1 macrophages was lower in the case of ∆mmpE infection compared to WT infection. This data supports the utility of the overexpression system in identifying potential target loci of MmpE using the HEK293T transfection model. The authors also demonstrate that the protein is a phosphatase, and the phosphatase activity of the protein is partially required for bacterial survival but not for the regulation of the VDR gene expression.

Weaknesses:

(1)   While the motifs can most certainly behave as NLSs, the overexpression of a mycobacterial protein in HEK293T cells can also result in artefacts of nuclear localisation. This is not unprecedented. Therefore, to prove that the protein is indeed secreted from BCG, and is able to elicit transcriptional changes during infection, I recommend that the authors (i) establish that the protein is indeed secreted into the host cell nucleus, and (ii) the NLS mutation prevents its localisation to the nucleus without disrupting its secretion.

We kindly appreciate the reviewer for the advice and will include the relevant experiments in the revised manuscript. The localization of WT MmpE and the NLS mutated MmpE will be tested in the BCG infected macrophages.

Demonstration that the protein is secreted: Supplementary Figure 3 - Immunoblotting should be performed for a cytosolic protein, also to rule out detection of proteins from lysis of dead cells. Also, for detecting proteins in the secreted fraction, it would be better to use Sauton's media without detergent, and grow the cultures without agitation or with gentle agitation. The method used by the authors is not a recommended protocol for obtaining the secreted fraction of mycobacteria.

We agree with the reviewer and we will further validate the secretion of MmpE using the tested protocol.

Demonstration that the protein localises to the host cell nucleus upon infection: Perform an infection followed by immunofluorescence to demonstrate that the endogenous protein of BCG can translocate to the host cell nucleus. This should be done for an NLS1-2 mutant expressing cell also.

We will add this experiment in the revised manuscript.

(2) In the RNA-seq analysis, the directionality of change of each of the reported pathways is not apparent in the way the data have been presented. For example, are genes in the cytokine-cytokine receptor interaction or TNF signalling pathway expressed more, or less in the ∆mmpE strain?

We thank the reviewer for pointing this out and fully agree that conventional KEGG pathway enrichment diagrams do not convey the directionality of individual gene expression changes within each pathway. While KEGG enrichment analysis identifies pathways that are statistically overrepresented among differentially expressed genes, it does not indicate whether individual genes within those pathways are upregulated or downregulated.

To address this, we re-analyzed the expression trends of DEGs within each significantly enriched KEGG pathway. The results show that key immune-related pathways, including cytokine–cytokine receptor interaction, TNF signaling, NF-κB signaling, and chemokine signaling, are collectively upregulated in THP-1 macrophages infected with ∆mmpE strain compared to those infected with the wild-type BCG strain. The full list of DEGs will be provided in the supplementary materials. The complete RNA-seq dataset has been deposited in the GEO database, and the accession number will be included in the revised manuscript.

(3) Several of these pathways are affected as a result of infection, while others are not induced by BCG infection. For example, BCG infection does not, on its own, produce changes in IL1β levels. As the author s did not compare the uninfected macrophages as a control, it is difficult to interpret whether ∆mmpE induced higher expression than the WT strain, or simply did not induce a gene while the WT strain suppressed expression of a gene. This is particularly important because the strain is attenuated. Does the attenuation have anything to do with the ability of the protein to induce lysosomal pathway genes? Does induction of this pathway lead to attenuation of the strain? Similarly, for pathways that seem to be downregulated in the ∆mmpE strain compared to the WT strain, these might have been induced upon infection with the WT strain but not sufficiently by the ∆mmpE strain due to its attenuation/ lower bacterial burden.

We thank the reviewer for the comment. We will update qRT-PCR data with the uninfected macrophages as a control in the revised manuscript.

Wild-type Mycobacterium bovis BCG strain still has the function of inhibiting phagosome maturation (Branzk et al., Nat Immunol, 2014; Weng et al., Nat Commun, 2022). Forrellad et al. previously identified Rv2577/MmpE as a virulence factor in M. tuberculosis and disruption of the MmpE gene impairs the ability of M. tuberculosis to arrest phagosome maturation (Forrellad et al., Front Microbiol, 2020). In our study, transcriptomic and qRTPCR data (Figures 4C and G, S4C) show that deletion of mmpE in M. bovis BCG leads to upregulation of lysosomal biogenesis and acidification genes, including TFEB, LAMP1, and vATPase. To further validate MmpE’s role in phagosome maturation, we will perform fluorescence colocalization assays in THP-1 macrophages infected with BCG/wt, ∆mmpE, complemented, and NLS-mutant strains. Co-staining with LAMP1 and LysoTracker will assess whether the ∆mmpE mutant is more efficiently trafficked to lysosomes.

Furthermore, CFU assays demonstrated that the ∆mmpE strain exhibits markedly reduced bacterial survival in both human THP-1 and murine RAW264.7 macrophages, as well as in mice, compared to the wild-type strain (Figures 4A and C, 6A). These findings suggest that the loss of MmpE compromises bacterial survival, likely due to enhanced lysosomal trafficking and acidification. This supports previous studies showing that increased lysosomal activity promotes mycobacterial clearance (Gutierrez et al., Cell, 2004; Pilli et al., Immunity, 2012).

(4) CHIP-seq should be performed in THP1 macrophages, and not in HEK293T. Overexpression of a nuclear-localised protein in a non-relevant line is likely to lead to several transcriptional changes that do not inform us of the role of the gene as a transcriptional regulator during infection.

We thank the reviewer for the comment. We performed ChIP-seq in HEK293T cells is based on the fact that this cell line is widely used in ChIP-based assays due to its high transfection efficiency, robust nuclear protein expression, and well-annotated genome (Lampe et al., Nat Biotechnol, 2024; Marasco et al., Cell, 2022). These features make HEK293T an ideal system for the initial identification of genome wide chromatin binding profiles of novel nuclear effectors such as MmpE.

Furthermore, we validated the major observations in THP-1 macrophages, including (i) RNAseq of THP-1 cells infected with either WT BCG or ∆mmpE strains revealed significant transcriptional changes in immune and lysosomal pathways (Figure 4A); (ii) Integrated analysis of CUT&Tag and RNA-seq data identified 298 genes in infected THP-1 cells that exhibited both MmpE binding and corresponding expression changes. Among these, VDR was validated as a direct transcriptional target of MmpE using EMSA and ChIP-PCR (Figures 5E-J, S5D-F). Notably, the signaling pathways associated with MmpE-bound genes, including PI3K-Akt-mTOR signaling and lysosomal function, substantially overlap with those transcriptionally modulated in infected THP-1 macrophages (Figures 4B-G, S4B-C, S5C-D), further supporting the biological relevance of the ChIP-seq data obtained from HEK293T cells.

(5) I would not expect to see such large inflammatory reactions persisting 56 days postinfection with M. bovis BCG. Is this something peculiar for an intratracheal infection with 1x107 bacilli? For images of animal tissue, the authors should provide images of the entire lung lobe with the zoomed-in image indicated as an inset.

We thank the reviewer for the comment. The lung inflammation peaked at days 21–28 and had clearly subsided by day 56 across all groups (Figure 6B), consistent with the expected resolution of immune responses to an attenuated strain like M. bovis BCG. This temporal pattern is in line with previous studies using intravenous or intratracheal BCG vaccination in mice and macaques, which also demonstrated robust early immune activation followed by resolution over time (Smith et al., Nat Microbiol, 2025; Darrah et al., Nature, 2020).

In this study, the infectious dose (1×10⁷ CFU intratracheally) was selected based on previous studies in which intratracheal delivery of 1×10⁷CFU produced consistent and measurable lung immune responses and pathology without causing overt illness or mortality (Xu et al., Sci Rep, 2017; Niroula et al., Sci Rep, 2025). We will provide whole-lung lobe images with zoomed-in insets in the revised manuscript.

(6) For the qRT-PCR based validation, infections should be performed with the MmpEcomplemented strain in the same experiments as those for the WT and ∆mmpE strain so that they can be on the same graph, in the main manuscript file. Supplementary Figure 4 has three complementary strains. Again, the absence of the uninfected, WT, and∆mmpE infected condition makes interpretation of these data very difficult.

We thank the reviewer for the comment. As suggested, we will conduct the qRT-PCR experiment including the uninfected, WT, ∆mmpE, Comp-MmpE, and the three complementary strains infecting THP-1 cells. The updated data will be provided in the revised manuscript.

(7) The abstract mentions that MmpE represses the PI3K-Akt-mTOR pathway, which arrests phagosome maturation. There is not enough data in this manuscript in support of this claim. Supplementary Figure 5 does provide qRT-PCR validation of genes of this pathway, but the data do not indicate that higher expression of these pathways, whether by VDR repression or otherwise, is driving the growth restriction of the ∆mmpE strain.

We thank the reviewer for the comment. The role of MmpE in phagosome maturation was previously characterized. Disruption of mmpE impairs the ability of M. tuberculosis to arrest lysosomal trafficking (Forrellad et al., Front Microbiol, 2020). In this study, we further found that MmpE suppresses the expression of key lysosomal genes, including TFEB, LAMP1, LAMP2, and ATPase subunits (Figure 4G), suggesting MmpE is involved in arresting phagosome maturation. As noted, the genes in the PI3K–Akt–mTOR pathway are upregulated in ∆mmpE-infected macrophages (Figure S5C).

To functionally validate this, we will conduct two complementary experimental approaches:

(i) Immunofluorescence assays: We will assess phagosome maturation and lysosomal fusion in THP-1 cells infected with BCG/wt, ∆mmpE, Comp-MmpE, and NLS mutant strains. Colocalization of intracellular bacteria with LAMP1 and LysoTracker will be quantified to determine whether the ∆mmpE strain is more efficiently trafficked to lysosomes.

(ii) CFU assays: We will perform CFU assays in THP-1 cells infected with BCG/wt or ∆mmpE in the presence or absence of PI3K-Akt-mTOR pathway inhibitors (e.g., Dactolisib), to assess whether activation of this pathway contributes to the intracellular growth restriction observed in the ∆mmpE strain.

(8) The relevance of the NLS and the phosphatase activity is not completely clear in the CFU assays and in the gene expression data. Firstly, there needs to be immunoblot data provided for the expression and secretion of the NLS-deficient and phosphatase mutants. Secondly, CFU data in Figure 3A, C, and E must consistently include both the WT and ∆mmpE strain.

We thank the reviewer for the comment. We will provide immunoblot data for the expression and secretion of the NLS-deficient and phosphatase mutants. Additionally, we will revise Figure 3A, 3C, and 3E to consistently include both the WT and ΔmmpE strains in the CFU assays.

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Author response:

Reviewer #1 (Public review):

Summary:

This work by Govorunova et al. identified three naturally blue-shifted channelrhodopsins (ChRs) from ancyromonads, namely AnsACR, FtACR, and NlCCR. The phylogenetic analysis places the ancyromonad ChRs in a distinct branch, highlighting their unique evolutionary origin and potential for novel applications in optogenetics. Further characterization revealed the spectral sensitivity, ionic selectivity, and kinetics of the newly discovered AnsACR, FtACR, and NlCCR. This study also offers valuable insights into the molecular mechanism underlying the function of these ChRs, including the roles of specific residues in the retinal-binding pocket. Finally, this study validated the functionality of these ChRs in both mouse brain slices (for AnsACR and FtACR) and in vivo in Caenorhabditis elegans (for AnsACR), demonstrating the versatility of these tools across different experimental systems.

In summary, this work provides a potentially valuable addition to the optogenetic toolkit by identifying and characterizing novel blue-shifted ChRs with unique properties.

Strengths:

This study provides a thorough characterization of the biophysical properties of the ChRs and demonstrates the versatility of these tools in different ex vivo and in vivo experimental systems. The mutagenesis experiments also revealed the roles of key residues in the photoactive site that can affect the spectral and kinetic properties of the channel.

We thank the Reviewer for his/her positive evaluation of our work.

Weaknesses:

While the novel ChRs identified in this work are spectrally blue-shifted, there still seems to be some spectral overlap with other optogenetic tools. The authors should provide more evidence to support the claim that they can be used for multiplex optogenetics and help potential end-users assess if they can be used together with other commonly applied ChRs. Additionally, further engineering or combination with other tools may be required to achieve truly orthogonal control in multiplexed experiments.

To demonstrate the usefulness of ancyromonad ChRs for multiplex optogenetics as a proof of principle, we co-expressed AnsACR with the red-shifted cation-conducting ChR Chrimson and measured net photocurrent generated by this combination as a function of the wavelength. We found that it is hyperpolarizing in the blue region of the spectrum, and depolarizing at the red region. In the revision, we added a new panel (Figure 1D) showing these results and the following paragraph to the main text:

“To test the possibility of using AnsACR in multiplex optogenetics, we co-expressed it with the red-shifted CCR Chrimson (Klapoetke et al., 2014) fused to an EYFP tag in HEK293 cells. We measured the action spectrum of the net photocurrents with 4 mM Cl^- in the pipette, matching the conditions in the neuronal cytoplasm (Doyon, Vinay et al. 2016). Figure 1D, black shows that the direction of photocurrents was hyperpolarizing upon illumination with λ<500 nm and depolarizing at longer wavelengths. A shoulder near 520 nm revealed a FRET contribution from EYFP (Govorunova, Sineshchekov et al. 2020), which was also observed upon expression of the Chrimson construct alone (Figure 1D, red)”.

In the C. elegans experiments, partial recovery of pharyngeal pumping was observed after prolonged illumination, indicating potential adaptation. This suggests that the effectiveness of these ChRs may be limited by cellular adaptation mechanisms, which could be a drawback in long-term experiments. A thorough discussion of this challenge in the application of optogenetics tools would prove very valuable to the readership.

We added the following paragraph to the revised Discussion:

“One possible explanation of the partial recovery of pharyngeal pumping that we observed after 15-s illumination, even at the highest tested irradiance, is continued attenuation of photocurrent during prolonged illumination (desensitization). However, the rate of AnsACR desensitization (Figure 1 – figure supplement 4A and Figure 1 – figure supplement 5A) is much faster than the rate of the pumping recovery, reducing the likelihood that desensitization is driving this phenomenon. Another possible reason for the observed adaptation is an increase in the cytoplasmic Cl^- concentration owing to AnsACR activity and hence a breakdown of the Cl^- gradient on the neuronal membrane. The C. elegans pharynx is innervated by 20 neurons, 10 of which are cholinergic (Pereira, Kratsios et al. 2015). A pair of MC neurons is the most important for regulation of pharyngeal pumping, but other pharyngeal cholinergic neurons, including I1, M2, and M4, also play a role (Trojanowski, Padovan-Merhar et al. 2014). Moreover, the pharyngeal muscles generate autonomous contractions in the presence of acetylcholine tonically released from the pharyngeal neurons (Trojanowski, Raizen et al. 2016). Given this complexity, further elucidation of pharyngeal pumping adaptation mechanisms is beyond the scope of this study.”

Reviewer #2 (Public review):

Summary:

Govorunova et al present three new anion opsins that have potential applications in silencing neurons. They identify new opsins by scanning numerous databases for sequence homology to known opsins, focusing on anion opsins. The three opsins identified are uncommonly fast, potent, and are able to silence neuronal activity. The authors characterize numerous parameters of the opsins.

Strengths:

This paper follows the tradition of the Spudich lab, presenting and rigorously characterizing potentially valuable opsins. Furthermore, they explore several mutations of the identified opsin that may make these opsins even more useful for the broader community. The opsins AnsACR and FtACR are particularly notable, having extraordinarily fast onset kinetics that could have utility in many domains. Furthermore, the authors show that AnsACR is usable in multiphoton experiments having a peak photocurrent in a commonly used wavelength. Overall, the author's detailed measurements and characterization make for an important resource, both presenting new opsins that may be important for future experiments, and providing characterizations to expand our understanding of opsin biophysics in general.

We thank the Reviewer for his/her positive evaluation of our work.

Weaknesses:

First, while the authors frequently reference GtACR1, a well-used anion opsin, there is no side-by-side data comparing these new opsins to the existing state-of-the-art. Such comparisons are very useful to adopt new opsins.

GtACR1 exhibits the peak sensitivity at 515 nm and therefore is poorly suited for combination with red-shifted CCRs or fluorescent sensors, unlike blue-light-absorbing ancyromonad ACRs. Nevertheless, we conducted side-by-side comparison of ancyromonad ChRs, GtACR1 and GtACR2, the latter of which has the spectral maximum at 470 nm. The results are shown in the new Figures 1E and F, and the new multipanel Figure 1 – figure supplement 4 added in the revision. We also added the following text, describing these results, to the revised Results section:

“Figures 1E and F show the dependence of the peak photocurrent amplitude and reciprocal peak time, respectively, on the photon flux density for ancyromonad ChRs and GtACRs. The current amplitude saturated earlier than the time-to-peak for all tested ChRs. Figure 1 – figure supplement 4A-E shows normalized photocurrent traces recorded at different photon densities. Quantitation of desensitization at the end of 1-s illumination revealed a complex light dependence (Figure 1, Figure Supplement 4F). Figure 1 – figure supplement 5 shows normalized photocurrent traces recorded in response to a 5-s light pulse of the maximal available intensity and the magnitude of desensitization at its end.”

Next, multiphoton optogenetics is a promising emerging field in neuroscience, and I appreciate that the authors began to evaluate this approach with these opsins. However, a few additional comparisons are needed to establish the user viability of this approach, principally the photocurrent evoked using the 2p process, for given power densities. Comparison across the presented opsins and GtACR1 would allow readers to asses if these opsins are meaningfully activated by 2P.

We carried out additional 2P experiments in ancyromonad ChRs, GtACR1 and GtACR2 and added their results to a new main-text Figure 6 and Figure 6 – figure supplement 1. We added the new section describing these results, “Two-photon excitation”, to the main text in the revision:

“To determine the 2P activation range of AnsACR, FtACR, and NlCCR, we conducted raster scanning using a conventional 2P laser, varying the excitation wavelength between 800 and 1,080 nm (Figure 6 – figure supplement 1). All three ChRs generated detectable photocurrents with action spectra showing maximal responses at ~925 nm for AnsACR, 945 nm for FtACR, and 890 nm for NlCCR (Figure 6A). These wavelengths fall within the excitation range of common Ti:Sapphire lasers, which are widely used in neuroscience laboratories and can be tuned between ~700 nm and 1,020-1,300 nm. To assess desensitization, cells expressing AnsACR, FtACR, or NlCCR were illuminated at the respective peak wavelength of each ChR at 15 mW for 5 seconds. GtACR1 and GtACR2, previously used in 2P experiments (Forli, Vecchia et al. 2018, Mardinly, Oldenburg et al. 2018), were included for comparison. The normalized photocurrent traces recorded under these conditions are shown in Figure 6B-F. The absolute amplitudes of 2P photocurrents at the peak time and at the end of illumination are shown in Figure 6G and H, respectively. All five tested variants exhibited comparable levels of desensitization at the end of illumination (Figure 6I).”

Reviewer #3 (Public review):

Summary:

The authors aimed to develop Channelrhodopsins (ChRs), light-gated ion channels, with high potency and blue action spectra for use in multicolor (multiplex) optogenetics applications. To achieve this, they performed a bioinformatics analysis to identify ChR homologues in several protist species, focusing on ChRs from ancyromonads, which exhibited the highest photocurrents and the most blue-shifted action spectra among the tested candidates. Within the ancyromonad clade, the authors identified two new anion-conducting ChRs and one cation-conducting ChR. These were characterized in detail using a combination of manual and automated patch-clamp electrophysiology, absorption spectroscopy, and flash photolysis. The authors also explored sequence features that may explain the blue-shifted action spectra and differences in ion selectivity among closely related ChRs.

Strengths:

A key strength of this study is the high-quality experimental data, which were obtained using well-established techniques such as manual patch-clamp and absorption spectroscopy, complemented by modern automated patch-clamp approaches. These data convincingly support most of the claims. The newly characterized ChRs expand the optogenetics toolkit and will be of significant interest to researchers working with microbial rhodopsins, those developing new optogenetic tools, as well as neuro- and cardioscientists employing optogenetic methods.

We thank the Reviewer for his/her positive evaluation of our work.

Weaknesses:

This study does not exhibit major methodological weaknesses. The primary limitation of the study is that it includes only a limited number of comparisons to known ChRs, which makes it difficult to assess whether these newly discovered tools offer significant advantages over currently available options.

We conducted side-by-side comparison of ancyromonad ChRs and GtACRs, wildly used for optical inhibition of neuronal activity. The results are shown in the new Figures 1E and F, and the new multipanel Figure 1 – figure supplement 4 and Figure 1 – figure supplement 5 added in the revision. We also added the following text, describing these results, to the revised Results section:

“Figures 1E and F show the dependence of the peak photocurrent amplitude and reciprocal peak time, respectively, on the photon flux density for ancyromonad ChRs and GtACRs. The current amplitude saturated earlier than the time-to-peak for all tested ChRs. Figure 1 – figure supplement 4A-E shows normalized photocurrent traces recorded at different photon densities. Quantitation of desensitization at the end of 1-s illumination revealed a complex light dependence (Figure 1, Figure Supplement 4F). Figure 1 – figure supplement 5 shows normalized photocurrent traces recorded in response to a 5-s light pulse of the maximal available intensity and the magnitude of desensitization at its end.”

Additionally, although the study aims to present ChRs suitable for multiplex optogenetics, the new ChRs were not tested in combination with other tools. A key requirement for multiplexed applications is not just spectral separation of the blue-shifted ChR from the red-shifted tool of interest but also sufficient sensitivity and potency under low blue-light conditions to avoid cross-activation of the respective red-shifted tool. Future work directly comparing these new ChRs with existing tools in optogenetic applications and further evaluating their multiplexing potential would help clarify their impact.

As a proof of principle, we co-expressed AnsACR with the red-shifted cation-conducting CCR Chrimson and demonstrated that the net photocurrent generated by this combination is hyperpolarizing in the blue region of the spectrum, and depolarizing at the red region. In the revision, we added a new panel (Figure 1D) showing these results and the following paragraph to the main text:

“To test the possibility of using AnsACR in multiplex optogenetics, we co-expressed it with the red-shifted CCR Chrimson (Klapoetke et al., 2014) fused to an EYFP tag in HEK293 cells. We measured the action spectrum of the net photocurrents with 4 mM Cl^- in the pipette, matching the conditions in the neuronal cytoplasm (Doyon, Vinay et al. 2016). Figure 1D, black shows that the direction of photocurrents was hyperpolarizing upon illumination with λ<500 nm and depolarizing at longer wavelengths. A shoulder near 520 nm revealed a FRET contribution from EYFP (Govorunova, Sineshchekov et al. 2020), which was also observed upon expression of the Chrimson construct alone (Figure 1D, red)”.

Reviewing Editor Comments:

The reviewers suggest that direct comparison to GtACR1 is the most important step to make this work more useful to the community.

We followed the Reviewers’ recommendations and carried out side-by-side comparison of ancyromonad ChRs and GtACR1 as well as GtACR2 (Figure 1E and F, Figure 1 – figure supplement 4, Figure 1 – figure supplement 5, and Figure 6). Note, however, that GtACR1’s spectral maximum is at 515 nm, which makes it poorly suitable for blue light excitation. Also, ChRs are known to perform very differently in different cell types and upon expression of their genes in different vector backbones, so our results cannot be generalized for all experimental systems. Each ChR user needs to select the most appropriate tool for his/her purpose by testing several candidates in his/her own experimental setting.

Reviewer #1 (Recommendations for the authors):

(1) The figure legend for Figure 2D-I appears to be incomplete. Please provide a detailed explanation of the panels.

In the revision, we have expanded the legend of Figure 2 to explain all individual panels.

(2) The meaning of the Vr shift (Y-axis in Figure 2H-I) should be clarified in the main text to aid reader understanding.

In the revision, we added the phrase “which indicated higher relative permeability to NO₃ than to Cl^-“ to explain the meaning of the Vr shift upon replacement of Cl^- with NO₃-.

(3) Adding statistical analysis for the peak and end photocurrent values in Figure 2D-F would strengthen the claim that there is minimal change in relative permeability during illumination.

In the revision, we added the V_r values for the peak photocurrent to Figure 2H-I, which already contained the V_r values for the end photocurrent, and carried out a statistical analysis of their comparison. The following sentence was added to the text in the revision:

“The V_r values of the peak current and that at the end of illumination were not significantly different by the two-tailed Wilcoxon signed-rank test (Fig. 2G), indicating no change in the relative permeability during illumination.”

(4) Figure 4H and I seem out of place in Figure 4, as the title suggests a focus on wild-proteins and AnsACR mutants. The authors could consider moving these panels to Figure 3 for better alignment with the content.

As noted below, we changed the panel order in Figure 4 upon the Reviewer’s request. In particular, former Figure 4I is Figure 4C in the revision, and former Figure 4H is now panel C in Figure 3 – figure supplement 1 in the revision. We rearranged the corresponding section of the text (highlighted yellow in the manuscript).

(5) The characterization section could be strengthened by including data on the pH sensitivity of FtACR, which is currently missing from the main figures.

Upon the Reviewer’s request, we carried out pH titration of FtACR absorbance and added the results as Figure 4B in the revision.

(6) The logic in Figure 4A-G appears somewhat disjointed. For example, Figure 4A shows pH sensitivity for WT AnsACR and the G86E mutant, while Figure 4 B-D shifts to WT AnsACR and the D226N mutant, and Figure 4E returns to the G86E mutant. Reorganizing or clarifying the flow would improve readability.

We followed the Reviewer’s advice and changed the panel order in Figure 4. In the revised version, the upper row (panels A-C) shows the pH titration data of the three WTs, the middle row (panels D-F) shows analysis of the AnsACR_D226N mutant, and the lower row (panels G-I) shows analysis of the AnsACR_G88E mutant. We also rearranged accordingly the description of these panels in the text.

(7) In Figure 5A, "NIACR" should likely be corrected to "NlCCR".

We corrected the typo in the revision.

(8) The statistical significance in Figure 6C and D is somewhat confusing. Clarifying which groups are being compared and using consistent symbols would improve interoperability.

In the revision, we improved the figure panels and legend to clarify that the comparisons are between the dark and light stimulation groups within the same current injection.

(9) The authors pointed out that at rest or when a small negative current was injected, the neurons expressing Cl- permeable ChRs could generate a single action potential at the beginning of photostimulation, as has been reported before. The authors could help by further discussing if and how this phenomenon would affect the applicability of such tools.

We mentioned in the revised Discussion section that activation of ACRs in the axons could depolarize the axons and trigger synaptic transmission at the onset of light stimulation, and this undesired excitatory effect need to be taken into consideration when using ACRs.

Reviewer #2 (Recommendations for the authors):

Govorunova et al present three new anion opsins that have potential applications in silencing neurons. This paper follows the tradition of the Spudich lab, presenting and rigorously characterizing potentially valuable opsins. Furthermore, they explore several mutations of the identified opsin that may make these opsins even more useful for the broader community. In general, I feel positively about this manuscript. It presents new potentially useful opsins and provides characterization that would enable its use. I have a few recommendations below, mostly centered around side-by-side comparisons to existing opsins.

(1) My primary concern is that while there is a reference to GtACR1, a highly used opsin first described by this team, they do not present any of this data side by side.

When evaluating opsins to use, it is important to compare them to the existing state of the art. As a potential user, I need to know where these opsins differ. Citing other papers does not solve this as, even within the same lab, subtle methodological differences or data plotting decisions can obscure important differences.

As we explained in the response to the public comments, we carried out side-by-side comparison of ancyromonad ChRs and GtACRs as requested by the Reviewer. The results are shown in the new Figures 1E and F, and the new multipanel Figure 1 – figure supplement 4 and Figure 1 – figure supplement 5, added in the revision. However, we would like to emphasize a limited usefulness of such comparative analysis, as ChRs are known to perform very differently in different cell types and upon expression of their genes in different vector backbones, so our results cannot be generalized for all experimental systems. Each ChR user needs to select the most appropriate tool for his/her purpose by testing several candidates in his/her own experimental setting.

(2) Multiphoton optogenetics is an emerging field of optogenetics, and it is admirable that the authors address it here. The authors should present more 2p characterization, so that it can be established if these new opsins are viable for use with 2P methods, the way GtACR1 is. The following would be very useful for 2P characterization:

Photocurrents for a given power density, compared to GtACR1 and GtACR2.

The new Figure 6 (B-F) added in the revision shows photocurrent traces recorded from the three ancyromonad ChRs and  two GtACRs upon 2P excitation of a given power density.

Comparing NICCR and FtACR's wavelength specificity and photocurrent. If these opsins are too weak to create reasonable 2P spectra, this difference should be discussed.

The new Figure 6A shows the 2P action spectra of all three ancyromonad ChRs.

A Trace and calculated photocurrent kinetics to compare 1P and 2P. This need not be the flash-based absorption characterization of Figure 3, but a side-by-side photocurrent as in Figure 2.

As mentioned above, photocurrent traces recorded from ancyromonad ChRs and GtACRs upon 2P excitation are shown in the new Figure 6 (B-F). However, direct comparison of the 2P data with the 1P data is not possible, as we used laser scanning illumination for the former and wild-field illumination for the latter.

Characterization of desensitization. As the authors mention, many opsins undergo desensitization, presenting the ratio of peak photocurrent vs that at multiple time points (probably up to a few seconds) would provide evidence for how effectively these constructs could be used in different scenarios.

We conducted a detailed analysis of desensitization under both 1P and 2P excitation. The new Figure 1 – figure supplement 4 and Figure 1 – figure supplement 5 show the data obtained under 1P excitation, and the new Figure 6 shows the data for 2P conditions.

I have to admit, that by the end of the paper, I was getting confused as to which of the three original constructs had which property, and how that was changing with each mutation. I would suggest that a table summarizing each opsin and mutation with its onset and offset kinetics, peak wavelength, photocurrent, and ion selectivity would greatly increase the ability to select and use opsins in the future.

In the revision, we added a table of the spectroscopic properties of all tested mutants as Supplementary File 2. This study did not aim to analyze other parameters listed by the Reviewer. We added the following sentence referring to this table to the main text:

“Supplementary File 2 contains the λ values of the half-maximal amplitude of the long-wavelength slope of the spectrum, which can be estimated more accurately from the action spectra than the λ of the maximum.”

It may be out of the scope of this manuscript, but if a soma localization sequence can be shown to remove the 'axonal spiking' (as described in line 441), this would be a significant addition to the paper.

Our previous study (Messier et al., 2018, doi: 10.7554/eLife.38506) showed that a soma localization sequence can reduce, but not eliminate, the axonal spiking. We plan to test these new ACRs with the trafficking motifs in the future.

NICCR appears to have the best photocurrents of all tested opsins in this paper. It seems odd that it was omitted from the mouse cortical neurons experiments.

We have not included analysis of NlCCR behavior in neurons because we are preparing a separate manuscript on this ChR.

Figure 6 would benefit from more gradation in the light powers used to silence and would benefit from comparison to GtACR. I suggest using a fixed current with a series of illumination intensities to see which of the three opsins (or GtACR) is most effective at silencing. At present, it looks binary, and a user cannot evaluate if any of these opsins would be better than what is already available.

In the revision, we added the data comparing the light sensitivity of AnsACR and FtACR with previously identified GtACR1 and GtACR2 (new Figure 1E and F) to help users compare these ACRs. Although they are less sensitive to light comparing to GtACR1 and GtACR2, they could still be activated by commercially available light sources if the expression levels are similar. Less sensitive ACRs may have less unwanted activation when using with other optogenetic tools.

Reviewer #3 (Recommendations for the authors):

Suggested Improvements to Experiments, Data, or Analyses:

(1) Line 25: "significantly exceeding those by previously known tools" and Line 408: "NlCCR is the most blue-shifted among ancyromonad ChRs and generates larger photocurrents than the earlier known CCRs with a similar absorption maximum." As noted in the public review, this statement applies only to a very specific subgroup of ChRs with spectral maxima below 450 nm. If the goal was to claim that NlCCR is a superior tool among a broader range of blue-light-activated ChRs, direct comparisons with state-of-the-art ChRs such as ChR2 T159C (Berndt et al., 2011), CatCh (Kleinlogel et al., 2014), CoChR (Klapoetke et al., 2014), CoChR-3M (Ganjawala et al., 2019), or XXM 2.0 (Ding et al., 2022) would be beneficial. If the goal was to demonstrate superiority among tools with spectra below 450 nm, I suggest explicitly stating this in the paper.

The Reviewer correctly inferred that we emphasized the superiority of NlCCR among tools with similar spectral maxima, not all blue-light-activated ChRs available for neuronal photoexcitation, most of which exhibit absorption maxima at longer wavelengths. To clarify this, we added “with similar spectral maxima” to the sentence in the original Line 25. The sentence in Line 408 already contains this clarification: “with a similar absorption maximum”.

(2) Lines 111-113: "The absorption spectra of the purified proteins were slightly blue-shifted from the respective photocurrent action spectra (Figure 1D), likely due to the presence of non-electrogenic cis-retinal-bound forms." I would be skeptical of this statement. The spectral shifts in NlCCR and AnsACR are small and may fall within the range of experimental error. The shift in FtACR is more apparent; however, if two forms coexist in purified protein, this should be reflected as two Gaussian peaks in the absorption spectrum (or at least as a broader total peak reflecting two states with close maxima and similar populations). On the contrary, the action spectrum appears to have two peaks, one potentially below 465 nm. Generally, neither spectrum appears significantly broader than a typical microbial rhodopsin spectrum. This question could be clarified by quantifying the widths of the absorption and action spectra or by overlaying them on the same axis. In my opinion, the two spectra seem very similar, and just appearance of the "bump" in the action spectum shifts the apparent maximum of the action spectrum to the red. If there were two states, then they should both be electrogenic, and the slight difference in spectra might be explained by something else (e.g. by a slight difference in the quantum yields of the two states).

As the Reviewer suggested, in the revision we added a new figure (Figure 1 – figure supplement 2), showing the overlay of the absorption and action spectra of each ancyromonad ChR. This figure shows that the absorption spectra are wider than the action spectra (especially in AnsACR and FtACR), which confirms our interpretation (contribution of the non-electrogenic blue-shifted cis-retinal-bound forms to the absorption spectrum). Note that the presence of such forms explaining a blue shift of the absorption spectrum has been experimentally verified in HcKCR1 (doi: 10.1016/j.cell.2023.08.009; 10.1038/s41467-025-56491-9). Therefore, we revised the text as follows:

“The absorption spectra of the purified proteins (Figure 1C) were slightly blue-shifted from the respective photocurrent action spectra (Figure 1 – figure supplement 3), likely due to the presence of non-electrogenic cis-retinal-bound forms. The presence of such forms, explaining the discrepancy between the absorption and the action spectra, was verified by HPLC in KCRs (Tajima et al. 2023, Morizumi et al., 2025).”

(3) Lines 135-136: "The SyncroPatch enables unbiased estimation of the photocurrent amplitude because the cells are drawn into the wells without considering their tag fluorescence." While SyncroPatch does allow unbiased selection of patched cells, it does not account for the fraction of transfected cells. Without a method to exclude non-transfected cells, which are always present in transient transfections, the comparison of photocurrents may be affected by the proportion of untransfected cells, which could vary between constructs. To clarify whether the statistically significant difference in the Kolmogorov-Smirnov test could indicate that the fraction of transfected cells after 48-72h differs between constructs, I suggest analyzing only transfected cells or reporting fractions of transfected cells by each construct.

The Reviewer correctly states that non-transfected cells are always present in transiently transfected cell populations. However, his/her suggestion to “exclude non-transfected cells” is not feasible in the absence of a criterion for such exclusion. As it is evident from our data, transient transfection results in a continuum of the amplitude values, and it is not possible to distinguish a small photocurrent from no photocurrent, considering the noise level. We would like, however, to emphasize that not excluding any cells provides an estimate of the overall potency of each ChR variant, which depends on both the fraction of transfected cells and their photocurrents. This approach mimics the conditions of in vivo experiments, when non-expressing cells also cannot be excluded.

(4) Line 176: "AnsACR and FtACR photocurrents exhibited biphasic rise." The fastest characteristic time is very close to the typical resolution of a patch-clamp experiment (RC = 50 μs for a 10 pF cell with a 5 MΩ series resistance). Thus, I am skeptical that the faster time constant of the biphasic opening represents a protein-specific characteristic time. It may not be fully resolved by patch-clamp and could simply result from low-pass filtering of a specific cell. I suggest clarifying this for the reader.

The Reviewer is right that the patch clamp setup acts as a lowpass filter. Earlier, we directly measured its time resolution (~15 μs) by recording the ultrafast (occurring on the ps time scale) charge movements related to the trans-cis isomerization (doi: 10.1111/php.12558). However, the lowpass filter of the setup can only slow the entire signal, but cannot lead to the appearance of a separate kinetic component (i.e. a monophasic process cannot become biphasic). Therefore, we believe that the biphasic photocurrent rise reflects biphasic channel opening rather than a measurement artifact. Two phases in the channel opening have also been detected in GtACR1 (doi: 10.1073/pnas.1513602112) and CrChR2 (10.1073/pnas.1818707116).

(5) Line 516: "The forward LED current was 900 mA." It would be more informative to report the light intensity rather than the forward current, as many readers may not be familiar with the specific light output of the used LED modules at this forward current.

We have added the light intensity value in the revision:

“The forward LED current was 900 mA (which corresponded to the irradiance of ~2 mW mm^-2)…”

(6) Lines 402-403: "The NlCCR ... contains a neutral residue in the counterion position (Asp85 in BR), which is typical of all ACRs. Yet, NlCCR does not conduct anions, instead showing permeability to Na+." This is not atypical for CCRs and has been demonstrated in previous works of the authors (CtCCR in Govorunova et al. 2021, ChvCCR1 in Govorunova et al. 2022). What is unique is the absence of negatively charged residues in TM2, as noted later in the current study. However, the absence of negatively charged residues in TM2 appears to be rare for ACRs as well. Not as a strong point of criticism, but to enhance clarity, I suggest analyzing the frequency of carboxylate residues in TM2 of ACRs to determine whether the unique finding is relevant to ion selectivity or to another property.

The Reviewer is correct that some CCRs lack a carboxylate residue in the D85 position, so this feature alone cannot be considered as a differentiating criterion. However, the complete absence of glutamates in TM2 is not rare in ACRs and is found, for example, in HfACR1 and CarACR2. We have discussed this issue in our earlier review (doi: 10.3389/fncel.2021.800313) and do not think that repeating this discussion in this manuscript is appropriate.

Recommendations for Writing and Presentation:

(1) Some figures contain incomplete or missing labels:

Figure 2: Panels D to I lack labels.

In the revision, we have expanded the legend of Figure 2 to explain all individual panels.

Figure 3 - Figure Supplement 1: Missing explanations for each panel.

In the revision, we changed the order of panes and explained all individual panels in the legend.

Figure 5 - Figure Supplement 1: Missing explanations for each panel.

No further explanation for individual panels in this Figure is needed because all panels show the action spectra of various mutants, the names of which are provided in the panels themselves. Repeating this information in the figure legend would be redundant.

(2) In Figure 2, "sem" is written in lowercase, whereas "SEM" is capitalized in other figures. Standardizing the format would improve consistency.

In the revision, we changed the font of the SEM abbreviation to the uppercase in all instances.

(3) Line 20: "spectrally separated molecules must be found in nature." There is no proof that they cannot be developed synthetically; rather, it is just difficult. I suggest softening this statement, as the findings of this study, together with others, will probably allow designing molecules with specified spectral properties in the future.

In the revision, we changed the cited sentence to the following:

“Multiplex optogenetic applications require spectrally separated molecules, which are difficult to engineer without disrupting channel function”.

(4) Line 216-219: "Acidification increased the amplitude of the fast current ~10-fold (Figure 4F) and shifted its Vr ~100 mV (Figure 3 - figure supplement 1D), as expected of passive proton transport. The number of charges transferred during the fast peak current was >2,000 times smaller than during the channel opening, from which we concluded that the fast current reflects the movement of the RSB proton." The claim about passive transport of the RSB proton should be clarified, as typically, passive transport is not limited to exactly one proton per photocycle, and the authors observe the increase in the fast photocurrents upon acidification.

We thank the Reviewer for pointing out the confusing character of our description. To clarify the matter, we added a new photocurrent trace to Figure 4I in the revision recorded from AnsACR_G86E at 0 mV and pH 7.4. We have rewritten the corresponding section of Results as follows:

“Its rise and decay τ corresponded to the rise and decay τ of the fast positive current recorded from AnsACR_G86E at 0 mV and neutral pH, superimposed on the fast negative current reflecting the chromophore isomerization (Figure 4I, upper black trace). We interpret this positive current as an intramolecular proton transfer to the mutagenetically introduced primary acceptor (Glu86), which was suppressed by negative voltage (Figure 4I, lower black trace). Acidification increased the amplitude of the fast negative current ~10-fold (Figure 4I, black arrow) and shifted its V_r ~100 mV to more depolarized values (Figure 4 – figure supplement 2A). This can be explained by passive inward movement of the RSB proton along the large electrochemical gradient.”

Minor Corrections:

(1) Line 204: Missing bracket in "phases in the WT (Figure 4D."

The quoted sentence was deleted during the revision.

(2) Line 288: Typo-"This Ala is conserved" should probably be "This Met is conserved."

We mean here the Ala four residues downstream from the first Ala. To avoid confusion, we changed the cited sentence to the following:

“The Ala corresponding to BR’s Gly122 is also found in AnsACR and NlCCR (Figure 5A)…”

(3) Lines 702-704: Missing Addgene plasmid IDs in "(plasmids #XXX and #YYY, respectively)."

In the revision, we added the missing plasmid IDs.

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Author response:

General Statements:

The formation of three-dimensional tubes is a fundamental process in the development of organs and aberrant tube size leads to common diseases and congenital disorders, such as polycystic kidney disease, asthma, and lung hypoplasia. The apical (luminal) extracellular matrix (ECM) plays a critical role in epithelial tube morphogenesis during organ formation, but its composition and organization remain poorly understood. Using the Drosophila embryonic salivary gland as a model, we reveal a critical role for the PAPS Synthetase (Papss), an enzyme that synthesizes the universal sulfate donor PAPS, as a critical regulator of tube lumen expansion. Additionally, we identify two zona pellucida (ZP) domain proteins, Piopio (Pio) and Dumpy (Dpy) as key apical ECM components that provide mechanical support to maintain a uniform tube diameter.

The apical ECM has a distinct composition compared to the basal ECM, featuring a diverse array of components. Many studies of the apical ECM have focused on the role of chitin and its modification, but the composition of the non-chitinous apical ECM and its role, and how modification of the apical ECM affects organogenesis remain elusive. The main findings of this manuscript are listed below.

(1) Through a deficiency screen targeting ECM-modifying enzymes, we identify Papss as a key enzyme regulating luminal expansion during salivary gland morphogenesis. 

(2) Our confocal and transmission electron microscopy analyses reveal that Papss mutants exhibit a disorganized apical membrane and condensed aECM, which are at least partially linked to disruptions in Golgi structures and intracellular trafficking. Papss is also essential for cell survival and basal ECM integrity, highlighting the role of sulfation in regulating both apical and basal ECM.

(3) Salivary gland-specific overexpression of wild-type Papss rescues all defects in Papss mutants, but the catalytically inactive mutant form does not, suggesting that defects in sulfation are the underlying cause of the phenotypes.

(4) We identify two ZP domain proteins, Piopio (Pio) and Dumpy (Dpy), as key components of the salivary gland aECM. In the absence of Papss, Pio is progressively lost from the aECM, while the Dpy-positive aECM structure is condensed and detaches from the apical membrane, resulting in a narrowed lumen. 

(5) Mutations in pio or dpy, or in Notopleural (Np), which encodes a matriptase that cleaves Pio, cause the salivary gland lumen to develop alternating bulges and constrictions. Additionally, loss of pio results in loss of Dpy in the salivary gland lumen, suggesting that the Dpycontaining filamentous structures of the aECM is critical for maintaining luminal diameter, with Pio playing an essential role in organizing this structure.

(6) We further reveal that the cleavage of the ZP domain of Pio by Np is critical for the role of Pio in organizing the aECM structure.

Overall, our findings underscore the essential role of sulfation in organizing the aECM during tubular organ formation and highlight the mechanical support provided by ZP domain proteins in maintaining tube diameter. Mammals have two isoforms of Papss, Papss1 and Papss2. Papss1 shows ubiquitous expression, with higher levels in glandular cells and salivary duct cells, suggesting a high requirement for sulfation in these cell types. Papss2 shows a more restricted expression, such as in cartilage, and mutations in Papss2 have been associated with skeletal dysplasia in humans. Our analysis of the Drosophila Papss gene, a single ortholog of human Papss1 and Papss2, reveals its multiple roles during salivary gland development. We expect that these findings will provide valuable insights into the function of these enzymes in normal development and disease in humans. Our findings on the key role of two ZP proteins, Pio and Dpy, as major components of the salivary gland aECM also provide valuable information on the organization of the non-chitinous aECM during organ formation.

We believe that our results will be of broad interest to many cell and developmental biologists studying organogenesis and the ECM, as well as those investigating the mechanisms underlying human diseases associated with conserved mutations.

Point-by-point description of the revisions:

We are delighted that all three reviewers were enthusiastic about the work. Their comments and suggestions have improved the paper. The details of the changes we have made in response to each reviewer’s comments are included in italicized text below.

Reviewer #1 (Evidence, reproducibility and clarity):

PAPS is required for all sulfotransferase reactions in which a sulfate group is covalently attached to amino acid residues of proteins or to side chains of proteoglycans. This sulfation is crucial for properly organizing the apical extracellular matrix (aECM) and expanding the lumen in the Drosophila salivary gland. Loss of Papss potentially leads to decreased sulfation, disorganizing the aECM, and defects in lumen formation. In addition, Papss loss destabilizes the Golgi structures.

In Papss mutants, several changes occur in the salivary gland lumen of Drosophila. The tube lumen is very thin and shows irregular apical protrusions. There is a disorganization of the apical membrane and a compaction of the apical extracellular matrix (aECM). The Golgi structures and intracellular transport are disturbed. In addition, the ZP domain proteins Piopio (Pio) and Dumpy (Dpy) lose their normal distribution in the lumen, which leads to condensation and dissociation of the Dpy-positive aECM structure from the apical membrane. This results in a thin and irregularly dilated lumen.

(1) The authors describe various changes in the lumen in mutants, from thin lumen to irregular expansion. I would like to know the correct lumen diameter, and length, besides the total area, by which one can recognize thin and irregular.

We have included quantification of the length and diameter of the salivary gland lumen in the stage 16 salivary glands of control, Papss mutant, and salivary gland-specific rescue embryos (Figure 1J, K). As described, Papss mutant embryos have two distinct phenotypes, one group with a thin lumen along the entire lumen and the other group with irregular lumen shapes. Therefore, we separated the two groups for quantification of lumen diameter. Additionally, we have analyzed the degree of variability for the lumen diameter to better capture the range of phenotypes observed (Figure 1K’). These quantifications enable a more precise assessment of lumen morphology, allowing readers to distinguish between thin and irregular lumen phenotypes.

(2) The rescue is about 30%, which is not as good as expected. Maybe the wrong isoform was taken. Is it possible to find out which isoform is expressed in the salivary glands, e.g., by RNA in situ Hyb? This could then be used to analyze a more focused rescue beyond the paper.

Thank you for this point, but we do not agree that the rescue is about 30%. In Papss mutants, about 50% of the embryos show the thin lumen phenotype whereas the other 50% show irregular lumen shapes. In the rescue embryos with a WT Papss, few embryos showed thin lumen phenotypes. About 40% of the rescue embryos showed “normal, fully expanded” lumen shapes, and the remaining 60% showed either irregular (thin+expanded) or slightly overexpanded lumen. It is not uncommon that rescue with the Gal4/UAS system results in a partial rescue because it is often not easy to achieve the balance of the proper amount of the protein with the overexpression system. 

To address the possibility that the wrong isoform was used, we performed in situ hybridization to examine the expression of different Papss spice forms in the salivary gland. We used probes that detect subsets of splice forms: A/B/C/F/G, D/H, and E/F/H, and found that all probes showed expression in the salivary gland, with varying intensities. The original probe, which detects all splice forms, showed the strongest signals in the salivary gland compared to the new probes which detect only a subset. However, the difference in the signal intensity may be due to the longer length of the original probe (>800 bp) compared to other probes that were made with much smaller regions (~200 bp). Digoxigenin in the DIG labeling kit for mRNA detection labels the uridine nucleotide in the transcript, and the probes with weaker signals contain fewer uridines (all: 147; ABCFG, 29; D, 36; EFH, 66). We also used the Papss-PD isoform, for a salivary gland-specific rescue experiment and obtained similar results to those with Papss-PE (Figure 1I-L, Figure 4D and E). 

Furthermore, we performed additional experiments to validate our findings. We performed a rescue experiment with a mutant form of Papss that has mutations in the critical rescues of the catalytic domains of the enzyme, which failed to rescue any phenotypes, including the thin lumen phenotype (Figure 1H, J-L), the number and intensity of WGA puncta (Figure 3I, I’), and cell death (Figure 4D, E). These results provide strong evidence that the defects observed in Papss mutants are due to the lack of sulfation.  

(3) Crb is a transmembrane protein on the apicolateral side of the membrane. Accordingly, the apicolateral distribution can be seen in the control and the mutant. I believe there are no apparent differences here, not even in the amount of expression. However, the view of the cells (frame) shows possible differences. To be sure, a more in-depth analysis of the images is required. Confocal Z-stack images, with 3D visualization and orthogonal projections to analyze the membranes showing Crb staining together with a suitable membrane marker (e.g. SAS or Uif). This is the only way to show whether Crb is incorrectly distributed. Statistics of several papas mutants would also be desirable and not just a single representative image. When do the observed changes in Crb distribution occur in the development of the tubes, only during stage 16? Is papss only involved in the maintenance of the apical membrane? This is particularly important when considering the SJ and AJ, because the latter show no change in the mutants.

We appreciate your suggestion more thoroughly analyze Crb distribution. We adapted a method from a previous study (Olivares-Castiñeira and Llimargas, 2017) to quantify Crb signals in the subapical region and apical free region of salivary gland cells. Using E-Cad signals as a reference, we marked the apical cell boundaries of individual cells and calculated the intensity of Crb signals in the subapical region (along the cell membrane) and in the apical free region. We focused on the expanded region of the SG lumen in Papss mutants for quantification, as the thin lumen region was challenging to analyze. This quantification is included in Figure 2D. Statistical analysis shows that Crb signals were more dispersed in SG cells in Papss mutants compared to WT.

(4) A change in the ECM is only inferred based on the WGA localization. This is too few to make a clear statement. WGA is only an indirect marker of the cell surface and glycosylated proteins, but it does not indicate whether the ECM is altered in its composition and expression. Other important factors are missing here. In addition, only a single observation is shown, and statistics are missing.

We understand your concern that WGA localization alone may not be sufficient to conclude changes in the ECM. However, we observed that luminal WGA signals colocalize with Dpy-YFP in the WT SG (Figure 5-figure supplement 2C), suggesting that WGA detects the aECM structure containing Dpy. The similar behavior of WGA and Dpy-YFP signals in multiple genotypes further supports this idea. In Papss mutants with a thin lumen phenotype, both WGA and Dpy-YFP signals are condensed (Figure 5E-H), and in pio mutants, both are absent from the lumen (Figure 6B, D). We analyzed WGA signals in over 25 samples of WT and Papss mutants, observing consistent phenotypes. We have included the number of samples in the text. While we acknowledge that WGA is an indirect marker, our data suggest that it is a reliable indicator of the aECM structure containing Dpy. 

(5) Reduced WGA staining is seen in papss mutants, but this could be due to other circumstances. To be sure, a statistic with the number of dots must be shown, as well as an intensity blot on several independent samples. The images are from single confocal sections. It could be that the dots appear in a different Z-plane. Therefore, a 3D visualization of the voxels must be shown to identify and, at best, quantify the dots in the organ.

We have quantified cytoplasmic punctate WGA signals. Using spinning disk microscopy with super-resolution technology (Olympus SpinSR10 Sora), we obtained high-resolution images of cytoplasmic punctate signals of WGA in WT, Papss mutant, and rescue SGs with the WT and mutant forms of Papss-PD. We then generated 3D reconstructed images of these signals using Imaris software (Figure 3E-H) and quantified the number and intensity of puncta. Statistical analysis of these data confirms the reduction of the number and intensity of WGA puncta in Papss mutants (Figure 3I, I’). The number of WGA puncta was restored by expressing WT Papss but not the mutant form. By using 3D visualization and quantification, we have ensured that our results are not limited to a single confocal section and account for potential variations in Z-plane localization of the dots.

(6) A colocalization analysis (statistics) should be shown for the overlap of WGA with ManII-GFP.

Since WGA labels multiple structures, including the nuclear envelope and ECM structures, we focused on assessing the colocalization of the cytoplasmic WGA punctate signals and ManIIGFP signals. Standard colocalization analysis methods, such as Pearson’s correlation coefficient or Mander’s overlap coefficient, would be confounded by WGA signals in other tissues. Therefore, we used a fluorescent intensity line profile to examine the spatial relationship between WGA and ManII-GFP signals in WT and Papss mutants (Figure 3L, L’). 

(7) I do not understand how the authors describe "statistics of secretory vesicles" as an axis in Figure 3p. The TEM images do not show labeled secretory vesicles but empty structures that could be vesicles.

Previous studies have analyzed “filled” electron-dense secretory vesicles in TEM images of SG cells (Myat and Andrew, 2002, Cell; Fox et al., 2010, J Cell Biol; Chung and Andrew, 2014, Development). Consistent with these studies, our WT TEM images show these vesicles. In contrast, Papss mutants show a mix of filled and empty structures. For quantification, we specifically counted the filled electron-dense vesicles (now Figure 3W). A clear description of our analysis is provided in the figure legend.

(8) The quality of the presented TEM images is too low to judge any difference between control and mutants. Therefore, the supplement must present them in better detail (higher pixel number?).

We disagree that the quality of the presented TEM images is too low. Our TEM images have sufficient resolution to reveal details of many subcellular structures, such as mitochondrial cisternae. The pdf file of the original submission may not have been high resolution. To address this concern, we have provided several original high-quality TEM images of both WT and Papss mutants at various magnifications in Figure 2-figure supplement 2. Additionally, we have included low-magnification TEM images of WT and Papss mutants in Figure 2H and I to provide a clearer view of the overall SG lumen morphology. 

(9) Line 266: the conclusion that apical trafficking is "significantly impaired" does not hold. This implies that Papss is essential for apical trafficking, but the analyzed ECM proteins (Pio, Dumpy) are found apically enriched in the mutants, and Dumpy is even secreted. Moreover, they analyze only one marker, Sec15, and don't provide data about the quantification of the secretion of proteins.

We agree and have revised our statement to “defective sulfation affects Golgi structures and multiple routes of intracellular trafficking”. 

(10) DCP-1 was used to detect apoptosis in the glands to analyze acellular regions. However, the authors compare ST16 control with ST15 mutant salivary glands, which is problematic. Further, it is not commented on how many embryos were analyzed and how often they detect the dying cells in control and mutant embryos. This part must be improved.

Thank you for the comment. We agree and have included quantification. We used stage 16 samples from WT and Papss mutants to quantify acellular regions. Since DCP-1 signals are only present at a specific stage of apoptosis, some acellular regions do not show DCP-1 signals. Therefore, we counted acellular regions regardless of DCP-1 signals. We also quantified this in rescue embryos with WT and mutant forms of Papss, which show complete rescue with WT and no rescue with the mutant form, respectively. The graph with a statistical analysis is included (Figure 4D, E).

(11) WGA and Dumpy show similar condensed patterns within the tube lumen. The authors show that dumpy is enriched from stage 14 onwards. How is it with WGA? Does it show the same pattern from stage 14 to 16? Papss mutants can suffer from a developmental delay in organizing the ECM or lack of internalization of luminal proteins during/after tube expansion, which is the case in the trachea.

Dpy-YFP and WGA show overlapping signals in the SG lumen throughout morphogenesis. DpyYFP is SG enriched in the lumen from stage 11, not stage 14 (Figure 5-figure supplement 2). WGA is also detected in the lumen throughout SG morphogenesis, similar to Dpy. In the original supplemental figure, only a stage 16 SG image was shown for co-localization of Dpy-YFP and WGA signals in the SG lumen. We have now included images from stage 14 and 15 in Figure 5figure supplement 2C. 

Given that luminal Pio signals are lost at stage 16 only and that Dpy signals appear as condensed structures in the lumen of Papss mutants, it suggests that the internalization of luminal proteins is not impaired in Papss mutants. Rather, these proteins are secreted but fail to organize properly. 

(12) Line 366. Luminal morphology is characterized by bulging and constrictions. In the trachea, bulges indicate the deformation of the apical membrane and the detachment from the aECM. I can see constrictions and the collapsed tube lumen in Fig. 6C, but I don't find the bulges of the apical membrane in pio and Np mutants. Maybe showing it more clearly and with better quality will be helpful.

Since the bulging phenotype appears to vary from sample to sample, we have revised the description of the phenotype to “constrictions” to more accurately reflect the consistent observations. We quantified the number of constrictions along the entire lumen in pio and Np mutants and included the graph in Figure 6F.

(13) The authors state that Papss controls luminal secretion of Pio and Dumpy, as they observe reduced luminal staining of both in papss mutants. However, the mCh-Pio and Dumpy-YFP are secreted towards the lumen. Does papss overexpression change Pio and Dumpy secretion towards the lumen, and could this be another explanation for the multiple phenotypes? 

Thank you for the comment. To clarify, we did not observe reduced luminal staining of Pio and Dpy in Papss mutants, nor did we state that Papss controls luminal secretion of Pio and Dpy. In Papss mutants, Pio luminal signals are absent specifically at stage 16 (Figure 5H), whereas strong luminal Pio signals are present until stage 15 (Figure 5G). For Dpy-YFP, the signals are not reduced but condensed in Papss mutants from stages 14-16 (Figure 5D, H). 

It remains unclear whether the apparent loss of Pio signals is due to a loss of Pio protein in the lumen or due to epitope masking resulting from protein aggregation or condensation. As noted in our response to Comment 11 internalization of luminal proteins seems unaffected in Papss mutants; proteins like Pio and Dpy are secreted into the lumen but fail to properly organize. Therefore, we have not tested whether Papss overexpression alters the secretion of Pio or Dpy.

In our original submission, we incorrectly stated that uniform luminal mCh-Pio signals were unchanged in Papss mutants. Upon closer examination, we found these signals are absent in the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly. 

Regulation of luminal ZP protein level is essential to modulate the tube expansion; therefore, Np releases Pio and Dumpy in a controlled manner during st15/16. Thus, the analysis of Pio and Dumpy in NP overexpression embryos will be critical to this manuscript to understand more about the control of luminal ZP matrix proteins.

Thanks for the insightful suggestion. We overexpressed both the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. It is important to note that these overexpression experiments were done in the presence of the endogenous WT Np. 

Overexpression of Np.WT led to increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. In contrast, overexpression of Np.S990A resulted in a near complete loss of luminal mCh-Pio signals. Pio antibody signals remained strong at the apical membrane but was weaker in the luminal filamentous structures compared to WT. 

Due to the GFP tag present in the UAS-Np.S990A line, we could not reliably analyze Dpy-YFP signals because of overlapping fluorescent signals in the same channel. However, the filamentous Pio signals in the lumen co-localized with GFP signals, suggesting that these structures might also include Dpy-YFP, although this cannot be confirmed definitively. 

These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

(14) Minor:

Fig. 5 C': mChe-Pio and Dumpy-YFP are mixed up at the top of the images.

Thanks for catching this error.  It has been corrected.

Sup. Fig7. A shows Pio in purple but B in green. Please indicate it correctly.

It has been corrected.

Reviewer #1 (Significance):

In 2023, the functions of Pio, Dumpy, and Np in the tracheal tubes of Drosophila were published. The study here shows similar results, with the difference that the salivary glands do not possess chitin, but the two ZP proteins Pio and Dumpy take over its function. It is, therefore, a significant and exciting extension of the known function of the three proteins to another tube system. In addition, the authors identify papss as a new protein and show its essential function in forming the luminal matrix in the salivary glands. Considering the high degree of conservation of these proteins in other species, the results presented are crucial for future analyses and will have further implications for tubular development, including humans.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary:

There is growing appreciation for the important of luminal (apical) ECM in tube development, but such matrices are much less well understood than basal ECMs. Here the authors provide insights into the aECM that shapes the Drosophila salivary gland (SG) tube and the importance of PAPSS-dependent sulfation in its organization and function.

The first part of the paper focuses on careful phenotypic characterization of papss mutants, using multiple markers and TEM. This revealed reduced markers of sulfation (Alcian Blue staining) and defects in both apical and basal ECM organization, Golgi (but not ER) morphology, number and localization of other endosomal compartments, plus increased cell death. The authors focus on the fact that papss mutants have an irregular SG lumen diameter, with both narrowed regions and bulged regions. They address the pleiotropy, showing that preventing the cell death and resultant gaps in the tube did not rescue the SG luminal shape defects and discussing similarities and differences between the papss mutant phenotype and those caused by more general trafficking defects. The analysis uses a papss nonsense mutant from an EMS screen - I appreciate the rigorous approach the authors took to analyze transheterozygotes (as well as homozygotes) plus rescued animals in order to rule out effects of linked mutations.

The 2nd part of the paper focuses on the SG aECM, showing that Dpy and Pio ZP protein fusions localize abnormally in papss mutants and that these ZP mutants (and Np protease mutants) have similar SG lumen shaping defects to the papss mutants. A key conclusion is that SG lumen defects correlate with loss of a Pio+Dpy-dependent filamentous structure in the lumen. These data suggest that ZP protein misregulation could explain this part of the papss phenotype.

Overall, the text is very well written and clear. Figures are clearly labeled. The methods involve rigorous genetic approaches, microscopy, and quantifications/statistics and are documented appropriately. The findings are convincing, with just a few things about the fusions needing clarification.

Minor comments

(1) Although the Dpy and Qsm fusions are published reagents, it would still be helpful to mention whether the tags are C-terminal as suggested by the nomenclature, and whether Westerns have been performed, since (as discussed for Pio) cleavage could also affect the appearance of these fusions.

Thanks for the comment. Dpy-YFP is a knock-in line in which YFP is inserted into the middle of the dpy locus (Lye et al., 2014; the insertion site is available on Flybase). mCh-Qsm is also a knock-in line, with mCh inserted near the N-terminus of the qsm gene using phi-mediated recombination using the qsm^MI07716 line (Chu and Hayashi, 2021; insertion site available on Flybase). Based on this, we have updated the nomenclature from Qsm-mCh to mCh-Qsm throughout the manuscript to accurately reflect the tag position. To our knowledge, no western blot has been performed on Dpy-YFP or mCh-Qsm lines. We have mentioned this explicitly in the Discussion.  

(2) The Dpy-YFP reagent is a non-functional fusion and therefore may not be a wholly reliable reporter of Dpy localization. There is no antibody confirmation. As other reagents are not available to my knowledge, this issue can be addressed with text acknowledgement of possible caveats.

Thanks for raising this important point. We have added a caveat in the Discussion noting this limitation and the need for additional tools, such as an antibody or a functional fusion protein, to confirm the localization of Dpy.

(3) TEM was done by standard chemical fixation, which is fine for viewing intracellular organelles, but high pressure freezing probably would do a better job of preserving aECM structure, which looks fairly bad in Fig. 2G WT, without evidence of the filamentous structures seen by light microscopy. Nevertheless, the images are sufficient for showing the extreme disorganization of aECM in papss mutants.

We agree that HPF is a better method and intent to use the HPF system in future studies. We acknowledge that chemical fixation contributes to the appearance of a gap between the apical membrane and the aECM, which we did not observe in the HPF/FS method (Chung and Andrew, 2014). Despite this, the TEM images still clearly reveal that Papss mutants show a much thinner and more electron-dense aECM compared to WT (Figure 2H, I), consistent to the condensed WGA, Dpy, and Pio signals in our confocal analyses. As the reviewer mentioned, we believe that the current TEM data are sufficient to support the conclusion of severe aECM disorganization and Golgi defects in Papss mutants.

(4) The authors may consider citing some of the work that has been done on sulfation in nematodes, e.g. as reviewed here: https://pubmed.ncbi.nlm.nih.gov/35223994/ Sulfation has been tied to multiple aspects of nematode aECM organization, though not specifically to ZP proteins.

Thank you for the suggestion. Pioneering studies in C. elegans have highlighted the key role of sulfation in diverse developmental processes, including neuronal organization, reproductive tissue development, and phenotypic plasticity. We have now cited several works.  

Reviewer #2 (Significance):

This study will be of interest to researchers studying developmental morphogenesis in general and specifically tube biology or the aECM. It should be particularly of interest to those studying sulfation or ZP proteins (which are broadly present in aECMs across organisms, including humans).

This study adds to the literature demonstrating the importance of luminal matrix in shaping tubular organs and greatly advances understanding of the luminal matrix in the Drosophila salivary gland, an important model of tubular organ development and one that has key matrix differences (such as no chitin) compared to other highly studied Drosophila tubes like the trachea.

The detailed description of the defects resulting from papss loss suggests that there are multiple different sulfated targets, with a subset specifically relevant to aECM biology. A limitation is that specific sulfated substrates are not identified here (e.g. are these the ZP proteins themselves or other matrix glycoproteins or lipids?); therefore it's not clear how direct or indirect the effects of papss are on ZP proteins. However, this is clearly a direction for future work and does not detract from the excellent beginning made here.

My expertise: I am a developmental geneticist with interests in apical ECM

Reviewer #3 (Evidence, reproducibility and clarity):

In this work Woodward et al focus on the apical extracellular matrix (aECM) in the tubular salivary gland (SG) of Drosophila. They provide new insights into the composition of this aECM, formed by ZP proteins, in particular Pio and Dumpy. They also describe the functional requirements of PAPSS, a critical enzyme involved in sulfation, in regulating the expansion of the lumen of the SG. A detailed cellular analysis of Papss mutants indicate defects in the apical membrane, the aECM and in Golgi organization. They also find that Papss control the proper organization of the Pio-Dpy matrix in the lumen. The work is well presented and the results are consistent.

Main comments

- This work provides a detailed description of the defects produced by the absence of Papss. In addition, it provides many interesting observations at the cellular and tissular level. However, this work lacks a clear connection between these observations and the role of sulfation. Thus, the mechanisms underlying the phenotypes observed are elusive. Efforts directed to strengthen this connection (ideally experimentally) would greatly increase the interest and relevance of this work.

Thank you for this thoughtful comment. To directly test whether the phenotypes observed in Papss mutants are due to the loss of sulfation activity, we generated transgenic lines expressing catalytically inactive forms of Papss, UAS-PapssK193A, F593P, in which key residues in the APS kinase and ATP sulfurylase domains are mutated. Unlike WT UAS-Papss (both the Papss-PD or Papss-PE isoforms), the catalytically inactive UAS-Papssmut failed to rescue any of the phenotypes, including the thin lumen phenotype (Figure 1I-L), altered WGA signals (Figure I, I’) and the cell death phenotype (Figure 4D, E). These findings strongly support the conclusion that the enzymatic sulfation activity of Papss is essential for the developmental processes described in this study.  

- A main issue that arises from this work is the role of Papss at the cellular level. The results presented convincingly indicate defects in Golgi organization in Papss mutants. Therefore, the defects observed could stem from general defects in the secretion pathway rather than from specific defects on sulfation. This could even underly general/catastrophic cellular defects and lead to cell death (as observed).

This observation has different implications. Is this effect observed in SGs also observed in other cells in the embryo? If Papss has a general role in Golgi organization this would be expected, as Papss encodes the only PAPs synthatase in Drosophila.

Can the authors test any other mutant that specifically affect Golgi organization and investigate whether this produces a similar phenotype to that of Papss?

Thank you for the comment. To address whether the defects observed in Papss mutants stem from general disruption of the secretory pathway due to Golgi disorganization, we examined mutants of two key Golgi components: Grasp65 and GM130. 

In Grasp65 mutants, we observed significant defects in SG lumen morpholgy, including highly irregular SG lumen shape and multiple constrictions (100%; n=10/10). However, the lumen was not uniformly thin as in Papss mutants. In contrast, GM130 mutants–although this line was very sick and difficult to grow–showed relatively normal salivary glands morphology in the few embryos that survived to stage 16 (n=5/5). It is possible that only embryos with mild phenotypes progressed to this stages, limiting interpretation. These data have now been included in Figure 3-figure supplement 2. Overall, while Golgi disruption can affect SG morphology, the specific phenotypes seen in Papss mutants are not fully recapitulated by Grasp65 or GM130 loss. 

- A model that conveys the different observations and that proposes a function for Papss in sulfation and Golgi organization (independent or interdependent?) would help to better present the proposed conclusions. In particular, the paper would be more informative if it proposed a mechanism or hypothesis of how sulfation affects SG lumen expansion. Is sulfation regulating a factor that in turn regulates Pio-Dpy matrix? Is it regulating Pio-Dpy directly? Is it regulating a

product recognized by WGA?

For instance, investigating Alcian blue or sulfotyrosine staining in pio, dpy mutants could help to understand whether Pio, Dpy are targets of sulfation.

Thank you for the comment. We’re also very interested in learning whether the regulation of the Pio-Dpy matrix is a direct or indirect consequence of the loss of sulfation on these proteins. One possible scenario is that sulfation directly regulates the Pio-Dpy matrix by regulating protein stability through the formation of disulfide bonds between the conserved Cys residues responsible for ZP module polymerization. Additionally, the Dpy protein contains hundreds of EGF modules that are highly susceptible to O-glycosylation. Sulfation of the glycan groups attached to Dpy may be critical for its ability to form a filamentous structure. Without sulfation, the glycan groups on Dpy may not interact properly with the surrounding materials in the lumen, resulting in an aggregated and condensed structure. These possibilities are discussed in the Discussion.

We have not analyzed sulfation levels in pio or dpy mutants because sulfation levels in mutants of single ZP domain proteins may not provide much information. A substantial number of proteoglycans, glycoproteins, and proteins (with up to 1% of all tyrosine residues in an organism’s proteins estimated to be sulfated) are modified by sulfation, so changes in sulfation levels in a single mutant may be subtle. Especially, the existing dpy mutant line is an insertion mutant of a transposable element; therefore, the sulfation sites would still remain in this mutant. 

- Interpretation of Papss effects on Pio and Dpy would be desired. The results presented indicate loss of Pio antibody staining but normal presence of cherry-Pio. This is difficult to interpret. How are these results of Pio antibody and cherry-Pio correlating with the results in the trachea described recently (Drees et al. 2023)?

In our original submission, we stated that the uniform luminal mCh-Pio signals were not changed in Papss mutants, but after re-analysis, we found that these signals were actually absent from the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly. 

After cleavages by Np and furin, the Pio protein should have three fragments. The Nterminal region contains the N-terminal half of the ZP domain, and mCh-Pio signals show this fragment. The very C-terminal region should localize to the membrane as it contains the transmembrane domain. We think the middle piece, the C-terminal ZP domain, is recognized by the Pio antibody. The mCh-Pio and Pio antibody signals in the WT trachea (Drees et al., 2023) are similar to those in the SG. mCh-Pio signals are detected in the tracheal lumen as uniform signals, at the apical membrane, and in cytoplasmic puncta. Pio antibody signals are exclusively in the tracheal lumen and show more heterogenous filamentous signals. 

In Papss mutants, the middle fragment (the C-terminal ZP domain) seems to be most affected because the Pio antibody signals are absent from the lumen. The loss of Pio antibody signals could be due to protein degradation or epitope masking caused by aECM condensation and protein misfolding. This fragment seems to be key for interacting with Dpy, since Pio antibody signals always colocalize with Dpy-YFP. The N-terminal mCh-Pio fragment does not appear to play a significant role in forming a complex with Dpy in WT (but still aggregated together in Papss mutants), and this can be tested in future studies.

In response to Reviewer 1’s comment, we performed an additional experiment to test the role of Np in cleaving Pio to help organize the SG aECM. In this experiment, we overexpressed the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. Np.WT overexpression resulted in increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. However, overexpression of Np.S990A resulted in the absence of luminal mCh-Pio signals. Pio antibody signals were strong at the apical membrane but rather weak in the luminal filamentous structures. Since the UAS-Np.S990A line has the GFP tag, we could not reliably analyze Dpy-YFP signals due to overlapping Np.S990A.GFP signals in the same channel. However, the luminal filamentous Pio signals co-localized with GFP signals, and we assume that these overlapping signals could be Dpy-YFP signals. 

These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I. 

A proposed model of the Pio-Dpy aECM in WT, Papss, pio, and Np mutants has now been included in Figure 7.

-  What does the WGA staining in the lumen reveal? This staining seems to be affected differently in pio and dpy mutants: in pio mutants it disappears from the lumen (as dpy-YFP does), but in dpy mutants it seems to be maintained. How do the authors interpret these findings? How does the WGA matrix relate to sulfated products (using Alcian blue or sulfotyrosine)?

WGA binds to sialic acid and N-acetylglucosamine (GlcNAc) residues on glycoproteins and glycolipids. GlcNAc is a key component of the glycosaminoglycan (GAG) chains that are covalently attached to the core protein of a proteoglycan, which is abundant in the ECM. We think WGA detects GlcNAc residues in the components of the aECM, including Dpy as a core component, based on the following data. 1) WGA and Dpy colocalize in the lumen, both in WT (as thin filamentous structures) and Papss mutant background (as condensed rod-like structures), and 2) are absent in pio mutants. WGA signals are still present in a highly condensed form in dpy mutants. That’s probably because the dpy mutant allele (dpyov1) has an insertion of a transposable element (blood element) into intron 11 and this insertion may have caused the Dpy protein to misfold and condense. We added the information about the dpy allele to the Results section and discussed it in the Discussion.

Minor points:

- The morphological phenotypic analysis of Papss mutants (homozygous and transheterozygous) is a bit confusing. The general defects are higher in Papss homozygous than in transheterozygotes over a deficiency. Maybe quantifying the defects in the heterozygote embryos in the Papss mutant collection could help to figure out whether these defects relate to Papss mutation.

We analyzed the morphology of heterozygous Papss mutant embryos. They were all normal. The data and quantifications have now been added to Figure 1-figure supplement 3. 

- The conclusion that the apical membrane is affected in Papss mutants is not strongly supported by the results presented with the pattern of Crb (Fig 2). Further evidences should be provided. Maybe the TEM analysis could help to support this conclusion

We quantified Crb levels in the sub-apical and medial regions of the cell and included this new quantification in Figure 2D. TEM images showed variation in the irregularity of the apical membrane, even in WT, and we could not draw a solid conclusion from these images.

- It is difficult to understand why in Papss mutants the levels of WGA increase. Can the authors elaborate on this?

We think that when Dpy (and many other aECM components) are condensed and aggregated into the thin, rod-like structure in Papss mutants, the sugar residues attached to them must also be concentrated and shown as increased WGA signals.   

- The explanation about why Pio antibody and mcherry-Pio show different patterns is not clear. If the antibody recognizes the C-t region, shouldn't it be clearly found at the membrane rather than the lumen?

The Pio protein is also cleaved by furin protease (Figure 5B). We think the Pio fragment recognized by the antibody should be a “C-terminal ZP domain”, which is a middle piece after furin + Np cleavages. 

- The qsm information does not seem to provide any relevant information to the aECM, or sulfation.

Since Qsm has been shown to bind to Dpy and remodel Dpy filaments in the muscle tendon (Chu and Hayashi, 2021), we believe that the different behavior of Qsm in the SG is still informative. As mentioned briefly in the Discussion, the cleaved Qsm fragment may localize differently, like Pio, and future work will need to test this. We have shortened the description of the Qsm localization in the manuscript and moved the details to the figure legend of Figure 5-figure supplement 3.

Reviewer #3 (Significance):

Previous reports already indicated a role for Papss in sulfation in SG (Zhu et al 2005). Now this work provides a more detailed description of the defects produced by the absence of Papss. In addition, it provides relevant data related to the nature and requirements of the aECM in the SG. Understanding the composition and requirements of aECM during organ formation is an important question. Therefore, this work may be relevant in the fields of cell biology and morphogenesis.

☑️ peer.gos.ck-editor needs to set title so that annotations can show it

currently it is done by manually adding a title tag in the source code for the document that is saved in Peergos

where all the html and javascript encluses the source of the HTML document so the editor/capbiity gets loaded wwith the saved HTML content

1. category and tag pages are not working...
2. can we change category url from https://adswom.com/category/the-search-to-sql/ to https://adswom.com/the-search-to-sql/
3. post pages filter as per the category

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Reply to the reviewers

Manuscript number: RC- 2025-03073

Corresponding author(s): Shaul Yogev

1. General Statements [optional]

We kindly thank our reviewers for their enthusiasm, thoughtful feedback, and constructive suggestions on how to strengthen our manuscript. Below, we provide a point-by-point response to reviewer comments and outline the experiments we will do to address every concern that has been raised.

2. Description of the planned revisions

• *

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

This interesting study uses an unbiased genetic screen in C. elegans to identify SAX-1/NDR kinase as a regulator of dendritic branch elimination. Loss of SAX-1 results in an excess branching phenotype that is striking and highly penetrant. The authors identify several additional regulators of branch elimination (SAX-2, MOB-1, RABI-1, RAB-11.2) by using a candidate genetic screen aimed at factors that interact physically or genetically with SAX-1. They propose that SAX-1 acts by promoting membrane retrieval based on the nature of these interactors and the results of an imaging-based in vivo assay for endocytic puncta.

Major comments.

1. My biggest concern is that the phenotypes are only observed in temperature-sensitive dauer-constitutive mutant backgrounds, and not in wild-type dauers. That is, wild-type animals exiting dauer do not require SAX-1 for dendrite elimination. While this does not undermine the importance of the results, it does require more explanation. The authors write that "the requirement for sax-1... relies on specific physiological states of the dauer stage," but I do not understand what this means. Are they saying that daf-7 and daf-2 dauers are in a different "physiological state" than wild-type dauers? In what way? What is the evidence for this? A more rigorous explanation is needed. We agree that this is puzzling, and we thank the reviewer for recognizing that this does not undermine the importance of the results. There is ample evidence that daf-2 and daf-7 differ from starvation-induced dauers. For example, a recent preprint finds that the transcriptomes of these two mutants at dauer cluster much closer to each other than to starvation-induced dauers (Corchado et al. 2024). Older work has noted other differences, such as the time the dauer entry decision is made (Swanson and Riddle 1981), the synchronicity of dauer exit, the ability to force dauer entry in daf-d mutants, as well as additional dauer-unrelated phenotypes (reviewed in Karp 2018). We agree with the reviewer that this merits further clarifications and will perform the experiments suggested by the reviewer below:

To me, the simplest genetic explanation is that daf-7 and daf-2 are partially required for branch retraction in a manner redundant with sax-1, and the ts mutants are not fully wild-type at 15C. Thus, the sax-1 requirement is revealed only in these mutant backgrounds. Can the authors examine starvation-induced dauers of daf-7 or daf-2 raised continuously at 15C?

We will do this experiment.

daf-7 and daf-2 ts strains can form "partial dauers" that have a dauer-like appearance but are not SDS resistant. Could the difference between partial dauers and full dauers account for the difference in sax-1-dependence? The authors could use SDS selection of the daf-7 strain at 25C to ensure they are examining full dauers.

We tested daf-7 mutants with 1% SDS when we set up the system – they are fully dauer at 25°C and are SDS sensitive after exit. We will repeat this important control with daf-7; sax-1 double mutants.

The Bargmann lab has created a daf-2 FLP-OUT strain (ky1095ky1087) that allows cell-type-specific removal of daf-2. Could this be used to test for a cell-autonomous role of daf-2 in IL2Q related to branch elimination?

We can attempt this experiment. However, since IL2 promoters turn on prior to dauer, the interpretation would not be straightforward – it would be hard to exclude that a cell autonomous defect in dauer entry does not account for the IL2 dauer exit phenotype, even if branching appears normal.

These ideas are not a list of specific experiments the authors need to complete, rather they are meant to illustrate some possible approaches to the question. Whatever approach they use, it is important for them to more rigorously explain why SAX-1 is not required for branch removal in wild-type animals.

We completely agree. We will carry out the 15°C experiment, examine morphological characteristics and test SDS resistance. In addition, we will test neuronal markers that differ between dauers and non-dauers to determine whether the mutants are full or partial dauers at the relevant timepoints.

The SAX-2 localization (Fig. 4) and endocytosis assay (Fig. 6) results were not clear to me from the data shown. Overall a more rigorous analysis and presentation of the data would be important to make these conclusions convincing. This may involve refining the data presentation in the figures, modifying the claims (e.g., "we propose" vs "we find"), or saving some of the data to be more fully explored in a future paper. In my view, these figures are the biggest weak point of the manuscript and also are not important for the central conclusions (which are well supported and convincing), indeed these results are barely mentioned in the Abstract or last paragraph of Introduction.

We agree that the analysis and presentation of Figures 4 and 6 need to be improved. The presentation has already been updated, and the figures are clearer now. In the revision, we will increase sample size to provide stronger conclusions, consolidate some of the analysis and further improve presentation. While we agree with the reviewer that conclusions from these figures are not as strong as those drawn from genetic experiments, they do complement and support the conclusions of those other figures.

• In Fig. 4D, why is SAX-2 visible throughout the entire neuron and why is the "punctum" marked with an arrow also seen in the tagRFP channel? One gets the impression that some of the puncta may be background, bleed-through, or artifacts due to cell varicosities.

There is no bleed-through: this is most evident by looking at the brightest signals in the cell body (now labelled with an asterisk in a zoomed-out image) and noting that they do not bleed between channels. In sax-1 mutants, the SAX-2::GFP puncta are very obvious and distinguishable from the tagRFP channel. In control, SAX-2::GFP is very faint in the dendrite, so we increased the contrast to allow visualization. The reviewer is correct that under these conditions, some puncta look like the cytosolic fill. In the revision, we will re-analyze the data and will not consider these as bona-fide SAX-2 puncta, but rather cytosolic SAX-2 that accumulates due to constrictions and varicosities in the dendrite.

• Related to both Fig. 4 and Fig. 6, where does SAX-1 localize in IL2Q in dauer and post-dauer? Does its expression or localization change during branch retraction? Does it co-localize with SAX-2 or endocytic puncta?

We generated an endogenously tagged sax-1 with a 7xspGFP11 tag; however, this was below detection in the IL2s. For the revisions, we can test an overexpressed cDNA construct.

**Referee cross-commenting**

I think we all touched on similar points. I wanted to follow up on Reviewer 3's comment, "Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults." I thought this was an excellent point. It made me wonder if that might explain why the defect is only seen in daf-7 and daf-2 mutant backgrounds - maybe these strains retain partial dauer traits even after exit. Is there a specific experiment that they could do? Did you have specific characteristics of dauer morphology in mind for them to check? (Ideally something in the nervous system that can be scored quantitatively.)

Please see response to point #1 regarding experiments we will do to confirm the “dauer state” of daf-7 and daf-7; sax-1 double mutants.

Reviewer #1 (Significance (Required)):

A major strength of this work is the pioneering use of a novel system to study neuronal branch retraction. C. elegans has provided a powerful model for studying how dendrite branches form, but much less attention has been paid to how excess neuronal branches are removed. The post-dauer remodeling of IL2Q neurons provides an exciting and dramatic physiological example to explore this question.

This paper is notable for taking the first steps towards developing this innovative model. It does exactly what is needed at the outset of a new exploration - a forward genetic screen to discover the main regulators of the process. Using a combination of classical and modern genetic approaches, the authors bootstrap their way to a sizeable list of factors and a solid understanding of the properties of this system, for example that retraction of higher vs lower order dendrites show different genetic requirements.

We thank the reviewer for recognizing the novelty and significance of our work.

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

In this manuscript, the authors establish C. elegans IL2 neurons as a system in which to study dendrite pruning. They use the system to perform a genetic screen for pruning regulators and find an allele of sax-1. Unexpectedly sax-1 is only required for post-dauer pruning in two different genetic backgrounds that induce dauer formation, but not starvation-induced dauer formation. Sax-1/NDR kinase reduction has previously been associated with increased outgrowth and branching in other systems, so this is a new role for this protein. However, the authors show that proteins that work with Sax-1 in other systems, like sax-2/fry, also play a role in this pathway. The genetic experiments are beautiful and the findings are all clearly explained and strongly supported. The authors also examine sax-2 localization, which localizes sax-1 in other systems, and show it in puncta in dendrites that increase with dauer exit, consistent with function at the time of pruning. They also show that membrane trafficking regulators associated with NDR kinases function in the same pathway here, hinting that endocytosis may play a role during pruning as in Drosophila. The link to endocytosis was a little weak (see Major point below). Overall, this study describes a new system to study pruning and identifies NDR/fry/Rabs as regulators of pruning during dauer exit. The work is very high quality and both the imaging and genetics are extremely well done.

We thank the reviewer for their positive assessment of the manuscript.

Major points

1. The only place where there were any questions about the data was the last figure (6G and I). Here they use uptake of GFP secreted from muscle as a readout of endocytosis in IL2 neurons. They nicely show that more internalized puncta accumulate as animals exit dauer. The claim that this is reduced in sax-1 mutants doesn't seem to match the images shown well. In the image there are many more puncta in the GFP channel and much more accumulation of the RFP-tagged receptor everywhere. It seems like some additional analysis of this data is important to fully capture what is going on and whether this really represents an endocytic defect. We agree and will provide additional data in Figure 6. The specific discrepancy between the image and the quantification is because we showed a single focal plane rather than a projection. This does not capture all the puncta in a neurite. The current version shows a projection, making it evident that the mutants has fewer puncta compared to the control.

Reviewer #2 (Significance (Required)):

Neurite pruning is important in all animals with neurons. Genetic approaches have primarily been applied to the problem using Drosophila, so identifying a new model system in which to study it is an important step. Using this system, a pathway known to function in a different context is linked to pruning. Thus the study provides new insights into both pruning and this pathway.

We thank the reviewer for the positive assessment of our study’s significance.

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

Summary: Figueroa-Delgado et al. use a C. elegans neuro plasticity model to examine how dendrites are eliminated upon recovery from the stress induced larval stage, dauer. The authors performed a mutagenesis screen to identify novel regulators of dendrite elimination and revealed some surprising results. Branch elimination mechanism varies between 2{degree sign}, 3{degree sign}, and 4{degree sign} branches. The NDR kinase, SAX-1 and it's interactors (SAX-2 and MOB-2) are required for elimination of second and third order branches but not fourth order branches. Interestingly they showed that branch elimination varies depending on the stimulus of dendrite outgrowth such that the NDR kinase is required for branch elimination after genetically inducing the dauer stage but is not required if dauers are produced through food deprivation. The authors go a step further to include a small candidate screen looking at various pathways of membrane remodeling and identify additional regulators of dendrite elimination related to membrane trafficking including RABI-1, RAB-8, RAB-10, and RAB-11.2.

We thank the reviewer for their time and suggestions below

Major comments:

• While I find the data promising and exciting, several of the experiments have concerningly low sample sizes. Fig 3G, Fig 4G, Fig 5J and L, and Fig 6I all contain data sets that are fewer than 10 animals. Sample sizes should be stated specifically in the figure legends for all data represented in the graphs. We thank the reviewer for finding the data exciting. We agree that the sample sizes in some panels is low and will increase it in the revised version. Sample sizes are now specifically listed in the figure legends.

• All statements based on data not shown should be amended to include the data as a supplemental figure or edited to omit the statement based on withheld data. We agree. Some “not shown” data are already added to the current version of the manuscript and the rest will be added to the fully revised version, or the statements will be omitted.

• Rescue experiments (Fig 2J) should demonstrate failure to rescue from neighboring tissue types (hypodermis and muscle) to conclude cell autonomous rescue rather than a broadly acting factor. Thank you for the suggestion. We will use a hypodermal promoter and a muscle promoter driving SAX-1 cDNA expression to strengthen the claim of cell autonomy.

• Fig 4 needs quantification of higher order branches and SAX-2 proximity to branch nodes as these are discussed in the text. We will add this quantification.

Minor comments:

• Fig 1C-F, It appears like the shy87 allele produces animals of significantly different body sizes. It would improve rigor to normalize the dendrite coverage to body size in the quantification. We do not see a biologically meaningful size difference between shy87 and control, it may be the specific image shown. We will confirm this by measuring animal size for the final revision.

• Is the failure to eliminate branches an indication of incomplete dauer recovery? Do sax-1 mutants retain additional characteristics of dauer morphology in post dauer adults. This important point was also raised by Reviewer 1. We will test SDS sensitivity, morphological markers, and molecular markers to determine the dauer “state” of the mutants used in this study. The results will be included in the final revision.

• The text references multiple transgenic lines tested in Fig 2I-J but only one line is shown. Additional lines were visually examined under a fluorescent compound microscope but not imaged or quantified. We will add this quantification to the final revision.

• Fig 4F, Additional timepoints would enhance the sax-1 localization result and might provide insight into mechanism of action for sax-1. We will add the localization in post-dauer adults.

• Fig 6I Control and sax-1(ky491) example images should be provided in the supplement. We will add these images to the final revision.

**Referee cross-commenting**

I agree that we shared many of the same concerns.

There are several general assays for dauer characteristics that could be used here to determine if the post-dauer animals retain other characteristics of the dauer stage in addition to IL2 branches (SDS resistance, alae remodeling, pharyngeal bulb morphology, nictation behavior). The nictation behavior has been connected very nicely with IL2 neurons (Junho Lee's group). Additionally, FLP dendrites occupy the same space as the IL2 branches and outgrowth in post-dauers occurs in coordination with IL2 branch elimination - this might be another optional experiment, to check if FLP growth is impeded by persistent IL2 branches. All of these could be quantified similar to how the authors have already established with their IL2 model (FLP dendrite branches) or with a binary statistic.

Please see responses to Reviewer 1 and 3 above for the list of experiments to determine whether the animals fail to completely enter or exit dauer.

Reviewer #3 (Significance (Required)):

SIGNIFICANCE ============ These results describe a new role for the NDR kinase complex in dendrite pruning that has clinical significance to our understanding of human brain development and human health concerns in which pruning is dysregulated, such as observed in the case of autism. The authors use an established neuro-plasticity, C. elegans model (Schroeder et al. 2013) which provides a tractable and reproduceable platform for discovering the mechanism of dendrite pruning. These results would influence future work in the fields of cell biology of the neuron and disease models of brain development.

My expertise is in the field of C. elegans neuroscience and stress biology and have sufficient expertise to evaluate all aspects of this work.

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

Reviewer #1

• In Fig. 4C, the distinction between puncta in the primary or higher-order dendrites is not clear to me, and several puncta that I would have scored as primary are marked as higher-order.

We apologize for a mistake in the arrowhead color and overall presentation of this figure. It has been fixed in the current version.

• Related to this, in Fig. 4B are the two arrows meant to be white as in the top panel, or yellow as in the bottom panel?

We thank Reviewer #1 for their observation, and we apologize for our oversight. We fixed this in the current version.

• In Fig. 4, where in the head are we looking? It would help to show a more low-magnification view of the entire cell.

We added zoomed-out images and indicated where the zoomed in insets are taken from. We thank the reviewer for helping us improve the clarity of the data.

• The main sax-1 phenotype is increased SAX-2 puncta in dauer, but the branch retraction defect is in post-dauers. How is this relevant to the phenotype?

This is a very good point. The increase in SAX-2 puncta in sax-1 mutants is stronger during dauer-exit than in dauer, consistent with this being the time when SAX-1 functions. We agree that some earlier activity of SAX-1 cannot be excluded, and we do not assume that the effect on SAX-2 completely accounts for the pruning defects. This is now acknowledged in the text. However, given that both proteins function together in pruning, and given that the effect is strongest during dauer exit, we do believe that this data is informative and worth showing.

• The number of SAX-2 puncta in sax-1 mutants decreases almost to normal in post dauers. Is there a correlation between the number of remaining branches and the number of SAX-2 puncta? That is, do the many wild-type animals with "excess" SAX-2 puncta also fail to retract branches?

There is no correlation. In other words, the number of SAX-2 puncta does not instruct the extent of pruning. Please note the quantifications underestimate the number of SAX-2 puncta in the mutants, since they were only done on the primary dendrite. This is necessary because the mutant and control have different arbor size, so only branch order that can be appropriately compared are primary dendrites.

• The control post-dauer data in Fig. 4F and 4H are identical (re-used data) but the corresponding control dauer data in Fig. 4F and 4G are different. What is going on here?

We thank the reviewer for raising this point and apologize for the oversight in data presentation. In the revised manuscript, we now show all control and experimental data integrated into a single graph, ensuring that each dataset is represented accurately to provide a comparison between dauer and post dauer recovery conditions.

• Why are sample sizes so small for both strains in Fig. 4G compared to Fig. 4F and 4H?

We sincerely apologize for this mistake, some of the data was erroneously grouped in the original submission. The revised version contains an updated number of neurons, presented on the same graph, and in the final revision we will further increase sample size. We apologize again for this error.

• In Fig. 6C, why are the tagRFP (blue) puncta larger than the neurite? Aren't these meant to represent vesicles inside the surrounding neurite? One gets the impression that this is bleed-through from the GFP channel.

Based on EM, both an endocytic punctum and the diameter of the neuron are smaller than a single pixel. The apparent difference in size in fluorescence microscopy is because the puncta are brighter (they contain more membrane) and thus appear larger. In the current version, the improved presentation of the figure contains zoomed out images that clearly show that there is no bleed-through.

• In Fig. 6E and 6F, why are there no tagRFP (blue) puncta? Is CD8 not endocytosed at all if it lacks the nanobody sequence? One would expect the tagRFP (blue) signal to be the same in both strains and simply to lack yellow if the nanobody is not present.

CD8 lacks clear endocytosis motifs, which is why it is advantageous for labelling neurites and testing endocytosis when paired with an endocytic signal (Lee and Luo 1999; Kozik et al. 2010). Conversely, extracellular GFP binding to a membrane GFP antibody can induce endocytosis (for example, see (Tang et al., 2020)), likely by inducing clustering, although we are not familiar with work that explored the mechanism. In the updated version we included a rare example of an mCD8 punctum.

• The authors report a decrease in endocytic events in sax-1, but qualitatively it looks like there are vastly more puncta inside the neuron in Fig. 6H than in 6G.

We apologize for the presentation in the original version of Figure 6. This impression was because we showed single focal planes that only captured some of the signal. In the revised version we show projections, which makes it evident that there are fewer endocytic events in the mutant.

• In Fig. 6E and 6H, why are there so many GFP (yellow) puncta outside the neuron? What are these structures and why are they absent in the strain with the nanobody?

These puncta are secreted or muscle-associated GFP that has not been internalized by IL2Q neurons. They are present in all strains in this figure, this can be clearly seen in the zoomed-out images that have been added to the updated figure.

• What is the large central blue structure in Fig. 6H - is this the soma? - and why are puncta in this region not counted?

This is indeed the soma. In the updated version this can be clearly seen in the zoom-out. The large puncta in the soma were not counted because they may arise from the fusion of an unknown number of smaller puncta, and their precise number cannot be determined at the resolution of fluorescence microscopy.

• minor: there is text reading "40-" in the bottom panel of Fig. 6H. It is visible when printed but not on screen - adjust levels in Photoshop to reveal it.

We thank the reviewer for catching this oversight, it is now fixed.

Minor points:

1. At several points the authors emphasize the relationship of neurite remodeling to stress, e.g. Abstract and Discussion: "we adapted C. elegans IL2 sensory dendrites as a model [of...] stress-mediated dendrite pruning". It seems unnecessary and potentially misleading to treat this as a neuronal stress response. First, it conflates organismal and cellular stress - there is no reason to think that IL2 neurons are under cellular stress in dauer. In fact parasitic nematodes go through dauer-like stages as part of healthy development and probably have similar remodeling of IL2. Second, dendrite pruning occurs during dauer exit, which is the opposite of a stress response - it reflects a return to favorable conditions. We agree. We modified the abstract and discussion to avoid conflating organismal stress (the alleviation of which is relevant for triggering pruning) and cellular stress. Thank you for pointing this out.

In Fig. 1A, C. elegans is shown going directly from L1 to dauer in response to unfavorable conditions, which is incorrect. Animals proceed through L2 (in many cases actually an alternative L2d pre-dauer) and then molt into dauer (an alternative L3 stage) after completing L2.

We updated the schematic to include the L2d stage where commitment to dauer entry or resumption to reproductive development is made.

In Fig. 1B, please check if it is correct that hypodermis contacts the pharynx basement membrane as drawn. The schematic in the top panel makes it look like there is a single secondary branch and the quaternary branches are similar in length to the primary dendrite. The schematic in the bottom panel makes it look like the entire neuron is a small fraction of the length of the pharynx. Could these be drawn closer to scale?

The hypodermis does contact the pharynx basement membrane. We redrew the schematic for clarity.

Reviewer #2

For context, it might be helpful to know whether branching of other dendrites is increased in sax-1 mutants (as expected based on phenotypes in other animals) or decreased like IL2 neurons.

We examined the branching pattern of PVD, a polymodal nociceptive neuron (new Supplemental Figure 3). We find no significant difference between control and sax-1 or sax-2 mutants, suggesting that these genes function in the context of pruning. Recent work (Zhao et al. 2022) confirms that sax-1 is not required for PVD branching.

Minor:

"shy87 mutant dauers showed a minor reduction in secondary and tertiary branches compared to control (Figure 1G). These results indicate that shy87 is specifically required for the elimination of dauer-generated dendrite branches." Maybe temper the specificity claim some as the reduction in branches is definitely there.

We agree, the claim was tempered.

"three complimentary approaches" should be complementary

Thank you for noticing. We fixed this.

"In control animals, SAX-2 was mostly concentrated in the cell body (data not shown)" It might be nice to include some overview images that show the cell body for completeness.

We added zoomed-out images to the revised figure, thank you for the suggestion.

Reviewer #3

Minor comments:

• Fig 1G-H, are shy87 second and third order branch counts statistically different between dauer and post dauer adults? This comparison would strengthen the claim that these order branches fail to eliminate all together rather than undergo a partial elimination. We added this to Figure S2. The shy87 mutants show a complete failure in eliminating secondary branches (i.e. no difference between dauer and post-dauer) and a strong but incomplete defect in eliminating tertiary branches.

• Fig 4B-E Indicate branch order in the images, this is unclear and a point that is focused on in the text. Done.

• Discussion of Fig 1G from the text claims that shy87 is specifically required for branch elimination yet the data shows significant defects in branch outgrowth as well. This raises the question, are the branches abnormally stabilized that results in early underdevelopment and late atrophy? Authors should acknowledge alternative hypotheses. We agree and will revise the text accordingly. The difference between shy87 and control dauers, while statistically significant, is relatively minor and can only be detected by careful quantification, it is not apparent from looking at the images (in contrast for example to rab-8 and rab-10 mutants, where we acknowledge in the text that their branching defects might affect subsequent pruning.

• Authors reference a branch elimination process but don't outline what this would entail and where their results fit in. We apologize for being unclear. Given that sax-1 and sax-2 function together, one would intuitively expect to see SAX-2 being reduced in sax-1 mutants, yet the opposite is observed. On potential explanation is that SAX-1 does not directly control SAX-2 abundance, but that clearance of SAX-2 is part of the pruning process that both proteins regulate. This would explain the enrichment of SAX-2 in sax-1 mutants. However, additional models cannot be excluded, and we acknowledge this in the revised text.

References:

Corchado, Johnny Cruz, Abhishiktha Godthi, Kavinila Selvarasu, and Veena Prahlad. 2024. “Robustness and Variability in Caenorhabditis Elegans Dauer Gene Expression.” Preprint, bioRxiv, August 26. https://doi.org/10.1101/2024.08.15.608164.

Karp, Xantha. 2018. “Working with Dauer Larvae.” WormBook, August 9, 1–19. https://doi.org/10.1895/wormbook.1.180.1.

Kozik, Patrycja, Richard W Francis, Matthew N J Seaman, and Margaret S Robinson. 2010. “A Screen for Endocytic Motifs.” Traffic (Copenhagen, Denmark) 11 (6): 843–55. https://doi.org/10.1111/j.1600-0854.2010.01056.x.

Lee, T., and L. Luo. 1999. “Mosaic Analysis with a Repressible Cell Marker for Studies of Gene Function in Neuronal Morphogenesis.” Neuron 22 (3): 451–61.

Swanson, M. M., and D. L. Riddle. 1981. “Critical Periods in the Development of the Caenorhabditis Elegans Dauer Larva.” Developmental Biology 84 (1): 27–40. https://doi.org/10.1016/0012-1606(81)90367-5.

Tang, Rui, Christopher W Murray, Ian L Linde, et al. n.d. “A Versatile System to Record Cell-Cell Interactions.” eLife 9: e61080. https://doi.org/10.7554/eLife.61080.

Zhao, Ting, Liying Guan, Xuehua Ma, Baohui Chen, Mei Ding, and Wei Zou. 2022. “The Cell Cortex-Localized Protein CHDP-1 Is Required for Dendritic Development and Transport in C. Elegans Neurons.” PLOS Genetics 18 (9): e1010381. https://doi.org/10.1371/journal.pgen.1010381.

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

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The investigators undertook detailed characterization of a previously proposed membrane targeting sequence (MTS), a short N-terminal peptide, of the bactofilin BacA in Caulobacter crescentus. Using light microscopy, single molecule tracking, liposome binding assays, and molecular dynamics simulations, they provide data to suggest that this sequence indeed does function in membrane targeting and further conclude that membrane targeting is required for polymerization. While the membrane association data are reasonably convincing, there are no direct assays to assess polymerization and some assays used lack proper controls as detailed below. Since the MTS isn't required for bactofilin polymerization in other bacterial homologues, showing that membrane binding facilitates polymerization would be a significant advance for the field.

We agree that additional experiments were required to consolidate our results and conclusions. Please see below for a description of the new data included in the revised version of the manuscript.

Major concerns

(1) This work claims that the N-termina MTS domain of BacA is required for polymerization, but they do not provide sufficient evidence that the ∆2-8 mutant or any of the other MTS variants actually do not polymerize (or form higher order structures). Bactofilins are known to form filaments, bundles of filaments, and lattice sheets in vitro and bundles of filaments have been observed in cells. Whether puncta or diffuse labeling represents different polymerized states or filaments vs. monomers has not been established. Microscopy shows mis-localization away from the stalk, but resolution is limited. Further experiments using higher resolution microscopy and TEM of purified protein would prove that the MTS is required for polymerization.

We do not propose that the MTS is directly involved in the polymerization process and state this more clearly now in the Results and Discussion sections of the revised manuscript. To address this point, we performed transmission electron microscopy studies comparing the polymerization behavior of wild-type and mutant BacA variants. The results clearly show that the MTS-free BacA variant (∆2-8) forms polymers that are indistinguishable from those formed by the wild-type protein, when purified from an E. coli overproduction strain (new Figure 1–figure supplement 1). This finding is consistent with structural work showing that bactofilin polymerization is exclusively mediated by the conserved bactofilin domain (Deng et al, Nat Microbiol, 2019). However, at native expression levels, BacA only accumulates to ~200 molecules per cell (Kühn et al, EMBO J, 2006). Under these conditions, the MTS-mediated increase in the local concentration of BacA at the membrane surface and, potentially, steric constraints imposed by membrane curvature, may facilitate the polymerization process. This hypothesis has now been stated more clearly in the Results and Discussion sections.

For polymer-forming proteins, defined localized signals are typically interpreted as slow-moving or stationary polymeric complexes. A diffuse localization, by contrast, suggests that a protein exists in a monomeric or, at most, (small) oligomeric state in which it diffuses rapidly within the cell and is thus no longer detected as distinct foci by widefield microscopy. Our single-molecule data show that BacA variants that are no longer able to interact with the membrane (as verified by cell fractionation studies and in vitro liposome binding assays) have a high diffusion rate, similar to that measured for the non-polymerizing and non-membrane-bound F130R variant. These results demonstrate that a defect in membrane binding strongly reduces the ability of BacA to form polymeric assemblies. To support this hypothesis, we have now repeated all single-particle tracking experiments and included mVenus as a freely diffusible reference protein. Our data confirm that the mobilities of the ∆2-8 and F130R variants are similar and approach those of free mVenus, supporting the idea that the deficiency to interact with the membrane prevents the formation of extended polymeric structures (which should show much lower mobilities). To underscore the relevance of membrane binding for BacA assembly, we have now included a new experiment, in which we used the PbpC membrane anchor (PbpC_1-132-mcherry) to restore the recruitment of the ∆2-8 variant to the membrane (Figure 9 and Figure 9–figure supplement 1). The results obtained show that the ∆2-8 variant transitions from a diffuse localization to polar foci upon overproduction of PbpC_1-132-mcherry. The polymerization-impaired F130R variant, by contrast, remains evenly distributed throughout the cytoplasm under all conditions. These findings further support the idea that polymerization and membrane-association are mutually interdependent processes.

(2) Liposome binding data would be strengthened with TEM images to show BacA binding to liposomes. From this experiment, gross polymerization structures of MTS variants could also be characterized.

We do not have the possibility to perform cryo-electron microscopy studies of liposomes bound to BacA. However, the results of the cell fractionation and liposome sedimentation assays clearly support a critical role of the MTS in membrane binding.

(3) The use of the BacA F130R mutant throughout the study to probe the effect of polymerization on membrane binding is concerning as there is no evidence showing that this variant cannot polymerize. Looking through the papers the authors referenced, there was no evidence of an identical mutation in BacA that was shown to be depolymerized or any discussion in this study of how the F130R mutation might to analogous to polymerization-deficient variants in other bactofilins mentioned in these references.

Residue F130 in the C-terminal polymerization interface of BacA is conserved among bactofilin homologs, although its absolute position in the protein sequence may vary, depending on the length of the N-terminal unstructured tail. The papers cited in our manuscript show that an exchange of this conserved phenylalanine residue abolishes polymer formation. Nevertheless, we agree that it is important to verify the polymerization defect of the F130R variant in the system under study. We have now included size-exclusion chromatography data showing that BacA-F130R forms a low-molecular-weight complex, whereas the wild-type protein largely elutes in the exclusion volume, indicating the formation of large, polymeric species (new Figure 1–figure supplement 1). In addition, we performed transmission electron microscopy analyses of BacA-F130R, which verified the absence of larger oligomers (new Figure 1–figure supplement 2).

(4) Microscopy shows that a BacA variant lacking the native MTS regains the ability to form puncta, albeit mis-localized, in the cell when fused to a heterologous MTS from MreB. While this swap suggests a link between puncta formation and membrane binding the relationship between puncta and polymerization has not been established (see comment 1).

We show that a BacA variant lacking the MTS (∆2-8) regains the ability to form membrane-associated foci when fused to the MTS of MreB. By contrast, a similar variant that additionally carries the F130R exchange (preventing its polymerization) shows a diffuse cytoplasmic localization. In addition, we show that the F130R exchange leads to a loss of membrane binding and to a considerable increase in the mobility of the variants carrying the MTS of E. coli MreB. As described above, we now provide additional data demonstrating that elevated levels of the PbpC membrane anchor can reinstate polar localization for the ∆2-8 variant, whereas it fails to do so for the polymerization-deficient F130R variant (Figure 9 and Figure 9–figure supplement 1). Together, these results support the hypothesis that membrane association and polymerization act synergistically to establish localized bactofilin assemblies at the stalked cell pole.

(5) The authors provide no primary data for single molecule tracking. There is no tracking mapped onto microscopy images to show membrane localization or lack of localization in MTS deletion/ variants. A known soluble protein (e.g. unfused mVenus) and a known membrane bound protein would serve as valuable controls to interpret the data presented. It also is unclear why the authors chose to report molecular dynamics as mean squared displacement rather than mean squared displacement per unit time, and the number of localizations is not indicated. Extrapolating from the graph in figure 4 D for example, it looks like WT BacA-mVenus would have a mobility of 0.5 (0.02/0.04) micrometers squared per second which is approaching diffusive behavior. Further justification/details of their analysis method is needed. It's also not clear how one should interpret the finding that several of the double point mutants show higher displacement than deleting the entire MTS. These experiments as they stand don't account for any other cause of molecular behavior change and assume that a decrease in movement is synonymous with membrane binding.

We now provide additional information on the single-particle analysis. A new supplemental figure now shows a mapping of single-particle tracks onto the cells in which they were recorded for all proteins analyzed (Figure 2–figure supplement 1). Due to the small size of C. crescentus, it is difficult to clearly differentiate between membrane-associated and cytoplasmic protein species. However, overall, slow-diffusing particles tend to be localized to the cell periphery, supporting the idea that membrane-associated particles form larger assemblies (apart from diffusing more slowly due to their membrane association). In addition, we have included a movie that shows the single-particle diffusion dynamics of all proteins in representative cells (Figure 2-video 1). Finally, we have included a table that gives an overview of the number of cells and tracks analyzed for all proteins investigated (Supplementary file 1). Figure 2A and 4D show the mean squared displacement as a function of time, which makes it possible to assess whether the particles observed move by normal, Brownian diffusion (which is the case here). We repeated the entire single-particle tracking analysis to verify the data obtained previously and obtained very similar results. Among the different mutant proteins, only the K4E-K7E variant consistently shows a higher mobility than the MTS-free ∆2-8 variant, with MSD values similar to that of free mVenus. The underlying reason remains unclear. However, we believe that an in-depth analysis of this phenomenon is beyond the scope of this paper. We re-confirmed the integrity of the construct encoding the K4E/K7E variant by DNA sequencing and once again verified the size and stability of the fusion protein by Western blot analysis, excluding artifacts due to errors during cloning and strain construction.

We agree that the single-molecule tracking data alone are certainly not sufficient to draw firm conclusions on the relationship between membrane binding and protein mobility. However, they are consistent with the results of our other in vivo and in vitro analyses, which together indicate a clear correlation between the mobility of BacA and its ability to interact with the membrane and polymerize (processes that promote each other synergistically).

(6) The experiments that map the interaction surface between the N-terminal unstructured region of PbpC and a specific part of the BacA bactofilin domain seem distinct from the main focus of the paper and the data somewhat preliminary. While the PbpC side has been probed by orthogonal approaches (mutation with localization in cells and affinity in vitro), the BacA region side has only been suggested by the deuterium exchange experiment and needs some kind of validation.

The results of the HDX analysis per se are not preliminary and clearly show a change in the solvent accessibility of backbone amides in the C-terminal region in the bactofilin domain in the presence of the PbpC_1-13 peptide. However, we agree that additional experiments would be required to verify the binding site suggested by these data. We agree that further research is required to precisely map and verify the PbpC binding site. However, as this is not the main focus of the paper, we would like to proceed without conducting further experiments in this area.

We now provide additional data showing that elevated levels of the PbpC membrane anchor are able to recruit the MTS-free BacA variant (∆2-8) to the cytoplasmic membrane and stimulate its assembly at the stalked pole (Figure 9). These results now integrate Figure 8 more effectively into the overall theme of the paper.

Reviewer #2 (Public review):

Summary:

The authors of this study investigated the membrane-binding properties of bactofilin A from Caulobacter crescentus, a classic model organism for bacterial cell biology. BacA was the progenitor of a family of cytoskeletal proteins that have been identified as ubiquitous structural components in bacteria, performing a range of cell biological functions. Association with the cell membrane is a common property of the bactofilins studied and is thought to be important for functionality. However, almost all bactofilins lack a transmembrane domain. While membrane association has been attributed to the unstructured N-terminus, experimental evidence had yet to be provided. As a result, the mode of membrane association and the underlying molecular mechanics remained elusive.

Liu at al. analyze the membrane binding properties of BacA in detail and scrutinize molecular interactions using in-vivo, in-vitro and in-silico techniques. They show that few N-terminal amino acids are important for membrane association or proper localization and suggest that membrane association promotes polymerization. Bioinformatic analyses revealed conserved lineage-specific N-terminal motifs indicating a conserved role in protein localization. Using HDX analysis they also identify a potential interaction site with PbpC, a morphogenic cell wall synthase implicated in Caulobacter stalk synthesis. Complementary, they pinpoint the bactofilin-interacting region within the PbpC C-terminus, known to interact with bactofilin. They further show that BacA localization is independent of PbpC.

Strengths:

These data significantly advance the understanding of the membrane binding determinants of bactofilins and thus their function at the molecular level. The major strength of the comprehensive study is the combination of complementary in vivo, in vitro and bioinformatic/simulation approaches, the results of which are consistent.

Thank you for this positive feedback.

Weaknesses:

The results are limited to protein localization and interaction, as there is no data on phenotypic effects. Therefore, the cell biological significance remains somewhat underrepresented.

We agree that it is interesting to investigate the phenotypic effects caused by the reduced membrane binding activity of BacA variants with defects in the MTS. We have now included phenotypic analyses that shed light on the role of region C1 in the localization of PbpC and its function in stalk elongation under phosphate-limiting conditions (see below).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

To address the missing estimation of biological relevance, some additional experiments may be carried out.

For example, given that BacA localizes PbpC by direct interaction, one might expect an effect on stalk formation if BacA is unable to bind the membrane or to polymerize. The same applies to PbpC variants lacking the C1 region. As the mutant strains are available, these data are not difficult to obtain but would help to compare the effect of the deletions with previous data (e.g. Kühn et al.) even if the differences are small.

We have now analyzed the effect of the removal of region C1 on the ability of mVenus-PbpC to promote stalk elongation in C. crescentus under phosphate starvation. Interestingly, our results show that the lack of the BacA-interaction motif impairs the recruitment of the fusion protein to the stalked pole, but it does not interfere with its stimulatory effect on stalk biogenesis. Thus, the polar localization of PbpC does not appear to be critical for its function in localized peptidoglycan synthesis at the stalk base. These results are now shown in Figure 8–Figure supplement 4. The results obtained may be explained by residual transient interactions of mVenus-PbpC with proteins other than BacA at the stalked pole. Notably, PbpC has also been implicated in the attachment of the stalk-specific protein StpX to components of the outer membrane at the stalk base. The polar localization of PbpC may therefore be primarily required to ensure proper StpX localization, consistent with previous work by Hughes et al. (Mol Microbiol, 2013) showing that StpX is partially mislocalized in a strain producing an N-terminally truncated PbpC variant that no longer localizes to the stalk base.

We have also attempted to investigate the ability of the Δ2-8 and F130R variants of BacA-mVenus to promote stalk elongation under phosphate starvation. However, the levels of the WT, Δ2-8 and F130R proteins and their stabilities were dramatically different after prolonged incubation of the cells in phosphate-limited medium, so that it was not possible to draw any firm conclusions from the results obtained (not shown).

In addition, the M23-like endopeptidase LdpA is proposed to be a client protein of BacA (in C. crescentus, Billini et al. 2018, and H. neptunium or R. rubrum, Pöhl et al. 2024). In H. neptunium, it is suggested that the interaction is mediated by a cytoplasmic peptide of LmdC reminiscent of PbpC. This should at least be commented on. It would be interesting to see, if LpdA in C. crescentus is also delocalized and if so, this could identify another client protein of BacA.

We agree that it would be interesting to study the role of BacA in LdpA function. However, we have not yet succeeded in generating a stable fluorescent protein fusion to LdpA, which currently makes it impossible to study the interplay between these two proteins in vivo. The focus of the present paper is on the mode of interaction between bactofilins and the cytoplasmic membrane and on the mutual interdependence of membrane binding and bactofilin polymerization. Given that PbpC is so far the only verified interaction partner of BacA in C. crescentus, we would like to limit our analysis to this client protein.

Further comments:

L105: analyze --> analyzed

Done.

L169: Is there any reason why the MTS of E. coli MreB was doubled?

Previous work has shown that two tandem copies of the N-terminal amphiphilic helix of E. coli MreB were required to partially target a heterologous fusion partner protein (GFP) to the cytoplasmic membrane of E. coli cells (Salje et al, 2011).

Fig. S3:

a) Please decide which tag was used (mNG or mVenus) and adapt the figure or legend accordingly.<br /> b) In the legend for panel (C), please describe how the relative amounts were calculated, as the fractions arithmetically cannot add to > 100%. I guess each band was densiometrically rated and independently normalized to the whole-cell signal?

The fluorescent tag used was mNeonGreen, as indicated in the figure. We have now corrected the legend accordingly. Thank you for making us aware of the wrong labeling of the y-axis. We have now corrected the figure and describe the method used to calculate the plotted values in the legend.

Legend of Fig 1b: It is not clear to me, to which part of panel B the somewhat cryptic LY... strain names belong. I suggest putting them either next to the images, to delete them, or at least to unify the layout (compare, e.g. to Fig S7). (I would delete the LY numbers and stay with the genes/mutations throughout. This is just a suggestion).

These names indicate the strains analyzed in panel B, and we have now clarified this in the legend. It is more straightforward to label the images according to the mutations carried by the different strains. Nevertheless, we would like to keep the strain names in the legend, so that the material used for the analysis can be clearly identified.

Fig. 2a: As some of the colors are difficult to distinguish, I suggest sorting the names in the legend within the graph according to the slope of the curves (e.g. K4E K7E (?) on top and WT being at the bottom).

Thank you for this suggestion. We have now rearranged the labels as proposed.

In the legend (L924), correct typo "panel C" to "panel B".

Done.

Fig. 3: In the legend, I suggest deleting the abbreviations "S" and "P" as they do not show up in the image. In line 929, I suggest adding: average "relative" amount... or even more precisely: "average relative signal intensities obtained..."

We have removed the abbreviations and now state that the bars indicate the “average relative signal intensities” obtained for the different fractions.

Fig 4d: same suggestion as for Fig. 2a.

Done.

Fig 8: In the legend (L978), delete 1x "the"

Done.

L258 and Fig. S5: The expression "To account for biases in the coverage of bacterial species" seems somewhat unclear. I suggest rephrasing and adding information from the M+M section here (e.g. from L593, if this is meant).

We now state that this step in the analysis pipeline was performed “To avoid biases arising from the over-representation of certain bacterial species in UniProt”.

I appreciate the outline of the workflow in panel (a) of Fig. S5. It would be even more useful when some more details about the applied criteria for filtering would be provided (e.g. concerning what is meant with "detailed taxonomic information" or "filter out closely related sequences". Does the latter mean that only one bactofilin sequence per species was used? (As quite many bacteria have more than one but similar bactofilins.)

We removed sequences from species with unclear phylogeny (e.g. candidate species whose precise taxonomic position has not yet been determined). For many pathogenic species, numerous strains have been sequenced. To account for this bias, only one sequence from clusters of highly similar bactofilin sequences (>90% identity) was retained per species. This information has now been included in the diagram. It is true that many bacteria have more than one bactofilin homolog. However, the sequences of these proteins are typically quite different. For instance, the BacA and BacB from C. crescentus only share 52% identity. Therefore, our analysis does not systematically eliminate bactofilin paralogs that coexist in the same species.

L281: Although likely, I am not sure if membrane binding has ever been shown for a bactofilin from these phyla. (See also L 380.) Is there an example? Otherwise, membrane binding may not be a property of these bactofilins.

To our knowledge, the ability of bactofilins from these clades to interact with membranes has not been investigated to date. We agree that the absence of an MTS-like motif may indicate that they lack membrane binding activity, and we have now stated this possibility in the Results and Discussion.

L285: See comment above concerning the M23-like peptidase LpdA. Although not yet directly shown for C. crescentus, it seems likely that BacACc does also localize this peptidase in addition to PbpC. I suggest rephrasing, e.g. "known" --> "shown"

We now use the word “reported”.

L295 and Fig S8: PbpC is ubiquitous. Which criteria/filters have been applied to select the shown sequences?

C. crescentus PbpC is different from E. coli Pbp1C. It is characterized by distinctive, conserved N- and C-terminal tails and only found in C. crescentus and close relatives. The C. crescentus homolog of E. coli PbpC is called PbpZ (Yakhnina et al, J Bacteriol, 2013; Strobel et al, J Bacterol, 2014), whereas C. crescentus PbpC is related to E. coli PBP1A. We have now added this information to the text to avoid confusion.

L311: may replace "assembly" by "polymerization"

Done.

L320: bactofilin --> bactofilin domain?

Yes, this was supposed to read “bactofilin domain”. Thank you for spotting this issue.

L324: The HDX analysis of BacA suggests that the exchange is slowed down in the presence of the PbpC peptide, which is indicative of a physical interaction between these two molecules. To corroborate the claim that BacA polymerization is critical for interaction with the peptide (resp. PbpC), this experiment should be carried out with the polymerization defective BacA version F130R.

(Or tone this statement down, e.g. show --> suggest.)

“suggest”

L386: undergoes --> undergo

Done.

L391-400: This idea is tempting but the suggested mechanism then would be restricted to bactofilins of C. crescentus and close relatives. The bactofilin of Rhodomicrobium, for example, was shown to localize dynamically and not to stick to a positively curved membrane.

In the vast majority of species investigated so far, bactofilins were found to associate with specifically curved membrane regions and to contribute to the establishment of membrane curvature. Unfortu­nately, the sequences of the three co-polymerizing bactofilin paralogs of R. vannielii DSM 166 studied by Richter et al (2023) have not been reported and the genome sequence of this strain is not publicly available. However, in related species with three bactofilin paralogs, only one paralog shows an MTS-like N-terminal peptide and another paralog typically contains an unusual cadherin-like domain of unknown function, as also reported for R. vannielii DSM 166. Therefore, the mechanism controlling the localization dynamics of bactofilins may be complex in the Rhodomicrobium lineage. Nevertheless, at native expression levels, the major bactofilin (BacA) of R. vannielii DSM 166 was shown to localize predominantly to the hyphal tips and the (incipient) bud necks, suggesting that regions of distinct membrane curvature could also play a role in its recruitment. We do not claim that all bactofilins recognize positive membrane curvature, which is clearly not the case. It rather appears as though the curvature preference of bactofilins varies depending on their specific function.

L405-406: I agree that localization of BacA has been shown to be independent of PbpC. However, this does not generally preclude an effect on BacA localization by other "client" or interacting proteins. (See also comment above about the putative BacA interactor LpdA). I suggest either to corroborate or to change this statement from "client binding" to "PbpC binding".

Thank you for pointing out the imprecision of this statement. We now conclude that “PbpC binding” is not critical for BacA assembly and positioning.

Suppl. Fig. S11: In the legend, please correct the copy-paste mismatch (...VirB...).

Done.

L482: delete 1x "at"

Done.

L484: may be better "soluble and insoluble fractions"?

We now describe the two fractions as “soluble and membrane-containing insoluble fractions” to make clear to all readers that membrane vesicles are found in the pellet after ultracentrifugation.

L489-490: check spelling immunoglobulin – immuneglobulin

Done.

L500 and 504: º_C --> ºC

Done.

Suppl. file X (HDX data): please check the table headline, table should be included in Suppl. file 1

We have now included a headline in this file (now Supplementary file 3).

'예술과 사회' 열쇳말에 다섯 편에 내가 더 포함하고 싶은 글이 있는지 찾아보기.

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Reply to the reviewers

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

*The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

Thank you for your positive feedback.

*There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms? *

In a separate technical manuscript describing the application of T-ChIC in mouse cells (Zeller, Blotenburg et al 2024, bioRxiv, 2024.05. 09.593364), we have provided a direct comparison of data quality between T-ChIC and other single-cell methods for chromatin-RNA co-profiling (Please refer to Fig. 1C,D and Fig. S1D, E, of the preprint). We show that compared to other methods, T-ChIC is able to better preserve the expected biological relationship between the histone modifications and gene expression in single cells.

*In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors? *

While we haven't profiled these other modifications using T-ChIC in Zebrafish, we have previously published high quality data on these histone modifications using the sortChIC method, on which T-ChIC is based (Zeller, Yeung et al 2023). In our comparison, we find that histone modification profiles between T-ChIC and sortChIC are very similar (Fig. S1C in Zeller, Blotenburg et al 2024). Therefore the method is expected to work as well for the other histone marks.

*T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary? *

We used the published scRNA-seq dataset with a larger number of cells to homogenize our cell type labels with these datasets, but we also cross-referenced our cluster-specific marker genes with ZFIN and homogenized the cell type labels with ZFIN ontology. This way our annotation is in line with previous datasets but not biased by it. Due the relatively smaller size of our data, we didn't expect to identify unique, rare cell types, but our full-length total RNA assay helps us identify non-coding RNAs such as miRNA previously undetected in scRNA assays, which we have now highlighted in new figure S1c .

*Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH? *

We appreciate that the ISH staining could be useful to validate the expression pattern of genes identified in this study. But to validate the relationships between the histone marks and gene expression, we need to combine these stainings with functional genomics experiments, such as PRC2-related knockouts. Due to their complexity, such experiments are beyond the scope of this manuscript (see also reply to reviewer #3, comment #4 for details).

*In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern? *

Thanks for the suggestion. In this revision, we have reanalysed a dataset of mouse ChIP-seq of H3K27me3 during mouse embryonic development by Xiang et al (Nature Genetics 2019) and find similar evidence of spreading of H3K27me3 signal from their pre-marked promoter regions at E5.5 epiblast upon differentiation (new Figure S4i). This observation, combined with the fact that the mechanism of pre-marking of promoters by PRC1-PRC2 interaction seems to be conserved between the two species (see (Hickey et al., 2022), (Mei et al., 2021) & (Chen et al., 2021)), suggests that the dynamics of H3K27me3 pattern establishment is conserved across vertebrates. But we think a high-resolution profiling via a method like T-ChIC would be more useful to demonstrate the dynamics of signal spreading during mouse embryonic development in the future. We have discussed this further in our revised manuscript.

Reviewer #1 (Significance (Required)):

*The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community. *

Thank you very much for your supportive remarks.

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

*Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and full-length transcriptome and applied the method on early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields. There are some concerns before the manuscript is considered for publication. *

We thank the reviewer for appreciating the quality of our study.

*Major concerns: *

1. • A major point of this study is to understand embryo development, especially gastrulation, with the power of scMulti-Omics assay. However, the current analysis didn't focus on deciphering the biology of gastrulation, i.e., lineage-specific pioneer factors that help to reform the chromatin landscape. The majority of the data analysis is based on the temporal dimension, but not the cell-type-specific dimension, which reduces the value of the single-cell assay. *

We focused on the lineage-specific transcription factor activity during gastrulation in Figure 4 and S8 of the manuscript and discovered several interesting regulators active at this stage. During our analysis of the temporal dimension for the rest of the manuscript, we also classified the cells by their germ layer and "latent" developmental time by taking the full advantage of the single-cell nature of our data. Additionally, we have now added the cell-type-specific H3K27-demethylation results for 24hpf in response to your comment below. We hope that these results, together with our openly available dataset would demonstrate the advantage of the single-cell aspect of our dataset.

1. *The cis-spreading of H3K27me3 with developmental time is interesting. Considering H3k27me3 could mark bivalent regions, especially in pluripotent cells, there must be some regions that have lost H3k27me3 signals during development. Therefore, it's confusing that the authors didn't find these regions (30% spreading, 70% stable). The authors should explain and discuss this issue. *

Indeed we see that ~30% of the bins enriched in the pluripotent stage spread, while 70% do not seem to spread. In line with earlier observations(Hickey et al., 2022; Vastenhouw et al., 2010), we find that H3K27me3 is almost absent in the zygote and is still being accumulated until 24hpf and beyond. Therefore the majority of the sites in the genome still seem to be in the process of gaining H3K27me3 until 24hpf, explaining why we see mostly "spreading" and "stable" states. Considering most of these sites are at promoters and show signs of bivalency, we think that these sites are marked for activation or silencing at later stages. We have discussed this in the manuscript ("discussion"). However, in response to this and earlier comment, we went back and searched for genes that show H3K27-demethylation in the most mature cell types (at 24 hpf) in our data, and found a subset of genes that show K27 demethylation after acquiring them earlier. Interestingly, most of the top genes in this list are well-known as developmentally important for their corresponding cell types. We have added this new result and discussed it further in the manuscript (Fig. 2d,e, , Supplementary table 3).

*Minors: *

1. • The authors cited two scMulti-omics studies in the introduction, but there have been lots of single-cell multi-omics studies published recently. The authors should cite and consider them. *

We have cited more single-cell chromatin and multiome studies focussed on early embryogenesis in the introduction now.

*2. T-ChIC seems to have been presented in a previous paper (ref 15). Therefore, Fig. 1a is unnecessary to show. *

Figure 1a. shows a summary of our Zebrafish TChIC workflow, which contains the unique sample multiplexing and sorting strategy to reduce batch effects, which was not applied in the original TChIC workflow. We have now clarified this in "Results".

1. *It's better to show the percentage of cell numbers (30% vs 70%) for each heatmap in Figure 2C. *

We have added the numbers to the corresponding legends.

1. *Please double-check the citation of Fig. S4C, which may not relate to the conclusion of signal differences between lineages. *

The citation seems to be correct (Fig. S4C supplements Fig. 2C, but shows mesodermal lineage cells) but the description of the legend was a bit misleading. We have clarified this now.

*5. Figure 4C has not been cited or mentioned in the main text. Please check. *

Thanks for pointing it out. We have cited it in Results now.

Reviewer #2 (Significance (Required)):

*Strengths: This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish. *

*Limitations: The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited. *

*Advance: The zebrafish single-cell datasets are valuable. The T-ChIC method is new and interesting. *

*The audience will be specialized and from basic research fields, such as developmental biology, epigenomics, bioinformatics, etc. *

*I'm more specialized in the direction of single-cell epigenomics, gene regulation, 3D genomics, etc. *

Thank you for your remarks.

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

*This manuscript introduces T‑ChIC, a single‑cell multi‑omics workflow that jointly profiles full‑length transcripts and histone modifications (H3K27me3 and H3K4me1) and applies it to early zebrafish embryos (4-24 hpf). The study convincingly demonstrates that chromatin-transcription coupling strengthens during gastrulation and somitogenesis, that promoter‑anchored H3K27me3 spreads in cis to enforce developmental gene silencing, and that integrating TF chromatin status with expression can predict lineage‑specific activators and repressors. *

*Major concerns *

1. *Independent biological replicates are absent, so the authors should process at least one additional clutch of embryos for key stages (e.g., 6 hpf and 12 hpf) with T‑ChIC and demonstrate that the resulting data match the current dataset. *

Thanks for pointing this out. We had, in fact, performed T-ChIC experiments in four rounds of biological replicates (independent clutch of embryos) and merged the data to create our resource. Although not all timepoints were profiled in each replicate, two timepoints (10 and 24hpf) are present in all four, and the celltype composition of these replicates from these 2 timepoints are very similar. We have added new plots in figure S2f and added (new) supplementary table (#1) to highlight the presence of biological replicates.

2. *The TF‑activity regression model uses an arbitrary R² {greater than or equal to} 0.6 threshold; cross‑validated R² distributions, permutation‑based FDR control, and effect‑size confidence intervals are needed to justify this cut‑off. *

Thank you for this suggestion. We did use 10-fold cross validation during training and obtained the R2 values of TF motifs from the independent test set as an unbiased estimate. However, the cutoff of R2 > 0.6 to select the TFs for classification was indeed arbitrary. In the revised version, we now report the FDR-adjusted p-values for these R2 estimates based on permutation tests, and select TFs with a cutoff of padj supplementary table #4 to include the p-values for all tested TFs. However, we see that our arbitrary cutoff of 0.6 was in fact, too stringent, and we can classify many more TFs based on the FDR cutoffs. We also updated our reported numbers in Fig. 4c to reflect this. Moreover, supplementary table #4 contains the complete list of TFs used in the analysis to allow others to choose their own cutoff.

3. *Predicted TF functions lack empirical support, making it essential to test representative activators (e.g., Tbx16) and repressors (e.g., Zbtb16a) via CRISPRi or morpholino knock‑down and to measure target‑gene expression and H3K4me1 changes. *

We agree that independent validation of the functions of our predicted TFs on target gene activity would be important. During this revision, we analysed recently published scRNA-seq data of Saunders et al. (2023) (Saunders et al., 2023), which includes CRISPR-mediated F0 knockouts of a couple of our predicted TFs, but the scRNAseq was performed at later stages (24hpf onward) compared to our H3K4me1 analysis (which was 4-12 hpf). Therefore, we saw off-target genes being affected in lineages where these TFs are clearly not expressed (attached Fig 1). We therefore didn't include these results in the manuscript. In future, we aim to systematically test the TFs predicted in our study with CRISPRi or similar experiments.

4. *The study does not prove that H3K27me3 spreading causes silencing; embryos treated with an Ezh2 inhibitor or prc2 mutants should be re‑profiled by T‑ChIC to show loss of spreading along with gene re‑expression. *

We appreciate the suggestion that indeed PRC2-disruption followed by T-ChIC or other forms of validation would be needed to confirm whether the H3K27me3 spreading is indeed causally linked to the silencing of the identified target genes. But performing this validation is complicated because of multiple reasons: 1) due to the EZH2 contribution from maternal RNA and the contradicting effects of various EZH2 zygotic mutations (depending on where the mutation occurs), the only properly validated PRC2-related mutant seems to be the maternal-zygotic mutant MZezh2, which requires germ cell transplantation (see Rougeot et al. 2019 (Rougeot et al., 2019)) , and San et al. 2019 (San et al., 2019) for details). The use of inhibitors have been described in other studies (den Broeder et al., 2020; Huang et al., 2021), but they do not show a validation of the H3K27me3 loss or a similar phenotype as the MZezh2 mutants, and can present unwanted side effects and toxicity at a high dose, affecting gene expression results. Moreover, in an attempt to validate, we performed our own trials with the EZH2 inhibitor (GSK123) and saw that this time window might be too short to see the effect within 24hpf (attached Fig. 2). Therefore, this validation is a more complex endeavor beyond the scope of this study. Nevertheless, our further analysis of H3K27me3 de-methylation on developmentally important genes (new Fig. 2e-f, Sup. table 3) adds more confidence that the polycomb repression plays an important role, and provides enough ground for future follow up studies.

*Minor concerns *

1. *Repressive chromatin coverage is limited, so profiling an additional silencing mark such as H3K9me3 or DNA methylation would clarify cooperation with H3K27me3 during development. *

We agree that H3K27me3 alone would not be sufficient to fully understand the repressive chromatin state. Extension to other chromatin marks and DNA methylation would be the focus of our follow up works.

*2. Computational transparency is incomplete; a supplementary table listing all trimming, mapping, and peak‑calling parameters (cutadapt, STAR/hisat2, MACS2, histoneHMM, etc.) should be provided. *

As mentioned in the manuscript, we provide an open-source pre-processing pipeline "scChICflow" to perform all these steps (github.com/bhardwaj-lab/scChICflow). We have now also provided the configuration files on our zenodo repository (see below), which can simply be plugged into this pipeline together with the fastq files from GEO to obtain the processed dataset that we describe in the manuscript. Additionally, we have also clarified the peak calling and post-processing steps in the manuscript now.

*3. Data‑ and code‑availability statements lack detail; the exact GEO accession release date, loom‑file contents, and a DOI‑tagged Zenodo archive of analysis scripts should be added. *

We have now publicly released the .h5ad files with raw counts, normalized counts, and complete gene and cell-level metadata, along with signal tracks (bigwigs) and peaks on GEO. Additionally, we now also released the source datasets and notebooks (.Rmarkdown format) on Zenodo that can be used to replicate the figures in the manuscript, and updated our statements on "Data and code availability".

*4. Minor editorial issues remain, such as replacing "critical" with "crucial" in the Abstract, adding software version numbers to figure legends, and correcting the SAMtools reference. *

Thank you for spotting them. We have fixed these issues.

Reviewer #3 (Significance (Required)):

The method is technically innovative and the biological insights are valuable; however, several issues-mainly concerning experimental design, statistical rigor, and functional validation-must be addressed to solidify the conclusions.

Thank you for your comments. We hope to have addressed your concerns in this revised version of our manuscript.

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Referee #1

Evidence, reproducibility and clarity

The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

There are several single-cell methodologies all claim to co-profile chromatin modifications and gene expression from the same individual cell, such as CoTECH, Paired-tag and others. Although T-ChIC employs pA-Mnase and IVT to obtain these modalities from single cells which are different, could the author provide some direct comparisons among all these technologies to see whether T-ChIC outperforms?

In current study, T-ChIC profiled H3K27me3 and H3K4me1 modifications, these data look great. How about other histone modifications (eg H3K9me3 and H3K36me3) and transcription factors?

T-ChIC can detect full length transcription from the same single cells, but in FigS3, the authors still used other published single cell transcriptomics to annotate the cell types, this seems unnecessary?

Throughout the manuscript, the authors found some interesting dynamics between chromatin state and gene expression during embryogenesis, independent approaches should be used to validate these findings, such as IHC staining or RNA ISH?

In Fig2 and FigS4, the authors showed H3K27me3 cis spreading during development, this looks really interesting. Is this zebrafish specific? H3K27me3 ChIP-seq or CutTag data from mouse and/or human embryos should be reanalyzed and used to compare. The authors could speculate some possible mechanisms to explain this spreading pattern?

Significance

The authors have a longstanding focus and reputation on single cell sequencing technology development and application. In this current study, the authors developed a novel single-cell multi-omic assay termed "T-ChIC" so that to jointly profile the histone modifications along with the full-length transcriptome from the same single cells, analyzed the dynamic relationship between chromatin state and gene expression during zebrafish development and cell fate determination. In general, the assay works well, the data look convincing and conclusions are beneficial to the community.

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Reply to the reviewers

Manuscript number: RC-2025-02879 Corresponding author(s): Matteo Allegretti; Alia dos Santos

1. General Statements

In this study, we investigated the effects of paclitaxel on both healthy and cancerous cells, focusing on alterations in nuclear architecture. Our novel findings show that:

• Paclitaxel-induced microtubule reorganisation during interphase alters the perinuclear distribution of actin and vimentin. The formation of extensive microtubule bundles, in paclitaxel or following GFP-Tau overexpression, coincides with nuclear shape deformation, loss of regulation of nuclear envelope spacing, and alteration of the nuclear lamina.

• Paclitaxel treatment reduces Lamin A/C protein levels via a SUN2-dependent mechanism. SUN2, which links the lamina to the cytoskeleton, undergoes ubiquitination and consequent degradation following paclitaxel exposure.

• Lamin A/C expression, frequently dysregulated in cancer cells, is a key determinant of cellular sensitivity to, and recovery from, paclitaxel treatment.

Collectively, our data support a model in which paclitaxel disrupts nuclear architecture through two mechanisms: (i) aberrant nuclear-cytoskeletal coupling during interphase, and (ii) multimicronucleation following defective mitotic exit. This represents an additional mode of action for paclitaxel beyond its well-established mechanism of mitotic arrest.

We thank the reviewers for their time and constructive feedback. We have carefully considered all comments and have carried out a full revision. The updated manuscript now includes additional data showing:

• Overexpression of microtubule-associated protein Tau causes similar nuclear aberration phenotypes to paclitaxel. This supports our hypothesis that increased microtubule bundling directly leads to nuclear disruption in paclitaxel during interphase.

• Paclitaxel's effects on nuclear shape and Lamin A/C and SUN2 expression levels occur independently of cell division.

• Reduced levels of Lamin A/C and SUN2 upon paclitaxel treatment occur at the protein level via ubiquitination of SUN2.

• The effects of paclitaxel on the nucleus are conserved in breast cancer cells.

Full Revision

We have also edited our text and added further detail to clarify points raised by the reviewers. We believe that our revised manuscript is overall more complete, solid and compelling thanks to the reviewers' comments.

1. Point-by-point description of the revisions

Reviewer #1 Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown.

We thank the reviewer for this valuable comment. To elucidate the mechanism behind the decrease in Lamin A/C and SUN2 levels, we have now performed several additional experiments. First, we performed RT-qPCR to quantify mRNA levels of these genes, relative to the housekeeping gene GAPDH (Supplementary Figure 3B and O). The levels of SUN2 and LMNA mRNA remained the same between control and paclitaxel-treated cells, indicating that this effect instead occurs at the protein level. We have also tested post-translational modifications as a potential regulatory mechanism for Lamin A/C and SUN2. In addition to the phosphorylation of Ser404 which we had already tested (Supplementary Figure 3C), we have now included additional Phos-tag gel and Western blotting data showing that the overall phosphorylation status of Lamin A/C is not affected by paclitaxel (Supplementary Figure 3E and F). We also pulled-down Lamin A/C from cell lysates and then Western blotted for polyubiquitin and acetyl-lysine, which showed that the ubiquitination and acetylation states of Lamin A/C are also not affected by paclitaxel (Supplementary Figure 3G-I). However, Western blots for polyubiquitin of SUN2 pulled down from cell lysates showed that paclitaxel treatment results in significant SUN2 ubiquitination (Figure 3M and N). Therefore, we propose that the downregulation of SUN2 following paclitaxel treatment occurs by ubiquitin-mediated proteolysis.

The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

We addressed this important point by using an alternative method to stabilise/bundle microtubules in interphase, namely by overexpressing GFP-Tau, as suggested by reviewer 2. Following GFP- Tau overexpression, large microtubule bundles were observed throughout the cytoplasm (Figure 4A), and this resulted in a significant decrease in nuclear solidity (Figure 4B). Furthermore, in cells where microtubule bundles extensively contacted the nucleus, the nuclear lamina became unevenly distributed and appeared patchy (Figure 4C). This supports our hypothesis that the aberrations to nuclear shape and Lamin A/C localisation in paclitaxel-treated cells are due to the presence of microtubules bundles surrounding the nucleus.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

We thank the reviewer for raising this important point. We have addressed this by conducting additional repeats for the cell confluency measurements to increase the statistical power of our experiments (Figure 5A). Our data now show that GFP-lamin A/C had a statistically significant effect on rescuing cell growth at both 1 nM and 5 nM paclitaxel, while Lamin A/C knockdown exacerbated the inhibition of cell growth at 5 nM paclitaxel but not 1 nM paclitaxel (Figure 5A). In addition, we note that the duration of paclitaxel treatment before assessing viability was specified in the figure legend: "Bar graph comparing cell viability between wild-type (red), GFP-Lamin A/C overexpression (green), and Lamin A/C knockdown (blue) cells following 20 h incubation in 0, 1, 5, or 10 nM paclitaxel." We also repeated cell viability analysis after 48 h incubation in paclitaxel instead of 20 h to allow for a longer time for differences to take effect (Figure 5B).

We also added figures showing the cytoskeletal reorganisation at both 1 and 10 nM in addition to 0 and 5 nM (Supplementary Figure 1A) showing that microtubule bundling and condensation of actin into puncta correlated with increased paclitaxel concentration. Vimentin colocalised well with microtubules at all concentrations.

We have also included in our results section further clarification for the use of 5nM paclitaxel in this study. The new section reads as follows: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

We thank the reviewer for pointing out the need for clarification in the wording of our manuscript. We have reworded the title and relevant sections of our abstract, introduction, and discussion to make it clearer that the effects of paclitaxel on the nucleus are due to a combination of aberrant nuclear cytoskeletal coupling during interphase and multimicronucleation following mitotic slippage. We have also added additional data in support of the effect of paclitaxel on nuclear architecture during interphase. For this, we used serum-starved cells (which divide only very slowly such that the majority of cells do not pass through mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data confirmed that paclitaxel's effects on nuclear solidity, and Lamin A/C and SUN2 proteins levels can occur independently of cell division (Figure 2C; Figure 3H-J). Finally, when we overexpressed GFP-Tau (as discussed above) we observed similar aberrations to nuclear solidity and Lamin A/C localisation. This indicates that these effects occur due to microtubule bundling in interphase, especially as in our study GFP-Tau did not lead to multimicronucleation or appear to affect mitosis (Figure 4).

Below are the main changes to the text regarding the interphase effect of paclitaxel:

• Title: "Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling"

• Abstract: "Overall, our data supports nuclear architecture disruption, caused by both aberrant nuclear-cytoskeletal coupling during interphase and exit from defective mitosis, as an additional mechanism for paclitaxel beyond mitotic arrest."

• Introduction: "Here we propose that cancer cells have increased vulnerability to paclitaxel both during interphase and following aberrant mitosis due to pre-existing defects in their NE and nuclear lamina."

• Discussion: "Overall, our work builds on previous studies investigating loss of nuclear integrity as an anti-cancer mechanism of paclitaxel separate from mitotic arrest14,20,21. We propose that cancer cells show increased sensitivity to nuclear deformation induced by aberrant nuclear-cytoskeletal coupling and multimicronucleation following mitotic slippage. Therefore, we conclude that paclitaxel functions in interphase as well as mitosis, elucidating how slowly growing tumours are targeted."

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

As mentioned above, we have now included additional information in our Results section to clarify our paclitaxel dose range: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

We appreciate the reviewer's summary and thank them for their time. We believe our comprehensive revisions have addressed all comments, strengthening the manuscript and making it more robust and compelling.

Reviewer #2 Evidence, reproducibility and clarity This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state- of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

We thank the reviewer for the summary and for highlighting our efforts in using the latest imaging technical advances.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

We thank the reviewer for highlighting the lack of clarity in our wording. We have revised the title, abstract and relevant sections of the introduction and discussion to clarify our message that the effects of paclitaxel on the nucleus arise from a combination of aberrant nuclear-cytoskeletal coupling during interphase and multimicronucleation following exit from defective mitosis. We have also included additional data where we used slow-dividing, serum-starved cells (under these conditions, the majority of cells do not undergo mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data show that even in these cells there is a clear effect of paclitaxel on nuclear solidity, and Lamin A/C and SUN2 protein levels, further supporting our hypothesis that these phenotypes can occur independently of cell division (Figure 2C; Figure 3H-J). Furthermore, we performed additional experiments where we used overexpression of GFP-Tau as an alternative method of stabilising microtubules in interphase and observed similar aberrations to nuclear solidity and Lamin A/C localisation. As GFP-Tau overexpression did not lead to micronucleation or appear to affect mitosis, these data support the hypothesis that nuclear aberrations occur due to microtubule bundling in interphase (Figure 4). We discuss these experiments in more detail below. Finally, we have reworded the introduction to better introduce alternative hypotheses and mechanisms for paclitaxel's activity.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

We would like to note that the paper from Xiang-Xi Xu's lab (Smith et al, 2021) was cited in our original manuscript (reference 14 in both the original and revised manuscripts). We have now also included additional review articles from the Xiang-Xi Xu lab (PMID:36368286 20 and PMID: 35048083 21). Furthermore, we have clarified the wording in both the introduction and discussion to better reflect the current understanding of paclitaxel's mechanism and alternative hypotheses.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

We thank the reviewer for suggesting this control experiment using the microtubule stabiliser Tau. We have now included these experiments in the revised version of the manuscript (Figure 4). The overexpression of GFP-Tau supports our hypothesis that cytoskeletal reorganisation in paclitaxel exerts mechanical stress on the nucleus during interphase, resulting in nuclear deformation and aberrations to the nuclear lamina. In particular, GFP-Tau overexpression resulted in large microtubule bundles throughout the cytoplasm (Figure 4A). Notably, in cells where these bundles extensively contacted the nucleus, we observed a significant decrease in nuclear solidity (Figure 4B) accompanied by changes in nuclear lamina organisation, including a patchy lamina phenotype, similar to that induced by paclitaxel (Figure 4C).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

We thank the reviewer for raising important points regarding the specificity of paclitaxel's effects. We agree that microtubule stabilisation can induce myriad cellular changes, including alterations to focal adhesions and other cytoskeletal components. Our focus on Lamin A/C and nuclear morphology is grounded both in the established clinical relevance of nuclear mechanics in cancer and builds on mechanistic work from other groups.

Lamin A/C expression is commonly altered in cancer, and nuclear morphology is frequently used in cancer diagnosis35. Lamin A/C also plays a crucial role in regulating nuclear mechanics32 and, importantly, determines cell sensitivity to paclitaxel14. However, the mechanism by which Lamin A/C determines sensitivity of cancer cells to paclitaxel is unclear.

Our data are consistent with Lamin A/C being a determinant of paclitaxel survival sensitivity. We also provide evidence that paclitaxel itself reduces Lamin A/C protein levels and disrupts its organisation at the nuclear envelope. We directly link these effects to microtubule bundling around the nucleus and degradation of force-sensing LINC component SUN2, highlighting the importance of nuclear architecture and mechanics to overall cellular function. Furthermore, we show that recovery from paclitaxel treatment depends on Lamin A/C expression levels. This has clinical relevance, as unlike cancer cells, healthy tissue with non-aberrant lamina would be able to selectively recover from paclitaxel treatment.

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

We agree with the reviewer that Supplementary Figure 6 does not add significant value to the manuscript and thank the reviewer for pointing this out. We have removed it from the manuscript accordingly.

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

We thank the reviewer for pointing out the missing information. For our cryo-ET experiments, we collected data from 9 lamellae from paclitaxel-treated cells and 6 lamellae from control cells, with each lamella derived from a single cell. This information has now been added to the figure legend (Figure 2F).

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

We appreciate the reviewer's concerns and have performed major revisions to strengthen the significance of our study. Specifically, we conducted two key sets of experiments to validate our original conclusions: serum starvation to control for the effects of cell division, and overexpression of the microtubule stabiliser Tau to demonstrate that paclitaxel can affect the nucleus via its microtubule bundling activity in interphase.

By elucidating the mechanistic link between microtubule stabilisation and nuclear-cytoskeletal coupling, our findings contribute to our understanding of paclitaxel's multifaceted actions in cancer cells.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

Reviewer #3 Evidence, reproducibility and clarity The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

We thank the reviewer for the positive feedback.

Although similar ideas are published, which may be suitable to be cited? • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review. • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

We thank the reviewer for bringing to our attention these important review articles. In our initial manuscript, we only cited the original paper (14, also reference 14 in the original manuscript). We have now included citations to the suggested publications (20,21).

We would also like to emphasise how our manuscript distinguishes itself from the work of Smith et al.14,20,21:

• Cell-type focus: In their study 14, Smith et al. examined the effect of paclitaxel on malignant ovarian cancer cells and proposed that paclitaxel's effects on the nucleus are limited to cancer cells. However, our data extends these findings by demonstrating paclitaxel's effects in both cancerous and non-cancerous backgrounds.

• Cytoskeletal reorganisation: Smith et al. show reorganisation of microtubules in paclitaxel-treated cells14. Our data show re-organisation of other cytoskeletal components, including F-actin and vimentin.

• Multimicronucleation: Smith et al. propose that paclitaxel-induced multimicronucleation occurs independently of cell division14. Although we observe progressive nuclear abnormalities during interphase over the course of paclitaxel treatment, our data do not support this conclusion; we find that multimicronucleation occurs only following mitosis.

• Direct link between microtubule bundling and nuclear aberrations: We show that nuclear aberrations caused by paclitaxel during interphase (distinct from multimicronucleation) are directly linked to microtubule bundling around the nucleus, suggesting they result from mechanical disruption and altered force propagation.

• Lamin A/C regulation: Consistent with Smith et al.14, we show that Lamin A/C depletion leads to increased sensitivity to paclitaxel treatment. However, we further demonstrate that paclitaxel itself leads to reduced levels of Lamin A/C and that this effect occurs independently of mitosis and is mediated via force-sensing LINC component SUN2. Upon SUN2 knockdown, Lamin A/C levels are no longer affected by paclitaxel treatment.

• Recovery: Finally, our work reveals that cells expressing low levels of Lamin A/C recover less efficiently after paclitaxel removal. This might help explain how cancer cells could be more susceptible to paclitaxel.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

We thank the reviewer for this comment. Our initial study aimed to understand the effects of paclitaxel on nuclear architecture in non-aberrant backgrounds. To show that the observed effects of paclitaxel are also applicable to cancer cells, we have now repeated our main experiments using MDA-MB-231 human breast cancer cells (Supplementary Figure 1B; Supplementary Figure 3P-T). Similar to our findings in human fibroblasts, paclitaxel treatment of MDA-MB-231 led to cytoskeletal reorganisation (Supplementary Figure 1B), a decrease in nuclear solidity (Supplementary Figure 3P), aberrant (patchy) localisation of Lamin A/C (Supplementary Figure 3Q), and a reduction in Lamin A/C and SUN2 levels (Supplementary Figure 3R-T).

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

We have added further detail to the figure legend for clarification: "Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Different colours distinguish individual α-tubulin clusters, representing individual microtubule filaments or filament bundles."

Generally, the figures need additional description to be clear.

We have added further clarification and detail to our figure legends.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

We changed this sentence to: "Figure 3 - Paclitaxel treatment results in aberrant organisation of the nuclear lamina and decreased Lamin A/C levels via SUN2."

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?.

We thank the reviewer for pointing this out and we agree that the ratio of Lamin A to Lamin C can vary with culture conditions. To confirm that paclitaxel treatment reduces total Lamin A/C levels regardless of this ratio, we repeated the Western blot analysis in three additional biological replicates using cells in which Lamin C levels exceeded Lamin A levels. These experiments confirmed a comparable decrease in total Lamin A/C levels. Figure 3B and 3C have been updated accordingly.

Also, the effect on Lamin A/C and SUN2 levels are not significant of robust.

Decreased Lamin A/C and SUN2 levels following paclitaxel treatment were consistently seen across three or more biological repeats (Figure 3B-C), and this could be replicated in a different cell type (MDA-MB-231) (Supplementary Figure 3R-T). Furthermore, Western blotting results are consistent with the patchy Lamin A/C distribution observed using confocal and STORM following paclitaxel treatment (Figure 3A; Supplementary Figure 3A), where Lamin A/C appears to be absent from discrete areas of the lamina.

Any mechanisms are speculated for the reason for the reduction?

We have now included additional data which aims to shed light on the mechanism behind the decrease in Lamin A/C and SUN2 levels following paclitaxel treatment. We found that SUN2 is selectively degraded during paclitaxel treatment. Immunoprecipitation of SUN2 followed by Western blotting against Polyubiquitin C showed increased SUN2 ubiquitination in paclitaxel (Figure 3M and N). Furthermore, in our original manuscript, we showed that Lamina A/C levels remained unaltered during paclitaxel treatment in cells where SUN2 had been knocked down. We propose that changes in microtubule organisation affect force propagation to Lamin A/C specifically via SUN2 and that this leads to Lamina A/C removal and depletion. Future work will be needed to fully understand this mechanism.

In addition to the findings described above, we report no significant changes in mRNA levels for LMNA or SUN2 in paclitaxel (Supplementary Figure 3B and O). Phos-tag gels followed by Western blotting analysis for Lamin A/C also did not detect changes to the overall phosphorylation status of Lamin A/C due to paclitaxel treatment. This is in agreement with our initial data showing no changes to Lamin A/C Ser 404 phosphorylation levels (Supplementary Figure 3E and F). Finally, Lamin A/C immunoprecipitation experiments followed by Western blotting for Polyubiquitin C and acetyl-lysine showed no significant changes in the ubiquitination and acetylation state of Lamin A/C in paclitaxel-treated cells (Supplementary Figure 3G-I).

Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

The nuclear lamina and LINC complex proteins play a critical role in regulating nuclear integrity, stiffness and mechanical responsiveness to external forces28,31-33,54,75, as well as in maintaining the nuclear intermembrane distance69,74. In particular, SUN-domain proteins physically bridge the nuclear lamina to the cytoskeleton through interactions with Nesprins, thereby preserving the perinuclear space distance30,69,74. Mutations in Lamins have been shown to disrupt chromatin organization, alter gene expression, and compromise nuclear structural integrity, and experiments with LMNA knockout cells reveal that nuclear mechanical fragility is closely coupled to nuclear deformation47. Furthermore, nuclear-cytoskeletal coupling is essential during processes such as cell migration, where cells undergo stretching and compression of the nucleus; weakening or loss of the lamina in such cases compromises cell movement47,73. In our work, we show that alterations to nuclear Lamin A/C and SUN2 by paclitaxel treatment coincide with nuclear deformations (Figure 2A-D, F, G; Figure 3A-D, F, G; Supplementary Figure 3A, P-T) and that these deformations are reversible following paclitaxel removal (Supplementary Figure 4B-D). Our experiments also demonstrate that Lamin A/C expression levels significantly influence cell growth, cell viability, and cell recovery in paclitaxel (Figure 5). Therefore, drawing on current literature and our results, we propose that, during interphase, paclitaxel induces severe nuclear aberrations through the combined effects of: i) increased cytoskeletal forces on the NE caused by microtubule bundling; ii) loss of ~50% Lamin A/C and SUN2; iii) reorganisation of nucleo-cytoskeletal components.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

We thank the reviewer for the feedback and valuable suggestions. In response, we have included experiments using human breast cancer cell line MDA-MB-231 to further corroborate our findings and interpretations. We believe these additions have improved the clarity, robustness and impact of our manuscript, and we are grateful for the reviewer's contributions to its improvement.

The title of the article makes a simple striking claim about the state of the scientific literature with a numerical estimate of the proportion of “fake” articles. Yet, by contrast to this title, in the text of the article, Heathers is highly critical of his own work.

James’ peer review of Heathers’ article

James Heathers often mentions the limitations of his research thus “peer-reviewing” his own article to the extent that he admits that this work is “incomplete”, “unsystematic” and “far flung”.

“This work is too incomplete to support responsible meta-analysis, and research that could more accurately define this figure does not exist yet. ~1 in 7 papers being fake represents an existential threat to the scientific enterprise.”

“While this is highly unsystematic, it produced a substantially higher figure. Correspondents reliably estimated 1-5% of all papers contain fabricated data, and 2-10% contain falsified results.”

“These values are too disparate to meta-analyze responsibly, and support only the briefest form of numerical summary: n=12 papers return n=16 individual estimates; these have a median of 13.95%, and 9 out of 16 of these estimates are between 13.4% and 16.9%. Given this, a rough approximation is that for any given corpus of papers, 1 in 7 (i.e. 14.3%) contain errors consistent with faking in at least one identifiable element.”

“The accumulation of papers collected here is, frankly, haphazard. It does not represent a mature body of literature. The papers use different methods of analyzing figures, data, or other features of scientific publications. They do not distinguish well between papers that have small problematic elements which are fake, or fake in their entirety. They analyze both small and large corpora of papers, which are in different areas of study and in journals of different scientific quality – and this greatly changes base rates;…”

“As a consequence, it would be prudent to immediately reproduce the result presented here as a formal systematic review. It is possible further figures are available after an exhaustive search, and also that pre registered analytical assumptions would modify the estimations presented.”

Heathers has also in an interview published in Retraction Watch (Chawla 2024) acknowledged pitfalls in this article such as:

“Heathers said he decided to conduct his study as a meta-analysis because his figures are “far flung.””

“They are a little bit from everywhere; it’s wildly nonsystematic as a piece of work,” he said.”

“Heathers acknowledged those limitations but argued that he had to conduct the analysis with the data that exist. “If we waited for the resources necessary to be able to do really big systematic treatments of a problem like this within a specific area, I think we’d be waiting far too long,” he said. “This is crucially underfunded.”

Built in opposition to Fanelli 2009, but it’s illogical

Heathers states in the abstract that his article is “in opposition” to Fanelli’s 2009 PloS One article (Fanelli 2009), yet that opposition is illogical and artificially constructed since there is no contradiction between 2% of scientists self-reporting having taking part in fabrication or falsification and an eventual much higher proportion of “fake scientific outputs”. Like most of what is wrong with Heather’s article, this is in fact acknowledged by the author who notes that the 2% figure “leaves us with no estimate of how much scientific output is fake” (bias in self-reporting, possibility of prolific authors, etc).

Fanelli 2009 is not cited in the way JH says it is cited

Whilst the opposition discussed above is illogical, it could be that the 2% figure is mis-cited by others as representing an estimate of fake scientific outputs thus probably underestimating the extent of fraud. Heathers suggests that this may indeed be the case, but also contradicts himself about how (Fanelli 2009), or the 2% figure coming from that publication, is typically used.

In one sentence, he writes that “the figure is overwhelmingly the salient cited fact in its 1513 citations” and that “this generally appears as some variant of “about 2% of scientists admitted to have fabricated, falsified or modified data or results at least once” (Frank et al. 2023)

whilst and in another sentence, he writes that “the typical phraseology used to express it – e.g. “the most serious types of misconduct, fabrication and falsification (i.e., data fraud), are relatively rare” (George 2016).

Those two sentences cited by Heathers are fundamentally different, the first one accurately reports that the 2% figure relates to individuals self-reporting, whilst the second one appears to relate to the prevalence of misconducts in the literature itself. How Fanelli 2009 is cited in the literature is an empirical question that can be studied by looking at citation contexts beyond the two examples given by Heathers. Given that a central justification for Heathers’ piece appears to be the misuse of this 2% figure, we sought to test whether this was the case.

A first surprise was that whilst the sentence attributed to (George 2016) can indeed be found in that publication (in the abstract), first it is not in a sentence citing (Fanelli 2009) nor the 2% figure, and, second, it is quoted selectively omitting a part of the sentence that nuances it considerably: “The evidence on prevalence is unreliable and fraught with definitional problems and with study design issues. Nevertheless, the evidence taken as a whole seems to suggest that cases of the most serious types of misconduct, fabrication and falsification (i.e., data fraud), are relatively rare but that other types of questionable research practices are quite common.” (Fanelli 2009) is discussed extensively by (George 2016), and some of the caveats, e.g. on self-reporting, are highlighted.

To go beyond those two examples, we constructed a comprehensive corpus of citation contexts, defined as the textual environment surrounding a paper's citation, including several words or sentences before and after the citation (see Methods section below). 737 citation contexts could be analysed. Out of those, the vast majority (533, or 72%) did not cite the 2% figure. Instead, they often referred to this article as a general reference together with other articles to make a broad point, or, focused on other numbers in particular those related to questionable research practices (Bordignon, Said, and Levy 2024). The 28% (204) citation contexts that did mention the 2% figure did so accurately in the majority of cases: 83% (170) of those did mention that it was self-reporting by scientists whilst 17% (34) of those, or 5% of the total citation contexts analysed were either ambiguous or misleading in that they suggested or claimed that the 2% figure related to scientific outputs.

Although the analysis above does not include all citation contexts, it is possible to conclude unambiguously that the 2% figure is not overwhelmingly the salient cited fact in relation to Fanelli 2009, and that when it is cited it is often accurately, i.e. as representing self-reporting by scientists. Whilst an exhaustive analysis is beyond the scope of this peer review, it is not uncommon to find in this corpus citations contexts that have an alarming tone about the seriousness of the problem of FFPs, e.g. “…a meta-analysis (Fanelli 2009) suggest that the few cases that do surface represent only the tip of a large iceberg." [DOI: 10.1177/0022034510384627]

Thus, the rationale for Heathers’ study appears to be misguided. The supposed lack of attention for the very serious problem of FFPs is not due to a minimisation of the situation fueled by a misinterpretation of Fanelli 2009. Importantly, even if that was the case, an attempt to draw attention by claiming that 1 in 7 papers are fake, a claim which according to the author himself is not grounded in solid facts, is not how the scientific literature should be used.

Methods for the construction of the corpus of citation contexts

We used Semantic Scholar, an academic database encompassing over 200 million scholarly documents from diverse sources including publishers, data providers, and web crawlers. Using the specific paper identifier for Fanelli's 2009 publication (d9db67acc223c9bd9b8c1d4969dc105409c6dfef), we queried the Semantic Scholar API to retrieve available citation contexts. Citation contexts were extracted from the "contexts" field within the JSON response pages, (see technical specifications).

The query looks like this: semanticscholar.org

The broad coverage of Semantic Scholar does not imply that citation contexts are always retrieved. The Semantic Scholar API provided citation contexts for only 48% of the 1452 documents citing the paper. To get more, we identified open access papers among the remaining 52% citing papers, retrieved their PDF location and downloaded the files. We used Unpaywall API, which is a database to be queried with a DOI in order to get open access information about a document. The query looks like this.

We downloaded 266 PDF files and converted them to text format using an online bulk PDF-to-text converter. These files were then processed using TXM, a specialized textual analysis tool. We used its concordancer function to identify the term "Fanelli" as a pivot term and check the reference being the good one (the 2009 paper in PlosOne). We did manual cleaning and appended the citation contexts to the previous corpus.

Through this comprehensive methodology, we ultimately identified 824 citation contexts, representing 54% (784) of all documents citing Fanelli's 2009 paper. This corpus comprised 48% of contexts retrieved from Semantic Scholar and an additional 6% obtained through semi-manual extraction from open access documents. 87 of those contexts were excluded from the analysis for a range of reasons including: context too short to conclude, language neither English nor French (shared languages of the authors of this review), duplicate documents (e.g. preprints), etc, leaving us with 737 contexts. They were first classified manually in two categories, those mentioning the 2% figure and those which did not. Then, for the first category, they were further classified manually in two categories depending on whether the figure was appropriately assigned to self-reporting of researchers or rather misleadingly suggesting that the 2% applied to research outputs.

Contributions

Investigation: FB collected the citation contexts.<br /> Data curation and formal analysis: RL and MS<br /> Writing – review & editing: RL, MS and FB

References

Bordignon, Frederique, Maha Said, and Raphael Levy. 2024. “Citation Contexts of [How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data, DOI: 10.1371/Journal.Pone.0005738].” Zenodo. https://doi.org/10.5281/zenodo.14417422.

Chawla, Dalmeet Singh. 2024. “1 in 7 Scientific Papers Is Fake, Suggests Study That Author Calls ‘Wildly Nonsystematic.’” Retraction Watch (blog). September 24, 2024. https://retractionwatch.com/2024/09/24/1-in-7-scientific-papers-is-fake-suggests-study-that-author-calls-wildly-nonsystematic/.

Fanelli, Daniele. 2009. “How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data.” PLOS ONE 4 (5): e5738. https://doi.org/10.1371/journal.pone.0005738.

Frank, Fabrice, Nans Florens, Gideon Meyerowitz-Katz, Jérôme Barriere, Éric Billy, Véronique Saada, Alexander Samuel, Jacques Robert, and Lonni Besançon. 2023. “Raising Concerns on Questionable Ethics Approvals - a Case Study of 456 Trials from the Institut Hospitalo-Universitaire Méditerranée Infection.” Research Integrity and Peer Review 8 (1): 9. https://doi.org/10.1186/s41073-023-00134-4.

George, Stephen L. 2016. “Research Misconduct and Data Fraud in Clinical Trials: Prevalence and Causal Factors.” International Journal of Clinical Oncology 21 (1): 15–21. https://doi.org/10.1007/s10147-015-0887-3.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

This is a revision of a manuscript previously submitted to Review Commons. The authors have partially addressed my comments, mainly by expanding the introduction and discussion sections. Sandy Schmid, a leading expert on the AP2 adaptor and CME, has been added as a co-corresponding author. The main message of the manuscript remains unchanged. Through overexpression of fluorescently tagged CCDC32, the authors propose that, in addition to its established role in AP2 assembly, CCDC32 also follows AP2 to the plasma membrane and regulates CCP maturation. The manuscript presents some interesting ideas, but there are still concerns regarding data inconsistencies and gaps in the evidence.

With due respect, we would argue that a role for CCDC32 in AP2 assembly is hardly ‘established’.  Rather a single publication reporting its role as a co-chaperone for AAGAP appeared while our manuscript was under review.  We find some similar and some conflicting results, which are described in our revised manuscript.  However, in combination our two papers clearly show that CCDC32, a previously unrecognized endocytic accessory protein, deserves further study.

(1) eGFP-CCDC32 was expressed at 5-10 times higher levels than endogenous CCDC32. This high expression can artificially drive CCDC32 to the cell surface via binding to the alpha appendage domain (AD)-an interaction that may not occur under physiological conditions.

While we acknowledge that overexpression of eGFP-CCDC32 could result in artificially driving it to CCPs, we do not believe this is the case for the following reasons:

i. The bulk of our studies (Figures 2-4) demonstrate the effects of siRNA knockdown on CCDC32 on CCP early stages of CME, and so it is likely that these functions require the presence of endogenous CCDC32 at nascent CCPs as detected with overexpressed eGFP-CCDC32 by TIRF imaging.

ii. At these levels of overexpression eGFP-CCDC32 fully rescues the effects of siRNA KD of endogenous CCCDC32 of Tfn uptake and CCP dynamics (Figure 6F,G). If the protein was artificially recruited to the AP2 appendage domain, one would expect it to compete with the recruitment of other EAPS to CCPs and hence exhibit defects in CCP dynamics. Indeed, we see the opposite: CCPs that are positive for eGFP-CCDC32 show normal dynamics and maturation rates, while CCPs lacking eGFP-CCDC32 are short-lived and more likely to be aborted (Figure 1C).

iii. We have identified two modes of binding of CCDC32 to AP2 adaptors: one is through canonical AP2-AD binding motifs, the second is through an a-helix in CCDC32 that, by modeling, docks only to the open conformation of AP2.  Overexpressed CCDC32 lacking this a-helix is not recruited to CCPs (Fig. 6 D,E), indicating that the canonical AP2 binding motifs are not sufficient to recruit CCDC32 to CCPs, even when overexpressed.

(2) Which region of CCDC32 mediates alpha AD binding? Strangely, the only mutant tested in this work, Δ78-98, still binds AP2, but shifts to binding only mu and beta. If the authors claim that CCDC32 is recruited to mature AP2 via the alpha AD, then a mutant deficient in alpha AD binding should not bind AP2 at all. Such a mutant is critical for establish the model proposed in this work.

We understand the reviewer’s confusion and thus devoted a paragraph in the discussion to this issue.  As revealed by AlphaFold 3.0 modeling (Figure S6) binding of CCDC32 to the alpha AD likely occurs via the 2 canonical AP2-AD binding motifs encoded in CCDC32. Given the highly divergent nature of AP2-AD binding motifs, we did not identify these motifs without the AlphaFold 3.0 modeling. While these interactions could be detected by GST-pull downs, they are apparently not of sufficient affinity to recruit CCDC32 to CCPs in cells. In the text, we now describe the a-helix we identified as being essential of CCP recruitment as ‘a’ AP2 binding site on CCDC32 rather than ‘the’ AP2 binding site.  Interestingly, and also discussed, Alphafold 3.0 identifies a highly predicted docking site on a-adaptin that is only accessible in the open, cargo-bound conformation of intact AP2.  This is also consistent with the inability of CCDC32(D78-99) to bind the a:µ2 hemi-complex in cell lysates.

We agree that further structural studies on CCDC32’s interactions with AP2 and its targeting to CCPs will be of interest for future work.

(3) The concept of hemicomplexes is introduced abruptly. What is the evidence that such hemicomplexes exist? If CCDC32 binds to hemicomplexes, this must occur in the cytosol, as only mature AP2 tetramers are recruited to the plasma membrane. The authors state that CCDC32 binds the AD of alpha but not beta, so how can the Δ78-98 mutant bind mu and beta?

We introduced the concept of hemicomplexes based on our unexpected (and now explicitly stated as such) finding that the CCDC32(D78-99) mutant efficiently co-IPs with a b2:µ2 hemicomplex.  As stated, the efficiency of this pulldown suggests that the presumed stable AP2 heterotetramer must indeed exist in equilibrium between the two a:s2 and b2:µ2 hemicomplexes, such that CCDC32(D78-99) can sequester and efficiently co-IP with the b2:µ2 hemicomplex.  A previous study, now cited, had shown that the b2:µ2 hemicomplex could partially rescue null mutations of a in C. elegans (PMID: 23482940).  We do not know how CCDC32 binds to the b2:µ2 hemicomplex and we did not detect these interactions using AlphaFold 3.0. However, these interactions could be indirect and involve the AAGAB chaperone.  It is also likely, based on the results of Wan et al. (PMID: 39145939), that the binding is through the µ2 subunit rather than b2. As mentioned above, and in our Discussion, further studies are needed to define the complex and multi-faceted nature of CCDC32-AP2 interactions.

(4) The reported ability of CCDC32 to pull down AP2 beta is puzzling. Beta is not found in the CCDC32 interactome in two independent studies using 293 and HCT116 cells (BioPlex). In addition, clathrin is also absent in the interactome of CCDC32, which is difficult to reconcile with a proposed role in CCPs. Can the authors detect CCDC32 binding to clathrin?

Based on the studies of Wan et al. (PMID: 39145939), it is likely that CCDC32 binds to µ2, rather than to the b2 in the b2:µ2 hemicomplex.  As to clathrin being absent from the CCDC32 pull down, this is as expected since the interactions of clathrin even with AP2 are weak in solution (as shown in Figure 5C, clathrin is not detected in our AP2 pull down) so as not to have spontaneous assembly of clathrin coats in the cytosol. Rather these interactions are strengthened by both the reduction in dimensionality that occurs on the membrane and by avidity of multivalent interactions.  For example, Kirchausen reported that 2 AP2 complexes are required to recruit one clathrin triskelion to the PM.

(5) Figure 5B appears unusual-is this a chimera?

Figure 5B shows an internal insertion of the eGFP tag into an unstructured region in the AP2 hinge. As we have previously shown (PMID: 32657003), this construct, unique among other commonly used AP2 tags, is fully functional.  We have rearranged the text in the Figure legend to make this clearer.

Figure 5C likely reflects a mixture of immature and mature AP2 adaptor complexes.

This is possible, but mature heterotetramers are by far the dominant species, otherwise the 4 subunits would not be immuno-precipitated at near stoichiometric levels with the a subunit.  Near stoichiometric IP with antibodies to the a-AD have been shown by many others in many cell types. 

(6) CCDC32 is reduced by about half in siRNA knockdown. Why not use CRISPR to completely eliminate CCDC32 expression?

Fortuitously, partial knockdown was essential to reveal this second function of CCDC32, as we have emphasized in our Discussion.  Wan et al, used CRISPR to knockout CCDC32 and reveal its essential role as a AAGAB co-chaperone.  In the complete absence of CCDC32 mature AP2 complexes fail to form.  However, under our conditions of partial CCDC32 depletion, the expression of AP2 heterotetramers is unaffected revealing a second function of CCDC32 at early stages of CME.  We expect that the co-chaperone function of CCDC32 is catalytic, while its role in CME is more structural; hence the different concentration dependencies, the former being less sensitive to KD than the latter.  This is one reason that many researchers are turning to CRISPRi for whole genome perturbation studies as many proteins play multiple roles that can be masked in KO studies.

Reviewer #2 (Public review):

Yang et al. describes CCDC32 as a new clathrin mediated endocytosis (CME) accessory protein. The authors show that CCDC32 binds directly to AP2 via a small alpha helical region and cells depleted for this protein show defective CME. Finally, the authors show that the CCDC32 nonsense mutations found in patients with cardio-facial-neuro-developmental syndrome (CFNDS) disrupt the interaction of this protein to the AP2 complex. The results presented suggest that CCDC32 may act as both a chaperone (as recently published) and a structural component of the AP2 complex.

Strengths:

The conclusions presented are generally well supported by experimental data and the authors carefully point out the differences between their results and the results by Wan et al. (PNAS 2024).

Weaknesses:

The experiments regarding the role of CCDC32 in CFNDS still require some clarifications to make them clearer to scientists working on this disease. The authors fail to describe that the CCDC32 isoform they use in their studies is different from the one used when CFNDS patient mutations were described. This may create some confusion. Also, the authors did not discuss that the frame-shift mutations in patients may be leading to nonsense mediated decay.

As requested we have more clearly described our construct with regard to the human mutations and added the possibility of NMD in the context of the human mutations.

Reviewer #3 (Public review):

In this manuscript, Yang et al. characterize the endocytic accessory protein CCDC32, which has implications in cardio-facio-neuro-developmental syndrome (CFNDS). The authors clearly demonstrate that the protein CCDC32 has a role in the early stages of endocytosis, mainly through the interaction with the major endocytic adaptor protein AP2, and they identify regions taking part in this recognition. Through live cell fluorescence imaging and electron microscopy of endocytic pits, the authors characterize the lifetimes of endocytic sites, the formation rate of endocytic sites and pits and the invagination depth, in addition to transferrin receptor (TfnR) uptake experiments. Binding between CCDC32 and CCDC32 mutants to the AP2 alpha appendage domain is assessed by pull down experiments. While interaction between CCDC32 and the alpha appendage domain of AP2 is clearly described, a discussion of potential association with other AP2 domains would be beneficial to understand the impact of CCDC32 in endocytosis.

The reviewer is correct. That CCDC32 also interacts with other subunits of AP2, is evident from the findings of Wan et al. and by the fact that the CCDC32(D78-99) mutant efficiently co-IPs with the b2:µ2 hemicomplex.  We expanded our discussion around this point. CCDC32 remains an, as yet, poorly characterized, but we now believe very interesting EAP worth further study.

Together, these experiments allow deriving a phenotype of CCDC32 knock-down and CCDC32 mutants within endocytosis, which is a very robust system, in which defects are not so easily detected. A mutation of CCDC32, mimicking CFNDS mutations, is also addressed in this study and shown to have endocytic defects.

In summary, the authors present a strong combination of techniques, assessing the impact of CCDC32 in clathrin mediated endocytosis and its binding to AP2.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) The authors must be clear about the differences between the CCDC32 isoform they used in their manuscript and the one used to describe the patient mutations. This could be done, for example, in the methods. This is essential for the capacity of other labs to reproduce, follow up and correctly cite these results.

We have added this information to the Methods. 

(2) I believe the authors have misunderstood what nonsense mediated decay is. NMD occurs at the mRNA level and requires a full genome context to occur (introns and exons). The fact that a mutant protein is expressed normally from a construct by no means prove that it does not happen. I believe that adding the possibility of NMD occurring would enrich the discussion.

Thank you, we have now done more homework and have added this possibility into our discussion of the mutant phenotype.  However, if a robust NMD mechanism resulted in a complete loss of CCDC42 protein, then the essential co-chaperone function reported by Wan et al, would result in complete loss of AP2.  A more detailed characterization of the cellular phenotype of these mutations, including assessing the expression levels of AP2 would be informative.

Reviewer #3 (Recommendations for the authors):

- It is not clear what the authors mean by '~30s lifetime cohort' (line 159). They refer to Figure 2H, which shows the % of CCPs. Can the authors explain exactly what kind of tracks they used for this analysis, for example which lifetime variations were accepted? Do they refer to the cohorts in Figure S4? In Figure S4, the most frequent tracks have lifetimes < 20 s (in contrast to what is stated in the main text). Why was this cohort not used?

The ‘30s cohort’ refers to CCPs with lifetimes between 25-35s which encompasses the most abundant species in control cells and CCDC32 KD cells, as shown by the probability curves in Figure 2H. Given the large number of CCPs analyzed we still have large numbers for our analyses n=5998 and 4418, for control and siRNA treated conditions, respectively.  Figure 2H shows the frequency of CCPs in cells treated with CCDC32 siRNA are shifted to shorter lifetimes. We have clarified this in the text.

- Figure S1: It is now clear, why the mutant versions of CCDC32 are not detected in this western blot. However, data that show the resistance of these proteins to siCCDC32 is still missing (S1 A is in the absence of siCCSC32 I assume, as the legend suggests). A western blot using an anti-GFP antibody, as the one used in Figure S1, after siRNA knock-known would provide clarity.

That these constructs all contain the same mutation in the siRNA target sequence gives us confidence that they are indeed resistant to siRNA.

- Note that the anti-CCDC32 antibody does not detect the eGFP-CCDC32(∆78-98) as well as full-length and is unable to detect eGFP-CCDC32(1-54)'. This phrase should belong to Figure S1 (B), not (A)

Corrected.

- The immunoprecipitations of CCDC32 and its mutants with AP2 and its subunits are partially confusing. In Figure 5, the authors show that CCDC32 interacts specifically with the alpha-AD, but not with the beta-AD of AP2. In Figure 6B and C, on the other hand, Co-IPs are shown also with the beta and the mu domain of AP2. This is understandable in the context of the full AP2. However, when interaction with the alpha domain (and sigma) is abolished through mutation of helix 78-98, why would beta and mu still interact, when the beta-AD cannot interact with CCDC32 on its own. Are there interaction sites expected outside the ADs in the beta or mu domains?

See responses to reviewer 1 above.  This result likely reflects the co-chaperone activity of CCDC32 as reported by Wan et al it likely due to their reported interactions of CCDC32 with the µ2 subnit of b2:µ2 hemicomplexes.

- Figure S6 D, E and F: How much confidence do the authors have on the AlphaFold predictions? Have the same binding poses been obtained repeatedly by independent predictions?

We provide, with a color scale, the confidence score for each interaction, which is very high (>90%). Of course, this is still a prediction that will need to be verified by further structural studies as we have stated.

Reviewer #1 (Public review):

Summary:

The mechanism by which WNT signals are received and transduced into the cell has been the topic of extensive research. Cell surface levels of the WNT receptors of the FZD family are subject to tight control and it's well established that the transmembrane ubiquitin ligases ZNRF3 and RNF43 target FZDs for degradation and that proteins of the R-spondin family block this effect. This manuscript explores the role that WNT proteins play in receptor internalization, recycling and degradation, and the authors provide evidence that WNTs promote interactions of FZD with the ubiquitin ligases. Using cells mutant in all 3 DVL genes, the authors demonstrate that this effect of WNT on FZD is DVL-independent.

Strengths:

Overall, the data are of good quality and support the authors' hypothesis. Strengths of this study is the use of CRISPR-mutated cell lines to establish genetic requirements for the various components. The finding that FZD internalization and degradation is WNT dependent and does not involve DVL is novel.

Weaknesses:

A weakness of the work includes a heavy reliance on overexpression of FZD proteins. To detect endogenous FZDs, the authors have inserted a V5 tag into the endogenous gene, which may affect their activity(ies).

Reviewer #2 (Public review):

In this manuscript Luo et al uncover that the ZNRF3/RNF43 E3 ubiquitin ligases participate in the selective endocytosis and degradation of FZD5/8 receptors in response to Wnt stimulation. In my opinion there are three significant findings of this study: 1) Wnt proteins are required for ZNRF3/RNF43 mediated endocytosis and degradation of FZD receptors and this constitutes an important negative regulatory loop. 2) Wnt can induce FZD endocytosis in the absence of ZNRF3/RNF43 but this does not influence total or cell surface levels. 3) The ZNRF3/RNF43 substrate selectivity for FZD5/8 over the other 8 Frizzleds. Of course, many questions remain, and new ones emerge as it is often the case, but these findings challenge our dogmatic view on how the ZNRF3/RNF43 regulate Wnt signaling and emphasize their role in Wnt-dependent Frizzled endocytosis/degradation and beta-catenin signaling.

This is an elegant study employing several CRISPR-edited cell lines to tag endogenous Frizzled receptors and to knockout ZNRF3/RNF43 and all three Dishevelled proteins. One major strength of the study is therefore the careful assessment of the roles of RNF43 and ZNFR3 in endogenous expression contexts. This is especially relevant since overexpression of membrane E3 ligases have been shown to ectopically degrade membrane proteins and could have blurred previous interpretations. A second strength is clarifying the role of Dishevelled proteins in FZD endocytosis. Indeed, although previous studies suggested that the Wnt-promoted interaction between FZD and RNF43/ZNFR3 was mediated through Dvl, the authors clearly show that this is not the case (using Dvl knockout cells and functional assays). Dvl proteins, on the other han,d are still required for ligand-independent FZD-endocytosis.

The only weakness pertains to the difference in signaling outcome, comparing elevated signaling seen when FZD levels are upregulated following ZNFR3/RNF43 KO vs ectopic overexpression. Indeed, the authors suggest that in the absence of RNF43/ZNFR3 the receptors could be recycled back to the PM and thereby contribute to increased signaling seen in the mutant cells. This has not been directly demonstrated.

Author response:

Reviewer #1 (Public review):

Summary:

In this manuscript, Gerken et al examined how neurons in the human medial temporal lobe respond to and potentially code dynamic movie content. They had 29 patients watch a long-form movie while neurons within their MTL were monitored using depth electrodes. They found that neurons throughout the region were responsive to the content of the movie. In particular, neurons showed significant responses to people, places, and to a lesser extent, movie cuts. Modeling with a neural network suggests that neural activity within the recorded regions was better at predicting the content of the movies as a population, as opposed to individual neural representations. Surprisingly, a subpopulation of unresponsive neurons performed better than the responsive neurons at decoding the movie content, further suggesting that while classically nonresponsive, these neurons nonetheless provided critical information about the content of the visual world. The authors conclude from these results that low-level visual features, such as scene cuts, may be coded at the neuronal level, but that semantic features rely on distributed population-level codes.

Strengths:

Overall, the manuscript presents an interesting and reasonable argument for their findings and conclusions. Additionally, the large number of patients and neurons that were recorded and analyzed makes this data set unique and potentially very powerful. On the whole, the manuscript was very well written, and as it is, presents an interesting and useful set of data about the intricacies of how dynamic naturalistic semantic information may be processed within the medial temporal lobe.

We thank the reviewer for their comments on our manuscript and for describing the strengths of our presented work

Weaknesses:

There are a number of concerns I have based on some of the experimental and statistical methods employed that I feel would help to improve our understanding of the current data.

In particular, the authors do not address the issue of superposed visual features very well throughout the manuscript. Previous research using naturalistic movies has shown that low-level visual features, particularly motion, are capable of driving much of the visual system (e.g, Bartels et al 2005; Bartels et al 2007; Huth et al 2012; Çukur et al 2013; Russ et al 2015; Nentwich et al 2023). In some of these papers, low-level features were regressed out to look at the influence of semantics, in others, the influence of low-level features was explicitly modeled. The current manuscript, for the most part, appears to ignore these features with the exception of scene cuts. Based on the previous evidence that low-level features continue to drive later cortical regions, it seems like including these as regressors of no interest or, more ideally, as additional variables, would help to determine how well MTL codes for semantic features over top of these lower-order variables.

We thank the reviewer for this insightful comment and for the relevant literature regarding visual motion in not only the primary visual system but in cortical areas as well. While we agree that the inclusion of visual motion as a regressor of no interest or as an additional variable would be overall informative in determining if single neurons in the MTL are driven by this level of feature, we would argue that our analyses already provide some insight into its role and that only the parahippocampal cortical neurons would robustly track this feature.

As noted by the reviewer, our model includes two features derived from visual motion: Camera Cuts (directly derived from frame-wise changes in pixel values)  and Scene Cuts (a subset of Camera Cuts restricted to changes in scene). As shown in Fig. 5a, decoding performance for these features was strongest in the parahippocampal cortex (~20%), compared to other MTL areas (~10%). While the entorhinal cortex also showed some performance for Scene Cuts (15%), we interpret this as being driven by the changes in location that define a scene, rather than by motion itself.

These findings suggest that while motion features are tracked in the MTL, the effect may be most robust in the parahippocampal cortex. We believe that quantifying more complex 3D motion in a naturalistic stimulus like a full-length movie is a significant challenge that would likely require a dedicated study. We agree this is an interesting future research direction and will update the manuscript to highlight this for the reader.

A few more minor points that would help to clarify the current results involve the selection of data for particular analyses. For some analyses, the authors chose to appropriately downsample their data sets to compare across variables. However, there are a few places where similar downsampling would be informative, but was not completed. In particular, the analyses for patients and regions may have a more informative comparison if the full population were downsampled to match the size of the population for each patient or region of interest. This could be done with the Monte Carlo sampling that is used in other analyses, thus providing a control for population size while still sampling the full population.

We thank the reviewer for raising this important methodological point. The decision not to downsample the patient- and region-specific analyses was deliberate, and we appreciate the opportunity to clarify our rationale.

Generally, we would like to emphasize that due to technical and ethical limitations of human single-neuron recordings, it is currently not possible to record large populations of neurons simultaneously in individual patients. The limited and variable number of recorded neurons per subject (Fig. S1) generally requires pooling neurons into a pseudo-populations for decoding, which is a well‐established standard in human single‐neuron studies (see e.g., (Jamali et al., 2021; Kamiński et al., 2017; Minxha et al., 2020; Rutishauser et al., 2015; Zheng et al., 2022)).

For the patient-specific analysis, our primary goal was to show that no single patient's data could match the performance of the complete pseudo-population. Crucially, we found no direct relationship between the number of recorded neurons and decoding performance; patients with the most neurons (patients 4, 13) were not top performers, and those with the fewest (patients 11, 14) were not the worst (see Fig. 4). This indicates that neuron count was not the primary limiting factor and that downsampling would be unlikely to provide additional insight.

Similarly, for the region-specific analysis, regions with larger neural populations did not systematically outperform those with fewer neurons (Fig. 5). Given the inherent sparseness of single-neuron data, we concluded that retaining the full dataset was more informative than excluding neurons simply to equalize population sizes.

We agree that this methodological choice should be transparent and explicitly justified in the text. We will add an explanation to the revised manuscript to justify why this approach was taken and how it differs from the analysis in Fig. 6.

Reviewer #2 (Public review):

Summary:

This study introduces an exciting dataset of single-unit responses in humans during a naturalistic and dynamic movie stimulus, with recordings from multiple regions within the medial temporal lobe. The authors use both a traditional firing-rate analysis as well as a sophisticated decoding analysis to connect these neural responses to the visual content of the movie, such as which character is currently on screen.

Strengths:

The results reveal some surprising similarities and differences between these two kinds of analyses. For visual transitions (such as camera angle cuts), the neurons identified in the traditional response analysis (looking for changes in firing rate of an individual neuron at a transition) were the most useful for doing population-level decoding of these cuts. Interestingly, this wasn't true for character decoding; excluding these "responsive" neurons largely did not impact population-level decoding, suggesting that the population representation is distributed and not well-captured by individual-neuron analyses.

The methods and results are well-described both in the text and in the figures. This work could be an excellent starting point for further research on this topic to understand the complex representational dynamics of single neurons during naturalistic perception.

We thank the reviewer for their feedback and for summarizing the results of our work.

(1) I am unsure what the central scientific questions of this work are, and how the findings should impact our understanding of neural representations. Among the questions listed in the introduction is "Which brain regions are informative for specific stimulus categories?". This is a broad research area that has been addressed in many neuroimaging studies for decades, and it's not clear that the results tell us new information about region selectivity. "Is the relevant information distributed across the neuronal population?" is also a question with a long history of work in neuroscience about localist vs distributed representations, so I did not understand what specific claim was being made and tested here. Responses in individual neurons were found for all features across many regions (e.g., Table S1), but decodable information was also spread across the population.

We thank the reviewer for this important point, which gets to the core of our study's contribution. While concepts like regional specificity are well-established from studies on the blood-flow level, their investigation at the single-neuron level in humans during naturalistic, dynamic stimulation remains a critical open question. The type of coding (sparse vs. distributed) on the other hand cannot be investigated with blood-flow studies as the technology lacks the spatial and temporal resolution.

Our study addresses this gap directly. The exceptional temporal resolution of single-neuron recordings allows us to move beyond traditional paradigms and examine cellular-level dynamics as they unfold in neuronal response on a frame-by-frame basis to a more naturalistic and ecologically valid stimulus. It cannot be assumed that findings from other modalities or simplified stimuli will generalize to this context.

To meet this challenge, we employed a dual analytical strategy: combining a classic single-unit approach with a machine learning-based population analysis. This allowed us to create a bridge between prior work and our more naturalistic data. A key result is that our findings are often consistent with the existing literature, which validates the generalizability of those principles. However, the differences we observe between these two analytical approaches are equally informative, providing new insights into how the brain processes continuous, real-world information.

We will revise the introduction and discussion to more explicitly frame our work in this context, emphasizing the specific scientific question driving this study, while also highlighting the strengths of our experimental design and recording methods.

(2) The character and indoor/outdoor labels seem fundamentally different from the scene/camera cut labels, and I was confused by the way that the cuts were put into the decoding framework. The decoding analyses took a 1600ms window around a frame of the video (despite labeling these as frame "onsets" like the feature onsets in the responsive-neuron analysis, I believe this is for any frame regardless of whether it is the onset of a feature), with the goal of predicting a binary label for that frame. Although this makes sense for the character and indoor/outdoor labels, which are a property of a specific frame, it is confusing for the cut labels since these are inherently about a change across frames. The way the authors handle this is by labeling frames as cuts if they are in the 520ms following a cut (there is no justification given for this specific value). Since the input to a decoder is 1600ms, this seems like a challenging decoding setup; the model must respond that an input is a "cut" if there is a cut-specific pattern present approximately in the middle of the window, but not if the pattern appears near the sides of the window. A more straightforward approach would be, for example, to try to discriminate between windows just after a cut versus windows during other parts of the video. It is also unclear how neurons "responsive" to cuts were defined, since the authors state that this was determined by looking for times when a feature was absent for 1000ms to continuously present for 1000ms, which would never happen for cuts (unless this definition was different for cuts?).

We thank the reviewer for the valuable comment regarding specifically the cut labels. The choice to label frames that lie in a time window of 520ms following a cut as positive was selected based on prior research and is intended to include the response onsets across all regions within the MTL (Mormann et al., 2008). We agree that this explanation is currently missing from the manuscript, and we will add a brief clarification in the revised version.

As correctly noted, the decoding analysis does not rely on feature onset but instead continuously decodes features throughout the entire movie. Thus, all frames are included, regardless of whether they correspond to a feature onset.

Our treatment of cut labels as sustained events is a deliberate methodological choice. Neural responses to events like cuts often unfold over time, and by extending the label, we provide our LSTM network with the necessary temporal window to learn this evolving signature. This approach not only leverages the sequential processing strengths of the LSTM (Hochreiter et al., 1997) but also ensures a consistent analytical framework for both event-based (cuts) and state-based (character or location) features.

(3) The architecture of the decoding model is interesting but needs more explanation. The data is preprocessed with "a linear layer of same size as the input" (is this a layer added to the LSTM that is also trained for classification, or a separate step?), and the number of linear layers after the LSTM is "adapted" for each label type (how many were used for each label?). The LSTM also gets to see data from 800 ms before and after the labeled frame, but usually LSTMs have internal parameters that are the same for all timesteps; can the model know when the "critical" central frame is being input versus the context, i.e., are the inputs temporally tagged in some way? This may not be a big issue for the character or location labels, which appear to be contiguous over long durations and therefore the same label would usually be present for all 1600ms, but this seems like a major issue for the cut labels since the window will include a mix of frames with opposite labels.

We thank the reviewer for their insightful comments regarding the decoding architecture. The model consists of an LSTM followed by 1–3 linear readout layers, where the exact number of layers is treated as a hyperparameter and selected based on validation performance for each label type. The initial linear layer applied to the input is part of the trainable model and serves as a projection layer to transform the binned neural activity into a suitable feature space before feeding it into the LSTM. The model is trained in an end-to-end fashion on the classification task.

Regarding temporal context, the model receives a 1600 ms window (800 ms before and after the labeled frame), and as correctly pointed out by the reviewer, LSTM parameters are shared across time steps. We do not explicitly tag the temporal position of the central frame within the sequence. While this may have limited impact for labels that persist over time (e.g., characters or locations), we agree this could pose a challenge for cut labels, which are more temporally localized.

This is an important point, and we will clarify this limitation in the revised manuscript and consider incorporating positional encoding in future work to better guide the model’s focus within the temporal window. Additionally, we will add a data table, specifying the ranges of hyperparameters in our decoding networks. Hyperparameters were optimized for each feature and split individually, but we agree that some more details on how these parameters were chosen are important and we will provide a data table in our revised manuscript giving more insights into the ranges of hyperparameters.

We thank the reviewer for this important point. We will clarify this limitation in the revised manuscript and note that positional encoding is a valuable direction to better guide the model’s focus within the temporal window. To improve methodological transparency, we will also add a supplementary table detailing the hyperparameter ranges used for our optimization process.

(4) Because this is a naturalistic stimulus, some labels are very imbalanced ("Persons" appears in almost every frame), and the labels are correlated. The authors attempt to address the imbalance issue by oversampling the minority class during training, though it's not clear this is the right approach since the test data does not appear to be oversampled; for example, training the Persons decoder to label 50% of training frames as having people seems like it could lead to poor performance on a test set with nearly 100% Persons frames, versus a model trained to be biased toward the most common class. [...]

We thank the reviewer for this critical and thoughtful comment. We agree that the imbalanced and correlated nature of labels in naturalistic stimuli is a key challenge.

To address this, we follow a standard machine learning practice: oversampling is applied exclusively to the training data. This technique helps the model learn from underrepresented classes by creating more balanced training batches, thus preventing it from simply defaulting to the majority class. Crucially, the test set remains unaltered to ensure our evaluation reflects the model's true generalization performance on the natural data distribution.

For the “Persons” feature, which appears in nearly all frames, defining a meaningful negative class is particularly challenging. The decoder must learn to identify subtle variations within a highly skewed distribution. Oversampling during training helps provide a more balanced learning signal, while keeping the test distribution intact ensures proper evaluation of generalization.

The reviewer’s comment—that we are “training the Persons decoder to label 50% of training frames as having people”—may suggest that labels were modified. We want to emphasize this is not the case. Our oversampling strategy does not alter the labels; it simply increases the exposure of the rare, underrepresented class during training to ensure the model can learn its pattern despite its low frequency.

We will revise the Methods section to describe this standard procedure more explicitly, clarifying that oversampling is a training-only strategy to mitigate class imbalance.

(5) Are "responsive" neurons defined as only those showing firing increases at a feature onset, or would decreased activity also count as responsive? If only positive changes are labeled responsive, this would help explain how non-responsive neurons could be useful in a decoding analysis.

We define responsive neurons as those showing increased firing rates at feature onset; we did not test for decreases in activity. We thank the reviewer for this valuable comment and will address this point in the revised manuscript by assessing responseness without a restriction on the direction of the firing rate.

(6) Line 516 states that the scene cuts here are analogous to the hard boundaries in Zheng et al. (2022), but the hard boundaries are transitions between completely unrelated movies rather than scenes within the same movie. Previous work has found that within-movie and across-movie transitions may rely on different mechanisms, e.g., see Lee & Chen, 2022 (10.7554/eLife.73693).

We thank the reviewer for pointing out this distinction and for including the relevant work from Lee & Chan (2022) which further contextualizes this distinction. Indeed, the hard boundaries defined in the cited paper differ slightly from ours. The study distinguishes between (1) hard boundaries—transitions between unrelated movies—and (2) soft boundaries—transitions between related events within the same movie. While our camera cuts resemble their soft boundaries, our scene cuts do not fully align with either category. We defined scene cuts to be more similar to the study’s hard boundaries, but we recognize this correspondence is not exact. We will clarify the distinctions between our scene cuts and the hard boundaries described in Zheng et al. (2022) in the revised manuscript, and will update our text to include the finding from Lee & Chan (2022).

Reviewer #3 (Public review):

This is an excellent, very interesting paper. There is a groundbreaking analysis of the data, going from typical picture presentation paradigms to more realistic conditions. I would like to ask the authors to consider a few points in the comments below.

(1) From Figure 2, I understand that there are 7 neurons responding to the character Summer, but then in line 157, we learn that there are 46. Are the other 39 from other areas (not parahippocampal)? If this is the case, it would be important to see examples of these responses, as one of the main claims is that it is possible to decode as good or better with non-responsive compared to single responsive neurons, which is, in principle, surprising.

We thank the reviewer for pointing out this ambiguity in the text. Yes, the other 39 units are responsive neurons from other areas. We will clarify to which neuronal sets the number of responsive neurons corresponds. We will also include response plots depicting the unit activity for the mentioned units.

(2) Also in Figure 2, there seem to be relatively very few neurons responding to Summer (1.88%) and to outdoor scenes (1.07%). Is this significant? Isn't it also a bit surprising, particularly for outdoor scenes, considering a previous paper of Mormann showing many outdoor scene responses in this area? It would be nice if the authors could comment on this.

We thank the reviewer for this insightful point. While a low response to the general 'outdoor scene' label seems surprising at first, our findings align with the established role of the parahippocampal cortex (PHC) in processing scenes and spatial layouts. In previous work using static images, each image introduces a new spatial context. In our movie stimulus, new spatial contexts specifically emerge at scene cuts. Accordingly, our data show a strong PHC response precisely at these moments. We will revise the discussion to emphasize this interpretation, highlighting the consistency with prior work.

Regarding the first comment, we did not originally test if the proportion of the units is significant using e.g. a binomial test. We will include the results of a binomial test for each region and feature pair in the revised manuscript.

(3) I was also surprised to see that there are many fewer responses to scene cuts (6.7%) compared to camera cuts (51%) because every scene cut involves a camera cut. Could this have been a result of the much larger number of camera cuts? (A way to test this would be to subsample the camera cuts.)

The decrease in responsive units for scene cuts relative to camera cuts could indeed be due to the overall decrease in “trials” from one label to the other. To test this, we will follow the reviewer’s suggestion and perform tests using sets of randomly subsampled camera cuts and will include the results in the revised manuscript.

(4) Line 201. The analysis of decoding on a per-patient basis is important, but it should be done on a per-session basis - i.e., considering only simultaneously recorded neurons, without any pooling. This is because pooling can overestimate decoding performances (see e.g. Quian Quiroga and Panzeri NRN 2009). If there was only one session per patient, then this should be called 'per-session' rather than 'per-patient' to make it clear that there was no pooling.

The per-patient decoding was indeed also a per-session decoding, as each patient contributed only a single session to the dataset. We will make note of this explicitly in the text to resolve the ambiguity.

(6) Lines 406-407. The claim that stimulus-selective responses to characters did not account for the decoding of the same character is very surprising. If I understood it correctly, the response criterion the authors used gives 'responsiveness' but not 'selectivity'. So, were people's responses selective (e.g., firing only to Summer) or non-selective (firing to a few characters)? This could explain why they didn't get good decoding results with responsive neurons. Again, it would be nice to see confusion matrices with the decoding of the characters. Another reason for this is that what are labelled as responsive neurons have relatively weak and variable responses.

We thank the reviewer for pointing out the importance of selectivity in addition to responsiveness. Indeed, our response criterion does not take stimulus selectivity into account and exclusively measures increases in firing activity after feature onsets for a given feature irrespective of other features.

We will adjust the text to reflect this shortcoming of the response-detection approach used here. To clarify the relationship between neural populations, we will add visualizations of the overlap of responsive neurons across labels for each subregion. These figures will be included in the revised manuscript.

In our approach, we trained separate networks for each feature to effectively mitigate the issue of correlated feature labels within the dataset (see earlier discussion). While this strategy effectively deals with the correlated features, it precluded the generation of standard confusion matrices, as classification was performed independently for each feature.

To directly assess the feature selectivity of responsive neurons, we will fit generalized linear models to predict their firing rates from the features. This approach will enable us to quantify their selectivity and compare it to that of the broader neuronal population.

(7) Line 455. The claim that 500 neurons drive decoding performance is very subjective. 500 neurons gives a performance of 0.38, and 50 neurons gives 0.33.

We agree with the reviewer that the phrasing is unclear. We will adjust our summary of this analysis as given in Line 455 to reflect that the logistic regression-derived neuronal rankings produce a subset which achieve comparable performance.

(8) Lines 492-494. I disagree with the claim that "character decoding does not rely on individual cells, as removing neurons that responded strongly to character onset had little impact on performance". I have not seen strong responses to characters in the paper. In particular, the response to Summer in Figure 2 looks very variable and relatively weak. If there are stronger responses to characters, please show them to make a convincing argument. It is fine to argue that you can get information from the population, but in my view, there are no good single-cell responses (perhaps because the actors and the movie were unknown to the subjects) to make this claim. Also, an older paper (Quian Quiroga et al J. Neurophysiol. 2007) showed that the decoding of individual stimuli in a picture presentation paradigm was determined by the responsive neurons and that the non-responsive neurons did not add any information. The results here could be different due to the use of movies instead of picture presentations, but most likely due to the fact that, in the picture presentation paradigm, the pictures were of famous people for which there were strong single neuron responses, unlike with the relatively unknown persons in this paper.

This is an important point and we thank the reviewer for highlighting a previous paradigm in which responsive neurons did drive decoding performance. Indeed, the fact that the movie, its characters and the corresponding actors were novel to patients could explain the disparity in decoding performance by way of weaker and more variable responses. We will include additional examples in the supplement of responses to features. Additionally, we will modify the text to emphasize the point that reliable decoding is possible even in the absence of a robust set of neuronal responses. It could indeed be the case that a decoder would place more weight on responsive units if they were present (as shown in the mentioned paper and in our decoding from visual transitions in the parahippocampal cortex).

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Referee #3

Evidence, reproducibility and clarity

Summary:

Cells need to adjust their gene expression pattern, including nutrient transporters and enzymes to process the available nutrient. How cells maintain the coordination between these processes is one of the most critical questions in biology. In this work authors elegantly combined a range of relevant experimental techniques, ranging from time-lapse microscopy, microfluidics, and mathematical modelling to address this question. Combining these methods, authors proposed a push-pull like mechanism, involving two pairs of repressors (Mth1, Std1 and Migs) in the glucose sensing network. In budding yeast there are multiple hexose transporter genes with varying affinity and transport rate. Authors postulated that on sensing glucose, cells switch between expressing high affinity glucose transporters (when extracellular glucose is low), and low affinity glucose transporters (in high extracellular glucose), and these processes are mediated by the pairs of repressors as mentioned earlier. Following the expressing patterns of fluorescently tagged hexose transporters and varying the extracellular glucose concentrations in media, authors proposed that pairs of repressors switch their activity depending on extracellular glucose level, and which is matched by the promoters of the hexose transporter genes to achieve optimality of glucose transport.

This study is elegantly designed and addressed an interesting question. The mechanism (push-pull involving two pairs of repressors) is plausible and justified by the data. Authors also presented a mathematical model and made predictions, which are also verified. We will recommend the publication of this work with minor modifications.

Major comments:

This study is well designed and experiments performed accordingly. We have only minor comments for revision.

Minor comments:

1. Although authors covered a wide array of literature, but while discussing tradeoffs and nutrient sensing, it will be good to include bacterial growth law and related literature, and physiological level tradeoffs should be discussed. Moreover, authors vouched that the push-pull mechanism helps to circumvent the rate-affinity tradeoff of the transporter, whereas expressing genes to more precisely corelate with the extracellular glucose level brings out physiological optimality. This rate-affinity tradeoff and its physiological role should be discussed clearly.
2. Authors described the ALCATRAS device in their previous publication, but for better clarity, a supplementary figure with schematic diagram and experimental plan should be included.
3. Microscopic images of transporter expression pattern should be shown as kymographs in the supplementary, in this version of the manuscript plots from processed microscopy images are shown only.
4. GFP was used to tag HXT1-7 as mentioned by the authors and expression of these genes are evaluated in separate experiments. We suggest including a schematic diagram describing the experimental design while using the microfluidic device and the experimental plan should be written in more detail in general. We found this part confusing. Did authors considered tagging two separate transporters with different fluorescent tag from either end of the affinity spectrum and showing the expression pattern in one experiment? Authors mentioned co expression of receptors at a particular glucose concentration over time, is this inferred from separate timelapse experiments? This need to be more clearly stated.
5. Please mark the second phase of media glucose concentration in panel 1C, 1% glucose phase is marked, please mark the other phases for clarity.
6. For the repressors to sense glucose and to initiate the push pull mechanism, there should be baseline glucose flux, which is not clearly mentioned in the manuscript. Authors mentioned that minimal intracellular glucose in absence of extracellular glucose and deployed a logistic function to increase intracellular glucose. The baseline glucose level is crucial, and authors should comment on this. Also, glucose mediated protection of HXT4 should be discussed in this context.
7. Figure 3B and 3C, details of the error bars should be mentioned in the figure legend.

Referee cross-commenting

All other reviewers also identified this study insightful and interesting, similar to our comments. We also agree with the suggestions made by other reviewers. Suggested changes and modifications can be addressed within a month as mentioned by most of the reviewers. Excellent point raised by other reviewers on technicalities and addressing those points will improve the readability of this work even more.

Significance

General assessment:

Use of innovative microfluidics platform to trap mother cells and following the gene expression pattern by fluorescence microscopy and combining the experimental approach with mathematical model are the strengths of this work. Whereas the proposed push-pull mechanism is not generalizable to other carbons. Model is merely used to fit the data, rather than making interesting predictions. Also how does the mechanism holds when cells are switched from other nutrient sources is also not clear in this work, which are the limitations of this work.

Advance

This work involves experimental technique and mathematical model to test the hypothesis. Use of custom-built microfluidics set up and live cell imaging to track gene expression levels in varying nutrient condition. This study links single cell level gene expression pattern to model and predict system level behavior. Nutrient sensing and subsequent rearrangement of gene regulatory network is an important question to address, and the proposed push-pull mechanism in this study adds up to the existing body of literature.

Audience:

This work is interdisciplinary and researchers across multiple fields will be interested in this work, including researchers interested in microbial nutrient sensing, systems biology, topology of gene regulatory network, metabolism, and general microbiology.

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Reply to the reviewers

Manuscript number: RC-2025-03083 Corresponding author(s): David Fay General Statements [optional] This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We greatly appreciate the input of the four reviewers, all of whom carried out a careful reading of our manuscript, provided useful suggestions for improvements, and were enthusiastic about the study including its thoroughness and utility to the field. Because the reviewers required no additional experiments, we were able to address their comments in writing.

However, in response to a comment from reviewer #4 we decided to add an additional new biological finding to our study given that our functional validation of proximity labeling targets was not extensive. Namely, we now show that a missense mutation affecting BCC-1, one of the top NEKL-MLT interactors identified by our proximity labeling screen, is a causative mutation (together with catp-1) in a strain isolated through a forward genetic screen for suppressors of nekl molting defects (new Fig 9C). This finding, combined with our genetic enhancer tests, further strengthens the functional relevance of proteins identified though our proximity labeling approach and highlights the synergy of proteomics combined with classical genetics.

Positive statements from reviewers include: Reviewer #1: Overall, this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner.

Reviewer #2: The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate... In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs.

Reviewer #3: Overall, the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. ...This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system.

Reviewer #4: Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data)... Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

Based on these reviews and comments, we believe that our manuscript is suitable for publication in a high-impact journal. 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 (Required)): *

*Proximity labeling has become a powerful tool for defining protein interaction networks and has been utilized in a growing number of multicellular model systems. However, while such an approach can efficiently generate a list of potential interactors, knowledge of the most appropriate controls and standardized metrics to judge the quality of the data are lacking. The study by Fay systematically investigates these questions using the C. elegans NIMA kinase family members NEKL-2 and NEKL-2 and their known binding partners MLT-2, MLT-3 and MLT-4. The authors perform eight TurboID experiments each with multiple NEKL and MLT proteins and explore general metrics for assessing experimental outcomes as well as how each of the individual metrics correlates with one another. They also compare technical and biological replicates, explore strategies for identifying false positives and investigate a number of variations in the experimental approach, such as the use of N- versus C-terminal tags, depletion of endogenous biotinylated proteins, combining auxin-inducible degradation, and the use of gene ontology analysis to identify physiological interactors. Finally, the authors validate their findings by demonstrating that a number of the candidate identified functionally interact with NEKL-2 or components of the WASH complex. *

Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. Below I list major and minor concerns.

Major concerns * 1. My biggest issue with the manuscript is that a lot is made of the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). The authors perform experiments using DIA and DDA approaches but do not directly compare the outcomes. As a result there is really no way to know if one approach is better than the other. I would suggest the authors either perform the necessary analysis to compare the two approaches or tone down their promotion of DIA.* We agree and have scaled back any statements comparing DDA to DIA as our manuscript did not address this directly. We also now point out this caveat in our closing thoughts section, while referencing other studies that compared the two (lines 926-929). Our main point was to convey that DIA worked well for our proximity labeling studies but has seen little use by the model organism field. Surprising (to us), DIA was also considerably less expensive than DDA options.

2. Line 75, The authors promote the use of data-independent acquisition (DIA) without defining what this approach is and how it differs from the more conventional data-dependent acquisition. As a non-mass spectroscopist, I found myself with lots of question concerning DIA, what it is and how it differs from DDA. I think it would really be helpful to expand the description of DIA and its comparison with DDA in the introduction. As non-mass-spectroscopists ourselves, we understand the reviewer's point. Because the paper is quite long, we were trying to avoid non-essential information. We have now added some information to explain some of the key differences between DDA and DIA. We have also included references for readers who may want to learn more. (lines 77-80)

Minor concerns: * Line 92 typo. I believe the authors meant to say NEKL-2-MLT-2-MLT-4. * Corrected. (line 95)

Line169. Is exogenous the correct word to use here? It suggests that you are talking about non-worm proteins, but I know you are not. Corrected. Changed to "Moreover, the detection of biotinylated proteins may be difficult if the bait-TurboID fusion is expressed at low levels..." (line 181).

Line 177 typo (D) should be (C). Corrected. (line 1122)

Figure 1C: Lucky Charms may sue you for infringement of their trademarked marshmallow treats. Thank you for picking up on this. The authors accept full responsibility for any resulting lawsuits.

Figure 1D. The NEKL-2::TurboID band is indicated with a green triangle in the figure but the figure legend states that green triangles indicate mNG::TurboID control. I know this triangle is a shade off the triangle that indicates mNG::TurboID but it's really hard to see the difference. All of the differently colored triangles in panel F are unnecessary. I would either just pick one color for all non-control bait proteins or better yet, only use a triangle to point to bands that are not obvious. For instance I don't need the triangles that point to NEKL-2 -3 and -4 fusion proteins. These are just distracting. We understand the reviewer's point. We colored the triangles to match the colors used for the proteins in the figures. We have now added "bright green triangles with white outlines" (Fig 1 legend) to indicate the Pdpy-7::mNG::TurboID control" and changed triangles in the corresponding figures. Although we would be fine with removing or changing the triangles, we think that they may aid somewhat with clarity.

Line: 316: Conceivably, another factor that could contribute to the counterintuitive upregulation of some proteins in the N2 samples is related to the fusion proteins that are being expressed in the TurboID lines. A partially functional bait protein (one with a level of activity similar to nekl-2(fd81) that may not result in an obvious phenotype) could directly or indirectly affect gene expression leading to lower levels of a subset of proteins in the TurboID samples. The same could be said for fusion proteins with a gain-of-function effect. This is an interesting idea, and we tested this possibility by looking for consistent overlap between N2-up proteins between biological replates of individual bait proteins. We now include a representative Venn diagram in S3C Fig to highlight this comparison. In summary, although we cannot rule out this possibility, our analysis did not support the widespread occurrence of this effect in our study. We also made certain that our statement regarding N2 up proteins was not too definitive. (lines 285-288)

*Fig 3 B-E. I am a little confused how the data in these graphs is normalized. For instance, I would have expected that for NEKL-3 in panel B, that the normalized (log2) intensity value in N2 be set at 0 as it is for NEKL-2. Maybe I just don't have enough information on how these plots were generated. * The difference is that in the N2 sample, NEKL-3 was detected but NEKL-2 was not. The numbers themselves are assigned by the Spectronaut software used to quantify the DIA results but are not meaningful beyond indicating relative amounts (intensity values) of a given protein within an individual biological experiment. We've added some lines to the figure legend to make this clearer. (lines 1165-1169)

*Figure 6C legend is not correct. * Corrected. (line 1214)

Line 575: Figure reference should be Fig. S5G. The authors should check to make sure all references to supplemental figures include correct panel information. Corrected. (line 464) In addition, we have now gone through the manuscript and added panel numbers references where applicable. Note that the addition of a new supplemental file has shifted the numbering.

Line 576. The authors reference a study by Artan and colleagues and report a weak correlation between their study and that of Artan. They reference figure S4 but it should be Fig S5H. Apologies and many thanks to the reviewer for catching these errors. (line 464)

Line 652. The authors note that numerous proteins were present at substantially reduced levels in the mNG::TurboID samples and suggest that sticky proteins may have been outcompeted or otherwise excluded from beads incubated with the mNG::TurboID lysates. Why would sticky proteins only be a problem in these samples? The reasoning is not clear to me. The idea was that in the sample with very high levels of biotinylated proteins (mNG::TurboID), the surface of the beads might become saturated with high-affinity biotinylated proteins. This could prevent or out complete the binding of random proteins that are not biotinylated but nevertheless have some affinity to the beads ("sticky" proteins). We have reworded this section to make this clearer. (lines 546-550)

Line 745: The term "bait overlaps" is a bit vague. Ultimately, I figured out what it meant but it was not immediately obvious. We have changed this to "overlap between baits" and made this section clearer. (line 624-628)

*S7B Fig. Why is actin missing from the eluate? * In S7B we refer to the purified eluate as the "eluate", which may have caused some confusion. In other sections of the manuscript, we refer to the bead-bound proteins as the "purified eluate" (Figs 1 and 5). For the purified eluate a portion of the streptavidin beads are boiled in sample buffer to elute the bound proteins before running a western. Actin would not be expected in these samples because it's (presumably) not biotinylated in our samples and doesn't detectably bind the beads. This result was seen in all relevant westerns in S1 Data. For consistency, however, we've gone through all our files to make sure we consistently use the term "purified eluate" versus "eluate", which is less specific.

L*ine 873: The authors state the extent of overlap in GO terms between the various experiments and provide percentages. I tried to extract this information from Figure 8C and came up with different values. For instance, in the case of Molecular Function, they state that they observed a 54% overlap between NEKL-2 and NEKL-3 but in the Venn diagram in Figure 8C I see that the NEKL-2 and NEKL-3 experiments had 71 (25+46) GO terms in common. Out of 98 GO terms for NEKL-2 or 104 for NEKL-3 the percentage I got is closer to 72. Am I analyzing this correctly? * Thanks for checking this. We believe our method for calculating the percent overlap is correct. In the case of NEKL-2/NEKL-3 overlap for Molecular Function, there are 131 total unique terms, of which 71 overlap, giving a 54% overlap. In the case of NEKL-2/NEKL-3 overlap for Biological Process, however, we made an error in arithmetic (415 unique, 239 overlap), such that the correct percentage is 58%, which we have corrected in the text.

*Reviewer #1 (Significance (Required)): *

*Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. *

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

*This study expanded the use of data-independent acquisition-mass spectrometry (DIA-MS) in TurboID proximity-labeling proteomics to identify novel interactors of NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4 complexes in C. elegans. The authors described several useful metrics to evaluate the quality of TurboID experiments, such as using the percentage of upregulated genes, the percentage of proteins present only in bait-TurboID experiments as compared to N2 controls, and the percentage of endogenously biotinylated carboxylases as internal controls. Further, the authors introduced methodological variability across 23 TurboID experiments and evaluated any improvement to the resulting data, such as N-terminally tagging bait proteins with TurboID, depleting endogenous carboxylases, and auxin-inducible degradation of known complex members. Finally, this study identified the kinase folding chaperone CDC-37 and the WASH complex component DDL-2 as novel interactors with the NEKL-MLT complexes through an RNAi-based enhancer approach following their identification by TurboID. *

Major comments: * The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate. We only have minor comments.*

Minor comments: * •In the western blot in Fig 1 why does the mNG::Turbo have two bands? * Thank you for point this out. To our knowledge this is a breakdown product that was especially prevalent in replicate 3 (also see S1 Data), which we chose to shown because all the NEKL-MLTs were clearly visible in this western. The expected size of the mNeonGreen::TurboID (including linker and tags) is ~68 kDa and our blots are roughly consistent those of Artan et al., (2001). This lower band was not evident in Exp 8. We have now included a statement in the figure legend to indicate that the upper band is the full-length protein whereas the lower band is likely to be a breakdown product (lines 1141-1142).

•Fig 2B is difficult to parse as a reader. Columns labeled "Upreg," "Downreg," "TurboID only," "N2 only," "Filter-1," "Filter-2," and "Epi %" could be moved to Supplemental. Fold change vs N2 could be represented as a bar chart, allowing for trends between fold change and the metrics Upreg %, Turbo %, and Carboxylase % to be seen more clearly. Further, rows headed "Carboxylase depletion," "DDA," and "Auxin treated" could be presented as separate panels to better match the distinct points made in the text. After serious consideration we have made several changes including the addition of S2 Fig, which may provide readers with a better visual representation of the bait and prey fold changes observed in all our experiments. However, we feel that the detailed data embedded in Fig 2 is the most concise and accurate means by which to convey our full results and is key to our methodological conclusions. As such we did not want to relegate this information to a supplemental table. We note that this figure was not found to be problematic by other reviewers, although we do understand the points made by this reviewer.

•Line 179: in vivo should be italicized Because journals differ in their stylistic practices, we are currently waiting before doing our final formatting. We did keep our use of Latin phrases consistently non-italicized in the draft.

•Lines 215-217: The comparison between Western blot expression levels and prior fluorescent reporter levels is unclear. Could be reformatted to make it clearer that relative expression of the different NEKL-MLTs in this study is consistent with prior data. We reformatted this sentence to improve clarity. (lines 205-207)

*•Lines 267-268: The final line of the passage is unclear and can be removed. * This sentence has been removed.

•Lines 311-313: This study is able to use the recovery of bait and known interactor proteins as internal controls to determine the quality of each experiment, but this may not always be the case for other users' experiments. The authors should comment on how Upreg %, a value influenced by many factors, can actually be used as a quality check when a bait protein has no known interactors. We have added language to highlight this point. (lines 344-348)

*•Line 702: There is a [new REF] that should be removed * As described above, we have now included this finding on bcc-1 as part of this manuscript (Fig 9C).

•The approach used mixed stage animals, but some genes oscillate or are transiently expressed. Please discuss cost-benefit of mixed stage vs syncing. This is an important point. We have added a discussion on the benefits and drawbacks of using mixed stages to the discussion. (lines 901-911)

*•Authors were working on hypodermally expressed proteins. It would be valuable to discuss what tissues are amenable to TurboID. Ie are the cases where there are few cells (anchor cell, glial sockets, etc) that it will be extremely challenging to perform this technique * We agree that certain tissues/proteins will not be amenable to proximity labeling. We believe that we have addressed this point together with the above comment throughout the manuscript and now on lines 936-940.

•Authors mention approaches such as nanobodies, split Turbo. Based on their experiences it would be valuable to add Discussion on strengths and weaknesses of these approaches to guide folks considering TurboID and DIA-MS experiments in C. elegans Because we have not tested these methods, we feel that we cannot provide a great deal of insight into these alternate approaches. We mention and reference these methods in the introduction so that readers are aware of them.

*Reviewer #2 (Significance (Required)): *

•Advance in technique: This study expands the use cases of data-independent acquisition MS method (DIA-MS) in C. elegans, which fragments all ions independent of the initial MS1 data. The benefits of this approach include better reproducibility across technical replicates and better recovery of low abundance peptides, which are critical for advancing our ability to capture weak and transient interactions.

•The use of DIA-MS in this study has improved our understanding of the partners of these NEKL-MLTs in membrane trafficking, molting, and cell adhesion within the epidermis.

•In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs.

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

*Summary: *

Fay and colleagues perform a series of proximity labeling experiments in C. elegans followed by thorough and rational analysis of the resulting biotinylated proteins identified by LC-MS/MS. The overall goals of the study are to evaluate different techniques and provide practical guidance on how to achieve success. The major takeaways are that integration of data-independent acquisition (DIA) along with comparison of endogenously tagged TurboID alleles to soluble TurboID expressed in the same tissue results in improved detection of bona-fide interactors and reduced numbers of false-positives.

*Major comments: *

Overall the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. I have no major concerns with the manuscript and focus primarily on improving the accessibility of this important contribution to the scientific community. As such, I suggest that the authors:

1) Provide more explanation of and rationale for using DIA. This is not yet a standard technique and most basic biomedical scientists will be unaware of the jargon. As I expect many labs in the C. elegans community and beyond will be interested in the guidance provided in this manuscript, the introduction offers a great opportunity to bring the reader up to speed, as opposed to sending them to the complicated proteomics analysis literature. We have added some additional context (lines 77-80) as well as new references. We note that getting into the technical differences between DIA and DDA, beyond what we briefly mention, would take a substantial amount of space, may not be of interest to many readers, and can be found through standard internet and (sigh) AI-based searches.

*2) Provide a better overview of the various protocols tested (Experiments 1-8). Maybe at the beginning of the results, and maybe with an accompanying schematic. As currently written, it is difficult to figure out details regarding how the experiments vary and why. * We have now added a short paragraph to better inform the reader at the front end regarding the major experiments. (lines 139-146).

3) As to be expected, expression of TurboID tags at endogenous levels via low abundance proteins in a complex multicellular system results in somewhat weak signals that flirt with the limit of detection. Perhaps by combining tagged alleles within the same complex (NEKL-3/MLT-3 or NEKL-2/MLT-2/MLT-4) the signals could be boosted? Tandem tags, either on one end or multiple ends of proteins might help as well. As the authors point out, a benefit of tagging the two NEKL-MLT complexes is that there are strong loss-of-function phenotypes (lethal molting defects) to help evaluate whether a tagging strategy results in a non-functional complex. THESE EXPERIMENTS ARE OPTIONAL and might simply be discussed at the authors discretion. These are interesting ideas that we have now incorporated into our discussion. (lines 936-940)

*Minor Comments: *

*1) Figure 3A is cropped on the right. * Thank you for catching this. Corrected.

*2) Better define [new REF] on line 702. * We have added new results (Fig 9C), obviating the need for this reference.

***Referee cross-comments** *

Overall, I am in agreement with, and supportive of, the other reviewers' comments.

*Reviewer #3 (Significance (Required)): *

*Significance: *

Proximity labeling is often proposed as a technique to determine interaction networks of proteins in vivo, but in practice it remains challenging for most labs to execute a successful experiment, especially within the context of multicellular model organisms. Fay and colleagues provide a much needed roadmap for how to best approach proximity labeling experiments in C. elegans that will likely apply to other model systems.

They establish a rigorous approach by choosing to endogenously tag components of two essential NEKL-MLT complexes required for C. elegans molting. These complexes are relatively low abundance as they are only expressed in a single cell type, the hyp7 epidermal syncytium. In addition, as inactivation of any member of the complexes results in molting defects, they have a powerful selection for functional tags. Thus, they have set a high bar for themselves in order to discern whether a given variation on the experimental approach results in improved detection of interactors and fewer false positives.

*Potential areas for improvement include lowering the expression level of the skin-specific soluble TurboID used to determine non-specific biotinylation events. This control results in much higher levels of biotinylation compared to the TurboID-tagged NEKL-MLT alleles and likely affects their analysis, which they openly admit. In addition, to reduce the high level of background biotinylation signals generated by endogenous carboxylases, they adopt a depletion strategy pioneered by other researchers but this does not offer major improvements in detection of specific signals. The source of these conflicting results remains to be determined. It is also curious that auxin-inducible degradation of components of the NEKL-MLT complexes did not robustly alter the resulting biotinylating capacity of other members. This approach should be evaluated in subsequent studies. Finally, as mentioned in Major Comment #3 (above), it would be interesting to see if combining TurboID tags within the same complex might improve signal-to-background ratios. *

This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system. I am a cell biologist that uses a variety of genetic, molecular and biochemical approaches, mostly centered around C. elegans. I have used LC/MS-MS in our studies but have relatively little expertise in evaluating all aspects of proteomic pipelines.

*Reviewer #4 (Evidence, reproducibility and clarity (Required)): *

*Fay et al. describe an extensive proximity labeling BioID study in C. elegans with TurboID and DIA-LCMS analysis. They chose the NEKL-2/3 kinases and their known interactors MLT-2/3/4 as TurboID-fused bait proteins (C- and partially N-terminal fusions encoded from CRISPR-mediated genome edited genes). With eight biological replicates (and three to four technical replicates each) and with the unmodified wildtype or mNeonGreen-TurboID expressing worms as controls, a comprehensive dataset was generated. Although starting from quite different abundances of the bait-fusions within the cell lysates all bait proteins and known complex-binding partners were convincingly enriched with capturing streptavidin beads after only one hour of incubation with the lysate. This confirms the general applicability of TurboID-BioID approach in C. elegans. The BioID method typically gives rise to large proteomics datasets (up to more than thousand proteins identified after biotin capture) with several tens to hundreds enriched proteins (against negative control strains) as potential proteins that localize proximal to the bait-TurboID protein. However, substantial variations of candidates between biological replicates are frequently observed in BioID experiments. The authors scrutinized their dataset towards indicative metrics, filters and cutoffs in order to separate high-confidence from low-confidence candidates. With the workflow applied the authors melt down the number of candidates to 15 proteins that were grouped in four functional groups reasonably associated to NEKL-MLT function. *

Successful BioID experiments depend on reliable enrichment quantification with mass spectrometry using control cell lines that require a carefully bait-tailored design. Those must adequately express TurboID controls matching the abundance of the bait-TurboID fusion protein and its biotinylation activity. After affinity capture, sample preparation and LCMS data acquisition there is no silver bullet towards the identification true bait neighbors. Fay et al. elaborately describe their considerations and workflow towards high-confidence candidates. The workflow considered (i) data analysis with Volcano plots to account for statistical reproducibility of biological replicates against negative controls, (ii) fraction of proteins only detected in the positive or negative controls thus evading the fold-enrichment quantification approach, (iii) evaluation of variations in carboxylase enrichment as a measure for variations in the general biotin capture quality between experiments, (iv) an assessment of technical reproducibility with scatter plots and Venn diagrams, (v) exclusion of potentially false positives, e.g. promiscuously biotinylated non-proximal proteins, through comparisons with control worms expressing a non-localized mNeonGreen-TurboID fusion protein, (vi) batch effects, (vii) the impact of endogenous biotinylated carboxylases through depletion, (viii) gene ontology analysis of enriched proteins, (ix) weighing data according to the quality of individual experiments according to the afore mentioned metrics, and finally (x) genetic interaction studies to functionally associate high-confidence candidates with the bait.

*Major comments: *

Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data). However, although the authors claim to seek for substrates of the kinase complex they drew no further attention to the phosphorylation status of the captured proteins. Haven't the MS data been analyzed in this respect? Information regarding this issue would enhance the manuscript. Data generation and method description appear reproducible for readers. Also, the statistical analyses appear adequate. The authors should also consider to deposit their MS raw and analysis data in a public repository (e.g. PRIDE) for future reviewing processes and as reference data for readers and followers. Our raw MS data have been deposited by the Arkansas Proteomics Facility. I have followed up to ensure that they are publicly available.

*Minor comments: *

The authors should combine supplementary data files to reduce the number of single files readers have to deal with. We have combined these files as suggested.

The authors should avoid the term "upregulation" or "increased biotinylation" when capture enrichment is meant. We agree with reviewer's point. We now use the terms enriched versus reduced or up versus down, depending on the context, and clearly define these terms. These changes have been incorporated throughout the manuscript.

*Reviewer #4 (Significance (Required)): *

The manuscript presents a robust BioID proteomics screening for co-localizing proteins of NEKL-2/3 kinases and their known interactors MLT-2/3/4. The ongoing validation of their functional interactions and whether the protein candidates reflect phosphorylation substrates or else remains elusive and is announced for upcoming manuscripts. The knowledge gain in terms of molecular mechanisms with NEKL-2/3 MLT-2/3/4 involvement in C. elegans is therefore limited to a table of - promising - interacting candidates that have to be studied further. Information about the phosphorylation status of the captured proteins from the MS data are not given. However, knowing the protein candidates will be of interest for groups working with these complexes (or the identified potentially interacting proteins) either in C. elegans or any other organism. Also, in-depth proteomics screenings with novel approaches such as BioID have to be established for individual organisms. For C. elegans there is only one prior BioID publication (Holzer et al. 2022). Many of the aspects discussed here have also been addressed earlier for BioIDs in other organisms and are not principally new. However, the presented study can be of conceptual interest for labs delving into or entangled with the BioID method in C. elegans or other organisms. The study addresses especially proteomics groups working on protein-protein interactions using proximity labeling/MS approaches. Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Fay and colleagues perform a series of proximity labeling experiments in C. elegans followed by thorough and rational analysis of the resulting biotinylated proteins identified by LC-MS/MS. The overall goals of the study are to evaluate different techniques and provide practical guidance on how to achieve success. The major takeaways are that integration of data-independent acquisition (DIA) along with comparison of endogenously tagged TurboID alleles to soluble TurboID expressed in the same tissue results in improved detection of bona-fide interactors and reduced numbers of false-positives.

Major comments:

Overall the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. I have no major concerns with the manuscript and focus primarily on improving the accessibility of this important contribution to the scientific community. As such, I suggest that the authors:

1. Provide more explanation of and rationale for using DIA. This is not yet a standard technique and most basic biomedical scientists will be unaware of the jargon. As I expect many labs in the C. elegans community and beyond will be interested in the guidance provided in this manuscript, the introduction offers a great opportunity to bring the reader up to speed, as opposed to sending them to the complicated proteomics analysis literature.
2. Provide a better overview of the various protocols tested (Experiments 1-8). Maybe at the beginning of the results, and maybe with an accompanying schematic. As currently written, it is difficult to figure out details regarding how the experiments vary and why.
3. As to be expected, expression of TurboID tags at endogenous levels via low abundance proteins in a complex multicellular system results in somewhat weak signals that flirt with the limit of detection. Perhaps by combining tagged alleles within the same complex (NEKL-3/MLT-3 or NEKL-2/MLT-2/MLT-4) the signals could be boosted? Tandem tags, either on one end or multiple ends of proteins might help as well. As the authors point out, a benefit of tagging the two NEKL-MLT complexes is that there are strong loss-of-function phenotypes (lethal molting defects) to help evaluate whether a tagging strategy results in a non-functional complex. THESE EXPERIMENTS ARE OPTIONAL and might simply be discussed at the authors discretion.

Minor Comments:

1. Figure 3A is cropped on the right.
2. Better define [new REF] on line 702.

Referee cross-comments

Overall, I am in agreement with, and supportive of, the other reviewers' comments.

Significance

Proximity labeling is often proposed as a technique to determine interaction networks of proteins in vivo, but in practice it remains challenging for most labs to execute a successful experiment, especially within the context of multicellular model organisms. Fay and colleagues provide a much needed roadmap for how to best approach proximity labeling experiments in C. elegans that will likely apply to other model systems.

They establish a rigorous approach by choosing to endogenously tag components of two essential NEKL-MLT complexes required for C. elegans molting. These complexes are relatively low abundance as they are only expressed in a single cell type, the hyp7 epidermal syncytium. In addition, as inactivation of any member of the complexes results in molting defects, they have a powerful selection for functional tags. Thus, they have set a high bar for themselves in order to discern whether a given variation on the experimental approach results in improved detection of interactors and fewer false positives.

Potential areas for improvement include lowering the expression level of the skin-specific soluble TurboID used to determine non-specific biotinylation events. This control results in much higher levels of biotinylation compared to the TurboID-tagged NEKL-MLT alleles and likely affects their analysis, which they openly admit. In addition, to reduce the high level of background biotinylation signals generated by endogenous carboxylases, they adopt a depletion strategy pioneered by other researchers but this does not offer major improvements in detection of specific signals. The source of these conflicting results remains to be determined. It is also curious that auxin-inducible degradation of components of the NEKL-MLT complexes did not robustly alter the resulting biotinylating capacity of other members. This approach should be evaluated in subsequent studies. Finally, as mentioned in Major Comment #3 (above), it would be interesting to see if combining TurboID tags within the same complex might improve signal-to-background ratios.

This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system. I am a cell biologist that uses a variety of genetic, molecular and biochemical approaches, mostly centered around C. elegans. I have used LC/MS-MS in our studies but have relatively little expertise in evaluating all aspects of proteomic pipelines.

Should this be an array?

Here, or in general?

I do think about this a lot. This is a nice, succinct way to put it. (Critique, though: "classism" is not the best way to put it. For better or worse, "privilege" is probably one of the best words we have for this. Separately: Since "privilege" became a staple of common rhetoric, I've mused a lot about trying to convince people to minimize the focus on "privilege" (to avoid the familiar kneejerk reactions from those hearing it who have associated it with overuse), with the intent to be to sway people instead by speaking about privilege without actually using the word "privilege" and speaking exclusively in terms of affordances*.)

See: https://hypothes.is/a/TCB5zClKEeyrIOu9mp-5TA and tag:"privilege vs affordance". (NB: Hypothes.is doesn't linkify the tag in the preceding annotation correctly.)

Reviewer #1 (Public review):

Summary:

The study by Klug et al. investigated the pathway specificity of corticostriatal projections, focusing on two cortical regions. Using a G-deleted rabies system in D1-Cre and A2a-Cre mice to retrogradely deliver channelrhodopsin to cortical inputs, the authors found that M1 and MCC inputs to direct and indirect pathway spiny projection neurons (SPNs) are both partially segregated and asymmetrically overlapping. In general, corticostriatal inputs that target indirect pathway SPNs are likely to also target direct pathway SPNs, while inputs targeting direct pathway SPNs are less likely to also target indirect pathway SPNs. Such asymmetric overlap of corticostriatal inputs has important implications for how the cortex itself may determine striatal output. Indeed, the authors provide behavioral evidence that optogenetic activation of M1 or MCC cortical neurons that send axons to either direct or indirect pathway SPNs can have opposite effects on locomotion and different effects on action sequence execution. The conclusions of this study add to our understanding of how cortical activity may influence striatal output and offer important new clues about basal ganglia function.

The conceptual conclusions of the manuscript are supported by the data, but the details of the magnitude of afferent overlap and causal role of asymmetric corticostriatal inputs on some behavioral outcomes may be a bit overstated given technical limitations of the experiments.

For example, after virally labeling either direct pathway (D1) or indirect pathway (D2) SPNs to optogenetically tag pathway-specific cortical inputs, the authors report that a much larger number of "non-starter" D2-SPNs from D2-SPN labeled mice responded to optogenetic stimulation in slices than "non-starter" D1 SPNs from D1-SPN labeled mice did. Without knowing the relative number of D1 or D2 SPN starters used to label cortical inputs, it is difficult to interpret the exact meaning of the lower number of responsive D2-SPNs in D1 labeled mice (where only ~63% of D1-SPNs themselves respond) compared to the relatively higher number of responsive D1-SPNs (and D2-SPNs) in D2 labeled mice. While relative differences in connectivity certainly suggest that some amount of asymmetric overlap of inputs exists, differences in infection efficiency and ensuing differences in detection sensitivity in slice experiments make determining the degree of asymmetry problematic.

It is also unclear if retrograde labeling of D1-SPN- vs D2-SPN- targeting afferents labels the same densities of cortical neurons. This gets to the point of specificity in some of the behavioral experiments. If the target-based labeling strategies used to introduce channelrhodopsin into specific SPN afferents label significantly different numbers of cortical neurons, might the difference in the relative numbers of optogenetically activated cortical neurons itself lead to behavioral differences?

I've tried using a hiking metaphor to describe a similar phenomenon (specifically, and perversely, as a preface when trying to explain second panel syndrome.

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Reply to the reviewers

__We thank the reviewers for the supportive suggestions and comments. We have addressed all comments underneath the original text in red. As suggested, we added to line numbers to the text and use these numbers to refer to the changes made. __

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

The manuscript is well written and presents solid data, most of which is statistically analyzed and sound. Given that the author's previous comprehensive publications on seipin organization and interactions, it might be beneficial (particularly in the title and abstract) to emphasize that this manuscript focuses on the metabolic regulation of lipid droplet assembly by Ldb16, to distinguish it from previous work. Perhaps one consideration, potentially interesting, involves changes in lipid droplet formation under the growth conditions used for galactose-mediated gene induction.

We thank the reviewer for the supportive comments and suggestions.

Comments: (1) Fig. 3 and 4. The galactose induction of lipid droplet biogenesis in are1∆/2∆ dga1∆ lro1∆ cells though activation of a GAL1 promoter fusion to DGA1 is a sound approach for regulating lipid droplet formation. Although unlikely, carbon sources can impact lipid droplet proliferation and (potentially interesting) metabolic changes under growth in non-fermentable carbon sources may impact lipid droplet biogenesis; in fact, oleate has significant effects (e.g. PMID: 21422231; PMID: 21820081). The GAL1 promoter is a very strong promoter and the overexpression of DGA1 via this heterologous promoter might itself cause unforeseen changes. Affirmation of the results using another induction system might be beneficial.

We thank the reviewer for these suggestions. In this study we focused on the organisation of the yeast seipin complex during the process of LD formation. We chose to use galactose-based induction of Dga1 because this is a well-established and widely used assay in the field, extensively characterized by many groups over the years. The tight control it provides, enabling synchronous and rapid LD induction, makes it the method of choice for many researchers. Importantly, the LDs formed using this assay are morphologically normal and involve the same components as LDs formed under other conditions.

Regarding the role of metabolism in LD formation, it is worth noting that galactose is metabolized by yeast primarily through fermentation, following its conversion to UDP-glucose. Therefore, its use does not involve drastic metabolic changes. The impact of metabolism in LD biogenesis is an interesting question but it falls beyond the scope of the current study.

(2) Fig. 3B. Although only representative images are shown, the panel convincingly shows that lipid droplets do form upon galactose induction of DGA1 in are1∆/2∆ dga1∆ lro1∆ cells. However, it does not show to what extent. Are lipid droplets synthesized at WT levels? How many cells were counted? How many lipid droplets per cell? Is there a statistical difference with respect to WT cells?

We did not assess these parameters in this study. The aim of the study was to assess the relations between components of the seipin complex with and without lipid droplets. For this purpose, inducing lipid droplet formation over a 4-hour period was sufficient to address that specific question. As mentioned above, LDs formed using this assay are morphologically normal and involve the same components as LDs formed under other conditions. This being said, it is known that prolonged overexpression of Dga1 (> 12hours) can lead to enlarged LDs.

(3) Fig. 2D. It is not clear how standard deviation can be meaningfully applied to two data points, let alone providing a p-value. For some of these experiments, triplicate trials might provide a more robust statistical sampling.

We thank the reviewer for this suggestion. We have added 2 more repeats to the Co-IP in figure 2.

Reviewer #1 (Significance (Required)):

Klug and Carvalho report on the lipid droplet architecture of the yeast seipin complex. Specifically, the mechanism of yeast seipin Sei1 binding to Ldo16 and the subsequent recruitment of Ldb45 is analyzed. These results follow from a recent publication (PMID: 34625558) from the same authors and aims to define a more precise role for the components of the seipin complex. Using photo-crosslinking, Ldo45 and Ldo16 interactions are analyzed in the context of lipid droplet assembly.

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

Summary:

Klug and Carvalho apply a photo-crosslinking approach, which has been extensively used in the Carvalho group, to investigate the subunit interactions of the seipin complex in yeast. The authors apply this approach to further study possible changes within the seipin complex following induction of neutral lipid synthesis and lipid droplet (LD) formation. The authors propose that Ldo45 makes contact with Ldb16 and that the seipin complex subunits assemble even in the absence of LDs.

Major comments:

Overall, this is a focused and well-executed study on one of the fundamental structural components of LDs. The study addresses the subunit interactions of the seipin complex but does not look into their functional consequences, for example how the mutations on Ldb16 that affect its interaction with Ldo45, influence LD formation; similarly, the authors make the interesting observation that Ldo16 may be differentially affected by the lack of neutral lipids (Fig. 3A) but this observation is not explored.

We thank the reviewer for this comment. The Ldb16 mutations analyzed in this study have been previously characterized by us (see Klug et al., 2021 – Figure 3) and exhibit a mild defect in lipid droplet (LD) formation. This phenotype is unlikely to result from impaired Ldo16/45 recruitment, as deletion of Ldo proteins causes only a very mild effect on LD formation (as shown in Teixeira et al., 2018 and Eisenberg-Bord et al., 2018).

We agree that the differential effect on Ldo proteins by the absence of neutral lipids is particularly interesting. However, its exploration falls outside of the scope of the current study and should be thoroughly investigated in the future.

1. For the crosslinking pull-downs (Fig. 1), it seems that the authors significantly overexpress (ADH1 promoter) the Ldb16 subunit that carries the various photoreactive amino acid residues, while keeping the other (tagged) seipin complex members at endogenous levels. Would not this imbalance affect the assembly of the complex and therefore the association of the different subunits with each other?

We thank the reviewer for this comment. The in vivo site-specific crosslinking is highly sensitive methodology to detect protein-protein interactions in a position-dependent manner. However, one of the caveats of the approach is the low efficiency of amber stop codon suppression and BPA incorporation. To mitigate this limitation, we (and others) induce the expression of the amber-containing protein (in this case Ldb16) from a strong constitutive promoter such as ADH1. Therefore, despite using a strong promoter, the overall levels of LDB16 remain comparable to endogenous levels due to the inherently low efficiency of amber suppression. Moreover, it is known that when not bound to Sei1, Ldb16 is rapidly degraded in a proteasome dependent manner (Wang, C.W. 2014), further preventing its accumulation.

Although the authors do show delta4 cells with no LDs (Fig. 3B, 0h), galactose-inducible systems in yeast are known to be leaky. Given that the authors' conclusion that the complex is "pre-assembled" irrespective of the addition of galactose, I think it would be important to confirm biochemically that there is no neutral lipid at time point 0. Alternatively, it may be better to simply compare wt vs dga1 lro1 or are1are2 mutants - there is no need for GAL induction since the authors look at one time point only.

Among the various regulable promoters, GAL1 shows a superior level of control. For example, expression of essential genes from GAL1 promoter frequently leads to cell death in glucose containing media, a condition that represses GAL1 promoter. Having said this, we cannot exclude that minute amounts of DGA1 are expressed prior galactose induction. However, if this is the case, the resulting levels of TAG are insufficient to be detected by sensitive lipid dyes and to induce LDs, as noted by the reviewer. Therefore, we believe our conclusions remain valid. This is consistent that we use in the text, where we refer to LD formation rather than complete loss of neutral lipids. To make this absolutely clear we replaced the word “presence” to “abundance” in line 236.

Lastly, we do not agree with the reviewer that using double mutants (are1/2 or dga1/lro1 mutants) would be sufficient since these mutations are not sufficient to abolish LD formation – a key aspect of this study. The GAL1 system allows us to monitor 2 time points in the same cells –no LDs (time 0h) and with LDs (Time 4h). The system proposed by the reviewer would only allow a snap shot of steady state levels in different cells rather than within the same cell culture.

Some methodological issues could be better detailed. For example, which of the three delta4 strains was used to induce neutral lipid in Fig. 4B? How exactly were the quantifications in Fig. 4D performed (I assume they were done under non-saturating band intensity conditions, as for some residues it is difficult to conclude whether the blot aligns with the quantification results).

We thank the reviewer for these comments. We have clarified the strain number in the figure legend of figure 4B (strain yPC12630).

We have also added the following text in rows 437-441 in the methods section: “Reactive bands were detected by ECL (Western Lightning ECL Pro, Perkin Elmer #NEL121001EA), and visualized using an Amersham Imager 600 (GE Healthcare Life Sciences). Data quantification was performed using Image Studio software (Li-Cor) to measure line intensity under non saturating conditions.”

"our findings support the notion that Ldo45 is important for early steps of LD formation as previously proposed" I find this statement confusing given that the authors claim that Ldo45 is already bound to the complex before LD formation.

We thank the reviewer for raising this important point. We believe that our findings support previous hypotheses on the role of Ldo45. It has been suggested that Ldo45 is important for the early stages of lipid droplet (LD) formation (Teixeira et al., 2018; Eisenberg-Bord et al., 2018). As such, Ldo45 would need to be recruited to the seipin complex before or at the onset of LD formation. The observation that Ldo45 is present at the complex prior to LD formation provides strong support for its role in the initial steps of this process.

To clarify this idea in the manuscript, we have revised the sentence on line 310 as follows:

“Irrespective of the mechanism, our findings support the notion that Ldo45 plays a role in the early steps of LD formation, as previously proposed…”

The model in Fig. 5 is essentially the same as the one shown in Fig. 1G.

To aid the reader and avoid confusion, we intentionally used a similar color scheme throughout the manuscript. This may contribute to the perception that the figures are very similar. However, there are clear distinctions between them. In Figure 1G, we summarize our findings regarding the positioning of Ldo45 within the complex and note that we do not yet have data on Ldo16. Building upon these findings, in Figure 5 we speculate where Ldo16 might interact with Ldb16 and highlight that recruitment of both Ldo16 and Ldo45 increases with neutral lipid availability.

Therefore, we believe that both figures serve distinct and complementary purposes, and that each is useful for communicating our overall message.

Minor comments

In the pull-downs in Fig. 2C, it seems that full-length Ldb16 is not enriched after the FLAG IP. What is the reason of this?

We thank the reviewer for raising this interesting aspect. We do not know why this occurs, but it is clear that full length Ldb16 is not efficiently pulled down. We could speculate that this has to do with access to the FLAG moiety at the C terminus that may become inaccessible due to interactions or folding in the long unstructured C-terminus of Ldb16. This might explain why when we truncate the C terminus in the 1-133 mutant we achieve a more efficient IP.

At the blots at Fig. 2C and 3A, the anti-Dpm1 Ab seems to recognize in the IP fractions a band labelled as non-specific, however this band is absent from the input.

We thank the reviewer for raising this. This non-specific band is the light chain of the antibody used in the pull down that detaches from the matrix during elution – thus not found in the input. This is a common non-specific band that appears in Co-IP blots.

Reviewer #2 (Significance (Required)):

Regulation of seipin function is essential for proper LD biogenesis in eukaryotes, so this study addresses a fundamental question in the field. As stated above some functional analysis that goes beyond the biochemistry would be beneficial. There is some overlap with a recently published paper from the Wang group that also examines the assembly of seipin in yeast.

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

The manuscript by Klug and Carvalho investigates the interaction of the yeast seipin complex (Sei1 and Ldb16) with Ldo45 and Ldo16. Using a site-specific photocrosslinking approach, the authors map some residues of the seipin complex in contact Ldo45, demonstrating that Ldo45 likely binds to Ldb16 in the center of the Sei1-Ldb16 complex. They find that both Ldo45 and Ldo16 copurify with Ldb16. Complex assembly is demonstrated to occur independently of the presence of neutral lipids. An Ldb16 mutant, harbouring the transmembrane domain (1-133) but lacking the cytosolic region (previously shown to allow normal LD formation and still bind to Sei1) showed photocrosslinks with Ldo45, but not Ldo16. No crosslinks between Sei1 and either Ldo45 or Ldo16 were detected.

Major: 1. Figure 2 shows CoIPs using different Ldb16 mutants/truncations to test for binding of Ldo45 and Ldo16. Both Ldo16 and Ldo45 copurify with full length Ldb16. Loss of the cytosolic part of Ldb16 strongly reduced binding of both Ldo45 and Ldo16, indicating that the TM-Helix-TM domain of Ldb16 (1-133) alone is not sufficient for proper binding of Ldo45 or Ldo16. The quantifications (2D and 2E) presented for this CoIP represent a n=2 with mean, standard deviation and statistics. To be a meaningful statistical analysis, the authors need to increase their n to at least n=3. In addition, they refer to the statistics they use here as "two-sided Fischer's T-test" in the respective Figure legend. To my knowledge, there is no such test, either it is Student's T-test or Fischer's exact test? Can the authors please clarify?

We thank the reviewer for this comment and suggestions. We have now included 2 additional repeats for this experiment and the results essentially support our conclusion.

The two-sided Fischer’s T-test is the name of the test in Graphpad- Prism. We wanted to acknowledge the test name so that the reader can trace the exact test we used in the program.

1. Figure 2E shows the same data as 2D with different normalization to highlight the differences between binding to the domain 1-133 per se and binding to this domain when the linker helix is mutated. These mutations seem to cause a further decrease in binding of both Ldo45 and Ldo16. Still, effects are rather small, and the n=2 does not allow any meaningful statistical tests. To make this point, the authors should increase their sample number (at least n=3) to show that this difference is indeed meaningful and to allow statistical analysis.

We thank the reviewer for this comment and suggestions. We have now included 2 additional repeats for this experiment and the results essentially support our conclusion.

For Ldo16, no crosslinks were detected with Ldb16 TM-HelixTM domain (Figure 1). In line, CoIP demonstrated that the interaction between Ldo16 and Ldb16 was strongly reduced when the Ldb16 domain 1-133 was used for IP. Still, additional mutation of the linker helix in this 1-133 domain further reduced this interaction (to a similar extend as for Ldo45). Could the authors please clarify why the additional mutations in the linker helix region also decreased the binding of Ldo16, though the authors conclude from their crosslinking approach in Fig. 1 that Ldo16 does not interact with this region?

We thank the reviewer for raising this point. Our negative crosslinking results for Ldo16 do not exclude the possibility of binding to that region; rather, they indicate that we were unable to detect Ldo16 there. Additionally, mutations in the linker helix may influence how Ldb16 interacts with seipin, including its positioning within the seipin ring and the membrane bilayer. These structural changes could, in turn, affect Ldo16 recruitment in ways that we do not fully understand.

Similarly, also in 4D, a quantification with n=2 is presented, showing that some of the crosslinks are more prominently detectable when LD biogenesis is induced. The findings of this manuscript are completely based on results obtained with CoIP and photocrosslinking, and quantification of a sufficient n to allow statistical analysis will be essential.

While we agree that additional experiments are useful for the Co-IP because of variability between experiments, this is less of a concern for the photocrosslinking experiments. In the case of photocrosslinking, we typically see much less variability and normally, for a given position, the effects are much more “black and white”- either there is a crosslink or not.

Why is there nowhere a blot with crosslinked Ldb16 bands shown (but only non-crosslinked Ldb16, e.g. Fig. 1C)?

We thank the reviewer for this comment. In all cases the amount of crosslinked product is very minor. This is particularly obvious in the case of Ldb16, where the non-crosslinked species dominates in the blots (as can be observed in figure S1B).

Figure 3: The authors conclude that galactose-induced expression of either Dga1, Lro1 or Are1 in cells lacking all four enzymes for neutral lipid synthesis (quadruple deletion mutant) increases the levels of Ldb16. However, I do not see any difference on the FLAG-Ldb16 blot when comparing Ldb16 levels in the quadruple deletion mutant with or without Dga1, Lro1 or Are1, and no quantification is presented that might reveal very subtle differences not visible on the blot.

We agree with the reviewer and modified the text to more accurately describe our results.

OPTIONAL: Have the authors considered to assess which sites/domains of Ldo45 and Ldo16 are employed to bind to Ldb16?

This is a logical next step that will be undertaken in a future study.

Minor: 1. Page numbers would have been helpful to refer to specific text sections.

Page numbers have been added

1. Figure 3C: Unclear to me why the authors label a part of their immunoblot where they detected HA with OSW5?

This was a mistake and has been corrected

1. Figure 4D and corresponding figure legend could be improved in respect to labeling to clarify.

we have added an X axis label and made extra clarifications in the legend

1. Please correct his sentence: "These variants we expressed in cells where the other subunits of the Sei1 complex were epitope tagged to facilitate detection and expressed their endogenous loci."

This sentence has been corrected

Reviewer #3 (Significance (Required)):

This is a short and interesting study completely based on UV-induced site-specific photocrosslinking and CoIPs that provides some new insights into the interaction surface between the Seipin complex and Ldo45 and the interaction between Ldo16 and Ldb16. Though in parts still premature, these findings will likely be of interest to the large community interested in lipid metabolism, expanding the role of Ldb16 from neutral lipid binding to regulator recruitment.

En este motor, tenemos el concepto de TAG: un componente sin datos.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public review):

Summary:

Phytophathogens including fungal pathogens such as F. graminearum remain a major threat to agriculture and food security. Several agriculturally relevant fungicides including the potent Quinofumelin have been discovered to date, yet the mechanisms of their action and specific targets within the cell remain unclear. This paper sets out to contribute to addressing these outstanding questions.

We appreciate the reviewer's accurate summary of our manuscript.

Strengths:

The paper is generally well-written and provides convincing data to support their claims for the impact of Quinofumelin on fungal growth, the target of the drug, and the potential mechanism. Critically the authors identify an important pyrimidine pathway dihydroorotate dehydrogenase (DHODH) gene FgDHODHII in the pathway or mechanism of the drug from the prominent plant pathogen F. graminearum, confirming it as the target for Quinofumelin. The evidence is supported by transcriptomic, metabolomic as well as MST, SPR, molecular docking/structural biology analyses.

We appreciate the reviewer's recognition of the strengths of our manuscript.

Weaknesses:

Whilst the study adds to our knowledge about this drug, it is, however, worth stating that previous reports (although in different organisms) by Higashimura et al., 2022 https://pmc.ncbi.nlm.nih.gov/articles/PMC9716045/ had already identified DHODH as the target for Quinofumelin and hence this knowledge is not new and hence the authors may want to tone down the claim that they discovered this mechanism and also give sufficient credit to the previous authors work at the start of the write-up in the introduction section rather than in passing as they did with reference 25? other specific recommendations to improve the text are provided in the recommendations for authors section below.

We appreciate the reviewer's suggestion. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of previous work on quinofumelin by Higashimura et al., 2022 in the discussion section to more effectively contextualize their contributions. Moreover, we have made revisions and provided responses in accordance with the recommendations.

Reviewer #2 (Public review):

Summary:

In the current study, the authors aim to identify the mode of action/molecular mechanism of characterized a fungicide, quinofumelin, and its biological impact on transcriptomics and metabolomics in Fusarium graminearum and other Fusarium species. Two sets of data were generated between quinofumelin and no treatment group, and differentially abundant transcripts and metabolites were identified. The authors further focused on uridine/uracil biosynthesis pathway, considering the significant up- and down-regulation observed in final metabolites and some of the genes in the pathways. Using a deletion mutant of one of the genes and in vitro biochemical assays, the authors concluded that quinofumelin binds to the dihydroorotate dehydrogenase.

We appreciate the reviewer's accurate summary of our manuscript.

Strengths:

Omics datasets were leveraged to understand the physiological impact of quinofumelin, showing the intracellular impact of the fungicide. The characterization of FgDHODHII deletion strains with supplemented metabolites clearly showed the impact of the enzyme on fungal growth.

We appreciate the reviewer's recognition of the strengths of our manuscript.

Weaknesses:

Some interpretation of results is not accurate and some experiments lack controls. The comparison between quinofumelin-treated deletion strains, in the presence of different metabolites didn't suggest the fungicide is FgDHODHII specific. A wild type is required in this experiment.

Potential Impact: Confirming the target of quinofumelin may help understand its resistance mehchanism, and further development of other inhibitory molecules against the target.

The manuscript would benefit more in explaining the study rationale if more background on previous characterization of this fungicide on Fusarium is given.

We appreciate the reviewer's suggestion. Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil. In our previous study, quinofumelin not only exhibited excellent antifungal activity against the mycelial growth and spore germination of F. graminearum, but also inhibited the biosynthesis of deoxynivalenol (DON). We have added this part to the introduction section.

Reviewer #3 (Public review):

Summary:

The manuscript shows the mechanism of action of quinofumelin, a novel fungicide, against the fungus Fusarium graminearum. Through omics analysis, phenotypic analysis, and in silico approaches, the role of quinofumelin in targeting DHODH is uncovered.

We appreciate the reviewer's accurate summary of our manuscript.

Strengths:

The phenotypic analysis and mutant generation are nice data and add to the role of metabolites in bypassing pyrimidine biosynthesis.

We appreciate the reviewer's recognition of the strengths of our manuscript.

Weaknesses:

The role of DHODH in this class of fungicides has been known and this data does not add any further significance to the field. The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

There is no mention of the other fungicide within this class ipflufenoquin, as there is ample data on this molecule.

We appreciate the reviewer's suggestion. We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions. The information regarding action mechanism of ipflufenoquin against filamentous fungi was added in discussion section.

Reviewer #1 (Recommendations for the authors):

(1) Given that the DHODH gene had been identified as a target earlier, could the authors perform blast experiments with this gene instead and let us know the percentage similarity between the FgDHODHII gene and the Pyricularia oryzae class II DHODH gene in the report by Higashimura et al., 2022.

BLAST experiment revealed that the percentage similarity between the FgDHODHII gene and the class II DHODH gene of P. oryzae was 55.41%. We have added the description ‘Additionally, the amino acid sequence of the FgDHODHII exhibits 55.41% similarity to that of DHODHII from Pyricularia oryzae, as previously reported (Higashimura et al., 2022)’ in section Results.

(2) Abstract:

The authors started abbreviating new terms e.g. DEG, DMP, etc but then all of a sudden stopped and introduced UMP with no full meaning of the abbreviation. Please give the full meaning of all abbreviations in the text, UMP, STC, RM, etc.

We have provided the full meaning for all abbreviations as requested.

(3) Introduction section:

The introduction talks very little about the work of other groups on quinofumelin. Perhaps add this information in and reference them including the work of Higashimura et al., 2022 which has done quite significant work on this topic but is not even mentioned in the background

We have added the work of other groups on quinofumelin in section introduction.

(4) General statements:

Please show a model of the pyrimidine pathway that quinofumelin attacks to make it easier for the reader to understand the context. They could just copy this from KEGG

We have added the model (Fig. 7).

(5) Line 186:

The authors did a great job of demonstrating interactions with the Quinofumelin and went to lengths to perform MST, SPR, molecular docking, and structural biology analyses yet in the end provide no details about the specific amino acid residues involved in the interaction. I would suggest that site-directed mutagenesis studies be performed on FgDHODHII to identify specific amino acid residues that interact with Quinofumelin and show that their disruption weakens Quinofumelin interaction with FgDHODHII.

Thank you for this insightful suggestion. We fully agree with the importance of elucidating the interaction mechanism. At present, we are conducting site-directed mutagenesis studies based on interaction sites from docking results and the mutation sites of FgDHODHII from the resistant mutants; however, due to the limitations in the accuracy of existing predictive models, this work remains ongoing. Additionally, we are undertaking co-crystallization experiments of FgDHODHII with quinofumelin to directly and precisely reveal their interaction pattern

(6) Line 76:

What is the reference or evidence for the statement 'In addition, quinofumelin exhibits no cross-resistance to currently extensively used fungicides, indicating its unique action target against phytopathogenic fungi.

If two fungicides share the same mechanism of action, they will exhibit cross resistance. Previous studies have demonstrated that quinofumelin retains effective antifungal activity against fungal strains resistant to commercial fungicides, indicating that quinofumelin does not exhibit cross-resistance with other commercially available fungicides and possesses a novel mechanism of action. Additionally, we have added the relevant inference.

(7) Line 80-82:

Again, considering the work of previous authors, this target is not newly discovered. Please consider toning down this statement 'This newly discovered selective target for antimicrobial agents provides a valuable resource for the design and development of targeted pesticides.'

We have rewritten the description of this sentence.

(8) Line 138: If the authors have identified DHODH in experimental groups (I assume in F. graminearum), what was the exact locus tag or gene name in F. graminearum, and why not just continue with this gene you identified or what is the point of doing a blast again to find the gene if the DHODH gene if it already came up in your transcriptomic or metabolic studies? This unfortunately doesn't make sense but could be explained better.

The information of FgDHODHII (gene ID: FGSG_09678) has been added. We have revised this part.

Reviewer #2 (Recommendations for the authors):

(1) Line 40:

Please add a reference.

We have added the reference

(2) Line 47:

Please add a reference.

We have added the reference.

(3) Line 50:

The lack of target diversity in existing fungicides doesn't necessarily serve as a reason for discovering new targets being more challenging than identifying new fungicides within existing categories, please consider adjusting the argument here. Instead, the authors can consider reasons for the lack of new targets in the field.

We have revised the description.

(4) Line 63:

Please cite your source with the new technology.

We have added the reference.

(5) Line 68:

What are you referring to for "targeted medicine", do you have a reference?

We have revised the description and the reference.

(6) Line 74:

One of the papers referred to "quinoxyfen", what are the similarities and differences between the two? Please elaborate for the readership.

Quinoxyfen, similar to quinofumelin, contains a quinoline ring structure. It inhibits mycelial growth by disrupting the MAP kinase signaling pathway in fungi (https://www.frac.info). In addition, quinoxyfen still exhibits excellent antifungal activity against the quinofumelin-resistant mutants (the findings from our group), indicating that action mechanism for quinofumelin and quinoxyfen differ.

(7) Line 84:

Please introduce why RNA-Seq was designed in the study first. What were the groups compared? How was the experiment set up? Without this background, it is hard to know why and how you did the experiment.

According to your suggestions, we have added the description in Section Results. In addition, the experimental process was described in Section Materials and methods as follows: A total of 20 mL of YEPD medium containing 1 mL of conidia suspension (1×105 conidia/mL) was incubated with shaking (175 rpm/min) at 25°C. After 24 h, the medium was added with quinofumelin at a concentration of 1 μg/mL, while an equal amount of dimethyl sulfoxide was added as the control (CK). The incubation continued for another 48 h, followed by filtration and collection of hyphae. Carry out quantitative expression of genes, and then analyze the differences between groups based on the results of DESeq2 for quantitative expression.

(8) Figures:

The figure labeling is missing (Figures 1,2,3 etc). Please re-order your figure to match the text

The figures have been inserted.

(9) Line. 97:

"Volcano plot" is a common plot to visualize DEGs, you can directly refer to the name.

We have revised the description.

(10) Figure 1d, 1e:

Can you separate down- and up-regulated genes here? Does the count refer to gene number?

The expression information for down- and up-regulated genes is presented in Figure 1a and 1b. However, these bubble plots do not distinguish down- and up-regulated genes. Instead, they only display the significant enrichment of differentially expressed genes in specific metabolic pathways. To more clearly represent the data, we have added the detailed counts of down- and up-regulated genes for each metabolic pathway in Supplementary Table S1 and S2. Here, the term "count" refers to differentially expressed genes that fall within a certain pathway.

(11) Line 111:

Again, no reasoning or description of why and how the experiment was done here.

Based on the results of KEGG enrichment analysis, DEMs are associated with pathways such as thiamine metabolism, tryptophan metabolism, nitrogen metabolism, amino acid sugar and nucleotide sugar metabolism, pantothenic acid and CoA biosynthesis, and nucleotide sugar production compounds synthesis. To specifically investigate the metabolic pathways involved action mechanism of quinofumelin, we performed further metabolomic experiments. Therefore, we have added this description according the reviewer’s suggestions.

(12) Figure 2a:

It seems many more metabolites were reduced than increased. Is this expected? Due to the antifungal activity of this compound, how sick is the fungus upon treatment? A physiological study on F. graminearum (in a dose-dependent manner) should be done prior to the omics study. Why do you think there's a stark difference between positive and negative modes in terms of number of metabolites down- and up-regulated?

Quinofumelin demonstrates exceptional antifungal activity against Fusarium graminearum. The results indicate that the number of reduced metabolites significantly exceeds the number of increased metabolites upon quinofumelin treatment. Mycelial growth is markedly inhibited under quinofumelin exposure. Prior to conducting omics studies, we performed a series of physiological and biochemical experiments (refer to Qian Xiu's dissertation https://paper.njau.edu.cn/openfile?dbid=72&objid=50_49_57_56_49_49&flag=free). Upon quinofumelin treatment, the number of down-regulated metabolites notably surpasses that of up-regulated metabolites compared to the control group. Based on the findings from the down-regulated metabolites, we conducted experiments by exogenously supplementing these metabolites under quinofumelin treatment to investigate whether mycelial growth could be restored. The results revealed that only the exogenous addition of uracil can restore mycelial growth impaired by quinofumelin.

Quinofumelin exhibits an excellent antifungal activity against F. graminearum. At a concentration of 1 μg/mL, quinofumelin inhibits mycelial growth by up to 90%. This inhibitory effect indicates that life activities of F. graminearum are significantly disrupted by quinofumelin. Consequently, there is a marked difference in down- and up-regulated metabolites between quinofumelin-treated group and untreated control group. The detailed results were presented in Figures 1 and 2.

(13) Figure 2e:

This is a good analysis. To help represent the data more clearly, the authors can consider representing the expression using fold change with a p-value for each gene.

To more clearly represent the data, we have incorporated the information on significant differences in metabolites in the de novo pyrimidine biosynthesis pathway, as affected by quinofumelin, in accordance with the reviewer’s suggestions.

(14) Line 142:

Please indicate fold change and p-value for statistical significance. Did you validate this by RT-qPCR?

We validated the expression level of the DHODH gene under quinofumelin treatment using RT-qPCR. The results indicated that, upon treatment with the EC50 and EC90 concentrations of quinofumelin, the expression of the DHODH gene was significantly reduced by 11.91% and 33.77%, respectively (P<0.05). The corresponding results have been shown in Figure S4.

(15) Line 145:

It looks like uracil is the only metabolite differentially abundant in the samples - how did you conclude this whole pathway was impacted by the treatment?

The experiments involving the exogenous supplementation of uracil revealed that the addition of uracil could restore mycelial growth inhibited by quinofumelin. Consequently, we infer that quinofumelin disrupts the de novo pyrimidine biosynthesis pathway. In addition, as uracil is the end product of the de novo pyrimidine biosynthesis pathway, the disruption of this pathway results in a reduction in uracil levels.

(16) Figure 3:

What sequence was used as the root of the tree? Why were the species chosen? Since the BLAST query was Homo sapiens sequence, would it be good to use that as the root?

FgDHODHII sequence was used as the root of the tree. These selected fungal species represent significant plant-pathogenic fungi in agriculture production. According to your suggestion, we have removed the BLAST query of Homo sapiens in Figure 3.

(17) Figure 4:

How were the concentrations used to test chosen?

Prior to this experiment, we carried out concentration-dependent exogenous supplementation experiments. The results indicated that 50 μg/mL of uracil can fully restore mycelial growth inhibited by quinofumelin. Consequently, we chose 50 μg/mL as the testing concentration.

(18) Line 164:

Why do you hypothesize supplementing dihydroorotate would restore resistance? The metabolite seemed accumulated in the treatment condition, whereas downstream metabolites were comparable or even depleted. The DHODH gene expression was suppressed. Would accumulation of dihydroorotate be associated with growth inhibition by quinofumelin? Please include the hypothesis and rationale for the experimental setup.

DHODH regulates the conversion of dihydroorotate to orotate in the de novo pyrimidine biosynthesis pathway. The inhibition of DHODH by quinofumelin results in the accumulation of dihydroorotate and the depletion of the downstream metabolites, including UMP, uridine and uracil. Consequently, downstream metabolites were considered as positive controls, while upstream metabolite dihydroorotate served as a negative control. This design further demonstrates DHODH as action target of quinofumelin against F. graminearum. In addition, the accumulation of dihydroorotate is not associated with growth inhibition by quinofumelin; however, but the depletion of downstream metabolites in the de novo pyrimidine biosynthesis pathway is closely associated with growth inhibition by quinofumelin.

(19) Line 168:

I'm not sure if this conclusion is valid from your results in Figure 4 showing which metabolites restore growth.

o minimize the potential influence of strain-specific effects, five strains were tested in the experiments shown in Figure 4. For each strain, the first row (first column) corresponds to control condition, while second row (first column) represents treatment with 1 μg/mL of quinofumelin, which completely inhibits mycelial growth. The second row (second column) for each strain represents the supplementation with 50 μg/mL of dihydroorotate fails to restore mycelial growth inhibited by quinofumelin. In contrast, the second row (third column, fourth column, fifth colomns) for each strain demonstrated that the supplementation of 50 μg/mL of UMP, uridine and uracil, respectively, can effectively restore mycelial growth inhibited by quinofumelin.

(20) Figure 5a:

The fact you saw growth of the deletion mutant means it's not lethal. However, the growth was severely inhibited.

Our experimental results indicate that the growth of the deletion mutant is lethal. The mycelial growth observed originates from mycelial plugs that were not exposed to quinofumelin, rather than from the plates amended with quinofumelin.

(21) Figure 5b:

Would you expect different restoration of growth in the presence of quinofumelin vs. no treatment? The wild type control is missing here. Any conclusions about the relationship between quinofumelin, FgDHODHII, and other metabolites in the pathway?

Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil.

(22) Figure 6b:

Lacking positive and negative controls (known binder and non-binder). What does the Kd (in comparison to other interactions) indicate in terms of binding strength?

We tested the antifungal activities of publicly reported DHODH inhibitors (such as leflunomide and teriflunomide) against F. graminearum. The results showed that these inhibitors exhibited no significant inhibitory effects against the strain PH-1. Therefore, we lacked an effective chemical for use as a positive control in subsequent experiments. Biacore experiments offers detailed insights into molecular interactions between quinofumelin and DHODHII. As shown in Figure 6b, the left panel illustrates the time-dependent kinetic curve of quinofumelin binding to DHODHII. Within the first 60 s after quinofumelin was introduced onto the DHODHII surface, it bound to the immobilized DHODHII on the chip surface, with the response value increasing proportionally to the quinofumelin concentration. Following cessation of the injection at 60 s, quinofumelin spontaneously dissociated from the DHODHII surface, leading to a corresponding decrease in the response value. The data fitting curve presented on the right panel indicates that the affinity constant KD of quinofumelin for DHODHII is 6.606×10-6 M, which falls within the typical range of KD values (10-3 ~ 10-6 M) for protein-small molecule interaction patterns. A lower KD value indicates a stronger affinity; thus, quinofumelin exhibits strong binding affinity towards DHODHII.

Reviewer #3 (Recommendations for the authors):

The authors should add information about the other molecule within this class, ipflufenoquin, and what is known about it. There are already published data on its mode of action on DHODH and the role of pyrimidine biosynthesis.

We have added the information regarding action mechanism of ipflufenoquin against filamentous fungi in discussion section.

The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions.

It is unclear how the protein model was established and this should be included. What species is the molecule from and how was it obtained? How are they different from Fusarium?

The three-dimensional structural model of F. graminearum DHODHII protein, as predicted by AlphaFold, was obtained from the UniProt database. Additionally, a detailed description along with appropriate citations has been incorporated in the ‘Manuscript’ file.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

This manuscript provides an initial characterization of three new missense variants of the PLCG1 gene associated with diverse disease phenotypes, utilizing a Drosophila model to investigate their molecular effects in vivo. Through the meticulous creation of genetic tools, the study assesses the small wing (sl) phenotype - the fly's ortholog of PLCG1 - across an array of phenotypes from longevity to behavior in both sl null mutants and variants. The findings indicate that the Drosophila PLCG1 ortholog displays aberrant functions. Notably, it is demonstrated that overexpression of both human and Drosophila PLCG1 variants in fly tissue leads to toxicity, underscoring their pathogenic potential in vivo.

Strengths:

The research effectively highlights the physiological significance of sl in Drosophila. In addition, the study establishes the in vivo toxicity of disease-associated variants of both human PLCG1 and Drosophila sl.

Weaknesses:

The study's limitations include the human PLCG1 transgene's inability to compensate for the Drosophila sl null mutant phenotype, suggesting potential functional divergence between the species. This discrepancy signals the need for additional exploration into the mechanistic nuances of PLCG1 variant pathogenesis, especially regarding their gain-of-function effects in vivo.

Overall:

The study offers compelling evidence for the pathogenicity of newly discovered disease-related PLCG1 variants, manifesting as toxicity in a Drosophila in vivo model, which substantiates the main claim by the authors. Nevertheless, a deeper inquiry into the specific in vivo mechanisms driving the toxicity caused by these variants in Drosophila could significantly enhance the study's impact.

Reviewer #2 (Public Review):

The manuscript by Ma et al. reports the identification of three unrelated people who are heterozygous for de novo missense variants in PLCG1, which encodes phospholipase C-gamma 1, a key signaling protein. These individuals present with partially overlapping phenotypes including hearing loss, ocular pathology, cardiac defects, abnormal brain imaging results, and immune defects. None of the patients present with all of the above phenotypes. PLCG1 has also been implicated as a possible driver for cell proliferation in cancer.

The three missense variants found in the patients result in the following amino acid substitutions: His380Arg, Asp1019Gly, and Asp1165Gly. PLCG1 (and the closely related PLCG2) have a single Drosophila ortholog called small wing (sl). sl-null flies are viable but have small wings with ectopic wing veins and supernumerary photoreceptors in the eye. As all three amino acids affected in the patients are conserved in the fly protein, in this work Ma et al. tested whether they are pathogenic by expressing either reference or patient variant fly or human genes in Drosophila and determining the phenotypes produced by doing so.

Expression in Drosophila of the variant forms of PLCG1 found in these three patients is toxic; highly so for Asp1019Gly and Asp1165Gly, much more modestly for His380Arg. Another variant, Asp1165His which was identified in lymphoma samples and shown by others to be hyperactive, was also found to be toxic in the Drosophila assays. However, a final variant, Ser1021Phe, identified by others in an individual with severe immune dysregulation, produced no phenotype upon expression in flies.

Based on these results, the authors conclude that the PLCG1 variants found in patients are pathogenic, producing gain-of-function phenotypes through hyperactivity. In my view, the data supporting this conclusion are robust, despite the lack of a detectable phenotype with Ser1021Phe, and I have no concerns about the core experiments that comprise the paper.

Figure 6, the last in the paper, provides information about PLCG1 structure and how the different variants would affect it. It shows that the His380, Asp1019, and Asp1165 all lie within catalytic domains or intramolecular interfaces and that variants in the latter two affect residues essential for autoinhibition. It also shows that Ser1021 falls outside the key interface occupied by Asp1019, but more could have been said about the potential effects of Ser1021Phe.

Overall, I believe the authors fully achieved the aims of their study. The work will have a substantial impact because it reports the identification of novel disease-linked genes, and because it further demonstrates the high value of the Drosophila model for finding and understanding gene-disease linkages.

Reviewer #3 (Public Review):

Summary:

The paper attempts to model the functional significance of variants of PLCG2 in a set of patients with variable clinical manifestations.

Strengths:

A study attempting to use the Drosophila system to test the function of variants reported from human patients.

Weaknesses:

Additional experiments are needed to shore up the claims in the paper. These are listed below.

Major Comments:

(1) Does the pLI/ missense constraint Z score prediction algorithm take into consideration whether the gene exhibits monoallelic or biallelic expression?

To our knowledge, pLI and missense Z don't consider monoallelic or biallelic expression. Instead, they reflect sequence constraint and are calculated based on the observed versus expected variant frequencies in population databases.

(2) Figure 1B: Include human PLCG2 in the alignment that displays the species-wide conserved variant residues.

We have updated Figure 1B and incorporated the alignment of PLCG2.

(3) Figure 4A:

Given that

(i) sl is predicted to be the fly ortholog for both mammalian PLCγ isozymes: PLCG1 and PLCG2 [Line 62]

(ii) they are shown to have non-redundant roles in mammals [Line 71]

(iii) reconstituting PLCG1 is highly toxic in flies, leading to increased lethality.

This raises questions about whether sl mutant phenotypes are specifically caused by the absence of PLCG1 or PLCG2 functions in flies. Can hPLCG2 reconstitution in sl mutants be used as a negative control to rule out the possibility of the same?

The studies about the non-redundant roles of PLCG1 and PLCG2 mainly concern the immune system.

We have assessed the phenotypes in the sl^T2A/Y; UAS-hPLCG2 flies. Expression of human PLCG2 in flies is also toxic and leads to severely reduced eclosion rate.

We have updated the manuscript with these results, and included the eclosion rate of sl^T2A/Y; UAS-hPLCG2 flies in the new Figure 4B.

(4) Do slT2A/Y; UAS-PLCG1Reference flies survive when grown at 22{degree sign}C? Since transgenic fly expressing PLCG1 cDNA when driven under ubiquitous gal4s, Tubulin and Da, can result in viable progeny at 22{degree sign}C, the survival of slT2A/Y; UAS-PLCG1Reference should be possible.

The eclosion rate of sl^T2A/Y >PLCG1^Reference flies at 22°C is slightly higher than at 25°C, but remains severely reduced compared to the UAS-Empty control. We have presented these results in the updated Figure S3.

and similarly

Does slT2A flies exhibit the phenotypes of (i) reduced eclosion rate (ii) reduced wing size and ectopic wing veins and (iii) extra R7 photoreceptor in the fly eye at 22{degree sign}C?

The mutant phenotypes are still observed at 22 °C.

If so, will it be possible to get a complete rescue of the slT2A mutant phenotypes with the hPLCG1 cDNA at 22{degree sign}C? This dataset is essential to establish Drosophila as an ideal model to study the PLCG1 de novo variants.

Thank you for the suggestion. It is difficult to directly assess the rescue ability of the PLCG1 cDNAs due to the toxicity. However, our ectopic expression assays show that the variants are more toxic than the reference with variable severities, suggesting that the variants are deleterious.

The ectopic expression strategy has been used to evaluate the consequence of genetic variants and has significantly contributed to the interpretation of their pathogenicity in many cases (reviewed in Her et al., Genome, 2024, PMID: 38412472).

(5) Localisation and western blot assays to check if the introduction of the de novo mutations can have an impact on the sub-cellular targeting of the protein or protein stability respectively.

Thank you for the suggestion.

We expressed PLCG1 cDNAs in the larval salivary glands and performed antibody staining (rabbit anti-Human PLCG1; 1:100, Cell Signaling Technology, #5690). The larval salivary gland are composed of large columnar epithelia cells that are ideal for analyzing subcellular localization of proteins. The PLCG1 proteins are cytoplasmic and localize near the cell surface, with some enrichment in the plasma membrane region. The variant proteins are detected, and did not show significant difference in expression level or subcellular distribution compared to the reference. We did not include this data.

(6) Analysing the nature of the reported gain of function (experimental proof for the same is missing in the manuscript) variants:

Instead of directly showing the effect of introducing the de novo variant transgenes in the Drosophila model especially when the full-length PLCG1 is not able to completely rescue the slT2A phenotype;

(i) Show that the gain-of-function variants can have an impact on the protein function or signalling via one of the three signalling outputs in the mammalian cell culture system: (i) inositol-1,4,5-trisphosphate production, (ii) intracellular Ca2+ release or (iii) increased phosphorylation of extracellular signal-related kinase, p65, and p38.

We appreciate the reviewer’s suggestion. We utilized the CaLexA (calcium-dependent nuclear import of LexA) system (Masuyama et al., J Neurogenet, 2012, PMID: 22236090) to assess the intracellular Ca²⁺ change associated with the expression of PLCG1 cDNAs in fly wing discs. The results show that, compared to the reference, expression of the D1019G or D1165G variants leads to elevated intracellular Ca²⁺ levels, similar to the hyperactive S1021F and D1165H variants. However, the H380R or L597F variants did not show a detectable phenotype in this assay. These results suggest that D1019G and D1165G are hyperactive variants, whereas H380R and L597F variant are not, or their effect is too mild to be detected in this assay. We have updated the related sections in the manuscript and Figures 5A and S5.

OR

(ii) Run a molecular simulation to demonstrate how the protein's auto-inhibited state can be disrupted and basal lipase activity increased by introducing D1019G and D1165G, which destabilise the association between the C2 and cSH2 domains. The H380R variant may also exhibit characteristics similar to the previously documented H335A mutation which leaves the protein catalytically inactive as the residue is important to coordinate the incoming water molecule required for PIP2 hydrolysis.

We utilized the DDMut platform, which predicts changes in the Gibbs Free Energy (ΔΔG) upon single and multiple point mutations (Zhou et al., Nucleic Acid Res, 2023, PMID: 37283042), to gain insight into the molecular dynamics changes of variants. The results are now presented in Figure S7.

Additionally, we performed Molecular dynamics (MD) simulations. The results show that, similar to the hyperactive D1165H variant, the D1019G and D11656G variants exhibit increased disorganization, with a higher root mean square deviations (RMSD) compared to the reference PLCG1.The data are also presented in the updated Figure S7.

(7) Clarify the reason for carrying out the wing-specific and eye-specific experiments using nub-gal4 and eyless-gal4 at 29˚C despite the high gal4 toxicity at this temperature.

We used high temperature and high expression level to see if the mild H380R and L597F variants could show phenotypes in this condition.

The toxicity of the two strong variants (D1019G and D1165G) has been consistently confirmed in multiple assays at different temperatures.

(8) For the sake of completeness the authors should also report other variants identified in the genomes of these patients that could also contribute to the clinical features.

Thank you!

The additional variants and their potential contributions to the clinical features are listed and discussed in Table 1 and its legend.

Reviewer #1 (Recommendations For The Authors):

The manuscript's significant contribution is tempered by a lack of comprehensive analysis using the generated genetic reagents in Drosophila. To enhance our understanding of the PLCG1 orthologs, I suggest the following:

(1) A more detailed molecular analysis to distinguish the actions of sl variants from the wild-type could be very informative. For example, utilizing the HA-epitope tag within the current UAS-transgenes could reveal more about the cellular dynamics and abundance of these variants, potentially elucidating mechanisms beyond gain-of-function.

We appreciate the reviewer’s suggestion. The UAS-sl cDNA constructs contain stop codon and do not express an HA-epitope tag. Alternatively, we utilized commercially available antibodies against human PLCG1 antibodies to assess the subcellular localization and protein stability by expressing the reference and variant PLCG1 cDNAs in Drosophila larval salivary glands. The reference proteins are cytoplasmic with some enrichment along the plasma membrane. However, we did not observe significant differences between the reference and variant proteins in this assay. We did not include this data.

(2) I suggest further investigating the relative contributions of developmental processes and acute (Adult) effects on the sl-variant phenotypes observed. For example, employing systems that allow for precise temporal control of gene expression, such as the temperature-sensitive Gal80, could differentiate between these effects, shedding light on the mechanisms that affect longevity and locomotion. This knowledge would be vital for a deeper understanding of the corresponding human disorders and for developing therapeutic interventions.

We appreciate the reviewer’s suggestion. We utilized Tub-GAL4, Tub-GAL80^ts to drive the expression of sl wild-type or variant cDNAs, and performed temperature shifts after eclosion to induce expression of the cDNAs only in adult flies. The sl^D1184G variant (corresponding to PLCG1^D1165G) caused severely reduced lifespan and the flies mostly die within 10 days. The sl^D1041G variant (corresponding to PLCG1^D1019G) led to reduced longevity and locomotion. The sl^H384R variant (corresponding to PLCG1^H380R) showed only a mild effect on longevity and no significant effect on climbing ability. These results suggest that the two strong variants (sl<sup>D1041G<sup> and sl^D1184G) contribute to both developmental and acute effects while the H384R variant mainly contributes to developmental stages.

I also suggest a more refined analysis of overexpression toxicity. Rather than solely focusing on ubiquitous transgene expression, overexpressing transgene in endogenous pattern using sl-t2a-Gal4 may yield a more nuanced understanding of the pathogenic mechanisms of gain-of-function mutations, particularly in the pathogenesis associated with these variants exclusively located in the coding regions.

We appreciate the reviewer’s suggestion. We therefore performed the experiments using sl^T2A to drive overexpression ofPLCG1cDNAs in heterozygous female progeny with one copy of wild-type sl+ (sl^T2A/ yw > UAS-cDNAs). In this context, expression of PLCG1<sup>Reference<sup>, PLCG1^H380RorPLCG1^L597F is viable whereas expression of PLCG1^D1019G or PLCG1^D1165G is lethal, suggesting that the PLCG1^D1019G and PLCG1^D1165G variants exert a strong dominant toxic effect while the PLCG1^H380Rand PLCG1<sup>L597F<sup> are comparatively milder. Similar patterns have been consistently observed in other ectopic expression assays with varying degrees of severity. These results are updated in the manuscript and figures.

Reviewer #2 (Recommendations For The Authors):

The work in the paper could be usefully extended by determining the effects of expressing His380Phe and His380Ala in flies. These variants suppress PLCG1 activity, so their phenotype, if any, would be predicted not to be the same as His380Arg. Determining this would add further strength to the conclusions of the paper.

We thank the reviewer for the constructive suggestions! We have tested the enzymatic-dead H380A variant, which still exhibits toxicity when expressed in sl^T2A/Y hemizygous flies, but it is not toxic in heterozygous females suggesting that the reduced eclosion rate is likely not directly associated with enzymatic activity. We have updated the manuscript and figures accordingly.

Reviewer #1 (Public review):

The objectives of this research are to understand how key selector transcription factors, Tal1, Gata2, Gata3, determine GABAergic vs glutamatergic neuron fate from the rhombencephalic V2 precursor domain and how their spatiotemporal expression is controlled by upstream regulators. Toward these goals, the authors have generated an impressive array of scRNA, scATAC-seq, and CUT&Tag datasets obtained from dissociated E12.5 ventral R1 dissections. The rV2 was subsetted with well-known markers. The authors use an extensive set of computational approaches to identify temporal patterns of chromatin accessibility, TF motif binding activities (footprints), gene expression and regulatory motifs at the different selector gene loci. These analyses are used to predict upstream regulators, candidate accessible CREs, and DNA binding motifs through which the selectors may be controlled in rV2 by upstream regulators. Further analyses predict auto- and cross-regulatory interactions for maintenance of selector expression and the downstream effectors of alternative transmitter identities controlled by the selectors. The authors have achieved their aim of making predictions about upstream and downstream selector TF regulatory networks; their conclusions and predictions are largely well supported. The work clearly illustrates the daunting gene regulatory complexity likely at play in controlling rV2 transmitter fate.

This is data-rich study and a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators and motifs identified in the study. The strengths of this work are the overall high quality of the datasets and in depth analyses. Through its comprehensive data and predictions, it is likely to have impact in advancing the understanding of GABAergic vs glutamatergic neuron fate decisions. The authors present a "simplified" gene regulatory model. However, the model does not illustrate the complexity of potential stage-specific upstream TF interactions with Tal1 and Vsx2 selector genes uncovered in TF footprinting analyses. While this seems nearly impossible to achieve given the plethora of potential functional TF inputs, the authors should consider assembling a focussed model by selectively illustrating the most robust, evidence-backed upstream TF input predictions, which are considered the strongest candidates for future hypothesis-driven perturbation experiments. It seems Insm1, Sox4, E2f1, Ebf1 and Tead2 TFs might be the strongest upstream candidates for future testing of Tal1 activation given the extensive analyses of their spatiotemporal expression patterns relative to Tal1, presented in Fig 4.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The objective of this research is to understand how the expression of key selector transcription factors, Tal1, Gata2, Gata3, involved in GABAergic vs glutamatergic neuron fate from a single anterior hindbrain progenitor domain is transcriptionally controlled. With suitable scRNAseq, scATAC-seq, CUT&TAG, and footprinting datasets, the authors use an extensive set of computational approaches to identify putative regulatory elements and upstream transcription factors that may control selector TF expression. This data-rich study will be a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators identified in the study. The data are displayed in some of the main and supplemental figures in a way that makes it difficult to appreciate and understand the authors' presentation and interpretation of the data in the Results narrative. Primary images used for studying the timing and coexpression of putative upstream regulators, Insm1, E2f1, Ebf1, and Tead2 with Tal1 are difficult to interpret and do not convincingly support the authors' conclusions. There appears to be little overlap in the fluorescent labeling, and it is not clear whether the signals are located in the cell soma nucleus.

Strengths:

The main strength is that it is a data-rich compilation of putative upstream regulators of selector TFs that control GABAergic vs glutamatergic neuron fates in the brainstem. This resource now enables future perturbation-based hypothesis testing of the gene regulatory networks that help to build brain circuitry.

We thank Reviewer #1 for the thoughtful assessment and recognition of the extensive datasets and computational approaches employed in our study. We appreciate the acknowledgment that our efforts in compiling data-rich resources for identifying putative regulators of key selector transcription factors (TFs)—Tal1, Gata2, and Gata3—are valuable for future hypothesis-driven research.

Weaknesses:

Some of the findings could be better displayed and discussed.

We acknowledge the concerns raised regarding the clarity and interpretability of certain figures, particularly those related to expression analyses of candidate upstream regulators such as Insm1, E2f1, Ebf1, and Tead2 in relation to Tal1. We agree that clearer visualization and improved annotation of fluorescence signals are crucial to accurately support our conclusions. In our revised manuscript, we will enhance image clarity and clearly indicate sites of co-expression for Tal1 and its putative regulators, ensuring the results are more readily interpretable. Additionally, we will expand explanatory narratives within the figure legends to better align the figures with the results section.

Reviewer #2 (Public review):

Summary:

In the manuscript, the authors seek to discover putative gene regulatory interactions underlying the lineage bifurcation process of neural progenitor cells in the embryonic mouse anterior brainstem into GABAergic and glutamatergic neuronal subtypes. The authors analyze single-cell RNA-seq and single-cell ATAC-seq datasets derived from the ventral rhombomere 1 of embryonic mouse brainstems to annotate cell types and make predictions or where TFs bind upstream and downstream of the effector TFs using computational methods. They add data on the genomic distributions of some of the key transcription factors and layer these onto the single-cell data to get a sense of the transcriptional dynamics.

Strengths:

The authors use a well-defined fate decision point from brainstem progenitors that can make two very different kinds of neurons. They already know the key TFs for selecting the neuronal type from genetic studies, so they focus their gene regulatory analysis squarely on the mechanisms that are immediately upstream and downstream of these key factors. The authors use a combination of single-cell and bulk sequencing data, prediction and validation, and computation.

We also appreciate the thoughtful comments from Reviewer #2, highlighting the strengths of our approach in elucidating gene regulatory interactions that govern neuronal fate decisions in the embryonic mouse brainstem. We are pleased that our focus on a critical cell-fate decision point and the integration of diverse data modalities, combined with computational analyses, has been recognized as a key strength.

Weaknesses:

The study generates a lot of data about transcription factor binding sites, both predicted and validated, but the data are substantially descriptive. It remains challenging to understand how the integration of all these different TFs works together to switch terminal programs on and off.