Author Response:

We are proceeding without revisions as the first author has chosen to withdraw from the project and will not be contributing further.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

I can find no problems with the experiments performed in this study, but there are several results that are not easily explained. I would like to see more consideration of possible explanations. For example, one of the major differences between the the CESA structure from primary and secondary cell walls is the displacement of TM7 in the primary cell wall CESAs that leads to the formation of lipid exposed channel. Why does this vary between primary and secondary cell wall CESA proteins? Could it explain differences in the properties, such as crystallinity between primary and secondary cell wall cellulose?

At this time, the different position of TM helix 7 observed in our GmCesA structures is just an observation. We have some emerging evidence that this helix is also flexible in POCesA8 under certain conditions; however, we do not know whether this affects catalytic activity or cellulose coalescence. We have revised the text to avoid the interpretation that TM 7 repositioning is a characteristic feature of primary cell wall CesAs only.

Similarly, regarding the formation of the larger structures from mixtures of different CESA trimers. Why do they not form roseOes? Par;cularly as these appear to be forming 2-dimensional structures.

We have included additional data on the interaction between different CesA isoform trimers (Figure 6). To answer the reviewer’s ques;on, the most likely reasons for not observing closely packed roseOe-like structures are (a) steric interferences between the micelles harboring the individual CesA trimers, and (b) the lack of a stabilizing cellulose fiber.  This interpretation is supported by 2D class averages of dimers of CesA1 and CesA3 trimers (now shown in Fig. 6). The class averages show an ‘upside-down and side-by-side’ orientation of the two trimers, consistent with interferences between the solubilizing detergent micelles. The implica;ons of this non-physiological arrangement are discussed in the revised manuscript. In a biological membrane, the CesA trimers are confined to the same plane in the same orientation, which is likely necessary to form ordered arrangements.

What role does the NTD play in trimer formation given its apparent very high class specificity?

We have no data suggesting any contribution of the NTD to trimer formation. Recent work on moss CesA5 and similar AlphaFold predic;ons suggest that, for some CesAs, an extreme Nterminal region can interact with the beta sheet of the catalytic domain via beta-strand augmentation. Whether this interaction can contribute to CesA-CesA interactions remains unknown.

Reviewer #2 (Recommendations For The Authors):

The authors provide PDB codes but not EMDB codes for the EM maps, also I would encourage the authors to upload the raw micrographs to the EMPIAR database.

The EMDB codes are shown in Table 1 and data transfer to EMPIAR is ongoing.  

Page 6 line 144, the statement "All CesA isoforms show greatest catalytic activity at neutral pH" seems to contradict the data in Figure 1e and the subsequent statements. This sentence should be removed.

The text has been revised to indicate that CesA1 and CesA6 show highest activity under mild alkaline conditions.  

Page 6, line 150, the authors state "The affinities for substrate binding range from 1.4 mM for CesA1 to 0.6 and 2.4 mM for CesA3 and CesA6, respectively." How were the affinities determined? Is this the affinities or the Michaelis constants? Is it known whether CesAs are rapid equilibrium enzymes? This should be clarified.

The text now states that we performed Michaelis Menten kine;cs using the ‘UDP-Glo’ glycosyltransferase assay kit. We are uncertain about whether CesAs can be classified as rapid equilibrium enzymes. The rate-limiting step of cellulose biosynthesis has been proposed to be glycosyl transfer, rather than cellulose transloca;on.  To avoid any confusion, we changed the text from '…reveals Michaelis Menten constants for substrate binding of CesA1 and CesA3' to '…reveals Michaelis Menten constants for CesA1 and CesA3 with respect to UDP-Glc'.

Page 6, line 153, the authors state "CesA1's apparent Ki for UDP is roughly 0.8 mM, whereas this concentration is increased to about 1.2 to 1.5 mM for CesA6 and CesA3, respectively." From the Figure 1g legend, it appears that the authors performed additional experiments at different UDP-Glc concentrations in order to determine Ki that are not shown. This data should be included as a figure supplement as the data presented are insufficient to determine Ki (only IC50).

The UDP inhibition data show apparent IC50 values, and this has been corrected in the text. For each CesA isoform, the titration was done at one UDP-Glc concentration only.    

Page 8, line 202, the authors state that TM helix 7 of the primary cell wall CesAs is more flexible "as evidenced by weaker density." The density for the TM helix 7 should be shown. If the density shown in Supplementary Figure 3 corresponds to TM helices the number of the helices should be indicated as it is not immediately obvious from the amino acid residue numbers.

The densities for TM helix 7 of all CesA isoforms are shown in Supplemental Figure 3. The helices are now labeled to orient the reader.  

Reviewer #2 (Public Review)

The authors demonstrate via truncation that the N-terminus of the CesA is not involved in the interactions between the isoforms and propose that the CSR hook-like extensions are the primary mediator of trimer-trimer interactions. This argument would be strengthened by equivalent truncation experiments in which the CSR region is removed.

We performed the suggested experiment. We replaced the CSR in N-terminally truncated GmCesA1 and GmCesA3 with a 20-residue long linker. The resulting constructs assemble into homotrimeric complexes as observed for the wild type and only N-terminally truncated versions. However, the CSR-truncated constructs of the different isoforms do not interact with each other in vitro. Further, CSR-deleted GmCesA3 also does not interact with full-length CesA1, suggesting that two CSR domains of different isoforms are necessary for homotrimer interaction. This data is now shown as Fig. 5.  

Reviewer #3 (Recommendations For The Authors):

Major Points

(1) The authors state on Line 354 that they were unable to isolate heterotrimers, but they need to provide the data to support this claim; for example, it is important for readers to understand whether co-expression of all three CESAs leads to only homotrimers or only monomers. This information is essential to exclude model C in Figure 6.

We have revised the corresponding discussion and toned down the statement that heterotrimeric complexes did not form in our recombinant expression system. Co-expression of differently tagged secondary or primary cell wall CesAs in Sf9 cells has consistently resulted in negligible amounts of material that can be purified sequentially over different affinity matrices (corresponding to the tags on the recombinantly expressed CesAs – His, Strep, Flag). While this does not exclude the formation of a small fraction of hetero-oligomeric complexes (which could be trimers as observed in the structures or monomers interacting via their CSR regions), it demonstrates that CesAs favor the same isoform for trimer formation, rather than partnering with other isoforms. An example of such a purification is now shown as Supplemental Figure 8.

Determining whether heterotrimers are formed upon co-expression of different CesA isoforms requires high resolution structural analysis because co-purification of different isoforms can also be due to interactions between different homo-trimeric complexes, as demonstrated in this study.

While we cannot exclude that factors exist in planta that may prevent the formation of homotrimers and favor the formation of hetero-trimers, it is important to keep in mind that currently no experimental data supports the formation of hetero-trimeric complexes. Instead, our work demonstrates that existing data on CesA isoform interactions can be explained by the interaction of homotrimers of different isoforms.

(2) The evidence that the products of GmCEA1, GmCESA3, and GmCESA6 homotrimers are cellulose is that they consume UDP-glucose and produce a beta-glucanase-sensitive product. Other beta-glucans synthesized by similar GT2 family proteins (e.g. CSLDs, Yang et al., 2020 Plant Cell or CSLCs, Kim et al., 2020 PNAS) would be sensitive to this enzyme, and the product cannot truly be called cellulose unless it forms microfibrils. Previous reports of CESA activity in vitro have demonstrated that the products form genuine cellulose microfibrils rather than amorphous beta-glucan (via electron microscopy); extensively documented that the product is sensitive to beta-glucanase, but not other enzymes (e.g., callose or MLG degrading enzymes); provided linkage analysis of the product to conclusively demonstrate that it is a beta1,4-linked glucan; and documented a loss of activity when key catalytic residues were mutated (Purushotham et al., 2016 PNAS; Cho et al., 2017 Plant Phys; Purushotham et al., 2020 Science).

Other GT2 characterization efforts have documented activity to similar standards (e.g. CSLDs, Yang et al., 2020 Plant Cell or CSLFs, Purushotham et al., 2022 Science Advances). At least one independent method should be provided, and the TEM of the product is necessary for readers to appreciate whether the product forms true cellulose microfibrils.

There may be some confusion regarding the nomenclature. Therefore, we revised the second sentence of the Introduction to define ‘cellulose’ as a beta-1,4 linked glucose polymer, in accordance with the ‘Essentials of Glycobiology’. This is also consistent with enzyme nomenclature as the primary product of cellulose synthase is a single glucose polymer, and not a fibril. For example, most bacterial cellulose synthases only produce amorphous (single chain) cellulose. 

We show that the GmCesA products can be degraded with a beta-1,4 specific glucanase (cellulase), which demonstrates the formation of authentic cellulose. This study does not focus on the formation of fibrillar cellulose apart from suggesting a revised model for a microfibrilforming CSC.       

(3) The position of isoxaben-resistant mutations implies that primary cell wall CESAs form heterotrimers (Shim et al., 2018 Frontiers in Plant Biology). Indeed, in their previous description of the POCESA8 structure (Purushotham et al., 2020 Science), the authors discussed the position of isoxaben-resistant mutations as a way to justify the way that TM7 of one CESA can contribute to forming the cellulose translocation pore in the neighbouring CESA within a heterotrimer. However, in this manuscript, the authors document a different location for TM7 in the GmCEA1, GmCESA3, and GmCESA6 homotrimers, which would change the position of these resistance mutations. Please discuss.

As stated in the manuscript, we do not know what the functional implication of the TM7 flexibility may be, but we speculate that it could affect the alignment of the synthesized cellulose polymers. Regarding the previously reported POCesA8 structure, the mapping of one of the reported isoxaben resistance mutants to the C-terminus of TM7 was not used to justify the structure; the structure with its position of TM7 stands on its own.  Considering recent observations suggesting that isoxaben may affect cellulose biosynthesis via secondary effects, we prefer not to speculate on the mechanism by which these mutations cause the apparent resistance to isoxaben (PMID: 37823413).

(4) The authors present no evidence that GmCESA1/3/6 are involved in primary cell wall synthesis. Please include gene expression information (documenting widespread expression consistent with primary CESAs) and rigorous molecular phylogenetic analysis (or references to these published data) to clarify that these are indeed primary cell wall CESAs.

This has been addressed. We have included additional figures (Fig. 1 and S1B) that show the strong and wide distribution of the selected CesAs in soybean leaves, their co-expression with primary cell wall markers, and their phylogenetic clustering with Arabidopsis primary cell wall CesAs.  

(5) Several small changes need to be made to the abstract to ensure that it aligns with the data: Line 28: add "in vitro" arer "their assembly into homotrimeric complexes" Line 28: change "stabilized by the PCR" to "presumably stabilized by the PCR".

We inserted ‘in vitro’ as requested. We did not insert the second modification as requested since CesA trimers are stabilized by the PCR. This is a fact arising from several experimentally determined CesA trimer structures.  

(6) In all graphs in all figures it is unclear what the sample size is and what the bars represent. These must be stated in the figure legends. It is best practice to plot individual data points so that readers can easily interpret both the sample size and the variation.

The sample sizes and error bars are now defined in the relevant figure legends.

(7) The methods need to unambiguously define GmCESA1, GmCESA3, GmCESA6 protein identities using appropriate accession numbers.

The accession codes are now provided in the Methods.

Minor Points

(1) Does CESA1 have higher activity in Figure 1D because of the pH at which the assay was conducted (see Figure 1E)? Could this difference in activity or pH preference have also affected their capacity to resolve TM7 of CESA1?

We consistently observe higher in vitro catalytic activity of CesA1, compared to CesA3 and CesA6. Activity assays are performed at a pH of 7.5, roughly halfway between the activity maxima of CesA3 and CesA1/6. At this pH, we expect activity differences to arise from factors other than the buffer pH. As detailed above, we do not know whether the conformational flexibility of TM helix 7 affects catalytic activity.

(2) Line 55: The authors should cite additional papers that also provide insight into CESA structure (e.g. Qiao et al 2021 PNAS).

A recent publication on moss CesA5 has been included. Qiao et al unfortunately report on a dimeric assembly of a fragment of Arabidopsis thaliana’s CesA3 catalytic domain, which we consider non-physiological. We added a brief statement in the Discussion explaining that our GmCesA3 structure is inconsistent with the dimeric arrangement reported by Qiao et al.

(3) Line 95: these references are about secondary cell wall CESA isoforms, but there are more appropriate references for the primary CESAs that should be included in place of these papers.

Fagard et al report on growth defects in roots and dark-grown hypocotyls linked to Arabidopsis CesA 1 and CesA6, which are primary cell wall CesAs. Nevertheless, we have included two additional recent publications from the Meyerowitz and Persson labs.

(4) Line 121-122: Please cite a specific figure that supports this claim, since the (Purushotham et al., 2020) reference refers to POCESA8 enrichment results, but the claims are about the GmCESA1/3/6 enrichment.

The POCesA8 reference has been removed. The classification into monomers and trimers arises from the data processing described in this manuscript and is consistent with similar results obtained for POCesA8.

(5) Line 314: It is more appropriate to use "enzyme activity" rather than "cellulose synthesis".

We prefer to use cellulose biosynthesis since the enzyme produces cellulose.

(6) Figure 1: please add colour to the graphs to clarify which trend lines belong to which data series (especially Figure 1G).

The figure (now Fig. 2) has been revised as suggested.  

(7) Figure 2D: It's not clear which parts are GmCESA and which are POCESA8; please clarify the figure legend.

Thank you, the legend has been revised accordingly (now Fig. 3).

(8) In Figure 5, It's not clear that the one CESA is maintained at a steady concentration throughout the assay since there is only a bar for that CESA at the highest concentration (e.g. in Figure 5A, the blue bar for CESA1 only appears on the right-most assay, but there was CESA1 in all assays, so this should be indicated).

In the panel the reviewer is referring to, the blue bar corresponds to the activity measured for only CesA1 at a concentration of 20 µM. The red columns (indicated as ‘Mix’) represent the activities measured in the presence of 20 µM of CesA1 plus increasing concentrations of CesA3. The purple columns represent activities obtained for only CesA3 at the indicated concentrations. Numerical addition of the activities of CesA1 alone at 20 µM (blue column) and CesA 3 alone (purple columns) gives rise to the gray columns, now indicated by a capital ‘sigma’ sign. We are unclear on how the figure could be improved, but we have revised the legend to avoid confusion.    

(9) Figure 5 legend needs to be clarified to indicate whether monomers or homotrimers were used in the assays.

This is now shown as Fig. 7 and the legend has been revised as requested. The experiments were performed with the trimeric CesA fractions.

(10) There seem to be some random dots near the top of Figures 6B & 6C

Removed. Thank you.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper investigates the effects of the explicit recognition of statistical structure and sleep consolidation on the transfer of learned structure to novel stimuli. The results show a striking dissociation in transfer ability between explicit and implicit learning of structure, finding that only explicit learners transfer structure immediately. Implicit learners, on the other hand, show an intriguing immediate structural interference effect (better learning of novel structure) followed by successful transfer only after a period of sleep.

Strengths:

This paper is very well written and motivated, and the data are presented clearly with a logical flow. There are several replications and control experiments and analyses that make the pattern of results very compelling. The results are novel and intriguing, providing important constraints on theories of consolidation. The discussion of relevant literature is thorough. In sum, this work makes an exciting and important contribution to the literature.

Weaknesses:

There have been several recent papers which have identified issues with alternative forced choice (AFC) tests as a method of assessing statistical learning (e.g. Isbilen et al. 2020, Cognitive Science). A key argument is that while statistical learning is typically implicit, AFC involves explicit deliberation and therefore does not match the learning process well. The use of AFC in this study thus leaves open the question of whether the AFC measure benefits the explicit learners in particular, given the congruence between knowledge and testing format, and whether, more generally, the results would have been different had the method of assessing generalization been implicit. Prior work has shown that explicit and implicit measures of statistical learning do not always produce the same results (eg. Kiai & Melloni, 2021, bioRxiv; Liu et al. 2023, Cognition).

The authors argued in their response to this point that this issue could have quantitative but not qualitative impacts on the results, but we see no reason that the impact could not be qualitative. In other words, it should be acknowledged that an implicit test could potentially result in the implicit group exhibiting immediate structure transfer.

We thank the reviewer for their feedback and added a statement in our discussion section acknowledging the possible effects of alternative measures of learning.

Given that the explicit/implicit classification was based on an exit survey, it is unclear when participants who are labeled "explicit" gained that explicit knowledge. This might have occurred during or after either of the sessions, which could impact the interpretation of the effects and deserves discussion.

We agree with the mentioned shortcoming in principle, although there are good methodological reasons for this, as discussed in our previous response. We added a statement on this topic to our discussion to make the potential issues and our reasoning in the design decision more transparent for the reader.

Reviewer #2 (Public review):

Summary:

Sleep has not only been shown to support the strengthening of memory traces, but also their transformation. A special form of such transformation is the abstraction of general rules from the presentation of individual exemplars. The current work used large online experiments with hundreds of participants to shed further light on this question. In the training phase participants saw composite items (scenes) that were made up of pairs of spatially coupled (i.e., they were next to each other) abstract shapes. In the initial training, they saw scenes made up of six horizontally structured pairs and in the second training phase, which took place after a retention phase (2 min awake, 12 hour incl. sleep, 12 h only wake, 24 h incl. sleep), they saw pairs that were horizontally or vertically coupled. After the second training phase, a two-alternativesforced-choice (2-AFC) paradigm, where participants had to identify true pairs versus randomly assembled foils, was used to measure performance on all pairs. Finally, participants were asked five questions to identify, if they had insight into the pair structure and post-hoc groups were assigned based on this. Mainly the authors find that participants in the 2 minute retention experiment without explicit knowledge of the task structure were at chance level performance for the same structure in the second training phase, but had above chance performance for the vertical structure. The opposite was true for both sleep conditions. In the 12 h wake condition these participants showed no ability to discriminate the pairs from the second training phase at all.

Strengths:

All in all, the study was performed to a high standard and the sample size in the implicit condition was large enough to draw robust conclusions. The authors make several important statistical comparisons and also report an interesting resampling approach. There is also a lot of supplemental data regarding robustness.

Weaknesses:

My main concern regards the small sample size in the explicit group and the lack of experimental control.

We thank the reviewer for the valuable feedback throughout the review process. The issues mentioned here have been addressed in our previous response.

Reviewer #3 (Public review):

In this project, Garber and Fiser examined how the structure of incidentally learned regularities influences subsequent learning of regularities, that either have the same structure or a different one. Over a series of six online experiments, it was found that the structure (spatial arrangement) of the first set of regularities affected learning of the second set, indicating that it has indeed been abstracted away from the specific items that have been learned. The effect was found to depend on the explicitness of the original learning: Participants who noticed regularities in the stimuli were better at learning subsequent regularities of the same structure than of a different one. On the other hand, participants whose learning was only implicit had an opposite pattern: they were better in learning regularities of a novel structure than of the same one. However, when an overnight sleep separated the first and second learning phases, this opposite effect was reversed and came to match the pattern of the explicit group, suggesting that the abstraction and transfer in the implicit case were aided by memory consolidation.

In their revision the authors addressed my major comments successfully and I commend them for that.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

We would encourage the authors to add text to the manuscript that acknowledges/discusses the two issues pointed out in our review.

We added relevant passages to the discussion section of the manuscript.

Reviewer #2 (Recommendations for the authors):

The authors have improved some sections of the manuscript and this is reflected in my assessment. The major weaknesses remain unchanged. Since my review is published alongside the paper, readers can make up their own mind regarding their severity.

My only hard ask would be to add that the study was not preregistered into the main manuscript as I asked before! I am surprised that the authors are so reluctant to honestly state this fact....

We have not stated this fact in our manuscript until now since our understanding is that papers that report preregistered studies state and cite their preregistration in their method section, while any omission of such a statement by default conveys that no preregistration occurred. In fact, we cannot recall encountering papers with statements of no-preregistration in the literature. Nevertheless, we have no issue stating that our study was not preregistered and per the reviewer's request, we have added such an explicit statement in our manuscript.

Reviewer #3 (Recommendations for the authors):

*  I strongly urge the authors to remove the Results sub-sections from Methods.

We thank the reviewer for highlighting this issue arising from our previous layout, which we decided to handle the following way. We re-labeledl the subsections in question as “Additional Analyses” to avoid confusion, we removed any redundant findings already reported in Results of the main text, and we moved a small number of more substantial findings from the Methods Section to the main text Results as requested. We believe that this solution constitutes the most readable option, as we do not clutter the main results with extensive sanity checks and results

of minor interest, while we also do not need to establish experiment-wise result sections in the Supplementary Materials, which would further disperse information interested readers might look for.

*  Authors report that in Experiment 4 "Participants with explicit knowledge (n=23) show the same pattern of results as they did in Experiment 1", but that seems inaccurate, as they did learn novel pairs in Exp4 whereas they did not in Exp1. This can be seen in the figure and also in Methods-Results: "performing above chance for ... pairs of a novel structure (M=69.6, SE=5.9, d=0.69, t(22)=3.33 p=0.012, BF=13.6) in the second training phase"

We thank the reviewer for pointing out this error in our interpretation of the results and adjusted the section in question to better align with what our result actually shows.

Author response:

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

Reviewer #1 (Public review):

Summary:

Multiple compounds that inhibit ATP-sensitive potassium (KATP) channels also chaperone channels to the surface membrane. The authors used an artificial intelligence (AI)-based virtual screening (AtomNet) to identify novel compounds that exhibit chaperoning effects on trafficking-deficient disease-causing mutant channels. One compound, which they named Aekatperone, acts as a low affinity, reversible inhibitor and effective chaperone. A cryoEM structure of KATP bound to Aekatperone showed that the molecule binds at the canonical inhibitory site.

Strengths and weaknesses:

The details of the AI screening itself are inevitably opaque, but appear to differ from classical virtual screening in not involving any physical docking of test compounds into the target site. The authors mention criteria that were used to limit the number of compounds, so that those with high similarity to known binders and 'sequence identity' (does this mean structural identity) were excluded. The identified molecules contain sulfonylurea-like moieties. How different are they from other sulfonylure4as?

We thank the reviewers for the questions. As part of the library preparation, molecules with greater than 0.5 Tanimoto similarity in ECFP4 space to any known binders of the target protein and its homologs within 70% sequence identity were excluded to increase the possibility of identifying novel hits. After scoring and ranking the molecules by the AtomNet® technology, a diversity clustering was performed using the Butina algorithm (Butina D. Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets, J. Chem. Inf. Comput. Sci. 1999, 39, 747–750) with a Tanimoto similarity cutoff of 0.35 in ECFP4 space to minimize selection of structurally similar scaffolds for the final compound buy-list. We have revised the results and methods sections to make this clear.

Sulfonylureas are defined by their core structure comprising a sulfonyl group (–S(=O)₂) and a urea moiety (–NH–CO–NH–). While some compounds identified in our study contain a sulfonamide group (R-S(=O) ₂-NR₂), they differ structurally from sulfonylureas by lacking the key urea group and by incorporating unique R-group substitutions (we have now added this to Figure 1A legend). For example, compound C27 (Z2068224500) includes a sulfonamide group but not a urea moiety. Likewise, C45 (Aekatperone, Z1620764636) contains a sulfonamide group along with an aromatic, nitrogen-rich heterocyclic ring, but no urea group. Additionally, the R-groups in these compounds are more complex than the simple aromatic or alkyl chains typical of sulfonylureas. They include heterocyclic aromatic systems and nitrogen-rich structures, which likely influence their binding properties and lipophilicity. These structural differences suggest distinct functional and pharmacological profiles as supported by our biochemical and functional studies.

The experimental work confirming that Aekatperone acts to traffic mutant KATP channels to the surface and acts as a low affinity, reversible, inhibitor is comprehensive and clear, with very convincing cell biological and patch-clamp data, as is the cryoEM structural analysis, for which the group are leading experts. In addition to the three positive chaperone-effective molecules, the authors identified a large number of compounds that are predicted binders but apparently have no chaperoning effect. Did any of them have inhibitory action on channels? If so, does this give clues to separating chaperoning from inhibitory effects?

This is an interesting question. Evidence from cryo-EM, biochemical and electrophysiology studies reveal a critical role of Kir6.2 N-terminus in K_ATP channel assembly and gating, and that pharmacological chaperones like glibenclamide, repaglinide, carbamazepine, and now aekatperone exert their chaperoning and inhibitory effects by stabilizing the interaction between Kir6.2 N-terminus and the SUR1-ABC core. This stabilization, while promoting the assembly of Kir6.2 and SUR1 to “chaperone” trafficking-impaired mutant channels to the cell surface, also inhibits the channel by restricting the Kir6.2 C-terminal domain from rotating to an open state. An additional mechanism by which these compounds inhibit channel activity is by preventing SUR1-NBD dimerization, which mediates physiological activation of the channel by MgADP (see review: Driggers CM, Shyng SL. Mechanistic insights on K_ATP channel regulation from cryo-EM structures. J Gen Physiol. 2023 Jan 2;155(1): e202113046, PMID: 36441147). From our compound screening, we did find some compounds that showed mild inhibition of the channel by electrophysiology but no obvious chaperone effects by western blots. It is possible that small chaperoning effects of some compounds showing mild channel inhibition effects were missed due to the lower sensitivity of the western blot assay compared to electrophysiology. Alternatively, these compounds could inhibit channels by preventing SUR1NBD dimerization without stabilizing the Kir6.2 N-terminus, which is required for the chaperone effect based on our model. Unfortunately, we did not find any compounds that show chaperone effects but no channel inhibition effects, which is consistent with our understanding of how this type of K_ATP chaperones work (i.e. by stabilizing Kir6.2 N-terminus interaction with SUR1’s ABC core).

The authors suggest that the novel compound may be a promising therapeutic for treatment of congenital hyperinsulinism due to trafficking defective KATP mutations. Because they are low affinity, reversible, inhibitors. This is a very interesting concept, and perhaps a pulsed dosing regimen would allow trafficking without constant channel inhibition (which otherwise defeats the therapeutic purpose), although it is unclear whether the new compound will offer advantages over earlier low-affinity sulfonylurea inhibitor chaperones. These include tolbutamide which has very similar affinity and effect to Aekatperone. As the authors point out this (as well as other sulfonlyureas) are currently out of favor because of potential adverse cardiovascular effects, but again, it is unclear why Aekatperone should not have the same concerns.

We thank the reviewer for the comments. This is clearly an important question to address in the future. While we have not directly tested the effects of Aekatperone on cardiac functions, we did assess its inhibitory effect on cells expressing the cardiac K_ATP channel isoform (SUR2A/Kir6.2). Our results indicate that Aekatperone exhibits higher sensitivity toward the pancreatic K_ATP channel isoform (SUR1/Kir6.2) compared to the cardiac isoform. However, we acknowledge that Aekatperone could still have cardiotoxic effects through its potential action on other channels, such as the hERG channel.

It is worth noting that tolbutamide, despite its known cardiotoxic effects, does not exert these effects through cardiac K_ATP channel inhibition. This has been demonstrated in studies showing no inhibitory effect of tolbutamide on SUR2A/Kir6.2 channels and on channels formed by Kir6.2 and SUR1 harboring the S1238Y mutation (also shown as S1237Y in some studies using a different SUR1 isoform)--the amino acid substitution found in SUR2A at the corresponding position (Ashfield R, Gribble FM, Ashcroft SJ, Ashcroft FM. Identification of the high-affinity tolbutamide site on the SUR1 subunit of the K_ATP channel. Diabetes. 1999 Jun;48(6):1341-7, PMID: 10342826). This suggests that tolbutamide’s cardiotoxic effects might involve other targets like the hERG channel. Interestingly, tolbutamide contains a hydrophobic tail and aromatic rings that align well with the structural features for hERG interaction (Garrido A, Lepailleur A, Mignani SM, Dallemagne P, Rochais C. hERG toxicity assessment: Useful guidelines for drug design. Eur J Med Chem. 2020 Jun 1;195:112290, PMID: 32283295). In contrast, highaffinity sulfonylureas such as glibenclamide and glimepiride, which have additional benzamide moieties, are associated with lower cardiovascular risks (Douros A, Yin H, Yu OHY, Filion KB, Azoulay L, Suissa S. Pharmacologic Differences of Sulfonylureas and the Risk of Adverse Cardiovascular and Hypoglycemic Events. Diabetes Care. 2017, 40:1506-1513, PMID:

28864502). Given these considerations, a comprehensive assessment of Aekatperone’s potential cardiotoxicity is crucial. Future studies involving in silico modeling, in vitro, and in vivo experiments will be essential to evaluate Aekatperone’s interaction with hERG and other offtarget effects. These efforts will help clarify its safety profile. This point has now been added to the Discussion.

Reviewer #2 (Public review):

Summary:

In their study 'AI-Based Discovery and CryoEM Structural Elucidation of a KATP Channel Pharmacochaperone', ElSheikh and colleagues undertake a computational screening approach to identify candidate drugs that may bind to an identified binding pocket in the SUR1 subunit of

KATP channels. Other KATP channel inhibitors such as glibenclamide have been previously shown to bind in this pocket, and in addition to inhibition KATP channel function, these inhibitors can very effectively rescue cell surface expression of trafficking deficient KATP mutations that cause excessive insulin secretion (Congenital Hyperinsulinism). However, a challenge for their utility for treatment of hyperinsulinism has been that they are powerful inhibitors of the channels that are rescued to the channel surface. In contrast, successful therapeutic pharmacochaperones (eg. CFTR chaperones) permit function of the channels rescued to the cell membrane. Thus, a key criteria for the authors' approach in this case was to identify relatively low affinity compounds that target the glibenclamide binding site (and be washed off) - these could potentially rescue KATP surface expression, but also permit KATP function.

Strengths:

The main findings of the manuscript include:

(1) Computational screening of a large virtual compound library, followed by functional screening of cell surface expression, which identified several potential candidate pharmacochaperones that target the glibenclamide binding site.

(2) Prioritization and functional characterization of Aekatperone as a low affinity KATP inhibitor which can be readily 'washed off' in patch clamp, and cell based efflux assays. Thus the drug clearly rescues cell surface expression, but can be manipulated experimentally to permit function of rescued channels.

(3) Determination of the binding site and dynamics of this candidate drug by cryo-EM, and functional validation of several residues involved in drug sensitivity using mutagenesis and patch clamp.

The experiments are well-conceived and executed, and the study is clearly described. The results of the experiments are very straightforward and clearly support the conclusions drawn by the authors. I found the study to provide important new information about KATP chaperone effects of certain drugs, with interesting considerations in terms of ion channel biology and human disease.

Weaknesses:

I don't have any major criticisms of the study as described, but I had some remaining questions that could be addressed in a revision.

(1) The chaperones can effectively rescue KATP trafficking mutants, but clearly not as strongly as the higher affinity inhibitor glibenclamide. Is this relationship between inhibitory potency, and efficacy of trafficking an intrinsic challenge of the approach? I suspect that it may be an intractable problem in the sense that the inhibitor bound conformation that underlies the chaperone effect cannot be uncoupled from the inhibited gating state. But this might not be true (many partial agonist drugs with low efficacy can be strongly potent, for example). In this case, the approach is really to find a 'happy medium' of a drug that is a weak enough inhibitor to be washed away, but still strong enough to exert some satisfactory chaperone effect. Could some additional clarity be added in the discussion on whether the chaperone and gating effects can be 'uncoupled'.

Thank you for the suggestion. A similar question was raised by Reviewer 1, which was addressed above (public review, point 2). We have now added more discussion to clarify this point.

(2) Based on the western blots in Figure 2B, the rescue of cell surface expression appears to require a higher concentration of AKP compared to the concentration response of channel inhibition (~9 microM in Figure 3, perhaps even more potent in patch clamp in Figure 2C). Could the authors clarify/quantify the concentration response for trafficking rescue?

Thank you for bringing up this observation. Indeed, the pharmacochaperone effects of Aekatperone as well as other previously published K_ATP pharmacochaperones require higher concentrations compared to their inhibitory effects on surface-expressed channels. This difference likely stems from the necessity for these compounds to cross the cell membrane and interact with newly synthesized channels in the endoplasmic reticulum, where the trafficking rescue occurs. We estimate that effective pharmacochaperone activity for Aekatperone can be achieved at concentrations ranging from 50 to 100 µM in cells expressing trafficking-deficient K_ATP channel mutants, higher than that required for inhibition of surface-expressed channels (~9 µM IC50). Future work could focus on medicinal chemistry modifications, for example esterification of Aekatperone (Zhou G. Exploring Ester Prodrugs: A Comprehensive Review of Approaches, Applications, and Methods. Pharmacology & Pharmacy, 2024, 15, 269-284). Once inside the cell, the esters would be cleaved by endogenous esterases to release the active compound, ensuring efficient intracellular delivery. This strategy could potentially improve membrane permeability and bioavailability of the compound, which would lower the required concentrations to achieve desired chaperoning effects.

(3) A future challenge in the application of pharmacochaperones of this type in hyperinsulinism may be the manipulation of chaperone concentration in order to permit function. In experiments it is straightforward to wash off the chaperone, but this would not be the case in an organism. I wondered if the authors had attempted to rescue channel function with diazoxide ine presence of AKP, rather than after washing off (ie. is AKP inhibition insurmountable, or can it be overcome by sufficient diazoxide).

Thank you for raising this important point. We have previously shown (Martin GM et al. Pharmacological Correction of Trafficking Defects in ATP-sensitive Potassium Channels Caused by Sulfonylurea Receptor 1 Mutations. J Biol Chem. 2016, 291: 21971-21983, PMID: 27573238) that diazoxide, which stabilizes K_ATP channels in an open conformation, also reduces physical association between Kir6.2 N-terminus and SUR1 as demonstrated by reduced crosslinking of engineered azido-phenylalanine (an unnatural amino acid) at Kir6.2 N-terminal amino acid 12 position to SUR1. Incubating cells with diazoxide did not rescue the trafficking mutants but actually further reduced the maturation efficiency of trafficking mutants. For this reason, we did not include diazoxide during Aekatperone incubation and instead added diazoxide after Aekatperone washout to potentiate the activity of mutant channels rescued to the cell surface. In vivo, we envision testing alternating Aekatperone and diazoxide dosing to maximize functional rescue of K_ATP trafficking mutants.

(4) Do the authors have any information about the turnover time of KATP after washoff of the chaperone (how stable are the rescued channels at the cell surface)? This is a difficult question to probe when glibenclamide is used as a chaperone, but maybe much simpler to address with a lower affinity chaperone like AKP.

Thank you for your thoughtful comment. While we have not yet tested the duration of rescued K_ATP channels at the cell surface following Aekatperone washout, we have conducted similar studies with carbamazepine (Chen PC et al. Carbamazepine as a novel small molecule corrector of trafficking-impaired ATP-sensitive potassium channels identified in congenital hyperinsulinism. J Biol Chem. 2013, 288: 20942-20954, PMID: 23744072), another compound exhibiting reversible inhibitory and chaperone effects (apparent affinity between glibenclamide and Aekatperone). Our previous findings with carbamazepine showed that in cultured cells its chaperone effects were detectable as early as 1 hour and peaked around 6 hours after treatment. Furthermore, when carbamazepine was removed following a 16-hour treatment, the rescue effect persisted for up to 6 hours post-drug removal. These results provide a potential duration of the surface expression rescue effects of reversible pharmacochaperones.

Reviewer #1 (Recommendations for the authors):

The paper is well-written and comprehensive with only very minor essentially copy-editing needed. That said, it would be good if the authors could answer the main points raised above:

(1) What is the relevant Tanimoto parameters and sequence identity (does this mean structural identity) for the identified compounds?

As we answered above in response to the overall assessment, to facilitate the identification of novel hits, molecules with greater than 0.5 Tanimoto similarity in ECFP4 space to any known binders of the target protein and its homologs within 70% amino acid sequence identity were excluded from the commercial library. Additionally, after scoring and ranking the molecules by the AtomNet® technology, a diversity clustering was performed on the top 30,000 molecules using the Butina algorithm with a Tanimoto similarity cutoff of 0.35 in ECFP4 space to minimize selection of structurally similar scaffolds for the final compound buy-list.

(2) Did any of the identified putative binders have inhibitory action on channels? If so, does this give clues to separating chaperoning from inhibitory effects?

Please see response to the same question in the overall assessment above.

(3) Acknowledge that the identified compounds contain sulfonylurea-like moieties, and address why Aekatperone should (or perhaps does not) offer anything advantage over low affinity sulfonrylureas such as tolbutamide?

Please see response to the same question in the overall assessment above.

Reviewer #2 (Recommendations for the authors):

Thank you for assembling the interesting study, which I felt was well designed and communicated. The diverse approaches used in the study, with consistent findings, were definitely a strength. The core findings are also well distilled in the main body of the text, and although there is quite a lot of supplementary information, I felt that it was presented appropriately and well selected in terms of what would be important for readers hoping to learn more. In addition to the questions described above, I only had a few minor editorial issues that could be fixed related to presentation.

(1) Figure 1B. The colours and resolution of the chemical structures are difficult to see clearly and could be improved.

We have revised the figure accordingly.

(2) This is a minor wording point... first sentence of the discussion describes the drugs as pancreatic-selective, when it would be more clear to describe them as selective for the pancreatic isoform of KATP (Kir6.2/SUR1), or perhaps better as 'exhibiting ~4-5 fold selective for SUR1-containing KATP channels vs. SUR2A or SUR2B'.

We have changed the wording as suggested.

(3) As a curiosity (not necessary to do more experiments), but I am curious if the authors know whether there is any meaningful enhancement of trafficking of WT channels by AKP.

All pharmacochaperones we have identified to date including Aekatperone also slightly enhance WT channel surface expression (10-20%).

Reviewing editor recommendations:

(1) Given the modest resolution of the EM reconstruction, it is perhaps not entirely clear how AKP was assigned to the density observed. Specifically, it would be helpful to include a comparison of an AKP-free map and the current AKP map (filtered to a similar resolution) showing slice views of densities in the region around the inferred binding site. This would be very helpful in ascertaining whether the cryoEM reconstruction is an independent validation of the computational and functional experiments or whether the density inference depends on the additional knowledge.

We appreciate the editor’s suggestion. We have now added a Supplemental Figure (Supplementary Figure 7 in the revised manuscript) that compares our AKP-free cryoEM density deposited previously to the EMDB (EMD-26320) and the AKP-bound cryoEM density from this study, with cryoEM density (filtered to the same resolution) superimposed on the structural model.

(2) It could help to mention in brief what is a probable mechanism of AKP inhibition - that is how after binding of AKP, channel opening is restricted. Is it similar to that of other site A ligands?

Based on the strong Kir6.2 N-terminal cryoEM density observed in our AKP map, AKP most likely inhibits K_ATP channels by trapping the Kir6.2 N-terminus in the central cavity of SUR1’s ABC core thus preventing Kir6.2-C-terminal domain from rotating to an open conformation, similar to other ligands that stabilize the Kir6.2 N-terminus-SUR1 interface by binding to site A (such as tolbutamide and AKP), site B (such as repaglinide), or both site A and site B (such as glibenclamide). We have now included this in the revised Results and Discussion sections.

(3) In the context of the MD simulations, do other site A ligands (which from my understanding bind at a similar site) also exhibit similar flexibility as AKP? If there is information available on the flexibility of ligands of varying affinities, bound to the same site, maybe some correlative inferences can be drawn? However, in MD simulation trajectories it is not entirely uncommon for a ligand to simply get trapped in a local energy well. Since the authors have performed significant analysis of their MD results it could be worth mentioning/discussing such phenomena.

Previously published MD data addressing ligand dynamics, such as glibenclamide in the SUR1 pocket (Walczewska-Szewc K, Nowak W. Photo-Switchable Sulfonylureas Binding to ATPSensitive Potassium Channel Reveal the Mechanism of Light-Controlled Insulin Release. J Phys Chem B. 2021, 125: 13111-13121, PMID: 34825567), indicate a certain degree of flexibility. Unfortunately, we cannot directly compare these results, as the simulations were performed without the KNtp domain in the SUR1 cavity, which partially contributes to ligand stabilization. This is an issue we plan to investigate in the future.

In this study, we ran five independent MD simulations, each 500 ns long, resulting in a total of 2.5 μs of simulation time. Across all replicates, the ligand stayed in the same position, with variations mainly in the dynamics of the blurred segment. Considering the length of the simulations and the consistency across the runs, we believe this binding pose is stable and represents a global (or at least highly stable) energy minimum, consistent with the cryo-EM data.

(4) In electrophysiological assays, 10 uM AKP seems to inhibit all currents (Figure 2), but in the Rb+ flux assay ~10 uM appears to be the IC50. The reason for this difference is not entirely clear and it would help to comment on this.

Thank you for noticing the difference. The initial electrophysiological experiments were conducted using the very small amount of AKP provided to us from Atomwise. We estimated the concentration of the reconstituted AKP the best we could, but the concentration was likely to not be very accurate due to difficulty in handling the very small amount of the AKP powder. Subsequent Rb<sup>+>/sup> efflux experiments were conducted using a different, larger batch of AKP we purchased from Enamine. We have now stated this in the Methods section.

Author response:

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

Reviewer #1 (Public review):

Summary:

As reported above, this paper by Xu et al reports on a new method to combine the analysis of coevolutionary patterns with dynamic profiles to identify functionally important residues and reveal correlations between binding sites.

Strengths:

In general, coevolutionary analysis and MD analysis are carried out separately and while there have been attempts to compare the information provided by the two, no unified framework exists. Here, the authors convincingly demonstrate that integrating signals from Dynamics and coevolution gives information that substantially overcomes the one provided by either method in isolation. While other methods are useful, they do not capture how dynamics is fundamental to define function and thus sculpts coevolution, via the 3D structure of the protein. At the same time, the authors demonstrate how coevolution in turn also influences internal dynamics. The Networks they rebuild unveil information at an even higher level: the model starts pairwise but through network representation the authors arrive to community analysis, reporting on interaction patterns that are larger than simple couples.

Weaknesses:

The authors should

- Make an effort in suggesting/commenting the limits of applicability of their method;

We have added a sentence on Page 17, line 15 that describes the limitation of our method.

- Expand discussion on how DyNoPy compares to other methods;

A paragraph has been added to explain the comparison with other models (Page 3, line 18)

- Dynamic is not essential in all systems (structural proteins): The authors may want to comment on possible strategies they would use for other systems where their framework may not be suitable/applicable.

We agree with the reviewer that dynamics is not essential in all systems. In systems where there is limited role of dynamics in the function, the analysis done with DyNoPy is equivalent to conventional coevolution analysis, which can be consider one limitation of our method. Conversely, for dynamic proteins, combining functional dynamics descriptors with coevolution analysis using DyNoPy, helps in denoising information by deconvolution of communities. We have included this in the manuscript to highlight the suitability/applicability of the method.

Further, we have added a paragraph in the Introduction and conclusions highlighting the main difference between DyNoPy and existing computational tools like DCCM, KIN, and SPM and for your convenience it is provided below:

“Functional sites are often regulated by both, local and global interactions. Changes in these interactions are instrumental for functional events like substrate binding, catalysis, and conformational changes (18). The development of physical models of protein dynamics and the increase in available computational power has stimulated the adoption of computational techniques (19, 20) to investigate the conformational dynamics of proteins, an essential component of the many biological functions (21, 22). Different models have been proposed to describe the interactions between residues during simulations and network models have been particularly popular,  including methods on single structures and MD simulations data built by analysing the response to external forces on residue networks (23), by estimating the prevalence of non-covalent energy interaction networks in homologous proteins (24), or by analysing linear or non-linear correlation in atomic fluctuations (25, 26). These techniques have demonstrated their usefulness in extracting allosteric networks from structural data with applications in enzyme design (26).”

Reviewer #2 (Public review):

Summary:

Authors introduced a computational framework, DyNoPy, that integrates residue coevolution analysis with molecular dynamics (MD) simulations to identify functionally important residues in proteins. DyNoPy identifies key residues and residue-residue coupling to generate an interaction graph and attempts to validate using two clinically relevant β-lactamases (SHV-1 and PDC-3).

Strengths:

DyNoPy could not only show clinically relevance of mutations but also predict new potential evolutionary mutations. Authors have provided biologically relevant insights into protein dynamics which can have potential applications in drug discovery and understanding molecular evolution.

Weaknesses:

Although DyNoPy could show the relevance of key residues in active and non-active site residues, no experiments have been performed to validate their predictions.

We thank the reviewer for highlighting this point. We acknowledge that direct experimental validation of our predictions for DyNoPy has not yet been performed. However, we have provided explanations and evidence from experiments conducted on closely related homologs to support the relevance of key residues. These homologs share significant structural and functional similarity, which strengthens the reliability of our predictions.

In addition, they should compare their method with conventional techniques and show how their method could be different.

We thank all the reviewers for highlighting this oversight on our behalf. In Introduction and conclusion, we have added the following paragraphs:

“Functional sites are often regulated by both, local and global interactions. Changes in these interactions are instrumental for functional events like substrate binding, catalysis, and conformational changes (18). The development of physical models of protein dynamics and the increase in available computational power has stimulated the adoption of computational techniques (19, 20) to investigate the conformational dynamics of proteins, an essential component of the many biological functions (21, 22). Different models have been proposed to describe the interactions between residues during simulations and network models have been particularly popular,  including methods on single structures and MD simulations data built by analysing the response to external forces on residue networks (23), by estimating the prevalence of non-covalent energy interaction networks in homologous proteins (24), or by analysing linear or non-linear correlation in atomic fluctuations (25, 26). These techniques have demonstrated their usefulness in extracting allosteric networks from structural data with applications in enzyme design (26). ”

An explanation of "communities" divided in the work and how these communities are relevant to the article should be provided. In addition, choice of collective variables and their relevance in residue coupling movement is also not very well explained. Dynamics cross correlation map can also be a good method for understanding the residue movements and can explain the residue-residue coupling, it is not explained how DyNoPy is different from the conventional methods or can perform better.

The following sentences have been included in the manuscript to address the questions raised by the reviewer:

On Community Definition and relevance

DyNoPy identified coevolving residue pairs (scaled coevolution score >1) with interactions strongly correlated with protein functional motions (i.e., J values larger than zero). Applying network analysis on the combined dynamics-coevolution matrix helps us extracting higher-order interactions beyond pairwise coupling and detecting critical residues, which show multiple interactions with each other. Moreover, indirect long-range relationships, which would be hard to identify from numerical data, could be detected through community clustering. Community-based analysis offers a more comprehensive understanding of residue relationships and enables the visualization of residue couplings on the protein structure.

On Choice of collective variables:

DyNoPy works on the assumption that time-dependent interactions between critical residues, either having significant structural change or not will correlate with functional conformational motions. Since MD simulation data is high-dimensional, a time-dependent dynamic descriptor is required to extract the most relevant information for the process under study. A good collective variable (CV) should appropriately describe protein functional motions. Thus, a CV that detects the highest number of residue couplings is expected to be the most suitable descriptor (Mentioned in Page 22 Line 14). In our study, we tested 12 CVs, either focusing on the entire protein or on selected regions. And the best performed CV (the one identified the most residue couplings) was selected for further analysis. In practical applications, users can decide whether to focus on the most relevant global or local dynamics descriptor  depending on the dynamics of their specific system.

We have added a paragraph in the Introduction differentiating DyNoPy with other methods including DCCM. DCCM differs from DyNoPy in two aspects 1) it does not account for inter-residue coevolution 2) the correlation matrix captures correlations of atomic/residue movements associated with the whole intrinsic dynamics of the system, without filtering for the contributions to the important motions involved in the biological function. Additionally, any residue pair contributing to functional motion without itself undergoing any structural change will not be visible in this approach.

In the sentence "DyNoPy identified eight significant communities of strongly coupled residues within SHV-1 (Supporting Fig. S4A)" I could not find a clear description of eight significant communities.

The following sentences have been included in the results, methods and figure legends that define ‘significant community’:

‘DyNoPy identified eight meaningful communities, each consisting of at least three strongly coupled residues within SHV-1 (Supplementary Fig. S4A). All crucial catalytic residues and critical substitution sites previously mentioned participating in one of these communities with the exceptions of R₄₃, R₂₀₂, and S₁₃₀.’ (Page 8 Line 28)

‘A meaningful community should contain at least three residues.’ (Page 21 Line 2)

‘A reasonable residue community should contain at least three residues.’ (SI Page 11)

Again the description of communities is not clear to me in the following sentence "Detailed description of the other three communities is provided in the supporting information (Fig. S6)."

This following sentence has been rewritten.

‘Detailed description of communities with secondary importance for protein function (community 3, 8, and 9) is provided in the supplementary information (Supplementary Fig. S6).’ (Page 9, line 8)

In the sentence "N170 acts as an intermediary between N136 and E166". Kindly cite the reference figure to show N179 as intermediate residue.

This sentence has been rewritten to avoid any confusion.

‘Although DyNoPy did not detect this direct interaction between N136 and E166, the established relationship between N136 and N170 highlights the role of N136 in influencing E166.’ (Page 10 Line 8)

Please be careful with the numbers. In the sentence "These residues not only interact with each other directly but are also indirectly coupled via 21 other residues." I could count 22 other residues and not 21.

We thank the reviewer for spotting this error. This has now been corrected. All the communities are counted again.

‘These residues not only interact with each other directly but are also indirectly coupled via 22 other residues.’ (Page 12 Line 14)

In the sentence "Unlike other substitution sites that are adjacent to the active site, R₂₀₅ is situated more than 16 Å away from catalytic serine S₇₀". Please add this label somewhere in the figure.

The figure legends have been updated to include this. Distances have been added to community 4 Fig. 3 and community 6 Fig. 4. Residue index in the legend of Fig.3 has been included as subscript. Distance in the main text has been changed to be more accurate.

‘G₁₅₆ and A₁₄₆ are two functional important residues distant from the active site. G₁₅₆ is 21.3Å away from the catalytic S₇₀. A₁₄₆ is 16.8Å away from S₇₀.’ (Page 12 Line 2)

‘R₂₀₅ is a functional important residue that is 20.6Å away from the active site S₇₀.’ (Page 13 Line 10)

Please cite a reference in the sentence "This indicates that mutations on G238 would result in an alteration on protein catalytic function, as well as an increased flexibility of the protein, which strongly aligns with previous finding."

The citation has been added

‘This indicates that mutations on G238 would result in an alteration on protein catalytic function, as well as an increased flexibility of the protein, which strongly aligns with previous finding (62).’ (Page 15 Line 2)

Reviewer #3 (Public review):

Summary:

In this paper, Xu, Dantu and coworkers report a protocol for analyzing coevolutionary and dynamical information to identify a subset of communities that capture functionally relevant sites in beta-lactamases.

Strengths:

The combination of coevolutionary information and metrics from MD simulations is interesting for capturing functionally relevant sites, which can have implications in the fields of drug discovery but also in protein design.

Weaknesses:

The combination of coevolutionary information and metrics from MD simulations is not new as other protocols have been proposed along the years (the current version of the paper neglects some of them, see below), and there are a few parameters of the protocol that, in my opinion, should be better analyzed and discussed.

(1) As mentioned, the introduction of the paper lacks some important publications in the field of using graph theory to represent important interaction networks extracted from MD simulations (DOI: 10.1002/pro.4911), and also combining MD data with MSA to identify functionally relevant sites for enzyme design (doi: 10.1021/acscatal.4c04587, 10.1093/protein/gzae005).

We are very grateful for pointing us to these references. We have added a paragraph in the Introduction mentioning these and other computational tools similar to DyNoPy. Further, in conclusion we have highlighted the differences between DyNoPy and existing tools.

(2) The matrix used to apply graph theory (J_ij) is built from summing the scaled coevolution and degree of correlation values. The alpha and beta weights are defined, and the authors mention that alpha is set to 0.5, thus beta as well to fulfil with the alpha + beta = 1. Why a value of 0.5 has been selected? How this affects the overall results and conclusions extracted? The finding that many catalytically relevant residues are identified in the communities is not surprising given that such sites usually present a high conservation score.

This is an excellent question. Our present formulation allows the user to easily assess the influence of coevolution and dynamic couplings on the output. Setting alpha to 0.5, weights both evolutionary and dynamics information equally and has shown promising results in SHV-1 and PDC-3. As it has been presented in the manuscript, setting alpha to 1, i.e., purely utilising coevolution data does not let us identify critical residues effectively as all residues are included in the set (Supplementary Fig. S4 and S5). In future work, we would like to investigate the effect of scanning alpha from 0 to 1 on the final residue list, possibly on a larger set of proteins and protein families.

We would also like to point out that some of the residue pairs with coevolution scores in the top 1% have J-scores set to 0, as they lacked significant coupling to the functional dynamics.

(3) Another important point that needs further explanation is the selection of the relevant descriptor of protein dynamics. In this study two different strategies have been used (one more global the other more local), but more details should be provided regarding their choice. What is the best strategy according to the authors? Why not using the same strategy for both related systems? The obtained results using one methodology or the other will have a large impact on the dynamical score. Another related point is: what is the impact of the MD simulation length, how the MSA is generated and number of sequences used for MSA construction?

As in the case of many complex proteins, the flow of information occurs in β-lactamases via structural interactions (https://doi.org/10.7554/eLife.66567). These interactions occur both on a local level, as in the case of binding site residues or residues immediately surrounding the binding site; however, there are interactions far away (>20Å) from the binding site that have the ability to alter function. We have obtained this information from extensive surveys of clinical isolates and experimental data. To account for such interactions, a more global approach has to be taken. To answer the reviewer’s question: each system is unique and there is no one-fixed strategy. In short, the method used should be able to denoise information and the user is advised to fine-tune their findings by corroborating with experimental and clinical information.

The length of MD simulations is also system specific. Some systems effectively sample the functional dynamics within a shorter simulation time, while others take a long timescale MD simulation to converge. The results won’t change as long as the simulation has effectively sampled the functional dynamics associated with biological function.

The MSA is generated by the HH-Suite package as mentioned on Page 19 Line 19. More specifically, the MSA is constructed based on the UniRef30 database, where sequences are clustered, and each cluster contains sequences with at least 30% sequence identity. This provides a non-redundant set of protein sequences. Our package allows the automatic generation of MSAs from the database. For SHV-1, the alignment contains 18,175 protein sequences and for PDC-3, the alignment consists of 27,892 protein sequences. Full details of this protocol are published in Bibik et al. (https://doi.org/10.1093/bioinformatics/btae166). We have revised the methods section to include these details.

Other Minor Alterations

‘Fig. S1 and S2’ has been changed to ‘Supplementary Fig. S1 and S2’ for consistency (Page 6 Line 12)

(1) ‘Figure 5B’ has been changed to ‘Fig. 5B’ for consistency (Page 16 Line 11)

(2) All the ‘Figure’ has been changed to ‘Fig.’ in the SI for consistency

(3) Just as the suggestion, an alteration has been made on the Step 1 of Fig.1.

Author response:

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; different types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes. The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

Major comment: 

[Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in DeltaNotch signaling, could the authors analyze the effect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression affects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 2022. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is sufficient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.

Minor comments: 

-  PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.

We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.

-  page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.

We thank the reviewer for spotting this. This has now been corrected.

-  Figure 3C: Description of black crosses in the panels is required in the figure legend.

Thank you for spotting this. The legend has now been corrected.

-  Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?

We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.

-  In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.

These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a different seed value. This is now reflected in the text on page 12 and in figure 4.

-  The authors analyze the effect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.

We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in  figure 4 supplementary figure 1 and are referred to in the text on page 11.

-  The authors analyze the effect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.

We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.

-  I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.

We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.

-  Figure 5-figure supplement 2: panel labels A, B, C are missing. 

Thank you for bringing this to our attention. These have now been added.

– Figure 5-figure supplement 3: panel labels A, B, C are missing.

Thank you for bringing this to our attention. These have now been added.

Reviewer #1 (Significance):

Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too.  

In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.

Reviewer #2 (Evidence, reproducibility and clarity):

SUMMARY 

The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

Methodology: The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes affect the segmentation clock.

Model System: The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

Key Findings and Conclusions: (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

MAJOR COMMENTS

(1) The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).

We thank the reviewer for their positive comments about our work

(2) This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cellcell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.

We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space. 

(3) The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.

We support open source coding practices.

(4) In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'off' phase of the clock will ingressing cells be in-phase with their neighbours."  However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853]) Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence. 

However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.

MINOR COMMENTS 

(1) The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).

We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.

(2) The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.

We are very glad to see that the reviewer found that the manuscript was clearly presented.

(3) The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.

Thank you

(4) Minor suggestions: 

a. Page 26: In the Cell addition sub-section of the Methods section, correct all instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).

We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.

b. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

Thank you for the suggstion, we have now done this (see table 1 page 36).

Reviewer #2 (Significance):

GENERAL ASSESSMENT 

This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

ADVANCE

This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.  However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.

The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

AUDIENCE

Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from different vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

Reviewer #3 (Evidence, reproducibility and clarity): 

In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to different biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy.

Below are the comments I would like the authors to address:

(1) The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the effects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the effect of different parameters in greater detail.

One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with differing cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM that captures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available. 

While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting effect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model. 

Furthermore, it is unclear how one would simulate processes such as compactionextension using a one-dimensional model. The two different scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively different than a one-dimensional scenario where cells are introduced continuously along the spatial axis.

(2) I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?

Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the xaxis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.

(3) While studying the effect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes affected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.

We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the different modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve.  While interesting, this work is however outside the scope of the current manuscript.

(4) While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.

We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression. 

(5) Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?

We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:

The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.

Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript. 

Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.

Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).

(6) The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.

We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31. 

Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016). 

We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).

(7) Figure 3 and the associated text shows no effect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

Thank you for the suggestion. However, we would argue that the lack of effect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.

Reviewer #3 (Significance): 

The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work shows that a specific adenosine deaminase protein in Dictyostelium generates the ammonia that is required for tip formation during Dictyostelium development. Cells with an insertion in the ADGF gene aggregate but do not form tips. A remarkable result, shown in several different ways, is that the ADGF mutant can be rescued by exposing the mutant to ammonia gas. The authors also describe other phenotypes of the ADGF mutant such as increased mound size, altered cAMP signalling, and abnormal cell type differentiation. It appears that the ADGF mutant has defects in the expression of a large number of genes, resulting in not only the tip defect but also the mound size, cAMP signalling, and differentiation phenotypes.

Strengths:

The data and statistics are excellent.

Weaknesses:

(1) The key weakness is understanding why the cells bother to use a diffusible gas like ammonia as a signal to form a tip and continue development.

Ammonia serves as a crucial signalling molecule influencing both multicellular organization and differentiation in Dictyostelium (Francis, 1964; Bonner et al., 1989; Bradbury and Gross, 1989). Ammonia by raising the pH of the intracellular acidic vesicles of prestalk cells (Poole and Ohkuma, 1981; Gross et al, 1983), and the cytoplasm, is known to increase the speed of chemotaxing amoebae (Siegert and Weijer 1989; Van Duijn and Inouye, 1991), triggering multicellular movement (Bonner et al., 1988, 1989), favoring tipped mound development. The slug tip is known to release ammonia and the slime sheath at the back of the slug prevents diffusion (Bonner et al., 1989) and thus high ammonia levels at the back, help prespore differentiation (Newell et al., 1969). Ammonia favors slug migration than fruiting (Schindler and Sussman, 1977) and thus, ammonia coming from the tip may favor synchronized development of the entire colony. The tip exerts negative chemotaxis towards ammonia probably helping the slugs to move away from each other ensuring equal spacing of the fruiting bodies (Feit and Sollitto, 1987).

Ammonia released in pulses acts as a long-distance signalling molecule between colonies of yeast cells indicating depletion of nutrient resources and promoting synchronous development (Palkova et al., 1997; Palkova and Forstova, 2000). Similarly, ammonia diffusion may influence neighboring Dictyostelium colonies. Ammonia produced in millimolar concentrations (Schindler and Sussman, 1977) could ward off other predators in soil. For instance, ammonia released by Streptomyces symbionts of leaf-cutting ants is known to inhibit fungal pathogens (Dhodary and Spiteller, 2021). Additionally, ammonia recycling into amino acids, as observed in breast cancer proliferation (Spinelli et al., 2017), may also occur in starving Dictyostelium cells, supporting survival and differentiation. These findings suggest that ammonia acts as both a local and long-range regulatory signal, integrating environmental and cellular cues to coordinate multicellular development.

(2) The rescue of the mutant by adding ammonia gas to the entire culture indicates that ammonia conveys no positional information within the mound.

Ammonia is known to influence rapid patterning of Dictyostelium cells confined in a restricted environment (Sawai et al., 2002). Both neutral red staining (a marker for prestalk and ALCs) (Fig. S2) and the prestalk marker ecmA/ ecmB expression (Fig. 8C) in the adgf mutants suggest that the mounds have differentiated prestalk cells but are blocked in development. The mound arrest phenotype can be reversed by exposing the adgf mutant mounds to ammonia.

Based on cell cycle phases, there exists a dichotomy of cell types, that biases cell fate as prestalk or prespore (Weeks and Weijer, 1994; Jang and Gomer, 2011). Prestalk cells are enriched in acidic vesicles, and ammonia, by raising the pH of these vesicles and the cytoplasm (Davies et al 1993; Van Duijn and Inouye 1991), plays an active role in collective cell movement (Bonner et al., 1989). Thus, ammonia reinforces or maintains the positional information by elevating cAMP levels, favoring prespore differentiation (Bradbury and Gross, 1989; Riley and Barclay, 1990; Hopper et al., 1993).

(3) By the time the cells have formed a mound, the cells have been starving for several hours, and desperately need to form a fruiting body to disperse some of themselves as spores, and thus need to form a tip no matter what.

When the adgf mutants were exposed to ammonia just after tight mound formation, tips developed within 4 h (Fig. 6). In contrast, adgf mounds, not exposed to ammonia remained at the mound stage for at least 30 h. This demonstrates that starvation alone is not sufficient to drive tip development and ammonia serves as a cue that promotes the transition from mound to tipped mound formation.

Many mound arrest mutants are blocked in development and do not proceed to form fruiting bodies (Carrin et al., 1994). Further, not all the mound arrest mutants tested in this study were rescued by ADA enzyme (Fig. S3 A), and they continue to stay as mounds without dispersing as spores.

(4) One can envision that the local ammonia concentration is possibly informing the mound that some minimal number of cells are present (assuming that the ammonia concentration is proportional to the number of cells), but probably even a minuscule fruiting body would be preferable to the cells compared to a mound. This latter idea could be easily explored by examining the fate of the ADGF cells in the mound - do they all form spores? Do some form spores?

Or perhaps the ADGF is secreted by only one cell type, and the resulting ammonia tells the mound that for some reason that cell type is not present in the mound, allowing some of the cells to transdifferentiate into the needed cell type. Thus, elucidating if all or some cells produce ADGF would greatly strengthen this puzzling story.

A fraction of adgf mounds form bulkier spore heads by the end of 36 h as shown in Fig. 3. This late recovery may be due to the expression of other ADA isoforms. Mixing WT and adgf mutant cell lines results in a slug with the mutants occupying the prestalk region (Fig. 9) suggesting that WT ADGF favours prespore differentiation. However, it is not clear if ADGF is secreted by a particular cell type, as adenosine can be produced by both cell types, and the activity of three other intracellular ADAs may vary between the cell types. To address whether adgf expression is cell type-specific, prestalk and prespore cells will be isolated, and thereafter, adgf expression in each population will be examined.

ADGF activity is likely to be higher in the tip to remove excess adenosine, the tip-inhibiting molecule (Wang and Schaap, 1985), and our results also show that adgf - cells with high adenosine preferentially migrate to the prestalk than the prespore region when mixed with WT cells. Ammonia generated from adenosine deamination could thus drive tip development and prespore differentiation.

Reviewer #2 (Public review):

Summary:

The paper describes new insights into the role of adenosine deaminase-related growth factor (ADGF), an enzyme that catalyses the breakdown of adenosine into ammonia and inosine, in tip formation during Dictyostelium development. The ADGF null mutant has a pre-tip mound arrest phenotype, which can be rescued by the external addition of ammonia. Analysis suggests that the phenotype involves changes in cAMP signalling possibly involving a histidine kinase dhkD, but details remain to be resolved.

Strengths:

The generation of an ADGF mutant showed a strong mound arrest phenotype and successful rescue by external ammonia. Characterization of significant changes in cAMP signalling components, suggesting low cAMP signalling in the mutant and identification of the histidine kinase dhkD as a possible component of the transduction pathway. Identification of a change in cell type differentiation towards prestalk fate

Weaknesses:

(1) Lack of details on the developmental time course of ADGF activity and cell type type-specific differences in ADGF expression.

ADGF expression was examined at 0, 8, 12, and 16 h (Fig. 1), and the total ADA activity was assayed at 12 and 16 h (Fig. 4). Previously, the 12 h data was not included, but it has now been added (Fig. 4A). The adgf expression was found to be highest at 16 h and hence, the ADA assay was carried out at that time point. Since the ADA assay will also report the activity of other three isoforms, it will not exclusively reflect ADGF activity.

A fraction of adgf - mounds form bulkier spore heads by the end of 36 h as shown in Fig. 3. This late recovery may be due to the expression of the other ADA isoforms. Mixing WT and adgf mutant cell lines results in a slug with the mutants occupying the prestalk region (Fig. 9) suggesting that WT adgf favours prespore differentiation. However, it’s not clear if ADGF is secreted by a particular cell type, as adenosine can be produced by both cell types, and the activity of the other three intracellular ADAs may vary between the cell types. To address whether adgf expression is cell type-specific, prestalk and prespore cells will be isolated, and thereafter, adgf expression in each population will be examined.

ADGF activity is likely to be higher in the tip to remove excess adenosine, the tip-inhibiting molecule (Wang and Schaap, 1985), and our results also show that adgf - cells with high adenosine preferentially migrate to the prestalk than the prespore region when mixed with WT cells.

(2) The absence of measurements to show that ammonia addition to the null mutant can rescue the proposed defects in cAMP signalling.

The cAMP levels were measured at two time points 8 h and 12 h in the mutant. The adgf mutant in comparison to WT has lower ammonia levels (Fig. 6), diminished acaA expression (Fig. 7) and reduced cAMP levels (Fig. 7) both at 12 and 16 h of development. Ammonia is known to increase cAMP levels (Riley and Barclay, 1990; Feit et al., 2001). Thus, ammonia addition to the mutant is likely to increase acaA expression, thus increase cAMP levels (Riley and Barclay, 1990; Feit et al., 2001) thereby rescuing the defects in cAMP signalling.

(3) No direct measurements in the dhkD mutant to show that it acts upstream of adgf in the control of changes in cAMP signalling and tip formation.

The histidine kinases dhkD and dhkC are reported to modulate phosphodiesterase RegA activity, thereby maintaining cAMP levels (Singleton et al., 1998; Singleton and Xiong, 2013). By activating RegA, dhkD ensures proper cAMP distribution within the mound, which is essential for the patterning of prestalk and prespore cells, as well as for tip formation (Singleton and Xiong, 2013). Therefore, ammonia exposure to dhkD mutants is likely to regulate cAMP signalling and thereby tip formation.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary

Farkas and colleagues conducted a comparative neuroimaging study with domestic dogs and humans to explore whether social perception in both species is underpinned by an analogous distinction between animate and inanimate entities an established functional organizing principle in the primate and human brain. Presenting domestic dogs and humans with clips of three animate classes (dogs, humans, cats) and one inanimate control (cars), the authors also set out to compare how dogs and humans perceive their own vs other species. Both research questions have been previously studied in dogs, but the authors used novel dynamic stimuli and added animate and inanimate classes, which have not been investigated before (i.e., cats and cars). Combining univariate and multivariate analysis approaches, they identified functionally analogous areas in the dog and human occipitotemporal cortex involved in the perception of animate entities, largely replicating previous observations. This further emphasizes a potentially shared functional organizing principle of social perception in the two species. The authors also describe between- species divergencies in the perception of the different animate classes, arguing for a less generalized perception of animate entities in dogs, but this conclusion is not convincingly supported by the applied analyses and reported findings.

Strengths

Domestic dogs represent a compelling model species to study the neural bases of social perception and potentially shared functional organizing principles with humans and primates. The field of comparative neuroimaging with dogs is still young, with a growing but still small number of studies, and the present study exemplifies the reproducibility of previous research. Using dynamic instead of static stimuli and adding new stimuli classes, Farkas and colleagues successfully replicated and expanded previous findings, adding to the growing body of evidence that social perception is underpinned by a shared functional organizing principle in the dog and human occipito-temporal cortex.

Weaknesses

The study design is imbalanced, with only one category of inanimate objects vs. three animate entities. Moreover, based on the example videos, it appears that the animate stimuli also differed in the complexity of the content from the car stimuli, with often multiple agents interacting or performing goal-directed actions. Moreover, while dogs are familiar with cars, they are definitely of lower relevance and interest to them than the animate stimuli. Thus, to a certain extent, the results might also reflect differences in attention towards/salience of the stimuli.

We agree with the Reviewer and were aware that using only one class of inanimate objects but three classes of animate entities, along with the differences in complexity and relevance between the animate and the inanimate stimuli potentially elicited more attention to the inanimate condition and may have thus introduced a confound. We are revising the related limitation in the discussion to acknowledge this and to emphasize why we believe these differences do not compromise our main findings.

The methods section and rationale behind the chosen approaches were often difficult to follow and lacked a lot of information, which makes it difficult to judge the evidence and the drawn conclusions, and it weakens the potential for reproducibility of this work. For example, for many preprocessing and analysis steps, parameters were missing or descriptions of the tools used, no information on anatomical masks and atlas used in humans was provided, and it is often not clear if the authors are referring to the univariate or multivariate analysis.

We acknowledge the concerns regarding the clarity and completeness of the methods section and are significantly revising the descriptions of the methods. Of note, in humans, the Harvard-Oxford Cortical Structural Atlas (Frazier et al., 2005; Makris et al., 2006; Desikan et al., 2006; Goldstein et al., 2007), implemented within the FSL software package, was used for anatomical masks, while the Automated Anatomical Labeling atlas (Tzourio-Mazoyer et al., 2002) was used for assigning labels.

In regard to the chosen approaches and rationale, the authors generally binarize a lot of rich information. Instead of directly testing potential differences in the neural representations of the different animate entities, they binarize dissimilarity maps for, e.g. animate entity > inanimate cars and then calculate the overlap between the maps.

We thank the Reviewer for these comments and ideas. We also appreciate the second Reviewer for their related concerns and suggestions about the overlap calculation. Since the neural processing of different animate entities in the dog brain is largely unexplored, in some of our analyses we aimed to provide a straightforward and directly comparable characterization of animacy perception in the two species. We believe that a measure of how overlapping the neural representations of different animate classes are in the dog vs. the human visual cortex is a simple but meaningful and insightful characterization of how animacy perception is structured in the two species, despite the lack of spatial detail. Our decision to use binarization was based on these considerations. In response to this Reviewer’s request for providing richer information, in our revised manuscript, we will present more details and additional non-binarized calculations. Specifically, we are going to use nonbinarized data to present the response profiles of a broad, anatomically defined set of regions that have been related in other works to visual functions, to thus show where there is significant difference and overlap between the neural responses for the three animate classes in each species.

The comparison of the overlap of these three maps between species is also problematic, considering that the human RSA was constricted to the occipital and temporal cortex (there is now information on how they defined it) vs. whole-brain in dogs.

We thank this Reviewer for raising yet another relevant point about overlap calculation. We note that the overlap calculation for univariate results used the visually responsive cortex in both dogs and humans. The decision to restrict the multivariate analysis to the occipital and temporal lobes in humans, where the visual areas are, was to reduce computational load. Since RSA in dogs yielded significant voxels almost exclusively in the occipital and temporal cortices, we believe this decision did not introduce major bias in our results. This concern will also be discussed in our revised submission.

Of note, in the category- and class-boundary test, as for the other multivariate tests, the occipital and temporal cortex of humans was delineated based on the MNI atlas.

Considering that the stimuli do differ based on low-level visual properties (just not significantly within a run), the RSA would also allow the authors to directly test if some of the (dis)similarities might be driven by low-level visual features like they, e.g. did with the early visual cortex model. I do think RSA is generally an excellent choice to investigate the neural representation of animate (and inanimate) stimuli, but the authors should apply it more appropriately and use its full potential.

We thank the Reviewer for this suggestion. While this study did not aim to investigate the correlation between low-level visual features and animacy, the data is available, and the suggested analysis can be conducted in the future. This issue will also be discussed in our revised submission.

The authors localized some of the "animate areas" also with the early visual cortex model (e.g. ectomarginal gyrus, mid suprasylvian); in humans, it only included the known early visual cortex - what does this mean for the animate areas in dogs?

We thank the Reviewer for raising this point. Although the labels are the same, both EMG and mSSG are relatively large gyri, and the clusters revealed by each of the two analyses hardly overlap, with peak coordinates more than 12 mm apart for R EMG, and in different hemispheres for mSSG (but more than 11 mm apart even if projected on the same hemisphere). We will detail the differences and the overlaps in the revised submission.

The results section also lacks information and statistical evidence; for example, for the univariate region-of-interest (ROI) analysis (called response profiles) comparing activation strength towards each stimulus type, it is not reported if comparisons were significant or not, but the authors state they conducted t-tests. The authors describe that they created spheres on all peaks reported for the contrast animate > inanimate, but they only report results for the mid suprasylvian and occipital gyrus (e.g. caudal suprasylvian gyrus is missing).

We thank this Reviewer for catching these errors. The missing statistics will be provided in the revised manuscript. Also, we mistakenly named the peak in caudal suprasylvian gyrus occipital gyrus on the figure depicting the response profiles. This will also be corrected.

Furthermore, considering that the ROIs were chosen based on the contrast animate > inanimate stimuli, activation strength should only be compared between animate entities (i.e., dogs, humans, cats), while cars should not be reported (as this would be double dipping, after selecting voxels showing lower activation for that category).

We thank both Reviewers for raising this relevant point about potential double dipping. The aim of this analysis was to describe the relationship between the neural response elicited by the three animate stimulus classes, to show that the animacy-sensitive peaks are not the results of the standalone greater response to a single animate class. We conducted t-tests only to assess significant difference between these three animate conditions and no stats were performed or reported for any animate class vs. inanimate comparisons in these ROIs. In addition to providing the missing t-tests (comparing animate classes), we will present response profiles and corresponding statistics for a broad set of additional, independent ROIs, defined either anatomically or functionally by other studies in the revised version.

The descriptive data in Figure 3B (pending statistical evidence) suggests there were no strong differences in activation for the three species in dog and human animate areas. Thus, the ROI analysis appears to contradict findings from the binary analysis approach to investigate species preference, but the authors only discuss the results of the latter in support of their narrative for conspecific preference in dogs and do not discuss research from other labs investigating own-species preference.

Studying conspecific-preference was not the primary aim of this study. We only used our data to characterize the animate-sensitive regions from this aspect. The species-preference test provides an overall characterization of the entire animate-sensitive region, revealing a higher number of voxels with a maximal response to conspecific than other stimuli in dogs (and a similar tendency in humans), confirming previous evidence on neural conspecific preference in visual areas in both species. The response profiles presented so far describe only the ROIs around the main animate-sensitive peaks and, as the Reviewer points out, in most cases reveal no significant conspecific bias. We believe there is no contradiction here: the entire animate-sensitive region may weakly but still be conspecific-preferring, whereas the main animate-sensitive peaks are not; the centers of conspecific preference may be located elsewhere in the visual cortex and may be supported by mechanisms other than animacy-sensitivity. In the revised manuscript, we will elaborate more on this. Additionally, in response to other comments, and for a better and more coherent characterization of species preference (and animacy sensitivity) across the visual cortex, we will present response profiles for other, independently defined regions and explore conspecific-sensitivity in those additional regions as well. Furthermore, we will discuss related own-species preference literature in greater detail.

The authors also unnecessarily exaggerate novelty claims. Animate vs inanimate and own vs other species perceptions have both been investigated before in dogs (and humans), so any claims in that direction seem unsubstantiated - and also not needed, as novelty itself is not a sign of quality; what is novel, and a sign of theoretical advance besides the novelty, are as said the conceptual extension and replication of previous work.

We agree with this Reviewer regarding novelty claims in general, and we confirm that we had no intention to overstate the uniqueness of our results. We also did not mean to imply that this work would be the first one on animacy perception in dogs, which it obviously is not. But we understand that we could have been more explicit presenting our work as a conceptual extension and replication of previous works, and we are revising the wording of the discussion from this aspect.

Overall, more analyses and appropriate tests are needed to support the conclusions drawn by the authors, as well as a more comprehensive discussion of all findings.

We are thankful for all comments. We will revise the methods section to provide sufficient detail and ensure replicability; conduct additional analyses as detailed above; and provide a more comprehensive discussion of all findings.

Reviewer #2 (Public review):

Summary:

The manuscript reports an fMRI study looking at whether there is animacy organization in a non-primate, mammal, the domestic dog, that is similar to that observed in humans and non-human primates (NHPs). A simple experiment was carried out with four kinds of stimulus videos (dogs, humans, cats, and cars), and univariate contrasts and RSA searchlight analysis was performed. Previous studies have looked at this question or closely associated questions (e.g. whether there is face selectivity in dogs). The import of the present study is that it looks at multiple types of animate objects, dogs, humans, and cats, and tests whether there was overlapping/similar topography (or magnitude) of responses when these stimuli were compared to the inanimate reference class of cars. The main finding was of some selectivity for animacy though this was primarily driven by the dog stimuli, which did overlap with the other animate stimulus types, but far less so than in humans.

Strengths:

I believe that this is an interesting study in so far as it builds on other recent work looking at category-selectivity in the domestic dog. Given the limited number of such studies, I think it is a natural step to consider a number of different animate stimuli and look at their overlap. While some of the results were not wholly surprising (e.g. dog brains respond more selectively for dogs than humans or cats), that does not take away from their novelty, such as it is. The findings of this study are useful as a point of comparison with other recent work on the organization of high-level visual function in the brain of the domestic dog.

Weaknesses:

(1) One challenge for all studies like this is a lack of clarity when we say there is organization for "animacy" in the human and NHP brains. The challenge is by no means unique to the present study, but I do think it brings up two more specific topics.

First, one property associated with animate things is "capable of self-movement". While cognitively we know that cars require a driver, and are otherwise inanimate, can we really assume that dogs think of cars in the same way? After all, just think of some dogs that chase cars. If dogs represent moving cars as another kind of selfmoving thing, then it is not clear we can say from this study that we have a contrast between animate vs inanimate. This would not mean that there are no real differences in neural organization being found.

It was unclear whether all or some of the car videos showed them moving. But if many/most do, then I think this is a concern.

We thank this Reviewer for raising this relevant point about the potential animacy of cars for dogs and its implication for our results. Of note, two-thirds of our car stimuli showed a car moving (slow, accelerating, or fast). We acknowledge that these stimuli contained motionbased animacy cues, and in this regard, there was no clear difference between our animate and inanimate conditions, and possibly between some of the representations they elicited. However, our animate and inanimate stimuli differed in other key factors accounting for animacy organization, such as visual features including the presence of faces, bodies, body parts, postures, and certain aspects of biological motion. So we believe that this limitation does not compromise our main conclusions. We will elaborate on this point further in the revised discussion, also considering how dogs’ differential behavioral responses to cars and animate entities may provide additional insights in this regard.

Second, there is quite a lot of potential complexity in the human case that is worth considering when interpreting the results of this study. In the human case, some evidence suggests that animacy may be more of a continuum (Sha et al. 2015), which may reflect taxonomy (Connolly et al. 2012, 2016). However moving videos seem to be dominated more by signals relevant to threat or predation relative to taxonomy (Nastase et al. 2017). Some evidence suggests that this purported taxonomic organization might be driven by gradation in representing faces and bodies of animals based on their relative similarity to humans (Ritchie et al. 2021). Also, it may be that animacy organization reflects a number of (partially correlated) dimensions (Thorat et al. 2019, Jozwik et al. 2022). One may wonder whether the regions of (partial) overlap in animate responses in the dog brain might have some of these properties as well (or not).

We agree that it would be interesting to dissect which animacy-related factor(s) contribute to the observed animacy sensitivity in different regions, and although this was not the original aim of the study, we agree that we could have made better use of the variation in our stimuli to discuss this aspect. Specifically, some animacy features are shared by all three animate stimulus classes, namely the presence of biological motions, faces, and bodies. In contrast, animate classes differed in some other aspects, for example in how dogs perceived dogs, humans, and cats as social agents and in their potential behavioral goals towards them. It can therefore be argued that regions with two- and especially three-way overlapping activations are more probably involved in processing biological motion, face and body aspects, and non-overlapping ones the social agency- and behavioural goal-related aspects. In line with this, the shared animacy features are indeed ones that have been reported to be central in human animacy representation and that may have made the overlaps in human brain responses greater. We will provide a more detailed discussion of the results from this viewpoint in the revised manuscript.

(2) It is stated that previous studies provide evidence that the dog brain shows selectivity to "certain aspects of animacy". One of these already looked at selectivity for dog and human faces and bodies and identified similar regions of activity (Boch et al. 2023). An earlier study by Dilks et al. (2015), not cited in the present work (as far as I can tell), also used dynamic stimuli and did not suffer from the above limitations in choosing inanimate stimuli (e.g. using toy and scene objects for inanimate stimuli). But it only included human faces as the dynamic animate stimulus. So, as far as stimulus design, it seems the import of the present study is that it included a *third* animate stimulus (cats) and that the stimuli were dynamic.

We agree with this Reviewer that the findings of Dilks et al. (2015) are relevant to our study and have therefore cited them. However, the citation itself was imprecise and will be corrected in the revised manuscript.

(3) I am concerned that the univariate results, especially those depicted in Figure 3B, include double dipping (Kriegesorte et al. 2009). The analysis uses the response peak for the A > iA contrast to then look at the magnitude of the D, H, C vs iA contrasts. This means the same data is being used for feature selection and then to estimate the responses. So, the estimates are going to be inflated. For example, the high magnitudes for the three animate stimuli above the inanimate stimuli are going to inherently be inflated by this analysis and cannot be taken at face value. I have the same concern with the selectivity preference results in Figure 3E.

I think the authors have two options here. Either they drop these analyses entirely (so that the total set of analyses really mirrors those in Figure 4), or they modify them to address this concern. I think this could be done in one of two ways. One would be to do a within- subject standard split-half analysis and use one-half of the data for feature selection and the other for magnitude estimation. The other would be to do a between-subject design of some kind, like using one subject for magnitude estimation based on an ROI defined using the data for the other subjects.

We thank both Reviewers again for raising this important point about potential double dipping. We also thank this Reviewer for specific suggestions for split-half analyses – we agree that, had our original analyses involved double dipping, such a modification would be necessary. But, as we explained in our response above, this was not the case. Indeed, whereas we do visualize all four conditions in Fig. 3B, we only conducted t-tests to assess differences between the three animate conditions (the corresponding stats have been missing from the original manuscript but will be added during revision). So, importantly, we did not evaluate the magnitude of the D, H, C vs iA contrasts in any of the ROIs defined by animate-sensitive peaks; therefore, we believe that these analyses do not involve double dipping. This holds for the species preference results in Fig. 3E as well. We will clarify this in the revised manuscript. Of note, in response to a request by the other reviewer and to provide richer information about the univariate results, we will also provide response profiles and corresponding stats for a broad set of additional ROIs, defined either anatomically or functionally by other studies (e.g., Boch et al., 2023).

(4) There are two concerns with how the overlap analyses were carried out. First, as typically carried out to look at overlap in humans, the proportion is of overlapping results of the contrasts of interest, e.g, for face and body selectivity overlap (Schwarlose et al. 2006), hand and tool overlap (Bracci et al. 2012), or more recently, tool and food overlap (Ritchie et al. 2024). There are a number of ways of then calculating the overlap, with their own strengths and weaknesses (see Tarr et al. 2007). Of these, I think the Jaccard index is the most intuitive, which is just the intersection of two sets as a proportion of their union. So, for example, the N of overlapping D > iA and H > iA active voxels is divided by the total number of unique active voxels for the two contrasts. Such an overlap analysis is more standard and interpretable relative to previous findings. I would strongly encourage the authors to carry out such an analysis or use a similar metric of overlap, in place of what they have currently performed (to the extent the analysis makes sense to me).

We agree with this Reviewer that the Jaccard index is an intuitive and straightforward overlap measure. Importantly, for our overlap calculations we already use this measure (and a very similar one) – but we acknowledge that this was not clear from the original description. Specifically, for the multivariate overlap test, we used the Jaccard index exactly as described by this Reviewer. For the univariate overlap test, we use a very similar measure, with the only difference that there, to reference the search space, the intersection of specific animate-inanimate contrasts was divided by the total voxel number of animate-sensitive areas (which is highly similar to the union of the specific animate-inanimate contrasts). In the revised submission we will provide a more detailed explanation of the overlap calculations, making it explicit that we used the Jaccard index (and a variant of it).

Second, the results summarized in Figure 3A suggest multiple distinct regions of animacy selectivity. Other studies have also identified similar networks of regions (e.g. Boch et al. 2023). These regions may serve different functions, but the overlap analysis does not tell us whether there is overlap in some of these portions of the cortex and not in others. The overlap is only looked at in a very general sense. There may be more overlap locally in some portions of the cortex and not in others.

We thank this Reviewer for this comment, we agree that adding spatial specificity to these results will improve the manuscript. Therefore, during revision, we will assess the anatomical distribution of the overlap results, making use of a broad set of ROIs potentially relevant for animacy perception, defined either anatomically or functionally by other studies (e.g., Boch et al., 2023 for dogs).

(5) Two comments about the RSA analyses. First, I am not quite sure why the authors used HMAX rather than layers of a standardly trained ImageNet deep convolutional neural network. This strikes me also as a missed opportunity since many labs have looked at whether later layers of DNNs trained on object categorization show similar dissimilarity structures as category-selective regions in humans and NHPs. In so far as cross-species comparisons are the motivation here, it would be genuinely interesting to see what would happen if one did a correlation searchlight with the dog brain and layers of a DNN, a la Cichy et al. (2016).

We thank the Reviewer for this comment and suggestion. At the start of the project, HMAX was the most feasible model to implement given our time and expertise constrains. Additionally, the biologically motivated HMAX was also an appropriate choice, as it simulates the selective tuning of neurons in the primary visual cortex (V1) of primates, which is considered homologous with V1 in carnivores (Boch et al., 2024).

Although we agree that using DNNs have recently been extensively and successfully used to explore object representations and could provide valuable additional insights for dogs’ visual perception as well, we believe that adding a large set of additional analyses would stretch the frames of this manuscript, disproportionately shifting its focus from our original research question. Also, our experiment, designed with a different, more specific aim in mind, did not provide a large enough stimulus variety of animate stimuli for a general comparison of the cortical hierarchy underlying object representations in dog and human brains and thus our data are not an optimal starting point for such extensive explorations. Having said that, we are thankful for this Reviewer for the idea and will consider using a DNN to uncover dog’ visual cortical hierarchy in future studies with a better suited stimulus set. Furthermore, in accordance with eLife’s data-sharing policies, we will make the current dataset publicly available so further hypothesis and models can be tested.

Second, from the text is hard to tell what the models for the class- and categoryboundary effects were. Are there RDMs that can be depicted here? I am very familiar with RSA searchlight and I found the description of the methods to be rather opaque. The same point about overlap earlier regarding the univariate results also applies to the RSA results. Also, this is again a reason to potentially compare DNN RDMs to both the categorical models and the brains of both species.

In the revised manuscript we will provide a more detailed explanation of the methods used to determine class- and category-boundary effects. In short, the analysis we performed here followed Kriegeskorte et al. (2008), and the searchlight test looked for regions in which between-class/category differences were greater than within-class/category differences. We will also include RDMs. Additionally, we will provide anatomical details for the overlap results for RSA, just as for the univariate results, using the same independently defined broad set of ROIs, defined either anatomically or functionally by other studies (e.g., Boch et al., 2023 for dogs).

(6) There has been emphasis of late on the role of face and body selective regions and social cognition (Pitcher and Ungerleider, 2021, Puce, 2024), and also on whether these regions are more specialized for representing whole bodies/persons (Hu et al. 2020, Taubert, et al. 2022). It may be that the supposed animacy organization is more about how we socialize and interact with other organisms than anything about animacy as such (see again the earlier comments about animacy, taxonomy, and threat/predation). The result, of a great deal of selectivity for dogs, some for humans, and little for cats, seems to readily make sense if we assume it is driven by the social value of the three animate objects that are presented. This might be something worth reflecting on in relation to the present findings.

We thank the Reviewer for this suggestion. The original manuscript already discussed how motion-related animacy cues involved in social cognition may explain that animacysensitive regions reported in our study extend beyond those reported previously and also the role of biological motion in the observed across-species differences. This discussion of the role of visual diagnostic features and features that involved in perceiving social agents will be extended in the revised discussion, also in response to the first comment of this Reviewer, to reflect on how social cognition-related animacy cues may have affected our results in dogs.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Dad et al. explored the roles of cytosolic carboxypeptidase 5（CCP5）in the development of ependymal multicilia in the brain. CCP family are erasers of polyglutamylation of ciliary-axoneme microtubules. The authors generated a new mutant mouse of Agbl5 gene, which encodes CCP5, with deletion of its N-terminus and partial carboxypeptidase (CP) domain (named AGBL5M1/M1).

Strengths:

The mutant mice revealed lethal hydrocephalus due to degeneration of ependymal multicilia. Interestingly, this is in contrast with the phenotype of Agbl5 mutants with disruption solely in the CP domain of CCP5 (named AGBL5M2/M2) that did not develop hydrocephalus despite increased glutamylation levels in ependymal cilia as observed for AGBL5M1/M1 mutants. The study has been well-performed and the findings suggest a unique function of the N-domain of CCP5 in ependymal multicilia stability.

Weaknesses:

The content of this article is relatively descriptive and lacks molecular insights.

We thank the Reviewer’s positive comments. To address the molecular insights of the dysregulated planar cell polarity (PCP) in Agbl5^M1/M1 ependyma, we are planning to further assess the microtubule polarization and the expression/localization of PCP core proteins in ependymal cells. We also plan to quantify the intensity of actin networks around BB patches to better understand to which extent it is affected in the ependyma of the mutants and contributes to the impaired stability of BBs (Please see below).

We will also assess whether Agbl5 commonly functions in multiciliated cells of other organs.

Reviewer #2 (Public review):

Summary:

This study analyzed the consequences of Agbl5 mutation on ependymal cell development and function. The authors first characterize their mutant mouse line reporting a reduced lifespand and severe hydrocephalus. Next, they report a defect in ependymal cell cilia number and motility. They provide evidence for impaired basal body organisation and cilia glutamylation.

Strengths:

Description of a mutant mouse which implicates Cytosolic Carboxypeptidase 5 (the product of Agbl5 gene) for proper ependymal cells.

Weaknesses:

Description of phenotype is incomplete:

We thank the Reviewer’s constructive comments. We agree that more quantitative analysis of the phenotypes in Agbl5^M1/M1 will strengthen this study.

- Figure 3G - the sequence from the movie is not really informative. Providing beating frequencies as quantification of the data would be more informative.

We agree that quantification of the cilia beating frequencies and directions in these experiments will be more informative.

- Figure 3 - the quantification of actin network would strengthen the message.

We agree with the Reviewers. We will quantify the total intensity of actin around BB patch and the total intensity of actin per BB to determine to which extent the actin networks are affected in Agbl5^M1/M1 ependymal cells.

- Lines 219 -220 - the authors conclude “Taken together, in Agbl5^M1/M1 ependymal cells, the expression of genes promoting multiciliogenesis were not impaired but certain proteins associated with differentiated ependymal cells are not properly expressed”. However, they do not assess gene but protein expression (IF). In addition, their quantification shows differences in the number of FoxJ1 positive cells which indeed is an impaired expression.

We will clarify this statement.

- Microtubules are involved in the local organization of ciliary basal bodies (see Werner et al., Vladar et al.,2011; Boutin et al., 2014). It would be interesting for the authors to check whether the subapical network of microtubules is glutamylated or not during ependymal cell differentiation and how this network is affected in their mutants.

We thank the Reviewer’s suggestion. We agree this is an interesting point to look at. We will assess the glutamylation status of the subapical microtubule networks in differentiating ependymal cells and whether they are affected in the mutants.

- Showing the data mentioned in the discussion on Cep110 would be a nice addition to the paper.

These results will be provided.

- Line 354: "The latter serves as a component of tissue polarity that is required for asymmetric PCP protein localization in each cell (Boutin et al., 2014; Vladar et al., 2012)." The cited reference did not demonstrate that this microtubule network is required for asymmetric PCP localization.

We thank the Reviewer for critical reading. We will correct the citation.

Reviewer #3 (Public review):

Summary:

The authors developed a new Agbl5 KO allele, extending the deletion to the N-terminus of CCP5 to explore its function in mouse ependymal cells.

Strengths:

They show that the KO mice exhibit severe hydrocephalus due to disorganized and mislocated basal bodies. Additionally, they present evidence of both impaired beating coordination and a reduction in ciliary beating.

Weaknesses:

The manuscript is well-written but lacks specific interpretations of the results presented. Further experiments are needed to be fully convincing.

We thank the Reviewer’s comments. We plan to conduct the following experiments to strengthen this study.

(1) Quantify the intensity of actin staining around BB patches and its intensity relative to the number of BBs to assess to which extent the actin networks in Agbl5^M1/M1 ependymal cells are affected (please refer to the above response to the comments of Reviewer 2#).

(2) Co-stain tdTomato with cell specific markers to strengthen the spatial expression of tdTomato.

(3) Seek proper antibodies to determine the correlation between signals of GT335 and Ac-Tub in ependymal multicilia of Agbl5^M1/M1 mice.

(4) Quantitatively compare the size of ependymal cells in the wild-type and Agbl5^M1/M1 mice to address whether there is a consequence of possible dysfunction of primary cilia in the precursors of ependymal cells in the mutants. If so, we will further analyze how the primary cilia in the precursors of ependymal cells are affected in the mutants.

(5) Address whether the rotational polarity is affected in the Agbl5^M1/M1 mutant mice.

Author response:

To address Reviewer 1’s concerns, we will implement the following changes:

Comment 1: We will clarify that, even without direct comparisons within or across species, whether vertically transmitted microbes act as pioneering colonizers or integrate into an existing community is an important factor influencing their effect on community composition.

Comment 2: We will provide additional details on the biology of the surrogate frog Oophaga sylvatica, explain how tadpole manipulation might influence adhesion to the caregiver, and acknowledge that the lack of knowledge on the physiological mechanisms underlying tadpole attachment currently limits our discussion to speculation.

We will further clarify in the “Methods” section that SourceTracker’s ability to accurately estimate source proportions was assessed by evaluating how well it assigned training samples to their correct source environments. We will provide the predictions for the training set and describe how they informed our data preprocessing and analysis approach.

Comment 3: While we predicted that community distances between tadpoles and adults would be smaller in species with parental transport, we explicitly state that our results did not confirm this expectation. We thus see no contradiction in our discussion but will ensure that this point is more clearly communicated. In response to the reviewer’s suggestion, we will incorporate additional literature on how tadpoles’ skin microbial communities change over time and adapt to their environment. We will also expand on how the life history of L. longirostris—specifically, the frequent presence of adults in tadpole habitats—may facilitate horizontal microbiota transmission, potentially contributing to shorter community distances.

Comment 4: We will remove the network visualization to prevent any misinterpretation.

Additionally, following Reviewer 2’s suggestion, we will include data on the absolute abundance of ASVs shared between parent and offspring after one month of development to further support the manuscript.

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

INTRODUCTION & THEORY

(1) Can the authors please clarify why the first trial of extinction in a standard protocol does NOT produce the retrieval-extinction effect? Particularly as the results section states: "Importantly, such a short-term effect is also retrieval dependent, suggesting the labile state of memory is necessary for the short-term memory update to take effect (Fig. 1e)." The importance of this point comes through at several places in the paper:

1A. "In the current study, fear recovery was tested 30 minutes after extinction training, whereas the effect of memory reconsolidation was generally evident only several hours later and possibly with the help of sleep, leaving open the possibility of a different cognitive mechanism for the short-term fear dementia related to the retrieval-extinction procedure." ***What does this mean? The two groups in study 1 experienced a different interval between the first and second CS extinction trials; and the results varied with this interval: a longer interval (10 min) ultimately resulted in less reinstatement of fear than a shorter interval. Even if the different pattern of results in these two groups was shown/known to imply two different processes, there is absolutely no reason to reference any sort of cognitive mechanism or dementia - that is quite far removed from the details of the present study.

Indeed, the only difference between the standard extinction paradigm and the retrieval-extinction paradigm is the difference between the first and second CS extinction trials. It has been shown before that a second CS+ presented 1 hour after the initial retrieval CS+ resulted in the dephosphorylation of GluR1 in rats, which was indicative of memory destabilization. The second CS+ presented only 3 minutes after the initial retrieval CS+, as in the standard extinction training, did not cause the GluR1 dephosphorylation effect (Monfils et al., 2009). Therefore, an isolated presentation of the CS+ seems to be important in preventing the return of fear expression. Behaviorally, when the CSs were presented in a more temporally spaced (vs. mass presentation) or a more gradual manner in the extinction training, the fear amnesia effects were more salient (Cain et al., 2003, Gershman et al., 2013). It has also been suggested that only when the old memory and new experience (through extinction) can be inferred to have been generated from the same underlying latent cause, the old memory can be successfully modified (Gershman et al., 2017). On the other hand, if the new experiences are believed to be generated by a different latent cause, then the old memory is less likely to be subject to modification. Therefore, the way the first and 2nd CS are temporally organized (retrieval-extinction or standard extinction) might affect how the latent cause is inferred and lead to different levels of fear expression from a theoretical perspective. These findings, together with studies in both fear and drug memories using the retrieval-extinction paradigm (Liu et al., 2014, Luo et al., 2015, Schiller et al., 2010, Xue et al., 2012), seem to suggest that the retrieval-extinction and the standard extinction procedures engage different cognitive and molecular mechanisms that lead to significant different behavioral outcomes. 

In our study, we focus on the short-term and long-term amnesia effects of the retrieval-extinction procedure but also point out the critical role of retrieval in eliciting the short-term effect.

1B. "Importantly, such a short-term effect is also retrieval dependent, suggesting the labile state of memory is necessary for the short-term memory update to take effect (Fig. 1e)." ***As above, what is "the short-term memory update"? At this point in the text, it would be appropriate for the authors to discuss why the retrieval-extinction procedure produces less recovery than a standard extinction procedure as the two protocols only differ in the interval between the first and second extinction trials. References to a "short-term memory update" process do not help the reader to understand what is happening in the protocol.

Sorry for the lack of clarity here. By short-term memory update we meant the short-term amnesia in fear expression.

(2) "Indeed, through a series of experiments, we identified a short-term fear amnesia effect following memory retrieval, in addition to the fear reconsolidation effect that appeared much later."

***The only reason for supposing two effects is because of the differences in responding to the CS2, which was subjected to STANDARD extinction, in the short- and long-term tests. More needs to be said about how and why the performance of CS2 is affected in the short-term test and recovers in the long-term test. That is, if the loss of performance to CS1 and CS2 is going to be attributed to some type of memory updating process across the retrieval-extinction procedure, one needs to explain the selective recovery of performance to CS2 when the extinction-to-testing interval extends to 24 hours. Instead of explaining this recovery, the authors note that performance to CS1 remains low when the extinction-to-testing interval is 24 hours and invoke something to do with memory reconsolidation as an explanation for their results: that is, they imply (I think) that reconsolidation of the CS1-US memory is disrupted across the 24-hour interval between extinction and testing even though CS1 evokes negligible responding just minutes after extinction.

In our results, we did not only focus on the fear expression related to CS2. In fact, we also demonstrated that the CS1 related fear expression diminished in the short-term memory test but re-appeared in the long-term memory after the CS1 retrieval-extinction training.

The “…recovery of performance to CS2 when the extinction-to-testing interval extends to 24 hours…” is a result that has been demonstrated in various previous studies (Kindt and Soeter, 2018, Kindt et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012). That is, the reconsolidation framework stipulates that the pharmacological or behavioral intervention during the labile states of the reconsolidation window only modifies the fear memory linked to the reminded retrieval cue, but not for the non-reminded CS-US memory expression (but also see (Liu et al., 2014, Luo et al., 2015) for using the unconditioned stimulus as the reminder cue and the retrieval-extinction paradigm to prevent the return of fear memory associated with different CS).  In fact, we hypothesized the temporal dynamics of CS1 and CS2 related fear expressions were due to the interplay between the short-term and long-term (reconsolidation) effects of the retrieval-extinction paradigm in the last figure (Fig. 6). 

(3) The discussion of memory suppression is potentially interesting but, in its present form, raises more questions than it answers. That is, memory suppression is invoked to explain a particular pattern of results but I, as the reader, have no sense of why a fear memory would be better suppressed shortly after the retrieval-extinction protocol compared to the standard extinction protocol; and why this suppression is NOT specific to the cue that had been subjected to the retrieval-extinction protocol.

We discussed memory suppression as one of the potential mechanisms to account for the three characteristics of the short-term amnesia effects: cue-independence, temporal dynamics (short-term) and thought-control-ability relevance. According to the memory suppression theory, the memory suppression effect is NOT specific to the cue and this effect was demonstrated via the independent cue test in a variety of studies (Anderson and Floresco, 2022, Anderson and Green, 2001, Gagnepain et al., 2014, Zhu et al., 2022). Therefore, we suggest in the discussion that it might be possible the CS1 retrieval cue prompted an automatic suppression mechanism and yielded the short-term fear amnesia consistent with various predictions from the memory suppression theory:

“In our experiments, subjects were not explicitly instructed to suppress their fear expression, yet the retrieval-extinction training significantly decreased short-term fear expression. These results are consistent with the short-term amnesia induced with the more explicit suppression intervention (Anderson et al., 1994; Kindt and Soeter, 2018; Speer et al., 2021; Wang et al., 2021; Wells and Davies, 1994). It is worth noting that although consciously repelling unwanted memory is a standard approach in memory suppression paradigm, it is possible that the engagement of the suppression mechanism can be unconscious. For example, in the retrieval-induced forgetting (RIF) paradigm, recall of a stored memory impairs the retention of related target memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner (Imai et al., 2014). Moreover, subjects with trauma histories exhibited more suppression-induced forgetting for both negative and neutral memories than those with little or no trauma (Hulbert and Anderson, 2018). Similarly, people with higher self-reported thought-control capabilities showed more severe cue-independent memory recall deficit, suggesting that suppression mechanism is associated with individual differences in spontaneous control abilities over intrusive thoughts (Küpper et al., 2014). It has also been suggested that similar automatic mechanisms might be involved in organic retrograde amnesia of traumatic childhood memories (Schacter et al., 2012; Schacter et al., 1996).”

3A. Relatedly, how does the retrieval-induced forgetting (which is referred to at various points throughout the paper) relate to the retrieval-extinction effect? The appeal to retrieval-induced forgetting as an apparent justification for aspects of the present study reinforces points 2 and 3 above. It is not uninteresting but needs some clarification/elaboration.

We introduced the retrieval-induced forgetting (RIF) to make the point that RIF was believed to be related to the memory suppression mechanism and the RIF effect can appear relatively early, consistent with what we observed in the short-term amnesia effect. We have re-written the manuscript to make this point clearer:

“It is worth noting that although consciously repelling unwanted memory is a standard approach in memory suppression paradigm, it is possible that the engagement of the suppression mechanism can be unconscious. For example, in the retrieval-induced forgetting (RIF) paradigm, recall of a stored memory impairs the retention of related target memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner (Imai et al., 2014). Moreover, subjects with trauma histories exhibited more suppression-induced forgetting for both negative and neutral memories than those with little or no trauma (Hulbert and Anderson, 2018). Similarly, people with higher self-reported thought-control capabilities showed more severe cue-independent memory recall deficit, suggesting that suppression mechanism is associated with individual differences in spontaneous control abilities over intrusive thoughts (Küpper et al., 2014).”

(4) Given the reports by Chalkia, van Oudenhove & Beckers (2020) and Chalkia et al (2020), some qualification needs to be inserted in relation to reference 6. That is, reference 6 is used to support the statement that "during the reconsolidation window, old fear memory can be updated via extinction training following fear memory retrieval". This needs a qualifying statement like "[but see Chalkia et al (2020a and 2020b) for failures to reproduce the results of 6]."

https://pubmed.ncbi.nlm.nih.gov/32580869/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115860/

We have incorporated the reviewer’s suggestion into the revised manuscript in both the introduction:

“Pharmacological blockade of protein synthesis and behavioral interventions can both eliminate the original fear memory expression in the long-term (24 hours later) memory test ( Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), resulting in the cue-specific fear memory deficit (Debiec et al., 2002; Lee, 2008; Nader, Schafe, & LeDoux, 2000). For example, during the reconsolidation window, retrieving a fear memory allows it to be updated through extinction training (i.e., the retrieval-extinction paradigm (Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), but also see (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; D. Schiller, LeDoux, & Phelps, 2020)”

And in the discussion:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

5A. What does it mean to ask: "whether memory retrieval facilitates update mechanisms other than memory reconsolidation"? That is, in what sense could or would memory retrieval be thought to facilitate a memory update mechanism?

It is widely documented in the literatures that memory retrieval renders the old memory into a labile state susceptible for the memory reconsolidation process. However, as we mentioned in the manuscript, studies have shown that memory reconsolidation requires the de novo protein synthesis and usually takes hours to complete. What remains unknown is whether old memories are subject to modifications other than the reconsolidation process. Our task specifically tested the short-term effect of the retrieval-extinction paradigm and found that fear expression diminished 30mins after the retrieval-extinction training. Such an effect cannot be accounted for by the memory reconsolidation effect.

5B. "First, we demonstrate that memory reactivation prevents the return of fear shortly after extinction training in contrast to the memory reconsolidation effect which takes several hours to emerge and such a short-term amnesia effect is cue independent (Study 1, N = 57 adults)."

***The phrasing here could be improved for clarity: "First, we demonstrate that the retrieval-extinction protocol prevents the return of fear shortly after extinction training (i.e., when testing occurs just min after the end of extinction)." Also, cue-dependence of the retrieval-extinction effect was assessed in study 2.

We thank the reviewer and have modified the phrasing of the sentence:

“First, we demonstrate that memory retrieval-extinction protocol prevents the return of fear expression shortly after extinction training and this short-term effect is memory reactivation dependent (Study 1, N = 57 adults).”

5C. "Furthermore, memory reactivation also triggers fear memory reconsolidation and produces cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults)." ***In study 2, the retrieval-extinction protocol produced a cue-specific disruption in responding when testing occurred 24 hours after the end of extinction. This result is interesting but cannot be easily inferred from the statement that begins "Furthermore..." That is, the results should be described in terms of the combined effects of retrieval and extinction, not in terms of memory reactivation alone; and the statement about memory reconsolidation is unnecessary. One can simply state that the retrieval-extinction protocol produced a cue-specific disruption in responding when testing occurred 24 hours after the end of extinction.

We have revised the text according to the reviewer’s comment.

“Furthermore, across different timescales, the memory retrieval-extinction paradigm triggers distinct types of fear amnesia in terms of cue-specificity and cognitive control dependence, suggesting that the short-term fear amnesia might be caused by different mechanisms from the cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults).”

5D. "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that both memory retrieval and intact prefrontal cortex functions were necessary for the short-term fear amnesia."

***This could be edited to better describe what was shown: E.g., "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that intact prefrontal cortex functions were necessary for the short-term fear amnesia after the retrieval-extinction protocol."

Edited:

“Finally, using continuous theta-burst stimulation (Study 3, N = 75 adults), we directly manipulated brain activity in the dorsolateral prefrontal cortex, and found that both memory reactivation and intact prefrontal cortex function were necessary for the short-term fear amnesia after the retrieval-extinction protocol.”

5E. "The temporal scale and cue-specificity results of the short-term fear amnesia are clearly dissociable from the amnesia related to memory reconsolidation, and suggest that memory retrieval and extinction training trigger distinct underlying memory update mechanisms."

***The pattern of results when testing occurred just minutes after the retrieval-extinction protocol was different from that obtained when testing occurred 24 hours after the protocol. Describing this in terms of temporal scale is unnecessary, and suggesting that memory retrieval and extinction trigger different memory update mechanisms is not obviously warranted. The results of interest are due to the combined effects of retrieval+extinction and there is no sense in which different memory update mechanisms should be identified with retrieval (mechanism 1) and extinction (mechanism 2).

We did not argue for different memory update mechanisms for the “retrieval (mechanism 1) and extinction (mechanism 2)” in our manuscript. Instead, we proposed that the retrieval-extinction procedure, which was mainly documented in the previous literatures for its association with the reconsolidation-related fear memory retention (the long-term effect), also had a much faster effect (the short-term effect). These two effects differed in many aspects, suggesting that different memory update mechanisms might be involved.

5F. "These findings raise the possibility of concerted memory modulation processes related to memory retrieval..."

***What does this mean?

As we mentioned in our response to the previous comment, we believe that the retrieval-extinction procedure triggers different types of memory update mechanisms working on different temporal scales.

(6) "...suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes..."

***What does it mean to say that the fear memory might be amenable to a more immediate effect?

We intended to state that the retrieval-extinction procedure can produce a short-term amnesia effect and have thus revised the text.

(7) "Parallel to the behavioral manifestation of long- and short-term memory deficits, concurrent neural evidence supporting memory reconsolidation theory emphasizes the long-term effect of memory retrieval by hypothesizing that synapse degradation and de novo protein synthesis are required for reconsolidation."

***This sentence needs to be edited for clarity.

We have rewritten this sentence:

“Corresponding to the long-term behavioral manifestation, concurrent neural evidence supporting memory reconsolidation hypothesis emphasizes that synapse degradation and de novo protein synthesis are required for reconsolidation.”

(8) "previous behavioral manipulations engendering the short-term declarative memory effect..."

***What is the declarative memory effect? It should be defined.

We meant the amnesia on declarative memory research, such as the memory deficit caused by the think/no-think paradigms. Texts have been modified for clarity:

“On the contrary, previous behavioral manipulations engendering the short-term amnesia on declarative memory, such as the think/no-think paradigm, hinges on the intact activities in brain areas such as dorsolateral prefrontal cortex (cognitive control) and its functional coupling with specific brain regions such as hippocampus (memory retrieval) (Anderson and Green, 2001; Wimber et al., 2015).”

(9) "The declarative amnesia effect emerges much earlier due to the online functional activity modulation..."

***Even if the declarative memory amnesia effect had been defined, the reference to online functional activity modulation is not clear.

We have rephrased the sentence:

“The declarative amnesia effect arises much earlier due to the more instant modulation of functional connectivity, rather than the slower processes of new protein synthesis in these brain regions.”

(10) "However, it remains unclear whether memory retrieval might also precipitate a short-term amnesia effect for the fear memory, in addition to the long-term prevention orchestrated by memory consolidation."

***I found this sentence difficult to understand on my first pass through the paper. I think it is because of the phrasing of memory retrieval. That is, memory retrieval does NOT precipitate any type of short-term amnesia for the fear memory: it is the retrieval-extinction protocol that produces something like short-term amnesia. Perhaps this sentence should also be edited for clarity.

We have changed “memory retrieval” to “retrieval-extinction” where applicable.

I will also note that the usage of "short-term" at this point in the paper is quite confusing: Does the retrieval-extinction protocol produce a short-term amnesia effect, which would be evidenced by some recovery of responding to the CS when tested after a sufficiently long delay? I don't believe that this is the intended meaning of "short-term" as used throughout the majority of the paper, right?

By “short-term”, we meant the lack of fear expression in the test phase (measured by skin conductance responses) shortly after the retrieval-extinction procedure (30 mins in studies 1 & 2 and 1 hour in study 3). It does not indicate that the effect is by itself “short-lived”.

(11) "To fully comprehend the temporal dynamics of the memory retrieval effect..."<br /> ***What memory retrieval effect? This needs some elaboration.

We’ve changed the phrase “memory retrieval effect” to “retrieval-extinction effect” to refer to the effect of retrieval-extinction on fear amnesia.

(12) "We hypothesize that the labile state triggered by the memory retrieval may facilitate different memory update mechanisms following extinction training, and these mechanisms can be further disentangled through the lens of temporal dynamics and cue-specificities."

***What does this mean? The first part of the sentence is confusing around the usage of the term "facilitate"; and the second part of the sentence that references a "lens of temporal dynamics and cue-specificities" is mysterious. Indeed, as all rats received the same retrieval-extinction exposures in Study 2, it is not clear how or why any differences between the groups are attributed to "different memory update mechanisms following extinction".

As the reviewer mentioned, if only one time point data were collected, we cannot differentiate whether different memory update mechanisms are involved. In study 2, however, the 3 groups only differed on the time onsets the reinstatement test was conducted. Accordingly, our results showed that the fear amnesia effects for CS1 and CS2 cannot be simply explained by forgetting: different memory update mechanisms must be at work to explain the characteristics of the SCR related to both CS1 and CS2 at three different time scales (30min, 6h and 24h). It was based on these results, together with the results from the TMS study (study 3), that we proposed the involvement of a short-term memory update mechanism in addition to the reconsolidation related fear amnesia (which should become evident much later) induced by the retrieval-extinction protocol.

(13) "In the first study, we aimed to test whether there is a short-term amnesia effect of fear memory retrieval following the fear retrieval-extinction paradigm."

***Again, the language is confusing. The phrase, "a short-term amnesia effect" implies that the amnesia itself is temporary; but I don't think that this implication is intended. The problem is specifically in the use of the phrase "a short-term amnesia effect of fear memory retrieval." To the extent that short-term amnesia is evident in the data, it is not due to retrieval per se but, rather, the retrieval-extinction protocol.

We have changed the wordings and replaced “memory retrieval” with “retrieval-extinction” where applicable.

(14) The authors repeatedly describe the case where there was a 24-hour interval between extinction and testing as consistent with previous research on fear memory reconsolidation. Which research exactly? That is, in studies where a CS re-exposure was combined with a drug injection, responding to the CS was disrupted in a final test of retrieval from long-term memory which typically occurred 24 hours after the treatment. Is that what the authors are referring to as consistent? If so, which aspect of the results are consistent with those previous findings? Perhaps the authors mean to say that, in the case where there was a 24-hour interval between extinction and testing, the results obtained here are consistent with previous research that has used the retrieval-extinction protocol. This would clarify the intended meaning greatly.

Our 24 hour test results after the retrieval-extinction protocol was consistent with both pharmacological and behavioral intervention studies in fear memory reconsolidation studies (Kindt and Soeter, 2018, Kindt et al., 2009, Liu et al., 2014, Luo et al., 2015, Monfils et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012) since the final test phase typically occurred 24 hours after the treatment. At the 24-hour interval, the memory reconsolidation effect would become evident either via drug administration or behavioral intervention (extinction training).

DATA

(15) Points about data:

5A. The eight participants who were discontinued after Day 1 in study 1 were all from the no-reminder group. Can the authors please comment on how participants were allocated to the two groups in this experiment so that the reader can better understand why the distribution of non-responders was non-random (as it appears to be)?

15B. Similarly, in study 2, of the 37 participants that were discontinued after Day 2, 19 were from Group 30 min, and 5 were from Group 6 hours. Can the authors comment on how likely these numbers are to have been by chance alone? I presume that they reflect something about the way that participants were allocated to groups, but I could be wrong.

We went back and checked out data. As we mentioned in the supplementary materials, we categorized subjects as non-responders if their SCR response to any CS was less than 0.02  in Day 1 (fear acquisition). Most of the discontinued participants (non-responders) in the no-reminder group (study 1) and the 30min & 24 h groups (study 2) were when the heating seasons just ended or were yet to start, respectively. It has been documented that human body thermal conditions were related to the quality of the skin conductance response (SCR) measurements (Bauer et al., 2022, Vila, 2004). We suspect that the non-responders might be related to the body thermal conditions caused by the lack of central heating.

15C. "Post hoc t-tests showed that fear memories were resilient after regular extinction training, as demonstrated by the significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group (t26 = 7.441, P < 0.001; Fig. 1e), while subjects in the reminder group showed no difference of fear recovery between CS+ and CS- (t29 = 0.797, P = 0.432, Fig. 1e)."

***Is the fear recovery index shown in Figure 1E based on the results of the first test trial only? How can there have been a "significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group" when the difference in responding to the CS+ and CS- is used to calculate the fear recovery index shown in 1E? What are the t-tests comparing exactly, and what correction is used to account for the fact that they are applied post-hoc?

As we mentioned in the results section of the manuscript, the fear recovery index was defined as “the SCR difference between the first test trial and the last extinction trial of a specific CS”. We then calculated the “differential fear recovery index” (figure legends of Fig. 1e) between CS+ and CS- for both the reminder and no-reminder groups. The post-hoc t-tests were used to examine whether there were significant fear recoveries (compare to 0) in both the reminder (t₂₉ = 0.797, P = 0.432, Fig. 1e) and no-reminder (t₂₆ = 7.441, P  < 0.001; Fig. 1e) groups. We realize that the description of Bonferroni correction was not specified in the original manuscript and hence added in the revision where applicable.

15D. "Finally, there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (t55 = -2.022, P = 0.048; Fig. 1c, also see Supplemental Material for direct test for the test phase)."

***Is this statement correct - i.e., that there is no statistically significant difference in fear recovery to the CS+ in the reminder and no reminder groups? I'm sure that the authors would like to claim that there IS such a difference; but if such a difference is claimed, one would be concerned by the fact that it is coming through in an uncorrected t-test, which is the third one of its kind in this paragraph. What correction (for the Type 1 error rate) is used to account for the fact that the t-tests are applied post-hoc? And if no correction, why not?

We are sorry about the typo.  The reviewer was correct that we meant to claim here that “… there is a significant difference between the differential fear recovery indexes between CS+ in the reminder and no-reminder groups (t₅₅ =- 2.022, P = 0.048; Fig. 1e)”.  Note that the t-test performed here was a confirmatory test following our two-way ANOVA with main effects of group (reminder vs. no-reminder) and time (last extinction trial vs. first test trial) on the differential CS SCR response (CS+ minus CS-) and we found a significant group x time interaction effect (F_1.55 = 4.087, P = 0.048, η² = 0.069). The significant difference between the differential fear recovery indexes was simply a re-plot of the interaction effect mentioned above and therefore no multiple correction is needed. We have reorganized the sequence of the sentences such that this t-test now directly follows the results of the ANOVA:

“The interaction effect was confirmed by the significant difference between the differential fear recovery indexes between CS1+ and CS2+ in the reminder and no-reminder groups (t₅₅ \= -2.022, P \= 0.048; Figure 1E, also see Supplemental Material for the direct test of the test phase).”

15E. In study 2, why is responding to the CS- so high on the first test trial in Group 30 min? Is the change in responding to the CS- from the last extinction trial to the first test trial different across the three groups in this study? Inspection of the figure suggests that it is higher in Group 30 min relative to Groups 6 hours and 24 hours. If this is confirmed by the analysis, it has implications for the fear recovery index which is partly based on responses to the CS-. If not for differences in the CS- responses, Groups 30 minutes and 6 hours are otherwise identical.

Following the reviewer’s comments, we went back and calculated the mean SCR difference of CS- between the first test trial and the last extinction trial for all three studies (see Author response image 1 below). In study 1, there was no difference in the mean CS- SCR (between the first test trial and last extinction trial) between the reminder and no-reminder groups (Kruskal-Wallis test , panel a), though both groups showed significant fear recovery even in the CS- condition (Wilcoxon signed rank test, reminder: P = 0.0043, no-reminder: P = 0.0037). Next, we examined the mean SCR for CS- for the 30min, 6h and 24h groups in study 2 and found that there was indeed a group difference (one-way ANOVA,F_2.76 = 5.3462, P = 0.0067, panel b), suggesting that the CS- related SCR was influenced by the test time (30min, 6h or 24h). We also tested the CS- related SCR for the 4 groups in study 3 (where test was conducted 1 hour after the retrieval-extinction training) and found that across TMS stimulation types (PFC vs. VER) and reminder types (reminder vs. no-reminder) the ANOVA analysis did not yield main effect of TMS stimulation type (F_1.71 = 0.322, P = 0.572) nor main effect of reminder type (F_1.71 = 0.0499, P = 0.824, panel c). We added the R-VER group results in study 3 (see panel c) to panel b and plotted the CS- SCR difference across 4 different test time points and found that CS- SCR decreased as the test-extinction delay increased (Jonckheere-Terpstra test, P = 0.00028). These results suggest a natural “forgetting” tendency for CS- related SCR and highlight the importance of having the CS- as a control condition to which the CS+ related SCR was compared with.

Author response image 1.

15F. Was the 6-hour group tested at a different time of day compared to the 30-minute and 24-hour groups; and could this have influenced the SCRs in this group?

For the 30min and 24h groups, the test phase can be arranged in the morning, in the afternoon or at night. However, for the 6h group, the test phase was inevitably in the afternoon or at night since we wanted to exclude the potential influence of night sleep on the expression of fear memory (see Author response table 1 below). If we restricted the test time in the afternoon or at night for all three groups, then the timing of their extinction training was not matched.

Author response table 1.

Nevertheless, we also went back and examined the data for the subjects only tested in the afternoon or at nights in the 30min and 24h groups to match with the 6h group where all the subjects were tested either in the afternoon or at night. According to Author response table 1 above, we have 17 subjects for the 30min group (9+8),18 subjects for the 24h group (9 + 9) and 26 subjects for the 6h group (12 + 14). As Author response image 2 shows, the SCR patterns in the fear acquisition, extinction and test phases were similar to the results presented in the original figure.

Author response image 2.

15G. Why is the range of scores in "thought control ability" different in the 30-minute group compared to the 6-hour and 24-hour groups? I am not just asking about the scale on the x-axis: I am asking why the actual distribution of the scores in thought control ability is wider for the 30-minute group?

We went back and tested whether the TCAQ score variance was the same across three groups. We found that there was significant difference in the variance of the TCAQ score distribution across three groups (F_2.155 = 4.324, P = 0.015, Levene test). However, post-hoc analyses found that the variance of TCAQ is not significantly different between the 30min and 6h groups (F_26.25 = 0.4788, P = 0.0697), nor between the 30min and 24h groups (i>F_26.25 = 0.4692, P = 0.0625). To further validate our correlational results between the TCAQ score and the fear recovery index, we removed the TCAQ scores that were outside the TCAQ score range of the 6h & 24h groups from the 30min group (resulting in 4 “outliner” TCAQ scores in the 30min group, panel a in Author response image 3 below) and the Levene test confirmed that the variance of the TCAQ scores showed no difference across groups after removing the 4 “outliner” data points in the 30min group (i>F_2.147 = 0.74028, P = 0.4788). Even with the 4 “outliers” removed from the 30min group, the correlational analysis of the TCAQ scores and the fear recovery index still yielded significant result in the 30min group (beta = -0.0148, t = -3.731, P = 0.0006, see panel b below), indicating our results were not likely due to the inclusion of subjects with extreme TCAQ scores.

Author response image 3.

(16) During testing in each experiment, how were the various stimuli presented? That is, was the presentation order for the CS+ and CS- pseudorandom according to some constraint, as it had been in extinction? This information should be added to the method section.

We mentioned the order of the stimuli in the testing phase in the methods section “… For studies 2 & 3, …a pseudo-random stimulus order was generated for fear acquisition and extinction phases of three groups with the rule that no same trial- type (CS1+, CS2+ and CS-) repeated more than twice. In the test phase, to exclude the possibility that the difference between CS1+ and CS2+ was simply caused by the presentation sequence of CS1+ and CS2+, half of the participants completed the test phase using a pseudo-random stimuli sequence and the identities of CS1+ and CS2+ reversed in the other half of the participants.”

(17) "These results are consistent with previous research which suggested that people with better capability to resist intrusive thoughts also performed better in motivated dementia in both declarative and associative memories."

***Which parts of the present results are consistent with such prior results? It is not clear from the descriptions provided here why thought control ability should be related to the present findings or, indeed, past ones in other domains. This should be elaborated to make the connections clear.

In the 30min group, we found that subjects’ TCAQ scores were negatively correlated with their fear recovery indices. That is, people with better capacity to resist intrusive thoughts were also less likely to experience the return of fear memory, which are consistent with previous results. Together with our brain stimulation results, the short-term amnesia is related to subject’s cognitive control ability and intact dlPFC functions. It is because of these similarities that we propose that the short-term amnesia might be related to the automatic memory suppression mechanism originated from the declarative memory research. Since we have not provided all the evidence at this point of the results section, we briefly listed the connections with previous declarative and associative memory research.

Reviewer #2 (Public Review):

The fear acquisition data is converted to a differential fear SCR and this is what is analysed (early vs late). However, the figure shows the raw SCR values for CS+ and CS- and therefore it is unclear whether the acquisition was successful (despite there being an "early" vs "late" effect - no descriptives are provided).

As the reviewer mentioned, the fear acquisition data was converted to a differential fear SCR and we conducted a two-way mixed ANOVA (reminder vs. no-reminder) x time (early vs. late part of fear acquisition) on the differential SCRs. We found a significant main effect of time (early vs. late; F_1.55 = 6.545, P = 0.013, η² = 0.106), suggesting successful fear acquisition in both groups. Fig. 1c also showed the mean differential SCR for the latter half of the acquisition phase in both the reminder and no-reminder groups and there was no significant difference in acquired SCRs between groups (early acquisition: t₅₅ = -0.063, P = 0.950; late acquisition: t₅₅ = -0.318, P = 0.751; Fig. 1c).

In Experiment 1 (Test results) it is unclear whether the main conclusion stems from a comparison of the test data relative to the last extinction trial ("we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS") or the difference relative to the CS- ("differential fear recovery index between CS+ and CS-"). It would help the reader assess the data if Figure 1e presents all the indexes (both CS+ and CS-). In addition, there is one sentence that I could not understand "there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (P=0.048)". The p-value suggests that there is a difference, yet it is not clear what is being compared here. Critically, any index taken as a difference relative to the CS- can indicate recovery of fear to the CS+ or absence of discrimination relative to the CS-, so ideally the authors would want to directly compare responses to the CS+ in the reminder and no-reminder groups. The latter issue is particularly relevant in Experiment 2, in which the CS- seems to vary between groups during the test and this can obscure the interpretation of the result.

In all the experiments, the fear recovery index (FRI) was defined as the SCR difference between the first test trial and the last extinction trial for any CS. Subsequently, the differential fear recovery index (FRI) was defined between the FRI of a specific CS+ and the FRI of the CS-. The differential FRI would effectively remove the non-specific time related effect (using the CS- FRI as the baseline). We have revised the text accordingly.

As we responded to reviewer #1, the CS- fear recovery indices (FIR) for the reminder and no-reminder groups were not statistically different (Kruskal-Wallis test , panel a, Author response image 1), though both groups showed significant fear recovery even in the CS- condition (Wilcoxon signed rank test, reminder: P = 0.0043, no-reminder: P = 0.0037, panel a). Next, we examined the mean SCR for CS- for the 30min, 6h and 24h groups in study 2 and found that there was indeed a group difference (one-way ANOVA,  one-way ANOVA,F_2.76 = 5.3462, P = 0.0067, panel b), suggesting that the CS- SCR was influenced by the test time delay. We also tested the CS- SCR for the 4 groups in study 3 and found that across TMS stimulation types (PFC vs. VER) and reminder types (reminder vs. no-reminder) the ANOVA analysis did not yield main effect of TMS stimulation type (F_1.71 = 0.322, P = 0.572) nor main effect of reminder type (F_1.71 = 0.0499, P = 0.824, panel c). We added the R-VER group results in study 3 (see panel c) to panel b and plotted the CS- SCR difference across 4 different test time points and found that CS- SCR decreased as the test-extinction delay increased (Jonckheere-Terpstra test, P = 0.00028). These results suggest a natural “forgetting” tendency for the CS- fear recovery index and highlight the importance of having the CS- as a control condition to compare the CS+ recovery index with (resulting in the Differential recovery index). Parametric and non-parametric analyses were adopted based on whether the data met the assumptions for the parametric analyses.

In Experiment 1, the findings suggest that there is a benefit of retrieval followed by extinction in a short-term reinstatement test. In Experiment 2, the same effect is observed on a cue that did not undergo retrieval before extinction (CS2+), a result that is interpreted as resulting from cue-independence, rather than a failure to replicate in a within-subjects design the observations of Experiment 1 (between-subjects). Although retrieval-induced forgetting is cue-independent (the effect on items that are suppressed [Rp-] can be observed with an independent probe), it is not clear that the current findings are similar. Here, both cues have been extinguished and therefore been equally exposed during the critical stage.

We appreciate the reviewer’s insight on this issue. Although in the discussion we raised the possibility of memory suppression to account for the short-term amnesia effect, we did not intend to compare our paradigm side-by-side with retrieval-induced forgetting. In our previous work (Wang et al., 2021), we reported that active suppression effect of CS+ related fear memory during the standard extinction training generalized to other CS+, yielding a cue-independent effect. In the current experiments, we did not implement active suppression; instead, we used the CS+ retrieval-extinction paradigm. It is thus possible that the CS+ retrieval cue may function to facilitate automatic suppression. Indeed, in the no-reminder group (standard extinction) of study 1, we did observe the return of fear expression, suggesting the critical role of CS+ reminder before the extinction training. Based on the results mentioned above, we believe our short-term amnesia results were consistent with the hypothesis that the retrieval CS+ (reminder) might prompt subjects to adopt an automatic suppress mechanism in the following extinction training, yielding cue-independent amnesia effects.

The findings in Experiment 2 suggest that the amnesia reported in Experiment 1 is transient, in that no effect is observed when the test is delayed by 6 hours. The phenomena whereby reactivated memories transition to extinguished memories as a function of the amount of exposure (or number of trials) is completely different from the phenomena observed here. In the former, the manipulation has to do with the number of trials (or the total amount of time) that the cues are exposed to. In the current study, the authors did not manipulate the number of trials but instead the retention interval between extinction and test. The finding reported here is closer to a "Kamin effect", that is the forgetting of learned information which is observed with intervals of intermediate length (Baum, 1968). Because the Kamin effect has been inferred to result from retrieval failure, it is unclear how this can be explained here. There needs to be much more clarity on the explanations to substantiate the conclusions.

Indeed, in our studies, we did not manipulate the amount of exposure (or number of trials) but only the retention interval between extinction and test. Our results demonstrated that the retrieval-extinction protocol yielded the short-term amnesia on fear memory, qualitatively different from the reconsolidation related amnesia proposed in the previous literatures. After examining the temporal dynamics, cue-specificity and TCAQ association with the short-term amnesia, we speculated that the short-term effect might be related to an automatic suppression mechanism. Of course, further studies will be required to test such a hypothesis.

Our results might not be easily compared with the “Kamin effect”, a term coined to describe the “retention of a partially learned avoidance response over varying time intervals” using a learning-re-learning paradigm (Baum, 1968, Kamin, 1957). However, the retrieval-extinction procedure used in our studies was different from the learning-re-learning paradigm in the original paper (Kamin, 1957) and the reversal-learning paradigm the reviewer mentioned (Baum, 1968).

There are many results (Ryan et al., 2015) that challenge the framework that the authors base their predictions on (consolidation and reconsolidation theory), therefore these need to be acknowledged. Similarly, there are reports that failed to observe the retrieval-extinction phenomenon (Chalkia et al., 2020), and the work presented here is written as if the phenomenon under consideration is robust and replicable. This needs to be acknowledged.

We thank the reviewer pointing out the related literature and have added a separate paragraph about other results in the discussion (as well as citing relevant references in the introduction) to provide a full picture of the reconsolidation theory to the audience:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

The parallels between the current findings and the memory suppression literature are speculated in the general discussion, and there is the conclusion that "the retrieval-extinction procedure might facilitate a spontaneous memory suppression process". Because one of the basic tenets of the memory suppression literature is that it reflects an "active suppression" process, there is no reason to believe that in the current paradigm, the same phenomenon is in place, but instead, it is "automatic". In other words, the conclusions make strong parallels with the memory suppression (and cognitive control) literature, yet the phenomena that they observed are thought to be passive (or spontaneous/automatic).

Ultimately, it is unclear why 10 mins between the reminder and extinction learning will "automatically" suppress fear memories. Further down in the discussion, it is argued that "For example, in the well-known retrieval-induced forgetting (RIF) phenomenon, the recall of a stored memory can impair the retention of related long-term memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner". I did not follow with the time delay between manipulation and test (20 mins) would speak about whether the process is controlled or automatic.

In our previous research, we showed that the memory suppression instruction together with the extinction procedure successfully prevented the return of fear expression in the reinstatement test trials 30mins after the extinction training (Wang et al., 2021). In the current experiments, we replaced the suppression instruction with the retrieval cue before the extinction training (retrieval-extinction protocol) and observed similar short-term amnesia effects. These results prompted us to hypothesize in the discussion that the retrieval cue might facilitate an automatic suppression process. We made the analogy to RIF phenomenon in the discussion to suggest that the suppression of (competing) memories could be unintentional and fast (20 mins), both of which were consistent with our results. We agree with the reviewer that this hypothesis is more of a speculation (hence in the discussion), and more studies are required to further test such a hypothesis. However, what we want to emphasize in this paper is the report of the short-term amnesia effects which were clearly not related to the memory reconsolidation effect in a variety of aspects.

Among the many conclusions, one is that the current study uncovers the "mechanism" underlying the short-term effects of retrieval extinction. There is little in the current report that uncovers the mechanism, even in the most psychological sense of the mechanism, so this needs to be clarified. The same applies to the use of "adaptive".

Whilst I could access the data on the OFS site, I could not make sense of the Matlab files as there is no signposting indicating what data is being shown in the files. Thus, as it stands, there is no way of independently replicating the analyses reported.

We have re-organized data on the OFS site, and they should be accessible now.

The supplemental material shows figures with all participants, but only some statistical analyses are provided, and sometimes these are different from those reported in the main manuscript. For example, the test data in Experiment 1 is analysed with a two-way ANOVA with the main effects of group (reminder vs no-reminder) and time (last trial of extinction vs first trial of the test) in the main report. The analyses with all participants in the sup mat used a mixed two-way ANOVA with a group (reminder vs no reminder) and CS (CS+ vs CS-). This makes it difficult to assess the robustness of the results when including all participants. In addition, in the supplementary materials, there are no figures and analyses for Experiment 3.

We are sorry for the lack of clarity in the supplementary materials. We have supplementary figures Fig. S1 & S2 for the data re-analysis with all the responders (learners + non-learners). The statistical analyses performed on the responders in both figures yielded similar results as those in the main text. For other analyses reported in the supplementary materials, we specifically provided different analysis results to demonstrate the robustness of our results. For example, to rule out the effects we observed in two-way ANOVA in the main text may be driven by the different SCR responses on the last extinction trial, we only tested the two-way ANOVA for the first trial SCR of test phase and these analyses provided similar results. Please note we did not include non-learners in these analyses (the texts of the supplementary materials).

Since we did not exclude any non-learners in study 3, all the results were already reported in the main text.

One of the overarching conclusions is that the "mechanisms" underlying reconsolidation (long term) and memory suppression (short term) phenomena are distinct, but memory suppression phenomena can also be observed after a 7-day retention interval (Storm et al., 2012), which then questions the conclusions achieved by the current study.

As we stated before, the focus of the manuscript was to demonstrate a novel short-term fear amnesia effect following the retrieval-extinction procedure. We discussed memory suppression as one of the potential mechanisms for such a short-term effect. In fact, the durability of the memory suppression effect is still under debate. Although Storm et al. (2012) suggested that the retrieval-induced forgetting can persist for as long as a week, other studies, however, failed to observe long-term forgetting (after 24 hrs; (Carroll et al., 2007, Chan, 2009). It is also worth noting that Storm et al. (2012) tested RIF one week later using half of the items the other half of which were tested 5 minutes after the retrieval practice. Therefore, it can be argued that there is a possibility that the long-term RIF effect is contaminated by the test/re-test process on the same set of (albeit different) items at different time onsets (5mins & 1 week).

Reviewer #3 (Public Review):

(1) The entire study hinges on the idea that there is memory 'suppression' if (1) the CS+ was reminded before extinction and (2) the reinstatement and memory test takes place 30 minutes later (in Studies 1 & 2). However, the evidence supporting this suppression idea is not very strong. In brief, in Study 1, the effect seems to only just reach significance, with a medium effect size at best, and, moreover, it is unclear if this is the correct analysis (which is a bit doubtful, when looking at Figure 1D and E). In Study 2, there was no optimal control condition without reminder and with the same 30-min interval (which is problematic, because we can assume generalization between CS1+ and CS2+, as pointed out by the authors, and because generalization effects are known to be time-dependent). Study 3 is more convincing, but entails additional changes in comparison with Studies 1 and 2, i.e., applications of cTBS and an interval of 1 hour instead of 30 minutes (the reason for this change was not explained). So, although the findings of the 3 studies do not contradict each other and are coherent, they do not all provide strong evidence for the effect of interest on their own.

Related to the comment above, I encourage the authors to double-check if this statement is correct: "Also, our results remain robust even with the "non-learners" included in the analysis (Fig. S1 in the Supplemental Material)". The critical analysis for Study 1 is a between-group comparison of the CS+ and CS- during the last extinction trial versus the first test trial. This result only just reached significance with the selected sample (p = .048), and Figures 1D and E even seem to suggest otherwise. I doubt that the analysis would reach significance when including the "non-learners" - assuming that this is what is shown in Supplemental Figure 1 (which shows the data from "all responded participants").

Our subjects were categorized based on the criteria specified in supplementary table S1. More specifically, we excluded the non-responders (Mean CS SCR < 0.02 uS  in the fear acquisition phase), and non-learners and focused our analyses on the learners. Non-responders were dismissed after day 1 (the day of fear acquisition), but both learners and non-learners finished the experiments. This fact gave us the opportunity to examine data for both the learners and the responders (learners + non-learners). What we showed in fig. 1D and E were differential SCRs (CS+ minus CS-) of the last extinction trials and the differential fear recovery indices (CS+ minus CS-), respectively. We have double checked the figures and both the learners (Fig. 1) and the responders (i.e. learners and non-learners, supplementary Fig. 1) results showed significant differences between the reminder and no-reminder groups on the differential fear recovery index.

Also related to the comment above, I think that the statement "suggesting a cue-independent short-term amnesia effect" in Study 2 is not correct and should read: "suggesting extinction of fear to the CS1+ and CS2+", given that the response to the CS+'s is similar to the response to the CS-, as was the case at the end of extinction. Also the next statement "This result indicates that the short-term amnesia effect observed in Study 2 is not reminder-cue specific and can generalize to the non-reminded cues" is not fully supported by the data, given the lack of an appropriate control group in this study (a group without reinstatement). The comparison with the effect found in Study 1 is difficult because the effect found there was relatively small (and may have to be double-checked, see remarks above), and it was obtained with a different procedure using a single CS+. The comparison with the 6-h and 24-h groups of Study 2 is not helpful as a control condition for this specific question (i.e., is there reinstatement of fear for any of the CS+'s) because of the large procedural difference with regard to the intervals between extinction and reinstatement (test).

In Fig. 2e, we showed the differential fear recovery indices (FRI) for the CS+ in all three groups. Since the fear recovery index (FRI) was calculated as the SCR difference between the first test trial and the last extinction trial for any CS, the differential fear recovery indices (difference between CS+ FRI and CS- FRI) not significantly different from 0 should be interpreted as the lack of fear expression in the test phase. Since spontaneous recovery, reinstatement and renewal are considered canonical phenomena in demonstrating that extinction training does not really “erase” conditioned fear response, adding the no-reinstatement group as a control condition would effectively work as the spontaneous recovery group and the comparison between the reinstatement and no-instatement groups turns into testing the difference in fear recovery using different methods (reinstatement vs. spontaneous recovery).

(2) It is unclear which analysis is presented in Figure 3. According to the main text, it either shows the "differential fear recovery index between CS+ and CS-" or "the fear recovery index of both CS1+ and CS2+". The authors should clarify what they are analyzing and showing, and clarify to which analyses the ** and NS refer in the graphs. I would also prefer the X-axes and particularly the Y-axes of Fig. 3a-b-c to be the same. The image is a bit misleading now. The same remarks apply to Figure 5.

We are sorry about the lack of clarity here. Figures 3 & 5 showed the correlational analyses between TCAQ and the differential fear recovery index (FRI) between CS+ and CS-. That is, the differential FRI of CS1+ (CS1+ FRI minus CS- FRI) and the differential FRI of CS2+ (CS2+ FRI minus CS- FRI).

We have rescaled both X and Y axes for figures 3 & 5 (please see the revised figures). 

(3) In general, I think the paper would benefit from being more careful and nuanced in how the literature and findings are represented. First of all, the authors may be more careful when using the term 'reconsolidation'. In the current version, it is put forward as an established and clearly delineated concept, but that is not the case. It would be useful if the authors could change the text in order to make it clear that the reconsolidation framework is a theory, rather than something that is set in stone (see e.g., Elsey et al., 2018 (https://doi.org/10.1037/bul0000152), Schroyens et al., 2022 (https://doi.org/10.3758/s13423-022-02173-2)).

In addition, the authors may want to reconsider if they want to cite Schiller et al., 2010 (https://doi.org/10.1038/nature08637), given that the main findings of this paper, nor the analyses could be replicated (see, Chalkia et al., 2020 (https://doi.org/10.1016/j.cortex.2020.04.017; https://doi.org/10.1016/j.cortex.2020.03.031).

We thank the reviewer’s comments and have incorporated the mentioned papers into our revised manuscript by pointing out the extant debate surrounding the reconsolidation theory in the introduction:

“Pharmacological blockade of protein synthesis and behavioral interventions can both eliminate the original fear memory expression in the long-term (24 hours later) memory test ( Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), resulting in the cue-specific fear memory deficit (Debiec et al., 2002; Lee, 2008; Nader, Schafe, & LeDoux, 2000). For example, during the reconsolidation window, retrieving a fear memory allows it to be updated through extinction training (i.e., the retrieval-extinction paradigm (Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), but also see (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; D. Schiller, LeDoux, & Phelps, 2020). ”

As well as in the discussion:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

Relatedly, it should be clarified that Figure 6 is largely speculative, rather than a proven model as it is currently presented. This is true for all panels, but particularly for panel c, given that the current study does not provide any evidence regarding the proposed reconsolidation mechanism.

We agree with the reviewer that Figure 6 is largely speculative. We realize that there are still debates regarding the retrieval-extinction procedure and the fear reconsolidation hypothesis. We have provided a more elaborated discussion and pointed out that figure 6 is only a working hypothesis and more work should be done to test such a hypothesis:

“Although mixed results have been reported regarding the durability of suppression effects in the declarative memory studies (Meier et al., 2011; Storm et al., 2012), future research will be needed to investigate whether the short-term effect we observed is specifically related to associative memory or the spontaneous nature of suppression (Figure 6C).”

Lastly, throughout the paper, the authors equate skin conductance responses (SCR) with fear memory. It should at least be acknowledged that SCR is just one aspect of a fear response, and that it is unclear whether any of this would translate to verbal or behavioral effects. Such effects would be particularly important for any clinical application, which the authors put forward as the ultimate goal of the research.

Again, we agree with the reviewer on this issue, and we have acknowledged that SCR is only one aspect of the fear response and caution should be exerted in clinical application:

“Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

(4) The Discussion quite narrowly focuses on a specific 'mechanism' that the authors have in mind. Although it is good that the Discussion is to the point, it may be worthwhile to entertain other options or (partial) explanations for the findings. For example, have the authors considered that there may be an important role for attention? When testing very soon after the extinction procedure (and thus after the reminder), attentional processes may play an important role (more so than with longer intervals). The retrieval procedure could perhaps induce heightened attention to the reminded CS+ (which could be further enhanced by dlPFC stimulation)?

We thank the reviewer for this suggestion and have added more discussion on the potential mechanisms involved. Unfortunately, since the literature on attention and fear recovery is rather scarce, it is even more of a speculation given our study design and results are mainly about subjects’ skin conductance responses (SCR).

(5) There is room for improvement in terms of language, clarity of the writing, and (presentation of the) statistical analyses, for all of which I have provided detailed feedback in the 'Recommendations for the authors' section. Idem for the data availability; they are currently not publicly available, in contrast with what is stated in the paper. In addition, it would be helpful if the authors would provide additional explanation or justification for some of the methodological choices (e.g., the 18-s interval and why stimulate 8 minutes after the reminder cue, the choice of stimulation parameters), and comment on reasons for (and implications of) the large amount of excluded participants (>25%).

We have addressed the data accessibility issue and added the justifications for the methodological choices as well as the excluded participants. As we mentioned in the manuscript and the supplementary materials, adding the non-learners into data analysis did not change the results. Since the non-responders discontinued after Day 1 due to their non-measurable spontaneous SCR signals towards different CS, it’s hard to speculate whether or how the results might have changed. However, participants’ exclusion rate in the SCR studies were relatively high (Hu et al., 2018, Liu et al., 2014, Raio et al., 2017, Schiller et al., 2010, Schiller et al., 2012, Wang et al., 2021). The non-responders were mostly associated with participants being tested in the winter in our tasks. Cold weather and dry skins in the winter are likely to have caused the SCR hard to measure (Bauer et al., 2022, Vila, 2004). Different intervals between the reinstating US (electric shock) and the test trials were used in the previous literature such as 10min (Schiller et al., 2010, Schiller et al., 2013) and 18 or 19s (Kindt and Soeter, 2018, Kindt et al., 2009, Wang et al., 2021). We stuck with the 18s reinstatement interval in the current experiment. For the cTBS stimulation, since the stimulation itself lasted less than 2mins, we started the cTBS 8min after the onset of reminder cue to ensure that any effect caused by the cTBS stimulation occurred during the hypothesized time window, where the old fear memory becomes labile after memory retrieval. All the stimulation parameters were determined based on previous literature, which showed that with the transcranial magnetic stimulation (TMS) on the human dorsolateral prefrontal cortex could disrupt fear memory reconsolidation (Borgomaneri et al., 2020, Su et al., 2022).

Finally, I think several statements made in the paper are overly strong in light of the existing literature (or the evidence obtained here) or imply causal relationships that were not directly tested.

We have revised the texts accordingly.

Reviewer #2 (Recommendations For The Authors):

On numerous occasions there are typos and the autocorrect has changed "amnesia" for "dementia".

We are sorry about this mistake and have revised the text accordingly.

Reviewer #3 (Recommendations For The Authors):

*"Neither of the studies reported in this article was preregistered. The data for both studies are publicly accessible at https://osf.io/9agvk". This excerpt from the text suggests that there are 2 studies, but there are 3 in the paper. Also, the data are only accessible upon request, not publicly available. I haven't requested them, as this could de-anonymize me as a reviewer.

We are sorry for the accessibility of the link. The data should be available to the public now.

*Please refrain from causal interpretations when they are not supported by the data:

- Figure 3 "thought-control ability only affected fear recovery"; a correlation does not provide causal evidence.

- "establishing a causal link between the dlPFC activity and short-term fear amnesia." I feel this statement is too strong; to what extent do we know for sure what the applied stimulation of (or more correct: near) the dlPFC does exactly?

We thank the reviewer for the suggestion and have changed the wording related to figure 3. On the other hand, we’d like to argue that the causal relationship between the dlPFC activity and short-term fear amnesia is supported by the results from study 3. Although the exact functional role of the TMS on dlPFC can be debated, the fact that the TMS stimulation on the dlPFC (compared to the vertex group) brought back the otherwise diminished fear memory expression can be viewed as the causal evidence between the dlPFC activity and short-term fear amnesia.

*The text would benefit from language editing, as it contains spelling and grammar mistakes, as well as wording that is vague or inappropriate. I suggest the authors check the whole text, but below are already some excerpts that caught my eye:

"preludes memory reconsolidation"; "old fear memory can be updated"; "would cause short-term memory deficit"; "the its functional coupling"; "Subjects (...) yielded more severe amnesia in the memory suppression tasks"; "memory retrieval might also precipitate a short-term amnesia effect"; "more SEVERE amnesia in the memory suppression tasks"; "the effect size of reinstatement effect"; "the previous literatures"; "towards different CS"; "failed to show SCR response to the any stimuli"; "significant effect of age of TMS"; "each subject' left hand"; "latter half trials"; "Differntial fear recovery"; "fear dementia"; "the fear reinstatement effects at different time scale is related to"; "fear reocery index"; "thought-control abiliites"; "performed better in motivated dementia"; "we tested that in addition to the memory retrieval cue (reminder), whether the"; "during reconsolidation window"; "consisitent with the short-term dementia"; "low level of shock (5v)"

We thank the reviewer for thorough reading and sorry about typos in the manuscript. We have corrected typos and grammar mistakes as much as we can find.

*In line with the remark above, there are several places where the text could still be improved.

- The last sentence of the Abstract is rather vague and doesn't really add anything.

- Please reword or clarify: "the exact functional role played by the memory retrieval remains unclear".

- Please reword or clarify: "the unbinding of the old memory trace".

- "suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes" shouldn't this rather read "in contrast with"?

We have modified the manuscript.

- In the Introduction, the authors state: "Specifically, memory reconsolidation effect will only be evident in the long-term (24h) memory test due to its requirement of new protein synthesis and is cue-dependent". They then continue about the more immediate memory update mechanisms that they want to study, but it is unclear from how the rationale is presented whether (and why (not)) they also expect this mechanism to be cue-dependent.

Most of the previous studies on the fear memory reconsolidation using CS as the memory retrieval cues have demonstrated that the reconsolidation effect is cue-dependent (Kindt and Soeter, 2018, Kindt et al., 2009, Monfils et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012). However, other studies using unconditioned stimulus retrieval-extinction paradigm showed that such protocol was able to prevent the return of fear memory expression associated with different CSs (Liu et al., 2014, Luo et al., 2015). In our task, we used CS+ as the memory retrieval cues and our results were consistent with results from previous studies using similar paradigms.

- "The effects of cTBS over the right dlPFC after the memory reactivation were assessed using the similar mixed-effect four-way ANOVA". Please clarify what was analyzed here.<br /> - "designing novel treatment of psychiatric disorders". Please make this more concrete or remove the statement.

This sentence was right after a similar analysis performed in the previous paragraph. While the previous graph focused on how the SCRs in the acquisition phase were modulated by factors such as CS+ (CS1+ and CS2+), reminder (reminder vs. no-reminder), cTBS site (right dlPFC vs. vertex) and trial numbers, this analysis focused instead on the SCR responses in the extinction training phase. We have made the modifications as the reviewer suggested.

*I have several concerns related to the (presentation) of the statistical analyses/results:<br /> - Some statistical analyses, as well as calculation of certain arbitrary indices (e.g., differential fear recovery index) are not mentioned nor explained in the Methods section, but only mentioned in the Results section.

We have added the explanation of the differential fear recovery index into the methods section:

“To measure the extent to which fear returns after the presentation of unconditioned stimuli (US, electric shock) in the test phase, we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS for each subject. Similarly, in studies 2 and 3, differential fear recovery index was defined as the difference between fear recovery indices of CS+ and CS- for both CS1+ and CS2+.”

- Figure 1C-E: It is unclear what the triple *** mean. Do they have the same meaning in Figure 1C and Figure 1E? I am not sure that that makes sense. The meaning is not explained in the figure caption (I think it is different from the single asterisk*) and is not crystal clear from the main text either.

We explained the triple *** in the figure legend (Fig. 1): ***P < 0.001. The asterisk placed within each bar in Figure 1C-E indicates the statistical results of the post-hoc test of whether each bar was significant. For example, the *** placed inside bars in Figure 1E indicates that the differential fear recovery index is statistically significant in the no-reminder group (P < 0.001).

- Supplemental Figure 1: "with all responded participants" Please clarify how you define 'responded participants' and include the n's.

We presented the criteria for both the responder/non-responder and the learner/non-learner in the table of the supplementary materials and reported the number of subjects in each category (please see supplement Table 1).

- "the differential SCRs (difference between CS+ and CS-) for the CS+". Please clarify what this means and/or how it is calculated exactly.

Sorry, it means the difference between the SCRs invoked by CS+ and CS- for both CS1+ (CS1+ minus CS-) and CS2+ (CS2+ minus CS-).

*I suggest that the authors provide a bit more explanation about the thought-control ability questionnaire. For example, the type of items, etc, as this is not a very commonly used questionnaire in the fear conditioning field.

We provided a brief introduction to the thought-control ability questionnaire in the methods section:

“The control ability over intrusive thought was measured by the 25-item Thought-Control Ability Questionnaire (TCAQ) scle(30). Participants were asked to rate on a five-point Likert-type scale the extent to which they agreed with the statement from 1 (completely disagree) to 5 (completely agree). At the end of the experiments, all participants completed the TCAQ scale to assess their perceived control abilities over intrusive thoughts in daily life(17).”

We have added further description of the item types to the TCAQ scale.

*The authors excluded more than 25% of the participants. It would be interesting to hear reasons for this relatively large number and some reflection on whether they think this selection affects their results (e.g., could being a (non)responder in skin conductance influence the susceptibility to reactivation-extinction in some way?).

Participants exclusion rate in the SCR studies were relatively high (Hu et al., 2018, Liu et al., 2014, Raio et al., 2017, Schiller et al., 2010, Schiller et al., 2012, Wang et al., 2021). The non-responders were mostly associated with participants being tested in the winter in our tasks. Cold weather and dry skins in the winter are likely to have caused the SCR hard to measure (Bauer et al., 2022, Vila, 2004).

*Minor comments that the authors may want to consider:

- Please explain abbreviations upon first use, e.g., TMS.

- In Figure 6, it is a bit counterintuitive that the right Y-axis goes from high to low.

We added the explanation of TMS:

“Continuous theta burst stimulation (cTBS), a specific form of repetitive transcranial magnetic stimulation (rTMS)…”

We are sorry and agree that the right Y-axis was rather counterintuitive. However, since the direction of the fear recovery index (which was what we measured in the experiment) and the short/long-term amnesia effect are of the opposite directions, plotting one index from low to high would inevitably cause the other index to go from high to low.

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Author response:

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

Recommendations for the authors:

Reviewer #1:

The authors have thoroughly changed the manuscript and addressed most of my concerns. I appreciate adding the activity assays of the C115/120S mutants, however, I suggest that the authors embed and also discuss these data more clearly. It also escaped my attention earlier that the positioning of the disulfide bond is 117-122 in the deposited PDBs instead of 115-120. The authors should carefully check which positioning is correct here.

We thank reviewer #1 for his or her careful assessment of our revised manuscript. As suggested, we detailed the results section “CrSBPase enzymatic activity” with additional numerical values, and discussed more clearly the comparisons of results for activity assays of mutants C115S and C120S in the section “Oligomeric states of CrSBPase”. Residues numbering was carefully proof-checked throughout the manuscript for correctness and homogeneity. C115 and C120 are numbered according to best databases consensus, ie. GenBank and Uniprot, and may differ from one database to another (including PDB) due to varying numbering rules. We clarified the chosen nomenclature in methods section “Cloning and mutagenesis of CrSBPase expression plasmids”.

Line 246-250: I think it is evident that the two SBPase structures superpose well given the sequence identity of more than 70%. However, it would be great to include a superposition of the two structures in Figure 1, especially with regard to the region harboring C115 and C120.

We added a panel showing superimposition of CrSBPase 7b2o and PpSBPase 5iz3 and made a close-up view around the region C115-C120 in supplementary figure 5. Given the density in information of figure 1 we prefer not to add additional images on it. Supplementary figure 5 was initially intended to illustrate sequence conservation/variation among homologs, thus fitting with the objective to compare past and present XRC results.

Line 255-266: I am again missing a panel in Figure 1 here, e.g. a side-by-side view of Xray vs AF2/3 structure.

We added another panel in supplementary figure 5 to visually compare side-by-side SBPase crystallographic structure 7b2o and our AF3 model. Again, for the sake of clarity we prefer not to overload figure 1 with additional panels. This will also enable thorough comparison of past XRC of PpSBPase, present XRC of CrSBPase, and various AF models (see below, oligomer comparisons).

Line 261-266: Did the authors predict dimers and tetramers using AF3? What are the confidence metrics in this case? Do the authors see differences to the monomer prediction in case a multimer is confidently predicted?

We modeled dimers and tetramers using AF3 and added them on supplementary figure 5 side by side with protomer of XRC model 7b2o and with monomer predicted by AF3. Color code for supplementary figure 5 panels F-H is according to AF standard representation of plDDT. Confidence metrics per residue correspond to very high reliability (navy blue) or, locally, confident prediction (cyan) and overall prediction scores range from pTM=0.85-0.91, a high-quality prediction. Interface prediction score is high for both dimer (ipTM=0.9) and tetramer (ipTM=0.82). We reported these data in supplementary figure 5 and corresponding updated legend. XRC and AF models all align with RMSD<0.5 Å, indicating a globally unchanged structure of the protomer in the various methods and oligomeric states.

Line 441: How does the oligomeric equilibrium change in C115/120S mutants? This information should be added for the mutants. Besides, the mAU units in Fig. 6 could be normalized to allow an easier comparison between the chromatograms of wt and mutants.

Change in oligomeric equilibrium is assessed by size-exclusion chromatography of WT and mutants C115S, C120S as reported in figure 6A. We made quantitative estimation of WT, and C115S and C120S mutants equilibrium by comparing maximal peak intensity and added this information in the text. Briefly, the oligomer ratio on a scale of 100 is 9:48:43 for WT, 42:25:33 for mutant C115S, and 29:17:54 for mutant C120S (ratio expressed as tetramer:dimer:monomer). We prefer not to normalize values of absorbance, but rather keep the actual measurement of absorbance at 280 nm on the chromatogram of figure 6, for the sake of consistency with the added text and for a more transparent report of the experiment.

Line 447: WT activity is 12.15+-2.15 and both mutants have a higher activity. The authors should check if their values (96% and 107%) are correct. Besides, did the authors check if the increase in C120S is statistically significant? My impression is that both mutants have a higher activity than the wildtype, in both correlating with increased fractions of the tetramer. This would also make sense, as the corresponding region is part of the tetramer interface in the crystal packing.

The reported activity values were checked for correctness. Wild-type SBPase specific activity at 12.5 ±2.15 µmol(NADPH) min^-1 mg(SBPase)^-1 was obtained by pre-incubating the enzyme with 1 µM CrTRXf2 supplemented with 1 mM DTT and 10 mM Mg²⁺, while the results of supplementary figure 14 reporting the comparison of activation of WT and mutants, with a variation of 107 or 96 %, were obtained with a slightly different protocol for pre-incubation of the enzyme with 10 mM DTT and 10 mM Mg²⁺. Please note that whether WT enzyme was assayed in 10 mM DTT 10 mM Mg or in 1 µM TRX 1 mM DTT 10 mM Mg, its specific activity appears equal within experimental error. Both mutants have nearly the same activity than the WT in the assay reported in supplementary figure 14: we fully agree that 107% (and 96%) variation is indeed not significant considering the uncertainty of the measurement (see error bars representing standard deviations of the mean in supplementary figure 14). We added this important information in the text. Even though both mutations stabilize the most active tetramer in untreated recombinant protein, we think that after reducting treatment both WT and mutants all reach the same maximal activity because they all form an equivalent proportion of the active tetramer versus alternative oligomeric states. We furhter interprete this piece of data as a decoupling of reduction and catalysis: in physiological conditions we assume that SBPase would initiate activation upon the reduction of disulfide bridges, including but not limited to C115-C120 that restricts the entry into fully active tetramer, at which point SBPase in reduced form reaches maximal activity until another post-translational signal eventually changes its conformation and oligomerisation.

We thank again reviewer 1 for his or her assessment and valuable suggestions.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

First, the authors confirm the up-regulation of the main genes involved in the three branches of the Unfolded Protein Response (UPR) system in diet-induced obese mice in AT, observations that have been extensively reported before. Not surprisingly, IRE1a inhibition with STF led to an amelioration of the obesity and insulin resistance of the animals. Moreover, non-alcoholic fatty liver disease was also improved by the treatment. More novel are their results in terms of thermogenesis and energy expenditure, where IRE1a seems to act via activation of brown AT. Finally, mice treated with STF exhibited significantly fewer metabolically active and M1-like macrophages in the AT compared to those under vehicle conditions. Overall, the authors conclude that targeting IRE1a has therapeutical potential for treating obesity and insulin resistance.

The study has some strengths, such as the detailed characterization of the effect of STF in different fat depots and a thorough analysis of macrophage populations. However, the lack of novelty in the findings somewhat limits the study´s impact on the field.

We thank the reviewer for the appreciation of our findings. We would use the opportunity to highlight several novelties. First, we characterized the relationship between the newly discovered CD9⁺ ATMs and the “M1-like” CD11c+ ATMs. Second, we demonstrated that M2 macrophage population was not reduced but instead increased in adipose tissue in obesity. Third, IRE1 inhibition does not improve thermogenesis by boosting M2 population, but instead, IRE1 inhibition suppresses pro-inflammatory macrophage populations including the M1-like ATMs.

Reviewer #3 (Public review):

Summary:

The manuscript by Wu D. et al. explores an innovative approach in immunometabolism and obesity by investigating the potential of targeting macrophage Inositol-requiring enzyme 1α (IRE1α) in cases of overnutrition. Their findings suggest that pharmacological inhibition of IRE1α could influence key aspects such as adipose tissue inflammation, insulin resistance, and thermogenesis. Notable discoveries include the identification of High-Fat Diet (HFD)-induced CD9⁺ Trem2+ macrophages and the reversal of metabolically active macrophages' activity with IRE1α inhibition using STF. These insights could significantly impact future obesity treatments.

Strengths:

The study's key strengths lie in its identification of specific macrophage subsets and the demonstration that inhibiting IRE1α can reverse the activity of these macrophages. This provides a potential new avenue for developing obesity treatments and contributes valuable knowledge to the field.

Weaknesses:

The research lacks an in-depth exploration of the broader metabolic mechanisms involved in controlling diet-induced obesity (DIO). Addressing this gap would strengthen the understanding of how targeting IRE1α might fit into the larger metabolic landscape.

We thank the reviewer for the appreciation of strengths in our manuscript. In particular, we appreciate the reviewer’s recommendation on the exploration of broader metabolic landscape, such as the effect of IRE1 inhibition on non-adipose tissue macrophages and metabolism. We agree that achieving these will certainly broaden the therapeutic potential of IRE1 inhibition to larger metabolic disorders and we will pursue these explorations in future studies.

Impact and Utility:

The findings have the potential to advance the field of obesity treatment by offering a novel target for intervention. However, further research is needed to fully elucidate the metabolic pathways involved and to confirm the long-term efficacy and safety of this approach. The methods and data presented are useful, but additional context and exploration are required for broader application and understanding.

Comments on revisions:

The author has revised the manuscript and addressed the most relevant comments raised by the reviewers. The paper is now significantly improved, though two minor issues remain.

(1) Studies were limited to male mice; this should be mentioned in the paper's Title.

Thanks for comment. We have modified the title to reflect the male mice only.

(2) Please include the sample size (n=) in all provided tables in the main manuscript and supplementary tables.

We have included the sample size in the main manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Bacterial effectors that interfere with the inner molecular workings of eukaryotic host cells are of great biological significance across disciplines. On the one hand they help us to understand the molecular strategies that bacteria use to manipulate host cells. On the other hand they can be used as research tools to reveal molecular details of the intricate workings of the host machinery that is relevant for the interaction/defence/symbiosis with bacteria. The authors investigate the function and biological impact of a rhizobial effector that interacts with and modifies, and curiously is modified by, legume receptors essential for symbiosis. The molecular analysis revealed a bacterial effector that cleaves a plant symbiosis signaling receptor to inhibit signaling and the host counterplay by phosphorylation via a receptor kinase. These findings have potential implications beyond bacterial interactions with plants.

Bao and colleagues investigated how rhizobial effector proteins can regulate the legume root nodule symbiosis. A rhizobial effector is described to directly modify symbiosis-related signaling proteins, altering the outcome of the symbiosis. Overall, the paper presents findings that will have a wide appeal beyond its primary field.

Out of 15 identified effectors from Sinorhizobium fredii, they focus on the effector NopT, which exhibits proteolytic activity and may therefore cleave specific target proteins of the host plant. They focus on two Nod factor receptors of the legume Lotus japonicus, NFR1 and NFR5, both of which were previously found to be essential for the perception of rhizobial nod factor, and the induction of symbiotic responses such as bacterial infection thread formation in root hairs and root nodule development (Madsen et al., 2003, Nature; Tirichine et al., 2003; Nature). The authors present evidence for an interaction of NopT with NFR1 and NFR5. The paper aims to characterize the biochemical and functional consequences of these interactions and the phenotype that arises when the effector is mutated.

Evidence is presented that in vitro NopT can cleave NFR5 at its juxtamembrane region. NFR5 appears also to be cleaved in vivo. and NFR1 appears to inhibit the proteolytic activity of NopT by phosphorylating NopT. When NFR5 and NFR1 are ectopically over-expressed in leaves of the non-legume Nicotiana benthamiana, they induce cell death (Madsen et al., 2011, Plant Journal). Bao et al., found that this cell death response is inhibited by the coexpression of nopT. Mutation of nopT alters the outcome of rhizobial infection in L. japonicus. These conclusions are well supported by the data.

The authors present evidence supporting the interaction of NopT with NFR1 and NFR5. In particular, there is solid support for cleavage of NFR5 by NopT (Figure 3) and the identification of NopT phosphorylation sites that inhibit its proteolytic activity (Figure 4C). Cleavage of NFR5 upon expression in N. benthamiana (Figure 3A) requires appropriate controls (inactive mutant versions) that have been provided, since Agrobacterium as a closely rhizobia-related bacterium might increase defense related proteolytic activity in the plant host cells.

We appreciate your recognition of the importance of appropriate controls in our experimental design. In response to your comments, we revised our manuscript to ensure that the figures and legends provide a clear description of the controls used. We also included a more detailed description of our experimental design at several places. In particular, we have highlighted the use of the protease-dead version of NopT as a control (NopT^C93S). Therefore, NFR5-GFP cleavage in N. benthamiana clearly depended on protease activity of NopT and not on Agrobacterium (Fig. 3A). In the revised text, we carefully revied the conclusion and do not conclude at this stage that NopT proteolyzes NFR5. However, our subsequent experiments, including in vitro experiments, clearly show that NopT is able to proteolyze NFR5.

Key results from N. benthamiana appear consistent with data from recombinant protein expression in bacteria. For the analysis in the host legume L. japonicus transgenic hairy roots were included. To demonstrate that the cleavage of NFR5 occurs during the interaction in plant cells the authors build largely on western blots. Regardless of whether Nicotiana leaf cells or Lotus root cells are used as the test platform, the Western blots indicate that only a small proportion of NFR5 is cleaved when co-expressed with nopT, and most of the NFR5 persists in its full-length form (Figures 3A-D). It is not quite clear how the authors explain the loss of NFR5 function (loss of cell death, impact on symbiosis), as a vast excess of the tested target remains intact. It is also not clear why a large proportion of NFR5 is unaffected by the proteolytic activity of NopT. This is particularly interesting in Nicotiana in the absence of Nod factor that could trigger NFR1 kinase activity.

Thank you for your comments regarding the cleavage of NFR5 by NopT and its functional implications. We acknowledge that our immunoblots indicate only a relatively small proportion of the NFR5 cleavage product. Possible explanations could be as follows:

(1) The presence of full-length NFR5 does not preclude a significant impact of NopT on function of NFR5, as NopT is able to interact with NFR5. In other words, the NopT-NFR5 and NopT-NFR1 interactions at the plasma membrane might influence the function of the NFR1/NFR5 receptor without proteolytic cleavage of NFR5. In fact, protease-dead NopT^C93S expressed in NGR234ΔnopT showed certain effects in L. japonicus (less infection foci were formed compared to NGR234ΔnopT Fig. 5E). In this context, it is worth mentioning that the non-acylated NopT^C93S (Fig. 1B) and NopT_USDA257 (Fig. 6B) proteins were unable to suppress NFR1/NFR5-induced cell death in N. benthamina, but this could be explained by the lack of acylation and altered subcellular localization.

(2) In the cleavage assay, only small portion of NFR5 could be detected for cleavage by NopT. However, this cleavage might be sufficient to suppress signaling pathways, leading to the observed phenotypic changes (loss of cell death in N. benthamiana; altered infection in L. japonicus). We do believe this is a great point, therefore, we carefully revised the conclusion about this point. Throughout the paper, we stated that the cleavage of NFR5 suppresses symbiotic signaling but not disrupt the symbiotic signaling. We also removed the conclusion that cleavage of NFR5 by NopT results in the function loss of NFR5.

(3) N. benthamiana co-expressing NFR1/NFR5 leads to strong cell death, which suggest that the NFR1 kinase activity might be constitutively active even in the absence of Nod factors. But why co-expression of symbiotic receptor leads to cell death and how kinase activity is active in the absence of Nod factor are not clear, which is of great interest to be studied.

(4) The proteolytic activity of NopT may be reduced by the interaction of NopT with other proteins such as NFR1, which phosphorylates NopT and inactivates its protease activity.

In our revised manuscript version, we provide now quantitative data for the efficiency of NFR5 cleavage by NopT in different expression systems used (Figure 3 and Supplemental Fig. 16). We have also improved our Discussion in this context.  

Comments on latest version:

The presentation of the figures and the language has greatly improved and the specific mistakes pointed out in the last review have been corrected. I especially appreciate the new images used to illustrate the observed mutant phenotypes, which are much clearer and easier to understand. The pictures used to illustrate the mutant phenotypes seem to be of more comparable root regions than before. Overall, the requested changes have been implemented, with some exceptions described below.

• Figure 1: New representative images are shown for BAX1 and CERK1. These pictures are more consistent with the phenotype seen in other treatments, but since the data has not changed, I presume the data from leaf discs (where the leaf discs for these treatments looked very different) previously shown is still included. The criteria for what was considered cell death is in my opinion still not described in the legend. The cell death/total ratio has been added for all leaf discs, as requested.

Thank you so much for carefully pointing out this. Cell death in leaf disc results in the formation of necrotic plaques, which restrains pathogens within deceased cells. These plaques commonly manifest as leaf dehydration, frequently accompanied by a translucent appearance. Brown and shriveled leaf discs serve as indicators of cell death. We have added these descriptions in the figure legend of Figure 1.

• Figure 2: the discussion of the figure now emphasizes direct protein interaction. There is still no size marker in 2D or a description of size in the figure legend, making it difficult to compare the result to Figure 3. If I understand the rebuttal comments correctly, there are other bands on the blot, including non-specific bands. This does not negate the need to include the full blot as a supplemental figure to show cleaved NFR5 as well as other bands. I do not see any other clarifications on this subject in the manuscript.

Thank you for your suggestion. In the revised manuscript, we have included the kDa range for all proteins detected in Figure.2D. The full blot of Co-IP assay was shown in Fig S2 (a new supplemental data). Yes, we detected some smaller bands after immunoblot, but we cannot give clear conclusion of what these bands are based on the current study. Interestingly, these smaller bands were immunoprecipitated by anti-FLAG beads, suggesting that these bands are some truncated peptides from NFR5.

• Figure 5: From the pictures, it is now easier to understand what is meant by "infection foci". Although there is no description in the methods of how these were distinguished from infection threads, I believe the images are clear enough.

Thank you for your helpful comment. In the revised manuscript, we have added the descriptions about this experiment in the method section and in the legend in Figure 5A.

• Figure 6: The changes in the discussion are appreciated, but panel E still misrepresents the evidence in the paper, as from the drawing it still seems that the cleaved NFR5 is somehow directly responsible for suppressing infection when this was not shown.

Thank you for your thoughtful comments. We appreciate your suggestion to the schematic model to illustrate the cleavage of NFR5 to suppressing rhizobia infection. In the revised manuscript, we have changed the model in Figure 6E.

Reviewer #2 (Public review):

Summary:

This manuscript presents data demonstrating NopT's interaction with Nod Factor Receptors NFR1 and NFR5 and its impact on cell death inhibition and rhizobial infection. The identification of a truncated NopT variant in certain Sinorhizobium species adds an interesting dimension to the study. These data try to bridge the gaps between classical Nod-factor-dependent nodulation and T3SS NopT effector-dependent nodulation in legume-rhizobium symbiosis. Overall, the research provides interesting insights into the molecular mechanisms underlying symbiotic interactions between rhizobia and legumes.

Strengths:

The manuscript nicely demonstrates NopT's proteolytic cleavage of NFR5, regulated by NFR1 phosphorylation, promoting rhizobial infection in L. japonicus. Intriguingly, authors also identify a truncated NopT variant in certain Sinorhizobium species, maintaining NFR5 cleavage but lacking NFR1 interaction. These findings bridge the T3SS effector with the classical Nod-factor-dependent nodulation pathway, offering novel insights into symbiotic interactions.

Weaknesses:

(1) In the previous study, when transiently expressed NopT alone in Nicotiana tobacco plants, proteolytically active NopT elicited a rapid hypersensitive reaction. However, this phenotype was not observed when expressing the same NopT in Nicotiana benthamiana (Figure 1A). Conversely, cell death and a hypersensitive reaction were observed in Figure S8. This raises questions about the suitability of the exogenous expression system for studying NopT proteolysis specificity.

We appreciate your attention to these plant-specific differences. Previous studies showed that NopT expressed in tobacco (N. tabacum) or in specific Arabidopsis ecotypes (with PBS1/RPS5 genes) causes rapid cell death (Dai et al. 2008; Khan et al. 2022). Khan et al. 2022 reported recently that cell death does not occur in N. benthamiana unless the leaves were transformed with PBS1/RPS5 constructs. Our data shown in Fig. S17 confirm these findings. As cell death is usually associated with induction of plant protease activities, we considered N. tabacum and A. thaliana plants as not suitable for testing NFR5 cleavage by NopT. In fact, no NopT/NFR5 experiments were not performed with these plants in our study. In response to your comment, we now better describe the N. benthamiana expression system and cite the previous articles_. Furthermore, we have revised the Discussion section to better emphasize effector-induced immunity in non-host plants and the negative effect of rhizobial effectors during symbiosis. Our revisions certainly provide a clearer understanding of the advantages and limitations of the _N. benthamiana expression system.

(2) NFR5 Loss-of-function mutants do not produce nodules in the presence of rhizobia in lotus roots, and overexpression of NFR1 and NFR5 produces spontaneous nodules. In this regard, if the direct proteolysis target of NopT is NFR5, one could expect the NGR234's infection will not be very successful because of the Native NopT's specific proteolysis function of NFR5 and NFR1. Conversely, in Figure 5, authors observed the different results.

Thank you for this comment, which points out that we did not address this aspect precisely enough in the original manuscript version. We improved our manuscript and now write that nfr1 and nfr5 mutants do not produce nodules (Madsen et al., 2003; Radutoiu et al., 2003) and that over-expression of either NFR1 or NFR5 can activate NF signaling, resulting in formation of spontaneous nodules in the absence of rhizobia (Ried et al., 2014). In fact, compared to the nopT knockout mutant NGR234ΔnopT, wildtype NGR234 (with NopT) is less successful in inducing infection foci in root hairs of L. japonicus (Fig. 5). With respect to formation of nodule primordia, we repeated our inoculation experiments with NGR234ΔnopT and wildtype NGR234 and also included a nopT over-expressing NGR234 strain into the analysis. Our data clearly showed that nodule primordium formation was negatively affected by NopT. The new data are shown in Fig. 5 of our revised version. Our data show that NGR234 infection is not really successful, especially when NopT is over-expressed. This is consistent with our observations that NopT targets Nod factor receptors in L. japonicus and inhibits NF signaling (NIN promoter-GUS experiments). Our findings indicate that NopT might be an “Avr effector” for L. japonicus. However, in other host plants of NGR234, NopT possesses a symbiosis-promoting role (Dai et al. 2008; Kambara et al. 2009). Such differences could be explained by different NopT targets in different plants (in addition to Nod factor receptors), which may influence the outcome of the infection process. Indeed, our work shows that NopT can interact with various kinase-dead LysM domain receptors, suggesting a role of NopT in suppression or activation of plant immunity responses depending on the host plant. We discuss such alternative mechanisms in our revised manuscript version and emphasize the need for further investigation to elucidate the precise mechanisms underlying the observed infection phenotype and the role of NopT in modulating symbiotic signaling pathways. In this context, we would also like to mention the new figures of our manuscript which are showing (i) the efficiency of NFR5 cleavage by NopT in different expression systems (Figure 3), (ii) the interaction between NopT^C93S and His-SUMO-NFR5JM-GFP (Supplementary Fig. 5), and (iii) cleavage of His-SUMO-NFPJM-GFP by NopT (Supplementary Figs. S8 and S9).

(3) In Figure 6E, the model illustrates how NopT digests NFR5 to regulate rhizobia infection. However, it raises the question of whether it is reasonable for NGR234 to produce an effector that restricts its own colonization in host plants.

Thank you for mentioning this point. We are aware of the possible paradox that the broad-host-range strain NGR234 produces an effector that appears to restrict its infection of host plants. As mentioned in our answer to the previous comment, NopT could have additional functions beyond the regulation of Nod factor signaling. In our revised manuscript version, we have modified our text as follows:

(1) We mention the potential evolutionary aspects of NopT-mediated regulation of rhizobial infection and discuss the possibility that interactions between NopT and Nod factor receptors may have evolved to fine-tune Nod factor signaling to avoid rhizobial hyperinfection in certain host legumes.

(2) We also emphasize that the presence of NopT may confer selective advantages in other host plants than L. japonicus due to interactions with proteins related to plant immunity. Like other effectors, NopT could suppress activation of immune responses (suppression of PTI) or cause effector-triggered immunity (ETI) responses, thereby modulating rhizobial infection and nodule formation. Interactions between NopT and proteins related to the plant immune system may represent an important evolutionary driving force for host-specific nodulation and explain why the presence of NopT in NGR234 has a negative effect on symbiosis with L. japonicus but a positive one with other legumes.

(4) The failure to generate stable transgenic plants expressing NopT in Lotus japonicus is surprising, considering the manuscript's claim that NopT specifically proteolyzes NFR5, a major player in the response to nodule symbiosis, without being essential for plant development.

We also thank for this comment. We have revised the Discussion section of our manuscript and discuss now our failure to generate stable transgenic L. japonicus plants expressing NopT. We observed that the protease activity of NopT in aerial parts of L. japonicus had a negative effect on plant development, whereas NopT expression in hairy roots was possible. Such differences may be explained by different NopT substrates in roots and aerial parts of the plant. In this context, we also discuss our finding that NopT not only cleaves NFR5 but is also able to proteolyze other proteins of L. japonicus such as LjLYS11, suggesting that NopT not only suppresses Nod factor signaling, but may also interfere with signal transduction pathways related to plant immunity. We speculate that, depending on the host legume species, NopT could suppress PTI or induce ETI, thereby modulating rhizobial infection and nodule formation.

Comments on revised version:

This version has effectively addressed most of my concerns. However, one key issue remains unresolved regarding the mechanism of NopT in regulating nodule symbiosis. Specifically, the explanation of how NopT catabolizes NFR5 to regulate symbiosis is still not convincing within the current framework of plant-microbe interaction, where plants are understood to genetically control rhizobial colonization.

While alternative regulatory mechanisms in plant-microbe interactions are plausible, the notion that the NRG234-secreted effector NopT could reduce its own infection by either suppressing plant immunity or degrading the symbiosis receptor remains unsubstantiated. I believe further revisions are needed in the discussion section to more clearly address and clarify these findings and any lingering uncertainties.

We appreciate your positive comments on the reason why NopT catabolizes NFR5 to regulate symbiosis. NopT belongs to pathogen effecftors YopT family and also cleavage Arabidopsis AtLYK5 and L. japonicus LjLYS11 which trigger immunity responses in plants. NFR5, AtLYK5 and LjLYS11 has the conserved amino acid motif at the juxtamembrane domain, leading to cleaving NFR5 by NopT during symbiosis. Besides, in plant-microbe interaction, effector HopB1 cleaves immune co-receptor BAK1 at the kinase domain to inhibit plant defense. The effect on cleavage of receptor may be positive or negative. NopT suppressing symbiosis may avoid preventing hyperinfection in the specific interaction between rhizobia and legumes. In the revised manuscript, we have emphasized this point more clearly in why NopT could reduce its own infection by either suppressing plant immunity in discussion.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Evaluation of the author's responses to the reviewer comments during the first review round

Reviewer's Comment:

Regardless of whether Nicotiana leaf cells or Lotus root cells are used as the test platform, the Western blots indicate that only a small proportion of NFR5 is cleaved when co-expressed with NopT, and most of the NFR5 persists in its full-length form (Figures 3A-D). It is not quite clear how the authors explain the loss of NFR5 function (loss of cell death, impact on symbiosis), as a vast excess of the tested target remains intact. It is also not clear why a large proportion of NFR5 is unaffected by the proteolytic activity of NopT. This is particularly interesting in Nicotiana in the absence of Nod factor that could trigger NFR1 kinase activity.

Summary of response:

• NopT could be interfering with the NFR1/NFR5 complex without proteolytic cleavage

• The cleaved fraction may still be sufficient to disrupt signaling pathways

• Elevated abundance of NFR5 relative to WT levels

• Add quantitative data for efficiency of NFR5 cleavage in different systems

Evaluation of response:

• The quantification of NFR5 cleavage efficiency is welcome, and there is some discussion of the possible reasons for the large proportion of uncleaved NFR5. It is clear that there is a large difference in cleavage efficiency between L. japonicus roots and N. benthamiana.

• The data is shown as a bar plot. Given that only 3 biological replicates are used, the data points should be shown, and there is too little data to provide sensible error bars. It would be better to simply make a dot-plot and indicate the mean for each sample. However, the main aim of the comment is addressed.

Thank you for your constructive comments regarding Figure S16. In the revised manuscript, we have presented these data into dot-Plot format.

Reviewer's Comment:

It is also difficult to evaluate how the ratios of cleaved and full-length protein change when different versions of NopT are present without a quantification of band strengths normalized to loading controls (Figure 3C, 3D, 3F). The same is true for the blots supporting NFR1 phosphorylation of NopT (Figure 4A).

Summary of response:

• Quantified proportion of cleaved and full length NFR5 in different systems (S14)

• Band strengths of immunoblots quantified (4B)

Evaluation of response:

• The quantification has been performed as requested and the data is shown as bar plots. This type of data is frequently displayed as part of the blot figure itself, printed under each respective lane, making it easier for the reader to connect the ratios to the band sizes. If data is shown in a plot, the data points should be shown on the plot, as described above.

Thank you for your constructive comments regarding Figure 3. In the revised manuscript, we have added the cleavage efficiency in the 3A-3D.

Reviewer's Comment:

Nodule primordia and infection threads are still formed when L. japonicus plants are inoculated with ∆nopT mutant bacteria, but it is not clear if these primordia are infected or develop into fully functional nodules (Figure 5). A quantification of the ratio of infected and non-infected nodules and primordia would reveal whether NopT is only active at the transition from infection focus to thread or perhaps also later in the bacterial infection process of the developing root nodule.

Summary of response:

• Additional experiments with NGR234 or NGR234ΔnopT mutants find no non-infected nodules (fig. 5)

Evaluation of response:

• The requested quantification has been done, although the support for the findings would be stronger if also mature nodules per plant were quantified and plotted. If non-infected nodules were neither present in NGR234 or NGR234ΔnopT, it would still be advisable to include images of cross-sections of the fully-developed nodules.

We appreciate your positive comments on the cross-sections of the fully-developed nodules. In the revised manuscript, we have added the cross-section images of nodules in the Figure S12.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors used a subset of a very large, previously generated 16S dataset to:<br /> (1) Assess age-associated features; and (2) develop a fecal microbiome clock, based on an extensive longitudinal sampling of wild baboons for which near-exact chronological age is known. They further seek to understand deviation from age-expected patterns and uncover if and why some individuals have an older or younger microbiome than expected, and the health and longevity implications of such variation. Overall, the authors compellingly achieved their goals of discovering age-associated microbiome features and developing a fecal microbiome clock. They also showed clear and exciting evidence for sex and rank-associated variation in the pace of gut microbiome aging and impacts of seasonality on microbiome age in females. These data add to a growing understanding of modifiers of the pace of age in primates, and links among different biological indicators of age, with implications for understanding and contextualizing human variation. However, in the current version, there are gaps in the analyses with respect to the social environment, and in comparisons with other biological indicators of age. Despite this, I anticipate this work will be impactful, generate new areas of inquiry, and fuel additional comparative studies.

Thank you for the supportive comments and constructive reviews.

Strengths:

The major strengths of the paper are the size and sampling depth of the study population, including the ability to characterize the social and physical environments, and the application of recent and exciting methods to characterize the microbiome clock. An additional strength was the ability of the authors to compare and contrast the relative age-predictive power of the fecal microbiome clock to other biological methods of age estimation available for the study population (dental wear, blood cell parameters, methylation data). Furthermore, the writing and support materials are clear, informative and visually appealing.

Weaknesses:

It seems clear that more could be done in the area of drawing comparisons among the microbiome clock and other metrics of biological age, given the extensive data available for the study population. It was confusing to see this goal (i.e. "(i) to test whether microbiome age is correlated with other hallmarks of biological age in this population"), listed as a future direction, when the authors began this process here and have the data to do more; it would add to the impact of the paper to see this more extensively developed.

Comparing the microbiome clock to other metrics of biological age in our population is a high priority (these other metrics of biological age are in Table S5 and include epigenetic age measured in blood, the non-invasive physiology and behavior clock (NPB clock), dentine exposure, body mass index, and blood cell counts (Galbany et al. 2011; Altmann et al. 2010; Jayashankar et al. 2003; Weibel et al. 2024; Anderson et al. 2021)). However, we have opted to test these relationships in a separate manuscript. We made this decision because of the complexity of the analytical task: these metrics were not necessarily collected on the same subjects, and when they were, each metric was often measured at a different age for a given animal. Further, two of the metrics (microbiome clock and NPB clock) are measured longitudinally within subjects but on different time scales (the NPB clock is measured annually while microbiome age is measured in individual samples). The other metrics are cross-sectional. Testing the correlations between them will require exploration of how subject inclusion and time scale affect the relationships between metrics.

We now explain the complexity of this analysis in the discussion in lines 447-450. In addition, we have added the NPB clock (Weibel et al. 2024) to the text in lines 260-262 and to Table S5.

An additional weakness of the current set of analyses is that the authors did not explore the impact of current social network connectedness on microbiome parameters, despite the landmark finding from members of this authorship studying the same population that "Social networks predict gut microbiome composition in wild baboons" published here in eLife some years ago. While a mother's social connectedness is included as a parameter of early life adversity, overall the authors focus strongly on social dominance rank, without discussion of that parameter's impact on social network size or directly assessing it.

Thank you for raising this important point, which was not well explained in our manuscript. We find that the signatures of social group membership and social network proximity are only detectable our population for samples collected close in time. All of the samples analyzed in  Tung et al. 2015 (“Social networks predict gut microbiome composition in wild baboons”) were collected within six weeks of each other. By contrast, the data set analyzed here spans 14 years, with very few samples from close social partners collected close in time. Hence, the effects of social group membership and social proximity are weak or undetectable. We described these findings in Grieneisen et al. 2021 and Bjork et al. 2022, and we now explain this logic on line 530, which states, “We did not model individual social network position because prior analyses of this data set find no evidence that close social partners have more similar gut microbiomes, probably because we lack samples from close social partners sampled close in time (Grieneisen et al. 2021; Björk et al. 2022).”

We do find small effects of social group membership, which is included as a random effect in our models of how each microbiome feature is associated with host age (line 529) and our models predicting microbiome Dage (line 606; Table S6).

Reviewer #2 (Public review):

Summary:

Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights into biological aging. Although 'microbiota age' holds potential as a proxy of biological age, it comes with limitations considering the gut microbial community can be influenced by various non-age related factors, and various age-related stressors may not manifest in changes in the gut microbiota. The work would benefit from a more comprehensive discussion, that includes the limitations of the study and what these mean to the interpretation of the results.

We agree and have text to the discussion that expands on the limitations of this study and what those limitations mean for the interpretation of the results. For instance, lines 395-400 read, “Despite the relative accuracy of the baboon microbiome clock compared to similar clocks in humans, our clock has several limitations. First, the clock’s ability to predict  individual age is lower than for age clocks based on patterns of DNA methylation—both for humans and baboons (Horvath 2013; Marioni et al. 2015; Chen et al. 2016; Binder et al. 2018; Anderson et al. 2021). One reason for this difference may be that gut microbiomes can be influenced by several non-age-related factors, including social group membership, seasonal changes in resource use, and fluctuations in microbial communities in the environment”

In addition, lines 405-411 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

Strengths:

The dataset this study is based on is impressive, and can reveal various insights into biological ageing and beyond. The analysis implemented is extensive and high-level.

Weaknesses:

The key weakness is the use of microbiota age instead of e.g., DNA-methylation-based epigenetic age as a proxy of biological ageing, for reasons stated in the summary. DNA methylation levels can be measured from faecal samples, and as such epigenetic clocks too can be non-invasive. I will provide authors a list of minor edits to improve the read, to provide more details on Methods, and to make sure study limitations are discussed comprehensively.

Thank you for this point. In response, we have deleted the text from the discussion that stated that non-invasive sampling is an advantage of microbiome clocks. In addition, we now propose a non-invasive epigenetic clock from fecal samples as an important future direction for our population (see line 450).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Abstract - The opening 2 sentences are not especially original or reflective of the potential value/ premise of the study. Members of this team have themselves measured variation in biological age in many different ways, and the implication that measuring a microbiome clock is easy or straightforward is not compelling. This paper is very interesting and provides unique insight, but I think overall there is a missed opportunity in the abstract to emphasize this, given the innovative science presented here. Furthermore, the last 2 sentences of the abstract are especially interesting - but missing a final statement on the broader significance of research outside of baboons.

We appreciate these comments and have revised the Abstract accordingly. The introductory sentences now read, “Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age.” (lines 31-34). The last two sentences of the abstract now read, “Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.” (lines 40-43).

If possible, it would be highly useful to present some comments on concordance in patterns at different levels. Are all ASVs assessed at both the family and genus levels? Do they follow similar patterns when assessed at different levels? What can we learn about the system by looking at different levels of taxonomic assignment?

The section on relationships between host age and individual microbiome features is already lengthy, so we have not added an analysis of concordance between different taxonomic levels. However, we added a justification for why we tested for age signatures in different levels of taxa to line 171, which reads, “We tested these different taxonomic levels in order to learn whether the degree to which coarse and fine-grained designations categories were associated with host age.”

To calculate the delta age - please clarify if this was done at the level of years, as suggested in Figure 3C, or at the level of months or portion months, etc?

Delta age is measured in years. This is now clarified in lines 294, 295, and 578.

Spelling mistake in table S12, cell B4 (Octovber)

Thank you. This typo has been corrected.

Given the start intro with vertebrates, the second paragraph needs some tweaking to be appropriate. Perhaps, "At least among mammals, one valuable marker of biological aging may lie in the composition and dynamics of the mammalian gut microbiome (7-10)." Or simply remove "mammalian".

We have updated this sentence based on your suggestions in line 54. It reads, “In mammals, one valuable marker of biological aging may lie in the composition and dynamics of the gut microbiome (Claesson et al. 2012; Heintz and Mair 2014; O’Toole and Jeffery 2015; Sadoughi et al. 2022).”

A rewrite at the end of the introduction is needed to avoid the almost direct repetition in lines 115-118 and 129-131 (including lit cited). One potentially effective way to approach this is to keep the predictions in the earlier paragraph and then more clearly center the approach and the overarching results statement in the latter paragraph. (I.e., "we find that season and social rank have stronger effects on microbiome age than early life events. Further, microbiome age does not predict host development or mortality.").

Thank you for pointing this out. We have re-organized the predictions in the introduction based on your suggestion. The alternative “recency effects” model now appears in the paragraph that starts in line 110. The final paragraph then centers on the overall approach and the results statement (lines 128-140)

Be clear in each case where taxon-level trends are discussed if it's at Family, Genus, or other level. It's there most, but not all, of the time.

We have gone through the text and clarified what taxa or microbiome feature was the subject of our analyses in any places where this was not clear.

In the legend for Figure 2, add clarification for how values to right versus left of the centered value should be interpreted with respect to age (e.g. "values to x of the center are more abundant in older individuals").

We now clarify in Figure 2C and 2D that “Positive values are more abundant in older hosts”.

Figure 3 - Are Panels A, B, and C all needed - can the value for all individuals not also be overlaid in the panel showing sex differences and the same point showing individuals with "old" and "young" microbiomes be added in the same plot if it was slightly larger?

We agree and have simplified Figure 3. We reduced the number of panels from three to two, and we added the information about how to calculate delta age to Panel A. We also moved the equation from the top of Panel C to the bottom right of Panel A.

Reviewer #2 (Recommendations for the authors):

Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights which in principle warrant publication. However, I do think the manuscript should be carefully revised. Below I list some minor revisions that should be implemented. Importantly, the authors should discuss in the Discussion the pros and cons of using 'microbiota age' as a proxy of 'biological age'. Further, the authors should provide more information on Methods, to make sure the study can be replicated.

Thank you for these important points. Based on your comments and those of the first reviewer, we have expanded our discussion of the limitations of using microbiota age as a proxy for biological age (see edits to the paragraph starting in line 395).

We have also expanded our methods around sample collection, DNA extraction, and sequencing to describe our sampling methods, strategies to mitigate and address possible contamination, and batch effects. See lines 483-490 and our citations to the original papers where these methods are described in detail.

(1) Lines 85-99: I think this paragraph could be revisited to make the assumptions clearer. For instance, the last sentence is currently a little confusing: are authors expecting males to exhibit old-for-age microbiomes already during the juvenile period?

This prediction has been clarified. Line 96 now reads, “Hence, we predicted that adult male baboons would exhibit gut microbiomes that are old-for-age, compared to adult females (by contrast, we expected no sex effects on microbiome age in juvenile baboons).”

(2) Lines 118-121: Could the authors discuss this assumption in relation to what has been observed e.g., in humans in terms of delays in gut microbiome development? Delayed/accelerated gut microbiome development has been studied before, so this assumption would be stronger if related to what we know from previous studies.

This comment refers to the sentence which originally stated, “However, we also expected that some sources of early life adversity might be linked to young-for-age gut microbiota. For instance, maternal social isolation might delay gut microbiome development due to less frequent microbial exposures from conspecifics.” We have slightly expanded the text here (line 117) to explain our logic. We now include citations for our predictions. We did not include a detailed discussion of prior literature on microbiome development in the interest of keeping the same level of detail across all sections on our predictions.

(3) As the authors discuss, various adversities can lead to old-for-age but also young-for-age microbiome composition. This should be discussed in the limitations.

We agree. This is now discussed in the sentence starting at line 371, which reads, “…deviations from microbiome age predictions are explained by socio-environmental conditions experienced by individual hosts, especially recent conditions, although the effect sizes are small and are not always directionally consistent.” In addition, the text starting at line 405 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

(4) In various places, e.g., lines 129-131, it is a little unclear at what chronological age authors are expecting microbiota to appear young/old-for-age.

This sentence was removed while responding to the comments from the first reviewer.

(5) Lines 132-133: this statement could be backed by stating that this is because the gut microbiota can change rapidly e.g., when diet changes (or whatever the authors think could be behind this).

We have added an expository sentence at line 123, including new citations. This sentence reads, “Indeed, gut microbiomes are highly dynamic and can change rapidly in response to host diet or other aspects of host physiology, behavior, or environments”.

We now cite:

· Hicks, A.L., et al. (2018). Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nature Communications 9, 1786.

· Kolodny, O., et al. (2019). Coordinated change at the colony level in fruit bat fur microbiomes through time. Nature Ecology & Evolution 3, 116-124.

· Risely, A., et al. (2021) Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat Commun 12, 6017.

(6) Lines 135-137: current or past season and social rank? This paragraph introduces the idea that it could be past rather than current socio-environmental factors that might predict microbiota age, so the authors should clarify this sentence.

We have clarified the information in this sentence. line 135 now reads, “In general, our results support the idea that a baboon’s current socio-environmental conditions, especially their current social rank and the season of sampling, have stronger effects on microbiome age than early life events—many of which occurred many years prior to sampling.”

(7) Lines 136-137: this sentence could include some kind of a conclusion of this finding. What might this mean?

We have added a sentence at line 138, which speculates that, “…the dynamism of the gut microbiome may often overwhelm and erase early life effects on gut microbiome age.”

(8) Use 'microbiota' or 'microbiome' across the manuscript; currently, the terms are used interchangeably. I don't have a strong opinion on this, although typically 'microbiota' is used when data comes from 16S rRNA.

We have updated the text to replace any instance of “microbiota” with “microbiome”. We use the term microbiome in the sense of this definition from the National Human Genome Research Institute, which defines a microbiome as “the community of microorganisms (such as fungi, bacteria and viruses) that exists in a particular environment”.

(9) Figure 1 legend: make sure to unify formatting; e.g., present sample sizes as N= or n=, rather than both, and either include or do not include commas in 4-digit values (sample sizes).

We have checked the formatting related to sample sizes and the use of commas in 4-digits in the main text and supplement. The formats are now consistent.

(10) Line 166: relative abundances surely?

Following Gloor et al. (2017), our analyses use centered log-ratio (CLR) transformations of read counts, which is the recommended approach for compositional data such as 16S rRNA amplicon read counts. CLR transformations are scale-invariant, so the same ratio is obtained in a sample with few read versus many reads. We now cite Gloor et al. (2017) at line 169 and in the methods in line 517, which reads “centered log ratio (CLR) transformed abundances (i.e., read counts) of each microbial phyla (n=30), family (n=290), genus (n=747), and amplicon sequence variance (ASV) detected in >25% of samples (n=358). CLR transformations are a recommended approach for addressing the compositional nature of 16S rRNA amplicon read count data (Gloor et al. 2017).”  

(11) Lines 167-172: were technical factors, e.g., read depth or sequencing batch, included as random effects?

Thank you for catching this oversight in the text. We did model sequencing depth and batch effects. The sentence starting at line 173 now reads, “For each of these 1,440 features, we tested its association with host age by running linear mixed effects models that included linear and quadratic effects of host age and four other fixed effects: sequencing depth, the season of sample collection (wet or dry), the average maximum temperature for the month prior to sample collection, and the total rainfall in the month prior to sample collection (Grieneisen et al. 2021; Björk et al. 2022; Tung et al. 2015). Baboon identity, social group membership, hydrological year of sampling, and sequencing plate (as a batch effect) were modeled as random effects.”

(12) Lines 175-180: When discussing how these alpha diversity results relate to previous findings, the authors should be clear about whether they talk about weighted or non-weighted measures of alpha diversity. - also maybe this should be included in the discussion rather than the results? Please consider this when revisiting the manuscript (see how it reads after edits).

Richness is the only unweighted metric, which we now clarify in line 181. We opted to retain the interpretation in the text in its original location to maintain the emphasis in the discussion on the microbiome clock results.

(13) Table S1 is very hard to interpret in the provided PDF format as columns are not presented side-by-side. It is currently hard to check model output for e.g., specific families. This needs to be revisited.

We agree. We believe that eLife’s submission portal automatically generates a PDF for any supplementary item. However, we also include the supplementary tables as an Excel workbook which has the columns presented side-by-side.

(14) Line 184: taxa meaning what? Unclear what authors refer to with this sentence, taxa across taxonomic levels, or ASVs, or what does the 51.6% refer to?

We have edited line 191 to clarify that this sentence refers to taxa at all taxonomic levels (phyla to ASVs).

(15) Line 191: a punctuation mark missing after ref (81).

We have added the missing period at the end of this sentence.

(16) Lines 189-197: this should go into the discussion in my opinion.

We have opted to retain this interpretation, now at line 183.

(17) Lines 215-219: Not sure what this means; do the authors mean features were not restricted to age-associated taxa, ie also e.g., diversity and other taxa-independent patterns were included? If so, the rest of the highlighted lines should be revisited to make this clear, currently to me it is very unclear what 'These could include features that are not strongly age-correlated in isolation' means. Currently, that sounds like some features included were only age-associated in combination with other features, but unclear how this relates to taxa-dependency/taxa-independency.

We agree this was not clear. We have revised line 224 to read, “We included all 9,575 microbiome features in our age predictions, as opposed to just those that were statistically significantly associated with age because removing these non-significant features could exclude features that contribute to age prediction via interactions with other taxa.”

(18) Line 403-407: There is now a paper showing epigenetic clocks can be built with faecal samples, so this argument is not valid. Please revisit in light of this publication: https://onlinelibrary.wiley.com/doi/epdf/10.1111/mec.17330

Thank you for bringing this paper to our attention. We deleted the text that describes epigenetic clocks as invasive, and we now cite this paper in line 450, which reads, “We also hope to measure epigenetic age in fecal samples, leveraging methods developed in Hanski et al. 2024.”

(19) Line 427: a punctuation mark/semicolon missing before However.

We have corrected this typo.

(20) Lines 419-428: I don't quite understand this speculation. Why would the priority of access to food lead to an old-looking gut microbiome? This paragraph needs stronger arguments, currently unclear and also not super convincing.

We agree this was confusing. We have revised this text to clarify the explanation. The text starting at line 424 now reads, “This outcome points towards a shared driver of high social status in shaping gut microbiome age in both males and females. While it is difficult to identify a plausible shared driver, one benefit shared by both high-ranking males and females is priority of access to food. This access may result in fewer foraging disruptions and a higher quality, more stable diet. At the same time, prior research in Amboseli suggests that as animals age, their diets become more canalized and less variable (Grieneisen et al. 2021). Hence aging and priority of access to food might both be associated with dietary stability and old-for-age microbiomes. However, this explanation is speculative and more work is needed to understand the relationship between rank and microbiome age.”

(21) Line 434: remove 'be'.

We have corrected this typo.

(22) Line 478: add information on how samples were collected; e.g., were samples collected from the ground? How was cross-contamination with soil microbiota minimised? Were samples taken from the inner part of depositions? These factors can influence microbiota samples quite drastically so detailed info is needed. Also what does homogenisation mean in this context? How soon were samples freeze-dried after sample collection?

We have expanded our methods with respect to sample collection. This text starts in line 483 and reads, “Samples were collected from the ground within 15 minutes of defecation. For each sample, approximately 20 g of feces was collected into a paper cup, homogenized by stirring with a wooden tongue depressor, and a 5 g aliquot of the homogenized sample was transferred to a tube containing 95% ethanol. While a small amount of soil was typically present on the outside of the fecal sample, mammalian feces contains 1000 times the number of microbial cells in a typical soil sample (Sender, Fuchs, and Milo 2016; Raynaud and Nunan 2014), which overwhelms the signal of soil bacteria in our analyses (Grieneisen et al. 2021). Samples were transported from the field in Amboseli to a lab in Nairobi, freeze-dried, and then sifted to remove plant matter prior to long term storage at -80°C.”

(23) Line 480 onwards: were negative controls included in extraction batches? Were samples randomised into extraction batches?

Yes, we included extraction blanks. These are now described in lines 495-500. This text reads, “We included one extraction blank per batch, which had significantly lower DNA concentrations than sample wells (t-test; t=-50, p < 2.2x10-16; Grieneisen et al. 2021). We also included technical replicates, which were the same fecal sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.”

(24) Were extraction, library prep, and sequencing negative controls included? Is data available?

We included extraction blanks (described above) and technical replicates, which were the same sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.

We have updated the data availability statement to read, “All data for these analyses are available on Dryad at https://doi.org/10.5061/dryad.b2rbnzspv. The 16S rRNA gene sequencing data are deposited on EBI-ENA (project ERP119849) and Qiita (study 12949). Code is available at the following GitHub repository: https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge”.

(25) Line 562: how were corrected microbiome delta ages calculated? Currently, the authors state x, y and z factors were corrected for, but it is unclear how this was done.

The paragraph starting at line 577 describes how microbiome delta age was calculated. We have made only a few changes to this text because we were not sure which aspects of these methods confused the reviewer. However, briefly, we calculated sample-specific microbiome Dage in years as the difference between a sample’s microbial age estimate, age_m from the microbiome clock, and the host’s chronological age in years at the time of sample collection, age_c. Higher microbiome Dages indicate old-for-age microbiomes, as age_m > age_c, and lower values (which are often negative) indicate a young-for-age microbiome, where age_c > age_m (see Figure 3).

(26) Line 579: typo 'as'.

We have corrected this typo.

Works Cited

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Anderson, Jordan A, Rachel A Johnston, Amanda J Lea, Fernando A Campos, Tawni N Voyles, Mercy Y Akinyi, Susan C Alberts, Elizabeth A Archie, and Jenny Tung. 2021. “High Social Status Males Experience Accelerated Epigenetic Aging in Wild Baboons.” Edited by George H Perry. eLife 10 (April):e66128. https://doi.org/10.7554/eLife.66128.

Binder, Alexandra M., Camila Corvalan, Verónica Mericq, Ana Pereira, José Luis Santos, Steve Horvath, John Shepherd, and Karin B. Michels. 2018. “Faster Ticking Rate of the Epigenetic Clock Is Associated with Faster Pubertal Development in Girls.” Epigenetics 13 (1): 85–94. https://doi.org/10.1080/15592294.2017.1414127.

Björk, Johannes R., Mauna R. Dasari, Kim Roche, Laura Grieneisen, Trevor J. Gould, Jean-Christophe Grenier, Vania Yotova, et al. 2022. “Synchrony and Idiosyncrasy in the Gut Microbiome of Wild Baboons.” Nature Ecology & Evolution, June, 1–10. https://doi.org/10.1038/s41559-022-01773-4.

Chen, Brian H., Riccardo E. Marioni, Elena Colicino, Marjolein J. Peters, Cavin K. Ward-Caviness, Pei-Chien Tsai, Nicholas S. Roetker, et al. 2016. “DNA Methylation-Based Measures of Biological Age: Meta-Analysis Predicting Time to Death.” Aging (Albany NY) 8 (9): 1844–59. https://doi.org/10.18632/aging.101020.

Claesson, Marcus J., Ian B. Jeffery, Susana Conde, Susan E. Power, Eibhlís M. O’Connor, Siobhán Cusack, Hugh M. B. Harris, et al. 2012. “Gut Microbiota Composition Correlates with Diet and Health in the Elderly.” Nature 488 (7410): 178–84. https://doi.org/10.1038/nature11319.

Galbany, Jordi, Jeanne Altmann, Alejandro Pérez-Pérez, and Susan C. Alberts. 2011. “Age and Individual Foraging Behavior Predict Tooth Wear in Amboseli Baboons.” American Journal of Physical Anthropology 144 (1): 51–59. https://doi.org/10.1002/ajpa.21368.

Gloor, Gregory B., Jean M. Macklaim, Vera Pawlowsky-Glahn, and Juan J. Egozcue. 2017. “Microbiome Datasets Are Compositional: And This Is Not Optional.” Frontiers in Microbiology 8. https://doi.org/10.3389/fmicb.2017.02224.

Grieneisen, Laura E., Mauna Dasari, Trevor J. Gould, Johannes R. Björk, Jean-Christophe Grenier, Vania Yotova, David Jansen, et al. 2021. “Gut Microbiome Heritability Is Nearly Universal but Environmentally Contingent.” Science 373 (6551): 181–86. https://doi.org/10.1126/science.aba5483.

Hanski, Eveliina, Susan Joseph, Aura Raulo, Klara M. Wanelik, Áine O’Toole, Sarah C. L. Knowles, and Tom J. Little. 2024. “Epigenetic Age Estimation of Wild Mice Using Faecal Samples.” Molecular Ecology 33 (8): e17330. https://doi.org/10.1111/mec.17330.

Heintz, Caroline, and William Mair. 2014. “You Are What You Host: Microbiome Modulation of the Aging Process.” Cell 156 (3): 408–11. http://dx.doi.org/10.1016/j.cell.2014.01.025.

Horvath, Steve. 2013. “DNA Methylation Age of Human Tissues and Cell Types.” Genome Biology 14 (10): R115. https://doi.org/10.1186/gb-2013-14-10-r115.

Jayashankar, Lakshmi, Kathleen M. Brasky, John A. Ward, and Roberta Attanasio. 2003. “Lymphocyte Modulation in a Baboon Model of Immunosenescence.” Clinical and Vaccine Immunology 10 (5): 870–75. https://doi.org/10.1128/CDLI.10.5.870-875.2003.

Marioni, Riccardo E., Sonia Shah, Allan F. McRae, Brian H. Chen, Elena Colicino, Sarah E. Harris, Jude Gibson, et al. 2015. “DNA Methylation Age of Blood Predicts All-Cause Mortality in Later Life.” Genome Biology 16 (1): 25. https://doi.org/10.1186/s13059-015-0584-6.

O’Toole, Paul W., and Ian B. Jeffery. 2015. “Gut Microbiota and Aging.” Science 350 (6265): 1214–15. https://doi.org/10.1126/science.aac8469.

Raynaud, Xavier, and Naoise Nunan. 2014. “Spatial Ecology of Bacteria at the Microscale in Soil.” PLOS ONE 9 (1): e87217. https://doi.org/10.1371/journal.pone.0087217.

Sadoughi, Baptiste, Dominik Schneider, Rolf Daniel, Oliver Schülke, and Julia Ostner. 2022. “Aging Gut Microbiota of Wild Macaques Are Equally Diverse, Less Stable, but Progressively Personalized.” Microbiome 10 (1): 95. https://doi.org/10.1186/s40168-022-01283-2.

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Weibel, Chelsea J., Mauna R. Dasari, David A. Jansen, Laurence R. Gesquiere, Raphael S. Mututua, J. Kinyua Warutere, Long’ida I. Siodi, Susan C. Alberts, Jenny Tung, and Elizabeth A. Archie. 2024. “Using Non-Invasive Behavioral and Physiological Data to Measure Biological Age in Wild Baboons.” GeroScience 46 (5): 4059–74. https://doi.org/10.1007/s11357-024-01157-5.

Author response:

We thank the reviewers for their thoughtful reading and review of our manuscript. These reviews make clear that, for this work to be complete, we must make progress on the following fronts:

(1) Expand the discussion to better incorporate alternate explanations of our data

(2) Improve data visualization and experimental support or an experimental refutation for the following concepts

a. Photoreceptor-derived lactate exported specifically from photoreceptors is utilized in the RPE TCA cycle

b. Photoreceptors can utilize lactate as a fuel source when starved of glucose

To address these concerns, we will focus our efforts on infusing ¹³C₆-glucose into rodΔglut1 mice. Lactate is not made without glucose, so this experiment should indicate whether glucose utilization in photoreceptors provides lactate to the RPE, and whether that lactate is used in the TCA cycle.

The reviewers also noted that changes in ¹³C labeling of RPE TCA cycle intermediates downstream of lactate is not obvious (between C57BL6J mice and AIPL1^-/-). We think that at least in part, this is a consequence of the way we presented the data. We will improve how we display our data so that the differences of incorporation of ¹³C in TCA cycle intermediates in control and AIPL1^-/- RPE is clearer.

Author response:

The issue of a control without blue light illumination was raised. Clearly without the light we will not obtain any signal in the fluorescence microscopy experiments, which would not be very informative. Instead, we changed the level of blue light illumination in the fluorescence microscopy experiments (figure 4A) and the response of the bacteria scales with dosage. It is very hard to find an alternative explanation, beyond that the blue light is stressing the bacteria and modulating their membrane potentials.

One of the referees refuses to see wavefronts in our microscopy data. We struggle to understand whether it is an issue with definitions (Waigh has published a tutorial on the subject in Chapter 5 of his book ‘The physics of bacteria: from cells to biofilms’, T.A.Waigh, CUP, 2024 – figure 5.1 shows a sketch) or something subtler on diffusion in excitable systems. We stand by our claim that we observe wavefronts, similar to those observed by Prindle et al¹ and Blee et al² for B. subtilis biofilms.

The referee is questioning our use of ThT to probe the membrane potential. We believe the Pilizota and Strahl groups are treating the E. coli as unexcitable cells, leading to their problems. Instead, we believe E. coli cells are excitable (containing the voltage-gated ion channel Kch) and we now clearly state this in the manuscript. Furthermore, we include a section here discussing some of the issues with ThT.

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Use of ThT as a voltage sensor in cells

ThT is now used reasonably widely in the microbiology community as a voltage sensor in both bacterial [Prindle et al]1 and fungal cells [Pena et al]12. ThT is a small cationic fluorophore that loads into the cells in proportion to their membrane potential, thus allowing the membrane potential to be measured from fluorescence microscopy measurements.

Previously ThT was widely used to quantify the growth of amyloids in molecular biology experiments (standardized protocols exist and dedicated software has been created)13 and there is a long history of its use14. ThT fluorescence is bright, stable and slow to photobleach.

Author response figure 1 shows a schematic diagram of the ThT loading in E. coli in our experiments in response to illumination with blue light. Similar results were previously presented by Mancini et al15, but regimes 2 and 3 were mistakenly labelled as artefacts.

Author response figure 1. Schematic diagram of ThT loading during an experiment with E. coli cells under blue light illumination i.e. ThT fluorescence as a function of time. Three empirical regimes for the fluorescence are shown (1, 2 and 3).

The classic study of Prindle et al on bacterial biofilm electrophysiology established the use of ThT in B. subtilis biofilms by showing similar results occurred with DiSc3 which is widely used as a Nernstian voltage sensor in cellular biology1 e.g. with mitochondrial membrane potentials in eukaryotic organisms where there is a large literature. We repeated such a comparative calibration of ThT with DiSc3 in a previous publication with both B. subtilis and P. aeruginosa cells2. ThT thus functioned well in our previous publications with Gram positive and Gram negative cells.

However, to our knowledge, there are now two groups questioning the use of ThT and DiSc3 as voltage sensors with E. coli cells15-16. The first by the Pilizota group claims ThT only works as a voltage sensor in regime 1 of Author response figure 1 using a method based on the rate of rotation of flagellar motors. Another slightly contradictory study by the Strahl group claims DiSc316 only acts as a voltage sensor with the addition of an ionophore for potassium which allows free movement of potassium through the E. coli membranes.

Our resolution to this contradiction is that ThT does indeed work reasonably well with E. coli. The Pilizota group’s model for rotating flagellar motors assumes the membrane voltage is not varying due to excitability of the membrane voltage (otherwise a non-linear Hodgkin Huxley type model would be needed to quantify their results) i.e. E. coli cells are unexcitable. We show clearly in our study that ThT loading in E. coli is a function of irradiation with blue light and is a stress response of the excitable cells. This is in contradiction to the Pilizota group’s model. The Pilizota group’s model also requires the awkward fiction of why cells decide to unload and then reload ThT in regimes 2 and 3 of Author response figure 1 due to variable membrane partitioning of the ThT. Our simple explanation is that it is just due to the membrane voltage changing and no membrane permeability switch needs to be invoked. The Strahl group’s16 results with DiSc3 are also explained by a neglect of the excitable nature of E. coli cells that are reacting to blue light irradiation. Adding ionophores to the E. coli membranes makes the cells unexcitable, reduces their response to blue light and thus leads to simple loading of DiSc3 (the physiological control of K+ in the cells by voltage-gated ion channels has been short circuited by the addition of the ionophore).

Further evidence of our model that ThT functions as a voltage sensor with E. coli include:

1) The 3 regimes in Author response figure 1 from ThT correlate well with measurements of extracellular potassium ion concentration using TMRM i.e. all 3 regimes in Author response figure 1 are visible with this separate dye (figure 1d).

2) We are able to switch regime 3 in Author response figure 1, off and then on again by using knock downs of the potassium ion channel Kch in the membranes of the E. coli and then reinserting the gene back into the knock downs. This cannot be explained by the Pilizota model.

We conclude that ThT works reasonably well as a sensor of membrane voltage in E. coli and the previous contradictory studies15-16 are because they neglect the excitable nature of the membrane voltage of E. coli cells in response to the light used to make the ThT fluoresce.

Three further criticisms of the Mancini et al method15 for calibrating membrane voltages include:

1) E. coli cells have clutches that are not included in their models. Otherwise the rotation of the flagella would be entirely enslaved to the membrane voltage allowing the bacteria no freedom to modulate their speed of motility.

2) Ripping off the flagella may perturb the integrity of the cell membrane and lead to different loading of the ThT in the E. coli cells.

3) Most seriously, the method ignores the activity of many other ion channels (beyond H+) on the membrane voltage that are known to exist with E. coli cells e.g. Kch for K+ ions. The Pilizota groups uses a simple Nernstian battery model developed for mitochondria in the 1960s. It is not adequate to explain our results.

An additional criticism of the Winkel et al study17 from the Strahl group is that it indiscriminately switches between discussion of mitochondria and bacteria e.g. on page 8 ‘As a consequence the membrane potential is dominated by H+’. Mitochondria are slightly alkaline intracellular organelles with external ion concentrations in the cytoplasm that are carefully controlled by the eukaryotic cells. E. coli are not i.e. they have neutral internal pHs, with widely varying extracellular ionic concentrations and have reinforced outer membranes to resist osmotic shocks (in contrast mitochondria can easily swell in response to moderate changes in osmotic pressure).

A quick calculation of the equilibrium membrane voltage of E. coli can be easily done using the Nernst equation dependent on the extracellular ion concentrations defined by the growth media (the intracellular ion concentrations in E. coli are 0.2 M K+ and 10-7 M H+ i.e. there is a factor of a million fewer H+ ions). Thus in contradiction to the claims of the groups of Pilizota15 and Strahl17, H+ is a minority determinant to the membrane voltage of E. coli. The main determinant is K+. For a textbook version of this point the authors can refer to Chapter 4 of D. White, et al’s ‘The physiology and biochemistry of prokaryotes’, OUP, 2012, 4th edition.

Even in mitochondria the assumption that H+ dominates the membrane potential and the cells are unexcitable can be questioned e.g. people have observed pulsatile depolarization phenomena with mitochondria18-19. A large number of K+ channels are now known to occur in mitochondrial membranes (not to mention Ca2+ channels; mitochondria have extensive stores of Ca2+) and they are implicated in mitochondrial membrane potentials. In this respect the seminal Nobel prize winning research of Peter Mitchell (1961) on mitochondria needs to be amended20. Furthermore, the mitochondrial work is clearly inapplicable to bacteria (the proton motive force, PMF, will instead subtly depend on non-linear Hodgkin-Huxley equations for the excitable membrane potential, similar to those presented in the current article). A much more sophisticated framework has been developed to describe electrophysiology by the mathematical biology community to describe the activity of electrically excitable cells (e.g. with neurons, sensory cells and cardiac cells), beyond Mitchell’s use of the simple stationary equilibrium thermodynamics to define the Proton Motive Force via the electrochemical potential of a proton (the use of the word ‘force’ is unfortunate, since it is a potential). The tools developed in the field of mathematical electrophysiology8 should be more extensively applied to bacteria, fungi, mitochondria and chloroplasts if real progress is to be made.

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Related to the previous point, we now cite articles from the Pilizota and Strahl groups in the main text (one from each group). Unfortunately, the space constraints of eLife mean we cannot make a more detailed discussion in the main article.

In terms of modelling the ion channels, the Hodgkin-Huxley type model proposes that the Kch ion channel can be modelled as a typical voltage-gated potassium ion channel i.e. with a 𝑛⁴ term in its conductivity. The literature agrees that Kch is a voltage-gated potassium ion channel based on its primary sequence³. The protein has the typical 6 transmembrane helix motif for a voltage-gated ion channel. The agent-based model assumes little about the structure of ion channels in E. coli, other than they release potassium in response to a threshold potassium concentration in their environment. The agent based model is thus robust to the exact molecular details chosen and predicts the anomalous transport of the potassium wavefronts reasonably well (the modelling was extended in a recent Physical Review E article(⁴). Such a description of reaction-anomalous diffusion phenomena has not to our knowledge been previously achieved in the literature⁵ and in general could be used to describe other signaling molecules.

1. Prindle, A.; Liu, J.; Asally, M.; Ly, S.; Garcia-Ojalvo, J.; Sudel, G. M., Ion channels enable electrical communication in bacterial communities. Nature 2015, 527, 59.

2. Blee, J. A.; Roberts, I. S.; Waigh, T. A., Membrane potentials, oxidative stress and the dispersal response of bacterial biofilms to 405 nm light. Physical Biology 2020, 17, 036001.

3. Milkman, R., An E. col_i homologue of eukaryotic potassium channel proteins. _PNAS 1994, 91, 3510-3514.

4. Martorelli, V.; Akabuogu, E. U.; Krasovec, R.; Roberts, I. S.; Waigh, T. A., Electrical signaling in three-dimensional bacterial biofilms using an agent-based fire-diffuse-fire model. Physical Review E 2024, 109, 054402.

5. Waigh, T. A.; Korabel, N., Heterogeneous anomalous transport in cellular and molecular biology. Reports on Progress in Physics 2023, 86, 126601.

6. Hodgkin, A. L.; Huxley, A. F., A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 1952, 117, 500.

7. Dawson, S. P.; Keizer, J.; Pearson, J. E., Fire-diffuse-fire model of dynamics of intracellular calcium waves. PNAS 1999, 96, 606.

8. Keener, J.; Sneyd, J., Mathematical Physiology. Springer: 2009.

9. Coombes, S., The effect of ion pumps on the speed of travelling waves in the fire-diffuse-fire model of Ca2+ release. Bulletin of Mathematical Biology 2001, 63, 1.

10. Blee, J. A.; Roberts, I. S.; Waigh, T. A., Spatial propagation of electrical signals in circular biofilms. Physical Review E 2019, 100, 052401.

11. Gorochowski, T. E.; Matyjaszkiewicz, A.; Todd, T.; Oak, N.; Kowalska, K., BSim: an agent-based tool for modelling bacterial populations in systems and synthetic biology. PloS One 2012, 7, 1.

12. Pena, A.; Sanchez, N. S.; Padilla-Garfias, F.; Ramiro-Cortes, Y.; Araiza-Villaneuva, M.; Calahorra, M., The use of thioflavin T for the estimation and measurement of the plasma membrane electric potential difference in different yeast strains. Journal of Fungi 2023, 9 (9), 948.

13. Xue, C.; Lin, T. Y.; Chang, D.; Guo, Z., Thioflavin T as an amyloid dye: fibril quantification, optimal concentration and effect on aggregation. Royal Society Open Science 2017, 4, 160696.

14. Meisl, G.; Kirkegaard, J. B.; Arosio, P.; Michaels, T. C. T.; Vendruscolo, M.; Dobson, C. M.; Linse, S.; Knowles, T. P. J., Molecular mechanisms of protein aggregation from global fitting of kinetic models. Nature Protocols 2016, 11 (2), 252-272.

15. Mancini, L.; Tian, T.; Guillaume, T.; Pu, Y.; Li, Y.; Lo, C. J.; Bai, F.; Pilizota, T., A general workflow for characterization of Nernstian dyes and their effects on bacterial physiology. Biophysical Journal 2020, 118 (1), 4-14.

16. Buttress, J. A.; Halte, M.; Winkel, J. D. t.; Erhardt, M.; Popp, P. F.; Strahl, H., A guide for membrane potential measurements in Gram-negative bacteria using voltage-sensitive dyes. Microbiology 2022, 168, 001227.

17. Derk te Winkel, J.; Gray, D. A.; Seistrup, K. H.; Hamoen, L. W.; Strahl, H., Analysis of antimicrobial-triggered membrane depolarization using voltage sensitive dyes. Frontiers in Cell and Developmental Biology 2016, 4, 29.

18. Schawarzlander, M.; Logan, D. C.; Johnston, I. G.; Jones, N. S.; Meyer, A. J.; Fricker, M. D.; Sweetlove, L. J., Pulsing of membrane potential in individual mitochondria. The Plant Cell 2012, 24, 1188-1201.

19. Huser, J.; Blatter, L. A., Fluctuations in mitochondrial membrane potential caused by repetitive gating of the permeability transition pore. Biochemistry Journal 1999, 343, 311-317.

20. Mitchell, P., Coupling of phosphorylation to electron and hydrogen transfer by a chemi-osmotic type of mechanism. Nature 1961, 191 (4784), 144-148.

21. Baba, T.; Ara, M.; Hasegawa, Y.; Takai, Y.; Okumura, Y.; Baba, M.; Datsenko, K. A.; Tomita, M.; Wanner, B. L.; Mori, H., Construction of Escherichia Coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Molecular Systems Biology 2006, 2, 1.

22. Schinedlin, J.; al, e., Fiji: an open-source platform for biological-image analysis. Nature Methods 2012, 9, 676.

23. Hartmann, R.; al, e., Quantitative image analysis of microbial communities with BiofilmQ. Nature Microbiology 2021, 6 (2), 151.

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

Critical synopsis of the articles cited by referee 2:

(1) ‘Generalized workflow for characterization of Nernstian dyes and their effects on bacterial physiology’, L.Mancini et al, Biophysical Journal, 2020, 118, 1, 4-14.

This is the central article used by referee 2 to argue that there are issues with the calibration of ThT for the measurement of membrane potentials. The authors use a simple Nernstian battery (SNB) model and unfortunately it is wrong when voltage-gated ion channels occur. Huge oscillations occur in the membrane potentials of E. coli that cannot be described by the SNB model. Instead a Hodgkin Huxley model is needed, as shown in our eLife manuscript and multiple other studies (see above). Arrhenius kinetics are assumed in the SNB model for pumping with no real evidence and the generalized workflow involves ripping the flagella off the bacteria! The authors construct an elaborate ‘work flow’ to insure their ThT results can be interpreted using their erroneous SNB model over a limited range of parameters.

(2) ‘Non-equivalence of membrane voltage and ion-gradient as driving forces for the bacterial flagellar motor at low load’, C.J.Lo, et al, Biophysical Journal, 2007, 93, 1, 294.

An odd de novo chimeric species is developed using an E. coli  chassis which uses Na+ instead of H+ for the motility of its flagellar motor. It is not clear the relevance to wild type E. coli, due to the massive physiological perturbations involved. A SNB model is using to fit the data over a very limited parameter range with all the concomitant errors.

(3) Single-cell bacterial electrophysiology reveals mechanisms of stress-induced damage’, E.Krasnopeeva, et al, Biophysical Journal, 2019, 116, 2390.

The abstract says ‘PMF defines the physiological state of the cell’. This statement is hyperbolic. An extremely wide range of molecules contribute to the physiological state of a cell. PMF does not even define the electrophysiology of the cell e.g. via the membrane potential. There are 0.2 M of K+ compared with 0.0000001 M of H+ in E. coli, so K+ is arguably a million times more important for the membrane potential than H+ and thus the electrophysiology!

Equation (1) in the manuscript assumes no other ions are exchanged during the experiments other than H+. This is a very bad approximation when voltage-gated potassium ion channels move the majority ion (K+) around!

In our model Figure 4A is better explained by depolarisation due to K+ channels closing than direct irreversible photodamage. Why does the THT fluorescence increase again for the second hyperpolarization event if the THT is supposed to be damaged? It does not make sense.

(4) ‘The proton motive force determines E. coli robustness to extracellular pH’, G.Terradot et al, 2024, preprint.

This article expounds the SNB model once more. It still ignores the voltage-gated ion channels. Furthermore, it ignores the effect of the dominant ion in E. coli, K+. The manuscript is incorrect as a result and I would not recommend publication.

In general, an important problem is being researched i.e. how the membrane potential of E. coli is related to motility, but there are serious flaws in the SNB approach and the experimental methodology appears tenuous.

Answers to specific questions raised by the referees

Reviewer #1 (Public Review):

Summary:

Cell-to-cell communication is essential for higher functions in bacterial biofilms. Electrical signals have proven effective in transmitting signals across biofilms. These signals are then used to coordinate cellular metabolisms or to increase antibiotic tolerance. Here, the authors have reported for the first time coordinated oscillation of membrane potential in E. coli biofilms that may have a functional role in photoprotection.

Strengths:

- The authors report original data.

- For the first time, they showed that coordinated oscillations in membrane potential occur in E. Coli biofilms.

- The authors revealed a complex two-phase dynamic involving distinct molecular response mechanisms.

- The authors developed two rigorous models inspired by 1) Hodgkin-Huxley model for the temporal dynamics of membrane potential and 2) Fire-Diffuse-Fire model for the propagation of the electric signal.

- Since its discovery by comparative genomics, the Kch ion channel has not been associated with any specific phenotype in E. coli. Here, the authors proposed a functional role for the putative K+ Kch channel : enhancing survival under photo-toxic conditions.

We thank the referee for their positive evaluations and agree with these statements.

Weaknesses:

- Since the flow of fresh medium is stopped at the beginning of the acquisition, environmental parameters such as pH and RedOx potential are likely to vary significantly during the experiment. It is therefore important to exclude the contributions of these variations to ensure that the electrical response is only induced by light stimulation. Unfortunately, no control experiments were carried out to address this issue.

The electrical responses occur almost instantaneously when the stimulation with blue light begins i.e. it is too fast to be a build of pH. We are not sure what the referee means by Redox potential since it is an attribute of all chemicals that are able to donate/receive electrons. The electrical response to stress appears to be caused by ROS, since when ROS scavengers are added the electrical response is removed i.e. pH plays a very small minority role if any.

- Furthermore, the control parameter of the experiment (light stimulation) is the same as that used to measure the electrical response, i.e. through fluorescence excitation. The use of the PROPS system could solve this problem.

>>We were enthusiastic at the start of the project to use the PROPs system in E. coli as presented by J.M.Krajl et al, ‘Electrical spiking in E. coli probed with a fluorescent voltage-indicating protein’, Science, 2011, 333, 6040, 345. However, the people we contacted in the microbiology community said that it had some technical issues and there have been no subsequent studies using PROPs in bacteria after the initial promising study. The fluorescent protein system recently presented in PNAS seems more promising, ‘Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance’, X.Jin et al, PNAS, 120, 3, e2208348120.

- Electrical signal propagation is an important aspect of the manuscript. However, a detailed quantitative analysis of the spatial dynamics within the biofilm is lacking. In addition, it is unclear if the electrical signal propagates within the biofilm during the second peak regime, which is mediated by the Kch channel. This is an important question, given that the fire-diffuse-fire model is presented with emphasis on the role of K+ ions.

We have presented a more detailed account of the electrical wavefront modelling work and it is currently under review in a physical journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

- Since deletion of the kch gene inhibits the long-term electrical response to light stimulation (regime II), the authors concluded that K+ ions play a role in the habituation response. However, Kch is a putative K+ ion channel. The use of specific drugs could help to clarify the role of K+ ions.

Our recent electrical impedance spectroscopy publication provides further evidence that Kch is associated with large changes in conductivity as expected for a voltage-gated ion channel (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

- The manuscript as such does not allow us to properly conclude on the photo-protective role of the Kch ion channel.

That Kch has a photoprotective role is our current working hypothesis. The hypothesis fits with the data, but we are not saying we have proven it beyond all possible doubt.

- The link between membrane potential dynamics and mechanosensitivity is not captured in the equation for the Q-channel opening dynamics in the Hodgkin-Huxley model (Supp Eq 2).

Our model is agnostic with respect to the mechanosensitivity of the ion channels, although we deduce that mechanosensitive ion channels contribute to ion channel Q.

- Given the large number of parameters used in the models, it is hard to distinguish between prediction and fitting.

This is always an issue with electrophysiological modelling (compared with most heart and brain modelling studies we are very conservative in the choice of parameters for the bacteria). In terms of predicting the different phenomena observed, we believe the model is very successful.

Reviewer #2 (Public Review):

Summary of what the authors were trying to achieve:

The authors thought they studied membrane potential dynamics in E.coli biofilms. They thought so because they were unaware that the dye they used to report that membrane potential in E.coli, has been previously shown not to report it. Because of this, the interpretation of the authors' results is not accurate.

We believe the Pilizota work is scientifically flawed.

Major strengths and weaknesses of the methods and results:

The strength of this work is that all the data is presented clearly, and accurately, as far as I can tell.

The major critical weakness of this paper is the use of ThT dye as a membrane potential dye in E.coli. The work is unaware of a publication from 2020 https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] that demonstrates that ThT is not a membrane potential dye in E. coli. Therefore I think the results of this paper are misinterpreted. The same publication I reference above presents a protocol on how to carefully calibrate any candidate membrane potential dye in any given condition.

We are aware of this study, but believe it to be scientifically flawed. We do not cite the article because we do not think it is a particularly useful contribution to the literature.

I now go over each results section in the manuscript.

Result section 1: Blue light triggers electrical spiking in single E. coli cells

I do not think the title of the result section is correct for the following reasons. The above-referenced work demonstrates the loading profile one should expect from a Nernstian dye (Figure 1). It also demonstrates that ThT does not show that profile and explains why is this so. ThT only permeates the membrane under light exposure (Figure 5). This finding is consistent with blue light peroxidising the membrane (see also following work Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] on light-induced damage to the electrochemical gradient of protons-I am sure there are more references for this).

The Pilizota group invokes some elaborate artefacts to explain the lack of agreement with a simple Nernstian battery model. The model is incorrect not the fluorophore.

Please note that the loading profile (only observed under light) in the current manuscript in Figure 1B as well as in the video S1 is identical to that in Figure 3 from the above-referenced paper (i.e. https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com]), and corresponding videos S3 and S4. This kind of profile is exactly what one would expect theoretically if the light is simultaneously lowering the membrane potential as the ThT is equilibrating, see Figure S12 of that previous work. There, it is also demonstrated by the means of monitoring the speed of bacterial flagellar motor that the electrochemical gradient of protons is being lowered by the light. The authors state that applying the blue light for different time periods and over different time scales did not change the peak profile. This is expected if the light is lowering the electrochemical gradient of protons. But, in Figure S1, it is clear that it affected the timing of the peak, which is again expected, because the light affects the timing of the decay, and thus of the decay profile of the electrochemical gradient of protons (Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com]).

We think the proton effect is a million times weaker than that due to potasium i.e. 0.2 M K+ versus 10-7 M H+. We can comfortably neglect the influx of H+ in our experiments.

If find Figure S1D interesting. There authors load TMRM, which is a membrane voltage dye that has been used extensively (as far as I am aware this is the first reference for that and it has not been cited https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1914430 [ncbi.nlm.nih.gov]/). As visible from the last TMRM reference I give, TMRM will only load the cells in Potassium Phosphate buffer with NaCl (and often we used EDTA to permeabilise the membrane). It is not fully clear (to me) whether here TMRM was prepared in rich media (it explicitly says so for ThT in Methods but not for TMRM), but it seems so. If this is the case, it likely also loads because of the damage to the membrane done with light, and therefore I am not surprised that the profiles are similar.

The vast majority of cells continue to be viable. We do not think membrane damage is dominating.

The authors then use CCCP. First, a small correction, as the authors state that it quenches membrane potential. CCCP is a protonophore (https://pubmed.ncbi.nlm.nih.gov/4962086 [pubmed.ncbi.nlm.nih.gov]/), so it collapses electrochemical gradient of protons. This means that it is possible, and this will depend on the type of pumps present in the cell, that CCCP collapses electrochemical gradient of protons, but the membrane potential is equal and opposite in sign to the DeltapH. So using CCCP does not automatically mean membrane potential will collapse (e.g. in some mammalian cells it does not need to be the case, but in E.coli it is https://www.biorxiv.org/content/10.1101/2021.11.19.469321v2 [biorxiv.org]). CCCP has also been recently found to be a substrate for TolC (https://journals.asm.org/doi/10.1128/mbio.00676-21 [journals.asm.org]), but at the concentrations the authors are using CCCP (100uM) that should not affect the results. However, the authors then state because they observed, in Figure S1E, a fast efflux of ions in all cells and no spiking dynamics this confirms that observed dynamics are membrane potential related. I do not agree that it does. First, Figure S1E, does not appear to show transients, instead, it is visible that after 50min treatment with 100uM CCCP, ThT dye shows no dynamics. The action of a Nernstian dye is defined. It is not sufficient that a charged molecule is affected in some way by electrical potential, this needs to be in a very specific way to be a Nernstian dye. Part of the profile of ThT loading observed in https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] is membrane potential related, but not in a way that is characteristic of Nernstian dye.

Our understanding of the literature is CCCP poisons the whole metabolism of the bacterial cells. The ATP driven K+ channels will stop functioning and this is the dominant contributor to membrane potential.

Result section 2: Membrane potential dynamics depend on the intercellular distance

In this chapter, the authors report that the time to reach the first intensity peak during ThT loading is different when cells are in microclusters. They interpret this as electrical signalling in clusters because the peak is reached faster in microclusters (as opposed to slower because intuitively in these clusters cells could be shielded from light). However, shielding is one possibility. The other is that the membrane has changed in composition and/or the effective light power the cells can tolerate (with mechanisms to handle light-induced damage, some of which authors mention later in the paper) is lower. Given that these cells were left in a microfluidic chamber for 2h hours to attach in growth media according to Methods, there is sufficient time for that to happen. In Figure S12 C and D of that same paper from my group (https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com]) one can see the effects of peak intensity and timing of the peak on the permeability of the membrane. Therefore I do not think the distance is the explanation for what authors observe.

Shielding would provide the reverse effect, since hyperpolarization begins in the dense centres of the biofilms. For the initial 2 hours the cells receive negligible blue light. Neither of the referee’s comments thus seem tenable.

Result section 3: Emergence of synchronized global wavefronts in E. coli biofilms

In this section, the authors exposed a mature biofilm to blue light. They observe that the intensity peak is reached faster in the cells in the middle. They interpret this as the ion-channel-mediated wavefronts moved from the center of the biofilm. As above, cells in the middle can have different membrane permeability to those at the periphery, and probably even more importantly, there is no light profile shown anywhere in SI/Methods. I could be wrong, but the SI3 A profile is consistent with a potential Gaussian beam profile visible in the field of view. In Methods, I find the light source for the blue light and the type of microscope but no comments on how 'flat' the illumination is across their field of view. This is critical to assess what they are observing in this result section. I do find it interesting that the ThT intensity collapsed from the edges of the biofilms. In the publication I mentioned https://www.sciencedirect.com/science/article/pii/S0006349519308793#app2 [sciencedirect.com], the collapse of fluorescence was not understood (other than it is not membrane potential related). It was observed in Figure 5A, C, and F, that at the point of peak, electrochemical gradient of protons is already collapsed, and that at the point of peak cell expands and cytoplasmic content leaks out. This means that this part of the ThT curve is not membrane potential related. The authors see that after the first peak collapsed there is a period of time where ThT does not stain the cells and then it starts again. If after the first peak the cellular content leaks, as we have observed, then staining that occurs much later could be simply staining of cytoplasmic positively charged content, and the timing of that depends on the dynamics of cytoplasmic content leakage (we observed this to be happening over 2h in individual cells). ThT is also a non-specific amyloid dye, and in starving E. coli cells formation of protein clusters has been observed (https://pubmed.ncbi.nlm.nih.gov/30472191 [pubmed.ncbi.nlm.nih.gov]/), so such cytoplasmic staining seems possible.

>>It is very easy to see if the illumination is flat (Köhler illumination) by comparing the intensity of background pixels on the detector. It was flat in our case. Protons have little to do with our work for reasons highlighted before. Differential membrane permittivity is a speculative phenomenon not well supported by any evidence and with no clear molecular mechanism.

Finally, I note that authors observe biofilms of different shapes and sizes and state that they observe similar intensity profiles, which could mean that my comment on 'flatness' of the field of view above is not a concern. However, the scale bar in Figure 2A is not legible, so I can't compare it to the variation of sizes of the biofilms in Figure 2C (67 to 280um). Based on this, I think that the illumination profile is still a concern.

The referee now contradicts themselves and wants a scale bar to be more visible. We have changed the scale bar.

Result section 4: Voltage-gated Kch potassium channels mediate ion-channel electrical oscillations in E. coli

First I note at this point, given that I disagree that the data presented thus 'suggest that E. coli biofilms use electrical signaling to coordinate long-range responses to light stress' as the authors state, it gets harder to comment on the rest of the results.

In this result section the authors look at the effect of Kch, a putative voltage-gated potassium channel, on ThT profile in E. coli cells. And they see a difference. It is worth noting that in the publication https://www.sciencedirect.com/science/article/pii/S0006349519308793 [sciencedirect.com] it is found that ThT is also likely a substrate for TolC (Figure 4), but that scenario could not be distinguished from the one where TolC mutant has a different membrane permeability (and there is a publication that suggests the latter is happening https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2958.2010.07245.x [onlinelibrary.wiley.com]). Given this, it is also possible that Kch deletion affects the membrane permeability. I do note that in video S4 I seem to see more of, what appear to be, plasmolysed cells. The authors do not see the ThT intensity with this mutant that appears long after the initial peak has disappeared, as they see in WT. It is not clear how long they waited for this, as from Figure S3C it could simply be that the dynamics of this is a lot slower, e.g. Kch deletion changes membrane permeability.

The work that TolC provides a possible passive pathway for ThT to leave cells seems slightly niche. It just demonstrates another mechanism for the cells to equilibriate the concentrations of ThT in a Nernstian manner i.e. driven by the membrane voltage.

The authors themselves state that the evidence for Kch being a voltage-gated channel is indirect (line 54). I do not think there is a need to claim function from a ThT profile of E. coli mutants (nor do I believe it's good practice), given how accurate single-channel recordings are currently. To know the exact dependency on the membrane potential, ion channel recordings on this protein are needed first.

We have good evidence form electrical impedance spectroscopy experiments that Kch increases the conductivity of biofilms  (https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04446, 'Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behavior', E.Akabuogu et al, ACS Nanoletters, 2024, in print.

Result section 5: Blue light influences ion-channel mediated membrane potential events in E. coli

In this chapter the authors vary the light intensity and stain the cells with PI (this dye gets into the cells when the membrane becomes very permeable), and the extracellular environment with K+ dye (I have not yet worked carefully with this dye). They find that different amounts of light influence ThT dynamics. This is in line with previous literature (both papers I have been mentioning: Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 [sciencedirect.com] and https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf [ars.els-cdn.com] especially SI12), but does not add anything new. I think the results presented here can be explained with previously published theory and do not indicate that the ion-channel mediated membrane potential dynamics is a light stress relief process.

The simple Nernstian battery model proposed by Pilizota et al is erroneous in our opinion for reasons outlined above. We believe it will prove to be a dead end for bacterial electrophysiology studies.

Result section 6: Development of a Hodgkin-Huxley model for the observed membrane potential dynamics

This results section starts with the authors stating: 'our data provide evidence that E. coli manages light stress through well-controlled modulation of its membrane potential dynamics'. As stated above, I think they are instead observing the process of ThT loading while the light is damaging the membrane and thus simultaneously collapsing the electrochemical gradient of protons. As stated above, this has been modelled before. And then, they observe a ThT staining that is independent from membrane potential.

This is an erroneous niche opinion. Protons have little say in the membrane potential since there are so few of them. The membrane potential is mostly determined by K+.

I will briefly comment on the Hodgkin Huxley (HH) based model. First, I think there is no evidence for two channels with different activation profiles as authors propose. But also, the HH model has been developed for neurons. There, the leakage and the pumping fluxes are both described by a constant representing conductivity, times the difference between the membrane potential and Nernst potential for the given ion. The conductivity in the model is given as gK*n^4 for potassium, gNa*m^3*h sodium, and gL for leakage, where gK, gNa and gL were measured experimentally for neurons. And, n, m, and h are variables that describe the experimentally observed voltage-gated mechanism of neuronal sodium and potassium channels. (Please see Hodgkin AL, Huxley AF. 1952. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116:449-72 and Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500-44).

In the 70 years since Hodgkin and Huxley first presented their model, a huge number of similar models have been proposed to describe cellular electrophysiology. We are not being hyperbolic when we state that the HH models for excitable cells are like the Schrödinger equation for molecules. We carefully adapted our HH model to reflect the currently understood electrophysiology of E. coli.

Thus, in applying the model to describe bacterial electrophysiology one should ensure near equilibrium requirement holds (so that (V-VQ) etc terms in authors' equation Figure 5 B hold), and potassium and other channels in a given bacterium have similar gating properties to those found in neurons. I am not aware of such measurements in any bacteria, and therefore think the pump leak model of the electrophysiology of bacteria needs to start with fluxes that are more general (for example Keener JP, Sneyd J. 2009. Mathematical physiology: I: Cellular physiology. New York: Springer or https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000144 [journals.plos.org])

The reference is to a slightly more modern version of a simple Nernstian battery model. The model will not oscillate and thus will not help modelling membrane potentials in bacteria. We are unsure where the equilibrium requirement comes from (inadequate modelling of the dynamics?)

Result section 7: Mechanosensitive ion channels (MS) are vital for the first hyperpolarization event in E. coli.

The results that Mcs channels affect the profile of ThT dye are interesting. It is again possible that the membrane permeability of these mutants has changed and therefore the dynamics have changed, so this needs to be checked first. I also note that our results show that the peak of ThT coincides with cell expansion. For this to be understood a model is needed that also takes into account the link between maintenance of electrochemical gradients of ions in the cell and osmotic pressure.

The evidence for permeability changes in the membranes seems to be tenuous.

A side note is that the authors state that the Msc responds to stress-related voltage changes. I think this is an overstatement. Mscs respond to predominantly membrane tension and are mostly nonspecific (see how their action recovers cellular volume in this publication https://www.pnas.org/doi/full/10.1073/pnas.1522185113 [pnas.org]). Authors cite references 35-39 to support this statement. These publications still state that these channels are predominantly membrane tension-gated. Some of the references state that the presence of external ions is important for tension-related gating but sometimes they gate spontaneously in the presence of certain ions. Other publications cited don't really look at gating with respect to ions (39 is on clustering). This is why I think the statement is somewhat misleading.

We have reworded the discussion of Mscs since the literature appears to be ambiguous. We will try to run some electrical impedance spectroscopy experiments on the Msc mutants in the future to attempt to remove the ambiguity.

Result section 8: Anomalous ion-channel-mediated wavefronts propagate light stress signals in 3D E. coli biofilms.

I am not commenting on this result section, as it would only be applicable if ThT was membrane potential dye in E. coli.

Ok, but we disagree on the use of ThT.

Aims achieved/results support their conclusions:

The authors clearly present their data. I am convinced that they have accurately presented everything they observed. However, I think their interpretation of the data and conclusions is inaccurate in line with the discussion I provided above.

Likely impact of the work on the field, and the utility of the methods and data to the community:

I do not think this publication should be published in its current format. It should be revised in light of the previous literature as discussed in detail above. I believe presenting it in it's current form on eLife pages would create unnecessary confusion.

We believe many of the Pilizota group articles are scientifically flawed and are causing the confusion in the literature.

Any other comments:

I note, that while this work studies E. coli, it references papers in other bacteria using ThT. For example, in lines 35-36 authors state that bacteria (Bacillus subtilis in this case) in biofilms have been recently found to modulate membrane potential citing the relevant literature from 2015. It is worth noting that the most recent paper https://journals.asm.org/doi/10.1128/mbio.02220-23 [journals.asm.org] found that ThT binds to one or more proteins in the spore coat, suggesting that it does not act as a membrane potential in Bacillus spores. It is possible that it still reports membrane potential in Bacillus cells and the recent results are strictly spore-specific, but these should be kept in mind when using ThT with Bacillus.

>>ThT was used successfully in previous studies of normal B. subtilis cells (by our own group and A.Prindle, ‘Spatial propagation of electrical signal in circular biofilms’, J.A.Blee et al, Physical Review E, 2019, 100, 052401, J.A.Blee et al, ‘Membrane potentials, oxidative stress and the dispersal response of bacterial biofilms to 405 nm light’, Physical Biology, 2020, 17, 2, 036001, A.Prindle et al, ‘Ion channels enable electrical communication in bacterial communities’, Nature, 2015, 527, 59-63). The connection to low metabolism pore research seems speculative.

Reviewer #3 (Public Review):

It has recently been demonstrated that bacteria in biofilms show changes in membrane potential in response to changes in their environment, and that these can propagate signals through the biofilm to coordinate bacterial behavior. Akabuogu et al. contribute to this exciting research area with a study of blue light-induced membrane potential dynamics in E. coli biofilms. They demonstrate that Thioflavin-T (ThT) intensity (a proxy for membrane potential) displays multiphasic dynamics in response to blue light treatment. They additionally use genetic manipulations to implicate the potassium channel Kch in the latter part of these dynamics. Mechanosensitive ion channels may also be involved, although these channels seem to have blue light-independent effects on membrane potential as well. In addition, there are challenges to the quantitative interpretation of ThT microscopy data which require consideration. The authors then explore whether these dynamics are involved in signaling at the community level. The authors suggest that cell firing is both more coordinated when cells are clustered and happens in waves in larger, 3D biofilms; however, in both cases evidence for these claims is incomplete. The authors present two simulations to describe the ThT data. The first of these simulations, a Hodgkin-Huxley model, indicates that the data are consistent with the activity of two ion channels with different kinetics; the Kch channel mutant, which ablates a specific portion of the response curve, is consistent with this. The second model is a fire-diffuse-fire model to describe wavefront propagation of membrane potential changes in a 3D biofilm; because the wavefront data are not presented clearly, the results of this model are difficult to interpret. Finally, the authors discuss whether these membrane potential changes could be involved in generating a protective response to blue light exposure; increased death in a Kch ion channel mutant upon blue light exposure suggests that this may be the case, but a no-light control is needed to clarify this.

In a few instances, the paper is missing key control experiments that are important to the interpretation of the data. This makes it difficult to judge the meaning of some of the presented experiments.

(1) An additional control for the effects of autofluorescence is very important. The authors conduct an experiment where they treat cells with CCCP and see that Thioflavin-T (ThT) dynamics do not change over the course of the experiment. They suggest that this demonstrates that autofluorescence does not impact their measurements. However, cellular autofluorescence depends on the physiological state of the cell, which is impacted by CCCP treatment. A much simpler and more direct experiment would be to repeat the measurement in the absence of ThT or any other stain. This experiment should be performed both in the wild-type strain and in the ∆kch mutant.

ThT is a very bright fluorophore (much brighter than a GFP). It is clear from the images of non-stained samples that autofluorescence provides a negligible contribution to the fluorescence intensity in an image.

(2) The effects of photobleaching should be considered. Of course, the intensity varies a lot over the course of the experiment in a way that photobleaching alone cannot explain. However, photobleaching can still contribute to the kinetics observed. Photobleaching can be assessed by changing the intensity, duration, or frequency of exposure to excitation light during the experiment. Considerations about photobleaching become particularly important when considering the effect of catalase on ThT intensity. The authors find that the decrease in ThT signal after the initial "spike" is attenuated by the addition of catalase; this is what would be predicted by catalase protecting ThT from photobleaching (indeed, catalase can be used to reduce photobleaching in time lapse imaging).

Photobleaching was negligible over the course of the experiments. We employed techniques such as reducing sample exposure time and using the appropriate light intensity to minimize photobleaching.

(3) It would be helpful to have a baseline of membrane potential fluctuations in the absence of the proposed stimulus (in this case, blue light). Including traces of membrane potential recorded without light present would help support the claim that these changes in membrane potential represent a blue light-specific stress response, as the authors suggest. Of course, ThT is blue, so if the excitation light for ThT is problematic for this experiment the alternative dye tetramethylrhodamine methyl ester perchlorate (TMRM) can be used instead.

Unfortunately the fluorescent baseline is too weak to measure cleanly in this experiment. It appears the collective response of all the bacteria hyperpolarization at the same time appears to dominate the signal (measurements in the eLife article and new potentiometry measurements).

(4) The effects of ThT in combination with blue light should be more carefully considered. In mitochondria, a combination of high concentrations of blue light and ThT leads to disruption of the PMF (Skates et al. 2021 BioRXiv), and similarly, ThT treatment enhances the photodynamic effects of blue light in E. coli (Bondia et al. 2021 Chemical Communications). If present in this experiment, this effect could confound the interpretation of the PMF dynamics reported in the paper.

We think the PMF plays a minority role in determining the membrane potential in E. coli. For reasons outlined before (H+ is a minority ion in E. coli compared with K+).

(5) Figures 4D - E indicate that a ∆kch mutant has increased propidium iodide (PI) staining in the presence of blue light; this is interpreted to mean that Kch-mediated membrane potential dynamics help protect cells from blue light. However, Live/Dead staining results in these strains in the absence of blue light are not reported. This means that the possibility that the ∆kch mutant has a general decrease in survival (independent of any effects of blue light) cannot be ruled out.

>>Both strains of bacterial has similar growth curve and also engaged in membrane potential dynamics for the duration of the experiment. We were interested in bacterial cells that observed membrane potential dynamics in the presence of the stress. Bacterial cells need to be alive to engage in membrane potential  dynamics (hyperpolarize) under stress conditions. Cells that engaged in membrane potential dynamics and later stained red were only counted after the entire duration. We believe that the wildtype handles the light stress better than the ∆kch mutant as measured with the PI.

(6) Additionally in Figures 4D - E, the interpretation of this experiment can be confounded by the fact that PI uptake can sometimes be seen in bacterial cells with high membrane potential (Kirchhoff & Cypionka 2017 J Microbial Methods); the interpretation is that high membrane potential can lead to increased PI permeability. Because the membrane potential is largely higher throughout blue light treatment in the ∆kch mutant (Fig. 3AB), this complicates the interpretation of this experiment.

Kirchhoff & Cypionka 2017 J Microbial Methods, using fluorescence microscopy, suggested that changes in membrane potential dynamics can introduce experimental bias when propidium iodide is used to confirm the viability of tge bacterial strains, B subtilis (DSM-10) and Dinoroseobacter shibae, that are starved of oxygen (via N2 gassing) for 2 hours. They attempted to support their findings by using CCCP in stopping the membrane potential dynamics (but never showed any pictoral or plotted data for this confirmatory experiment). In our experiment methodology, cell death was not forced on the cells by introducing an extra burden or via anoxia. We believe that the accumulation of PI in ∆kch mutant is not due to high membrane potential dynamics but is attributed to the PI, unbiasedly showing damaged/dead cells. We think that propidium iodide is good for this experiment. Propidium iodide is a dye that is extensively used in life sciences. PI has also been used in the study of bacterial electrophysiology (https://pubmed.ncbi.nlm.nih.gov/32343961/, ) and no membrane potential related bias was reported.

Throughout the paper, many ThT intensity traces are compared, and described as "similar" or "dissimilar", without detailed discussion or a clear standard for comparison. For example, the two membrane potential curves in Fig. S1C are described as "similar" although they have very different shapes, whereas the curves in Fig. 1B and 1D are discussed in terms of their differences although they are evidently much more similar to one another. Without metrics or statistics to compare these curves, it is hard to interpret these claims. These comparative interpretations are additionally challenging because many of the figures in which average trace data are presented do not indicate standard deviation.

Comparison of small changes in the absolute intensities is problematic in such fluorescence experiments. We mean the shape of the traces is similar and they can be modelled using a HH model with similar parameters.

The differences between the TMRM and ThT curves that the authors show in Fig. S1C warrant further consideration. Some of the key features of the response in the ThT curve (on which much of the modeling work in the paper relies) are not very apparent in the TMRM data. It is not obvious to me which of these traces will be more representative of the actual underlying membrane potential dynamics.

In our experiment, TMRM was used to confirm the dynamics observed using ThT. However, ThT appear to be more photostable than TMRM (especially towars the 2nd peak). The most interesting observation is that with both dyes, all phases of the membrane potential dynamics were conspicuous (the first peak, the quiescent period and the second peak). The time periods for these three episodes were also similar.

A key claim in this paper (that dynamics of firing differ depending on whether cells are alone or in a colony) is underpinned by "time-to-first peak" analysis, but there are some challenges in interpreting these results. The authors report an average time-to-first peak of 7.34 min for the data in Figure 1B, but the average curve in Figure 1B peaks earlier than this. In Figure 1E, it appears that there are a handful of outliers in the "sparse cell" condition that likely explain this discrepancy. Either an outlier analysis should be done and the mean recomputed accordingly, or a more outlier-robust method like the median should be used instead. Then, a statistical comparison of these results will indicate whether there is a significant difference between them.

The key point is the comparison of standard errors on the standard deviation.

In two different 3D biofilm experiments, the authors report the propagation of wavefronts of membrane potential; I am unable to discern these wavefronts in the imaging data, and they are not clearly demonstrated by analysis.

The first data set is presented in Figures 2A, 2B, and Video S3. The images and video are very difficult to interpret because of how the images have been scaled: the center of the biofilm is highly saturated, and the zero value has also been set too high to consistently observe the single cells surrounding the biofilm. With the images scaled this way, it is very difficult to assess dynamics. The time stamps in Video S3 and on the panels in Figure 2A also do not correspond to one another although the same biofilm is shown (and the time course in 2B is also different from what is indicated in 2B). In either case, it appears that the center of the biofilm is consistently brighter than the edges, and the intensity of all cells in the biofilm increases in tandem; by eye, propagating wavefronts (either directed toward the edge or the center) are not evident to me. Increased brightness at the center of the biofilm could be explained by increased cell thickness there (as is typical in this type of biofilm). From the image legend, it is not clear whether the image presented is a single confocal slice or a projection. Even if this is a single confocal slice, in both Video S3 and Figure 2A there are regions of "haze" from out-of-focus light evident, suggesting that light from other focal planes is nonetheless present. This seems to me to be a simpler explanation for the fluorescence dynamics observed in this experiment: cells are all following the same trajectory that corresponds to that seen for single cells, and the center is brighter because of increased biofilm thickness.

We appreciate the reviewer for this important observation. We have made changes to the figures to address this confusion. The cell cover has no influence on the observed membrane potential dynamics. The entire biofilm was exposed to the same blue light at each time. Therefore all parts of the biofilm received equal amounts of the blue light intensity. The membrane potential dynamics was not influenced by cell density (see Fig 2C). 

The second data set is presented in Video S6B; I am similarly unable to see any wave propagation in this video. I observe only a consistent decrease in fluorescence intensity throughout the experiment that is spatially uniform (except for the bright, dynamic cells near the top; these presumably represent cells that are floating in the microfluidic and have newly arrived to the imaging region).

A visual inspection of Video S6B shows a fast rise, a decrease in fluorescence and a second rise (supplementary figure 4B). The data for the fluorescence was carefully obtained using the imaris software. We created a curved geometry on each slice of the confocal stack. We analyzed the surfaces of this curved plane along the z-axis. This was carried out in imaris.

3D imaging data can be difficult to interpret by eye, so it would perhaps be more helpful to demonstrate these propagating wavefronts by analysis; however, such analysis is not presented in a clear way. The legend in Figure 2B mentions a "wavefront trace", but there is no position information included - this trace instead seems to represent the average intensity trace of all cells. To demonstrate the propagation of a wavefront, this analysis should be shown for different subpopulations of cells at different positions from the center of the biofilm. Data is shown in Figure 8 that reflects the velocity of the wavefront as a function of biofilm position; however, because the wavefronts themselves are not evident in the data, it is difficult to interpret this analysis. The methods section additionally does not contain sufficient information about what these velocities represent and how they are calculated. Because of this, it is difficult for me to evaluate the section of the paper pertaining to wave propagation and the predicted biofilm critical size.

The analysis is considered in more detail in a more expansive modelling article, currently under peer review in a physics journal, ‘Electrical signalling in three dimensional bacterial biofilms using an agent based fire-diffuse-fire model’, V.Martorelli, et al, 2024 https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

There are some instances in the paper where claims are made that do not have data shown or are not evident in the cited data:

(1) In the first results section, "When CCCP was added, we observed a fast efflux of ions in all cells"- the data figure pertaining to this experiment is in Fig. S1E, which does not show any ion efflux. The methods section does not mention how ion efflux was measured during CCCP treatment.

We have worded this differently to properly convey our results.

(2) In the discussion of voltage-gated calcium channels, the authors refer to "spiking events", but these are not obvious in Figure S3E. Although the fluorescence intensity changes over time, it's hard to distinguish these fluctuations from measurement noise; a no-light control could help clarify this.

The calcium transients observed were not due to noise or artefacts.

(3) The authors state that the membrane potential dynamics simulated in Figure 7B are similar to those observed in 3D biofilms in Fig. S4B; however, the second peak is not clearly evident in Fig. S4B and it looks very different for the mature biofilm data reported in Fig. 2. I have some additional confusion about this data specifically: in the intensity trace shown in Fig. S4B, the intensity in the second frame is much higher than the first; this is not evident in Video S6B, in which the highest intensity is in the first frame at time 0. Similarly, the graph indicates that the intensity at 60 minutes is higher than the intensity at 4 minutes, but this is not the case in Fig. S4A or Video S6B.

The confusion stated here has now been addressed. Also it should be noted that while Fig 2.1 was obtained with LED light source, Fig S4A was obtained using a laser light source. While obtaining the confocal images (for Fig S4A ), the light intensity was controlled to further minimize photobleaching. Most importantly, there is an evidence of slow rise to the 2nd peak in Fig S4B. The first peak, quiescence and slow rise to second peak are evident.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Scientific recommendations:

- Although Fig 4A clearly shows that light stimulation has an influence on the dynamics of cell membrane potential in the biofilm, it is important to rule out the contribution of variations in environmental parameters. I understand that for technical reasons, the flow of fresh medium must be stopped during image acquisition. Therefore, I suggest performing control experiments, where the flow is stopped before image acquisition (15min, 30min, 45min, and 1h before). If there is no significant contribution from environmental variations (pH, RedOx), the dynamics of the electrical response should be superimposed whatever the delay between stopping the flow stop and switching on the light.

In this current research study, we were focused on studying how E. coli cells and biofilms react to blue light stress via their membrane potential dynamics. This involved growing the cells and biofilms, stopping the media flow and obtaining data immediately. We believe that stopping the flow not only helped us to manage data acquisition, it also helped us reduce the effect of environmental factors. In our future study we will expand the work to include how the membrane potential dynamics evolve in the presence of changing environmental factors for example such induced by stopping the flow at varied times.

- Since TMRM signal exhibits a linear increase after the first response peak (Supplementary Figure 1D), I recommend mitigating the statement at line 78.

- To improve the spatial analysis of the electrical response, I suggest plotting kymographs of the intensity profiles across the biofilm. I have plotted this kymograph for Video S3 and it appears that there is no electrical propagation for the second peak. In addition, the authors should provide technical details of how R^2(t) is measured in the first regime (Figure 7E).

See the dedicated simulation article for more details. https://www.biorxiv.org/content/10.1101/2023.11.17.567515v1

- Line 152: To assess the variability of the latency, the authors should consider measuring the variance divided by the mean instead of SD, which may depend on the average value.

We are happy with our current use of standard error on the standard deviation. It shows what we claim to be true.

- Line 154-155: To truly determine whether the amplitude of the "action potential" is independent of biofilm size, the authors should not normalise the signals.

Good point. We qualitatively compared both normalized and unnormalized data. Recent electrical impedance spectroscopy measurements (unpublished) indicate that the electrical activity is an extensive quantity i.e. it scales with the size of the biofilms.

- To precise the role of K+ in the habituation response, I suggest using valinomycin at sub-inhibitory concentrations (10µM). Besides, the high concentration of CCCP used in this study completely inhibits cell activity. Not surprisingly, no electrical response to light stimulation was observed in the presence of CCCP. Finally, the Kch complementation experiment exhibits a "drop after the first peak" on a single point. It would be more convincing to increase the temporal resolution (1min->10s) to show that there is indeed a first and a second peak.

An interesting experiment for the future.

- Line 237-238: There are only two points suggesting that the dynamics of hyperpolarization are faster at higher irradiance(Fig 4A). The authors should consider adding a third intermediate point at 17µW/mm^2 to confirm the statement made in this sentence.

Multiple repeats were performed. We are confident of the robustness of our data.

- Line 249 + Fig 4E: It seems that the data reported on Fig 4E are extracted from Fig 4D. If this is indeed the case, the data should be normalised by the total population size to compare survival probabilities under the two conditions. It would also be great to measure these probabilities (for WT and ∆kch) in the presence of ROS scavengers.

- To distinguish between model fitting and model predictions, the authors should clearly state which parameters are taken from the literature and which parameters are adjusted to fit the experimental data.

- Supplementary Figure 4A: why can't we see any wavefront in this series of images?

For the experimental data, the wavefront was analyzed by employing the imaris software. We systematically created a ROI with a curved geometry within the confocal stack (the biofilm). The fluorescence of ThT was traced along the surface of the curved geometry was analyzed along the z-axis.

- Fig 7B: Could the authors explain why the plateau is higher in the simulations than in the biofilm experiments? Could they add noise on the firing activities?

See the dedicated Martorelli modelling article. In general we would need to approach stochastic Hodgkin-Huxley modelling and the fluorescence data (and electrical impedance spectroscopy data) presented does not have extensive noise (due to collective averaging over many bacteria cells).

- Supplementary Figure 4B: Why can't we see the second peak in confocal images?

The second peak is present although not as robust as in Fig 2B. The confocal images were obtained with a laser source. Therefore we tried to create a balance between applying sufficient light stress on the bacterial cells and mitigating photobleaching.

Editing recommendations:

The editing recommendations below has been applied where appropriate

- Many important technical details are missing (e.g. R^2, curvature, and 445nm irradiance measurements). Error bars are missing from most graphs. The captions should clearly indicate if these are single-cell or biofilm experiments, strain name, illumination conditions, number of experiments, SD, or SE. Please indicate on all panels of all figures in the main text and in the supplements, which are the conditions: single cell vs. biofilm, strains, medium, centrifugal vs centripetal etc..., where relevant. Please also draw error bars everywhere.

We have now made appropriate changes. We specifically use cells when we were dealing with single cells and biofilms when we worked on biofilms. We decided to describe the strain name either on the panel or the image description.

- Line 47-51: The way the paragraph is written suggests that no coordinated electrical oscillations have been observed in Gram-negative biofilms. However, Hennes et al (referenced as 57 in this manuscript) have shown that a wave of hyperpolarized cells propagates in Neisseria gonorrhoea colony, which is a Gram-negative bacterium.

We are now aware of this work. It was not published when we first submitted our work and the authors claim the waves of activity are due to ROS diffusion NOT propagating waves of ions (coordinated electrical wavefronts).

- Line 59: "stressor" -> "stress" or "perturbation".

The correction has been made.

- Line 153: Please indicate in the Material&Methods how the size of the biofilm is measured.

The biofilm size was obtained using BiofilmQ and the step by step guide for using BiofilmQ were stated..

- Figure 2A: Please provide associated brightfield images to locate bacteria.

- Line 186: Please remove "wavefront" from the caption. Fig2B only shows the average signal as a function of time.

This correction has been implemented.

- Fig 3B,C: Please indicate single cell and biofilm on the panels and also WT and ∆kch.

- Line 289: I suggest adding "in single cell experiments" to the title of this section.

- Fig 5A: blue light is always present at regular time intervals during regime I and II. The presence of blue light only in regime I could be misleading.

- Fig 5C: The curve in Fig 5D seems to correspond to the biofilm case. The curve given by the model, should be compared with the average curve presented in Fig 1D.

- Fig 6A, B, and C: These figures could be moved to supplements.

- Line 392: Replace "turgidity" with "turgor pressure".

- Fig 7C,E: Please use a log-log scale to represent these data and indicate the line of slope 1.

- Fig 7E: The x-axis has been cropped.

- Please provide a supplementary movie for the data presented in Fig 7E.

- Line 455: E. Coli biofilms do not express ThT.

- Line 466: "\gamma is the anomalous exponent". Please remove anomalous (\gamma can equal 1 at this stage).

- Line 475: Please replace "section" with "projection".

- Line 476: Please replace "spatiotemporal" with "temporal". There is no spatial dependency in either figure.

- Line 500: Please define Eikonal approximation.

- Fig 8 could be moved to supplements.

- Line 553: "predicted" -> "predict".

- Line 593: Could the authors explain why their model offers much better quantitative agreement?

- Line 669: What does "universal" mean in that context?

- Line 671: A volume can be pipetted but not a concentration.

- Line 676: Are triplicates technical or biological replicates?

- Sup Fig1: Please use minutes instead of seconds in panel A.

- Model for membrane dynamics: "The fraction of time the Q+ channel is open" -> "The dynamics of Q+ channel activity can be written". Ditto for K+ channel...

- Model for membrane dynamics: "the term ... is a threshold-linear". This function is not linear at all. Why is it called linear? Also, please describe what \sigma is.

- ABFDF model: "releasing a given concentration" -> "releasing a local concentration" or "a given number" but it's not \sigma anymore. Besides, this \sigma is unlikely related to the previous \sigma used in the model of membrane potential dynamics in single cells. Please consider renaming one or the other. Also, ions are referred to as C+ in the text and C in equation 8. Am I missing something?

Reviewer #2 (Recommendations For The Authors):

I have included all my comments as one review. I have done so, despite the fact that some minor comments could have gone into this section, because I decided to review each Result section. I thus felt that not writing it as one review might be harder to follow. I have however highlighted which comments are minor suggestions or where I felt corrections.

However, while I am happy with all my comments being public, given their nature I think they should be shown to authors first. Perhaps the authors want to go over them and think about it before deciding if they are happy for their manuscript to be published along with these comments, or not. I will highlight this in an email to the editor. I question whether in this case, given that I am raising major issues, publishing both the manuscript and the comments is the way to go as I think it might just generate confusion among the audience.

Reviewer #3 (Recommendations For The Authors):

I was unable to find any legends for any of the supplemental videos in my review materials, and I could not open supplemental video 5.

I made some comments in the public review about the analysis and interpretation of the time-to-fire data. One of the other challenges in this data set is that the time resolution is limited- it seems that a large proportion of cells have already fired after a single acquisition frame. It would be ideal to increase the time resolution on this measurement to improve precision. This could be done by imaging more quickly, but that would perhaps necessitate more blue light exposure; an alternative is to do this experiment under lower blue light irradiance where the first spike time is increased (Figure 4A).

In the public review, I mentioned the possible impact of high membrane potential on PI permeability. To address this, the experiment could be repeated with other stains, or the viability of blue light-treated cells could be addressed more directly by outgrowth or colony-forming unit assays.

In the public review, I mentioned the possible combined toxicity of ThT and blue light. Live/dead experiments after blue light exposure with and without ThT could be used to test for such effects, and/or the growth curve experiment in Figure 1F could be repeated with blue light exposure at a comparable irradiance used in the experiment.

Throughout the paper and figure legends, it would help to have more methodological details in the main text, especially those that are critical for the interpretation of the experiment. The experimental details in the methods section are nicely described, but the data analysis section should be expanded significantly.

At the end of the results section, the authors suggest a critical biofilm size of only 4 µm for wavefront propagation (not much larger than a single cell!). The authors show responses for various biofilm sizes in Fig. 2C, but these are all substantially larger. Are there data for cell clusters above and below this size that could support this claim more directly?

The authors mention image registration as part of their analysis pipeline, but the 3D data sets in Video S6B and Fig. S4A do not appear to be registered- were these registered prior to the velocity analysis reported in Fig. 8?

One of the most challenging claims to demonstrate in this paper is that these membrane potential wavefronts are involved in coordinating a large, biofilm-scale response to blue light. One possible way to test this might be to repeat the Live/Dead experiment in planktonic culture or the single-cell condition. If the protection from blue light specifically emerges due to coordinated activity of the biofilm, the Kch mutant would not be expected to show a change in Live/Dead staining in non-biofilm conditions.

Line 140: How is "mature biofilm" defined? Also on this same line, what does "spontaneous" mean here?

Line 151: "much smaller": Given that the reported time for 3D biofilms is 2.73 {plus minus} 0.85 min and in microclusters is 3.27 {plus minus} 1.77 min, this seems overly strong.

Line 155: How is "biofilm density" characterized? Additionally, the data in Figure 2C are presented in distance units (µm), but the text refers to "areal coverage"- please define the meaning of these distance units in the legend and/or here in the text (is this the average radius?).

Lines 161-162: These claims seem strong given the data presented before, and the logic is not very explicit. For example, in the second sentence, the idea that this signaling is used to "coordinate long-range responses to light stress" does not seem strongly evidenced at this point in the paper. What is meant by a long-range response to light stress- are there processes to respond to light that occur at long-length scales (rather than on the single-cell scale)? If so, is there evidence that these membrane potential changes could induce these responses? Please clarify the logic behind these conclusions.

Lines 235-236: In the lower irradiance conditions, the responses are slower overall, and it looks like the ThT intensity is beginning to rise at the end of the measurement. Could a more prominent second peak be observed in these cases if the measurement time was extended?

Line 242-243: The overall trajectories of extracellular potassium are indeed similar, but the kinetics of the second peak of potassium are different than those observed by ThT (it rises some minutes earlier)- is this consistent with the idea that Kch is responsible for that peak? Additionally, the potassium dynamics also reflect the first peak- is this surprising given that the Kch channel has no effect on this peak?

Line 255-256: Again, this seems like a very strong claim. There are several possible interpretations of the catalase experiment (which should be discussed); this experiment perhaps suggests that ROS impacts membrane potential, but does not obviously indicate that these membrane potential fluctuations mitigate ROS levels or help the cells respond to ROS stress. The loss of viability in the ∆kch mutant might indicate a link between these membrane potential experiments and viability, but it is hard to interpret without the no-light control I mention in the public review.

Lines 313-315: "The model predicts... the external light stress". Please clarify this section. Where this prediction arises from in the modeling work? Second, I am not sure what is meant by "modulates the light stress" or "keeps the cell dynamics robust to the intensity of external light stress" (especially since the dynamics clearly vary with irradiance, as seen in Figure 4A).

Line 322: I am not sure what "handles the ROS by adjusting the profile of the membrane potential dynamics" means. What is meant by "handling" ROS? Is the hypothesis that membrane potential dynamics themselves are protective against ROS, or that they induce a ROS-protective response downstream, or something else? Later in lines 327-8 the authors write that changes in the response to ROS in the model agree with the hypothesis, but just showing that ROS impacts the membrane potential does not seem to demonstrate that this has a protective effect against ROS.

Line 365-366: This section title seems confusing- mechanosensitive ion channels totally ablate membrane potential dynamics, they don't have a specific effect on the first hyperpolarization event. The claim that mechanonsensitive ion channels are specifically involved in the first event also appears in the abstract.

Also, the apparent membrane potential is much lower even at the start of the experiment in these mutants- is this expected? This seems to imply that these ion channels also have a blue light independent effect.

Lines 368, 371: Should be VGCCs rather than VGGCs.

Line 477: I believe the figure reference here should be to Figure 7B, not 6B.

Line 567-568: "The initial spike is key to registering the presence of the light stress." What is the evidence for this claim?

Line 592-594: "We have presented much better quantitative agreement..." This is a strong claim; it is not immediately evident to me that the agreement between model and prediction is "much better" in this work than in the cited work. The model in Figure 4 of reference 57 seems to capture the key features of their data. Clarification is needed about this claim.

Line 613: "...strains did not have any additional mutations." This seems to imply that whole genome sequencing was performed- is this the case?

Line 627: I believe this should refer to Figure S2A-B rather than S1.

Line 719: What percentage of cells did not hyperpolarize in these experiments?

Lines 751-754: As I mentioned above, significant detail is missing here about how these measurements were made. How is "radius" defined in 3D biofilms like the one shown in Video S6B, which looks very flat? What is meant by the distance from the substrate to the core, since usually in this biofilm geometry, the core is directly on the substrate? Most importantly, this only describes the process of sectioning the data- how were these sections used to compute the velocity of ThT signal propagation?

I also have some comments specifically on the figure presentation:

Normalization from 0 to 1 has been done in some of the ThT traces in the paper, but not all. The claims in the paper would be easiest to evaluate if the non-normalized data were shown- this is important for the interpretation of some of the claims.

Some indication of standard deviation (error bars or shading) should be added to all figures where mean traces are plotted.

Throughout the paper, I am a bit confused by the time axis; the data consistently starts at 1 minute. This is not intuitive to me, because it seems that the blue light being applied to the cells is also the excitation laser for ThT- in that case, shouldn't the first imaging frame be at time 0 (when the blue light is first applied)? Or is there an additional exposure of blue light 1 minute before imaging starts? This is consequential because it impacts the measured time to the first spike. (Additionally, all of the video time stamps start at 0).

Please increase the size of the scale bars and bar labels throughout, especially in Figure 2A and S4A.

In Figure 1B and D, it would help to decrease the opacity on the individual traces so that more of them can be discerned. It would also improve clarity to have data from the different experiments shown with different colored lines, so that variability between experiments can be clearly visualized.

Results in Figure 1E would be easier to interpret if the frequency were normalized to total N. It is hard to tell from this graph whether the edges and bin widths are the same between the data sets, but if not, they should be. Also, it would help to reduce the opacity of the sparse cell data set so that the full microcluster data set can be seen as well.

Biofilm images are shown in Figures 2A, S3A, and Video S3- these are all of the same biofilm. Why not take the opportunity to show different experimental replicates in these different figures? The same goes for Figure S4A and Video S6B, which again are of the same biofilm.

Figure 2C would be much easier to read if the curves were colored in order of their size; the same is true for Figure 4A and irradiance.

The complementation data in Figure S3D should be moved to the main text figure 3 alongside the data about the corresponding knockout to make it easier to compare the curves.

Fig.ure S3E: Is the Y-axis in this graph mislabeled? It is labeled as ThT fluorescence, but it seems that it is reporting fluorescence from the calcium indicator?

Video S6B is very confusing - why does the video play first forwards and then backwards? Unless I am looking very carefully at the time stamps it is easy to misinterpret this as a rise in the intensity at the end of the experiment. Without a video legend, it's hard to understand this, but I think it would be much more straightforward to interpret if it only played forward. (Also, why is this video labeled 6B when there is no video 6A?)

Author response:

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

Reviewer #1 (Public review):

This paper presents a comprehensive study of how neural tracking of speech is a ected by background noise. Using five EEG experiments and Temporal response function (TRF), it investigates how minimal background noise can enhance speech tracking even when speech intelligibility remains very high. The results suggest that this enhancement is not attention-driven but could be explained by stochastic resonance. These findings generalize across di erent background noise types and listening conditions, o ering insights into speech processing in real-world environments. I find this paper well-written, the experiments and results are clearly described. However, I have a few comments that may be useful to address.

I thank the reviewer for their positive feedback.

(1) The behavioral accuracy and EEG results for clear speech in Experiment 4 di er from those of Experiments 1-3. Could the author provide insights into the potential reasons for this discrepancy? Might it be due to linguistic/ acoustic di erences between the passages used in experiments? If so, what was the rationale behind using di erent passages across di erent experiments?

The slight di erences in behavior and EEG magnitudes may be due to several factors. Di erent participants took part in the di erent experiments (with some overlap). Stories and questions were generated using ChatGPT using the same approach, but di erent research assistants have supported story and question generation, and ChatGPT advanced throughout the course of the study, such that di erent versions were used over time (better version control was only recently introduced by OpenAI). The same Google voice was used for all experiments, so this cannot be a factor. Most critically, within each experiment, assignment of speech-clarity conditions to di erent stories was randomized, such that statistical comparisons are una ected by these minor di erences between experiments. The noise-related enhancement generalizes across all experiments, showing that minor di erences in experimental materials do not impact it.

(2) Regarding peak amplitude extraction, why were the exact peak amplitudes and latencies of the TRFs for each subject not extracted, and instead, an amplitude average within a 20 ms time window based on the group-averaged TRFs used? Did the latencies significantly di er across di erent SNR conditions?

Estimation of peak latency can be challenging if a deflection is not very pronounced in a participant. Especially the N1 was small for some conditions. Using the mean amplitude in a specific time window is very common practice in EEG research that mitigates this issue. Another, albeit less common, approach is to use a Jackknifing procedure to estimate each participant’s latencies (Smulders 2010 Psychophysiology; although this may sometimes not work well). For the revision, I used the Jackknifing approach to estimate peak latencies for each participant and condition, and extracted the mean amplitude around the peak latency. As expected, this approach provides very similar e ects as reported in the main article, here exemplified for Experiments 1 and 2. The results are thus not a ected by this data analysis choice. The estimated latencies di ered across SNRs, e.g., the N1 increased with decreasing SNR (this is less surprising/novel and was thus not added to the manuscript to avoid increasing the amount of information).

Author response image 1.

P1-minus-N1 amplitude for Experiment 1 and 2, using amplitudes centered on individually estimated peak latencies. The asterisk indicates a significant di erence from the clear speech condition (FDR-thresholded).

(3) How is neural tracking quantified in the current study? Does improved neural tracking correlate with EEG prediction accuracy or individual peak amplitudes? Given the di ering trends between N1 and P2 peaks in babble and speech-matched noise in experiment 3, how is it that babble results in greater envelope tracking compared to speech-matched noise?

Neural tracking is generally used for responses resulting from TRF analyses, crosscorrelations, or coherence, where the speech envelope is regressed against the brain signals (see review of Brodbeck & Simon 2020 Current Opinion in Physiology). Correlations between EEG prediction accuracy and individual peak amplitudes was not calculated because the data used for the analyses are not independent. The EEG prediction accuracy essentially integrates information over a longer time interval (here 0–0.4 s), whereas TRF amplitudes are more temporally resolved. If one were to shorten the time interval (e.g., 0.08–0.12 s), then EEG prediction accuracy would look more similar to the TRF results (because the TRF is convolved with the amplitude-onset envelope of the speech [predicted EEG] before calculating the EEG prediction accuracy). Regarding the enhancement di erence between speech-matched noise and babble, I have discussed a possible interpretation in the discussion section. The result is indeed surprising, but it replicates across two experiments (Experiments 3 and 4), and is consistent with previous work using speech-matched noise that did not find the enhancement. I reproduce the part of the discussion here.

“Other work, using a noise masker that spectrally matches the target speech, have not reported tracking enhancements (Ding and Simon, 2013; Zou et al., 2019; Synigal et al., 2023). However, in these works, SNRs have been lower (<10 dB) to investigate neural tracking under challenging listening conditions. At low SNRs, neural speech tracking decreases (Ding and Simon, 2013; Zou et al., 2019; Yasmin et al., 2023; Figures 1 and 2), thus resulting in an inverted u-shape in relation to SNR for attentive and passive listening (Experiments 1 and 2).”

“The noise-related enhancement in the neural tracking of the speech envelope was greatest for 12talker babble, but it was also present for speech-matched noise, pink noise, and, to some extent, white noise. The latter three noises bare no perceptional relation to speech, but resemble stationary, background buzzing from industrial noise, heavy rain, waterfalls, wind, or ventilation. Twelve-talker babble – which is also a stationary masker – is clearly recognizable as overlapping speech, but words or phonemes cannot be identified (Bilger, 1984; Bilger et al., 1984; Wilson, 2003; Wilson et al., 2012b). There may thus be something about the naturalistic, speech nature of the background babble that facilitates neural speech tracking.”

“Twelve-talker babble was associated with the greatest noise-related enhancement in neural tracking, possibly because the 12-talker babble facilitated neuronal activity in speech-relevant auditory regions, where the other, non-speech noises were less e ective.”

(4) The paper discusses how speech envelope-onset tracking varies with di erent background noises. Does the author expect similar trends for speech envelope tracking as well? Additionally, could you explain why envelope onsets were prioritized over envelope tracking in this analysis?

The amplitude-onset envelope was selected because several previous works have used the amplitude-onset envelope, our previous work that first observed the enhancement also used the amplitude-onset envelope, and the amplitude-onset envelope has been suggested to work better for speech tracking. This was added to the manuscript. For the manuscript revision, analyses were calculated for the amplitude envelope, largely replicating the results for the amplitude-onset envelope. The results for the amplitude envelope are now presented in the Supplementary Materials and referred to in the main text.

“The amplitude-onset envelope was selected because a) several previous works have used it (Hertrich et al., 2012; Fiedler et al., 2017; Brodbeck et al., 2018a; Daube et al., 2019; Fiedler et al., 2019), b) our previous work first observing the enhancement also used the amplitude-onset envelope (Yasmin et al., 2023; Panela et al., 2024), and c) the amplitude-onset envelope has been suggested to elicit a strong speech tracking response (Hertrich et al., 2012). Results for analyses using the amplitude envelope instead of the amplitude-onset envelope show similar e ects and are provided in the Supplementary Materials (Figure 1-figure supplement 1).”

Recommendations for the authors:

(1) Include all relevant parameters related to data analysis where applicable. For example, provide the filter parameters (Line 154, Line 177, Line 172), and the default parameters of the speech synthesizer (Line 131).

Additional filter information and parameter values are provided in the revised manuscript.

(2) Please share the data and codes or include a justification as to why the data cannot be shared.

Data and code are provided on OSF (https://osf.io/zs9u5/). A materials availability statement has been added to the manuscript.

Reviewer #2 (Public review):

The author investigates the role of background noise on EEG-assessed speech tracking in a series of five experiments. In the first experiment, the influence of di erent degrees of background noise is investigated and enhanced speech tracking for minimal noise levels is found. The following four experiments explore di erent potential influences on this e ect, such as attentional allocation, di erent noise types, and presentation mode. The step-wise exploration of potential contributors to the e ect of enhanced speech tracking for minimal background noise is compelling. The motivation and reasoning for the di erent studies are clear and logical and therefore easy to follow. The results are discussed in a concise and clear way. While I specifically like the conciseness, one inevitable consequence is that not all results are equally discussed in depth. Based on the results of the five experiments, the author concludes that the enhancement of speech tracking for minimal background noise is likely due to stochastic resonance. Given broad conceptualizations of stochastic resonance as a noise benefit this is a reasonable conclusion. This study will likely impact the field as it provides compelling support questioning the relationship between speech tracking and speech processing.

I thank the reviewer for the positive review and thoughtful feedback.

Recommendations for the authors:

As mentioned in the public review, I like the conciseness. However, some points might benefit from addressing them.

(1) The absence of comprehension e ects is on the one hand surprising, as the decreased intelligibility should (theoretically) be visible in this data. On the other hand, from my own experience, the generation of "good" comprehension questions is quite di icult. While it is mentioned in the methods section, that comprehension accuracy and gist rating go hand in hand, this is not the case here. I am wondering if the data here should be rather understood as "there is no di erence in intelligibility" or that comprehension assessment via comprehension questions is potentially not a valid measure.

I assume that the reviewer refers to Experiment 1, where SNRs approximately below 15 dB led to reduced gist ratings (used as a proxy for speech intelligibility; Davis and Johnsrude, 2003, J Neurosci; Ritz et al., 2022, J Neurosci). That story comprehension accuracy does not decrease could be due to the comprehension questions themselves (as indicated by the reviewer, “good” questions can be hard to generate, potentially having low sensitivity). On the other hand, speech for the most di icult SNR was still ‘reasonably’ intelligible (gist ratings suggest ~85% of words could be understood), and participants may still have been able to follow the thread of the story. I do not further discuss this point in the manuscript, since it is not directly related to the noise-related enhancement in the neural tracking response, because the enhancement was present for high SNRs for which gist ratings did not show a di erence relative to clear speech (i.e., 20 dB and above).

(2) However, if I understood correctly, the "lower" manipulation (same RMS for the whole sound stimulus) of experiment 3 was, what was also used in experiment 1. In experiment 3, unlike 1, there are comprehension e ects. I wondered if there are ideas about why that is.

Yes indeed, the ‘lower’ manipulation in Experiment 3 was also used in Experiments 1, 2, 4, and 5. The generation of the stimulus materials was similar across experiments. However, a new set of stories and comprehension questions was used for each experiment and the participants di ered as well (with some overlap). These aspects may have contributed to the di erence. 

(3) Concerning the prediction accuracy, for a naive reader, some surrounding information would be helpful: What is the purpose/expectation of this measure? Is it to show that all models are above chance?

EEG prediction accuracy was included here, mainly because it is commonly used in studies using TRFs. A reader may wonder about EEG prediction accuracy if it were not reported. The hypotheses of the current study are related to the TRF weights/amplitude. This was added to the manuscript.

“EEG prediction accuracy was calculated because many previous studies report it (e.g., Decruy et al., 2019; Broderick et al., 2021; Gillis et al., 2021; Weineck et al., 2022; Karunathilake et al., 2023), but the main focus of the current study is on the TRF weights/amplitude.”

(4) Regarding the length of training and test data I got confused: It says per story 50 25-s snippets. As the maximum length of a story was 2:30 min, those snippets were mostly overlapping, right? It seems that depending on the length of the story and the "location within the time series" of the snippets, the number of remaining non-over-lapping snippets is variable. Also, within training, the snippets were overlapping, correct? Otherwise, the data for training would be too short. Again, as a naive reader, is this common, or can overlapping training data lead to overestimations?

The short stories made non-overlapping windows not feasible, but the overlap unlikely a ects the current results. Using cross-correlation (Hertrich et al 2012 Psychophysiology; which is completely independent for di erent snippets) instead of TRFs shows the same results (now provided in the supplementary materials). In one of our previous studies where the enhancement was first observed (Yasmin et al. 2023 Neuropsychologia), non-overlapping data were used because the stories were longer. This makes any meaningful impact of the overlap very unlikely. Critically, speech-clarity levels were randomized and all analyses were conducted in the same way for all conditions, thus not confounding any of the results/conclusions. The methods section was extended to further explain the choice of overlapping data snippets.

“Speech-clarity levels were randomized across stories and all analyses were conducted similarly for all conditions. Hence, no impact of overlapping training data on the results is expected (consistent with noise-related enhancements observed previously when longer stories and non-overlapping data were used; Yasmin et al., 2023). Analyses using cross-correlation, for which data snippets are treated independently, show similar results compared to those reported here using TRFs (Figure 1figure supplement 2).”

(5) For experiment 1, three stories were clear, while the other 21 conditions were represented by one story each. Presumably, the ratio of 3:1 can a ect TRFs?

TRFs were calculated for each story individually and then averaged across three stories: either three clear stories, or three stories in babble for neighboring SNRs. Hence, the same number of TRFs were averaged for clear and noise conditions, avoiding exactly this issue. This was described in the methods section and is reproduced here:

“Behavioral data (comprehension accuracy, gist ratings), EEG prediction accuracy, and TRFs for the three clear stories were averaged. For the stories in babble, a sliding average across SNR levels was calculated for behavioral data, EEG prediction accuracy, and TRFs, such that data for three neighboring SNR levels were averaged. Averaging across three stories was calculated to reduce noise in the data and match the averaging of three stories for the clear condition.”

(6) Was there an overlap in participants?

Some participants took part in several of the experiments in separate sessions on separate days. This was added to the manuscript.

“Several participants took part in more than one of the experiments, in separate sessions on separate days: 7, 7, 9, 9, and 14 (for Experiments 1-5, respectively) participated only in one experiment; 3 individuals participated in all 5 experiments; 68 unique participants took part across the 5 experiments.”

(7) Can stochastic resonance also explain inverted U-shape results with vocoded speech?

This is an interesting question. Distortions to the neural responses to noise-vocoding may reflect internal noise, but this would require additional research. For example, the Hauswald study (2022 EJN), showing enhancements due to noise-vocoding, used vocoding channels that also reduced speech intelligibility. The study would ideally be repeated with a greater number of vocoding channels to make sure the e ects are not driven by increased attention due to reduced speech intelligibility. I did not further discuss this in detail in the manuscript as it would go too far away from the experiments of the current study.

(8) Typo in the abstract: box sexes is probably meant to say both sexes?

This text was removed, because more detailed gender identification is reported in the methods, and the abstract needed shortening to meet the eLife guidelines.

Reviewing Editor Comments:

Interesting series of experiments to assess the influence of noise on cortical tracking in di erent conditions, interpreting the results with the mechanism of stochastic resonance.

I thank the editor for their encouraging feedback.

For experiment 2, the author wishes to exclude the role of attention, by making participants perform a visual task. Data from low performers on the visual task was excluded, to avoid that participants attended the spoken speech. However, from the high performers on the visual task, how can you be sure that they did not pay attention to the auditory stimuli as well (as auditory attention is quite automatic, and these participants might be good at dividing their attention)? I understand that you can not ask participants about the auditory task during the experiment, but did you ask AFTER the experiment whether they were able to understand the stimuli? I think this is crucial for your interpretation.

Participants were not asked whether they were able to understand the stimuli. Participants would unlikely invest e ort/attention in understanding the stories in babble without a speech-related task. Nevertheless, for follow-up analyses, I removed participants who performed above 0.9 in the visual task (i.e., the high performers), and the di erence between clear speech and speech in babble replicates. In the plots, data from all babble conditions above 15 dB SNR (highly intelligible) were averaged, but the results look almost identical if all SNRs are averaged. Moreover, the correlation between visual task performance and the babble-related enhancement was not-significant. These analyses were added to the Supplementary Materials (Figure 2-figure supplement 1).  

Statistics: inconsistencies across experiments with a lot of simple tests (FDR corrected) and in addition sometimes rmANOVA added - if interactions in rmANOVA are not significant then all the simple tests might not be warranted. So a bit of double dipping and over-testing here, but on the whole the conclusions do not seem to be overstated.

The designs of the di erent experiments di ered, thus requiring di erent statistical approaches. Moreover, the di erent tests assess di erent comparisons. For all experiments, contrasting the clear condition to all noise conditions was the main purpose of the experiments. To correct for multiple comparison, the False Discovery Rate correction was used. Repeated-measures ANOVAs were conducted in addition to this – excluding the clear condition because it would not fit into a factorial structure (e.g., Experiment 3) or to avoid analyzing it twice (e.g., Experiment 5) – to investigate di erences between di erent noise conditions. There was thus no over-testing in the presented study.

Small points:

Question on methods: For each story, 50 25-s data snippets were extracted (Page 7, line 190). As you have stories with a duration of 1.5 to 2 minutes, does that mean there is a lot of overlap across data snippets? How does that influence the TRF/prediction accuracy?

The short stories made non-overlapping windows not feasible, but the overlap unlikely a ects the current results. Using cross-correlation (Hertrich et al 2012 Psychophysiology; which is completely independent for di erent snippets) instead of TRFs shows the same results (newly added Figure 1-figure supplement 2). In one of our previous studies where the enhancement was first observed (Yasmin et al. 2023 Neuropsychologia), non-overlapping data were used because the stories were longer. This makes any meaningful impact of the overlap very unlikely. Critically, speechclarity levels were randomized and all analyses were conducted in the same way for all conditions, thus not confounding any of the results/conclusions. The methods section was extended to further explain the choice of overlapping data snippets.

“Overlapping snippets in the training data were used to increase the amount of data in the training given the short duration of the stories. Speech-clarity levels were randomized across stories and all analyses were conducted similarly for all conditions. Hence, no impact of overlapping training data on the results is expected (consistent with noise-related enhancements observed previously when longer stories and non-overlapping data were used; Yasmin et al., 2023). Analyses using crosscorrelation, for which data snippets are treated independently, show similar results compared to those reported here using TRFs (Figure 1-figure supplement 2).”

Results Experiment 3: page 17, line 417: no di erences were found between clear speech and masked speech - is this a power issue (as it does look di erent in the figure, Figure 4b)?

I thank the editor for pointing this out. Indeed, I made a minor mistake. Two comparisons were significant after FDR-thresholding. This is now included in the revised Figure 4. I also made sure the mistake was not present for other analyses; which it was not.

Author response:

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

Reviewer #1 (Public review):

This paper examines the role of MLCK (myosin light chain kinase) and MLCP (myosin light chain phosphatase) in axon regeneration. Using loss-of-function approaches based on small molecule inhibitors and siRNA knockdown, the authors explore axon regeneration in cell culture and in animal models from central and peripheral nervous systems. Their evidence shows that MLCK activity facilitates axon extension/regeneration, while MLCP prevents it.

Major concerns:

(1) In the title, authors indicate that the observed effects from loss-of-function of MLCK/MLCP take place via F-actin redistribution in the growth cone. However, there are no experiments showing a causal effect between changes in axon growth mediated by MLCK/MLCP and F-actin redistribution.

Thank you for your comments. We revised the title of our manuscript to “MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin”. (line 3)

(2) The author combines MLCK inhibitors with Bleb (Figure 6), trying to verify if both pairs of inhibitors act on the same target/pathway. MLCK may regulate axon growth independent of NMII activity. However, this has very important implications for the understanding not only on how NMII works and affects axon extension, but also in trying to understand what MLCP is doing. One wonders if MLCP actions, which are opposite of MLCK, also independent of NMII activity? The authors, in the discussion section, try to find an explanation for this finding, but I consider it fails since the whole rationale of the manuscript is still around how MLCK and MLCP affect NMII phosphorylation.

Thank you for your comments. Although both MLCK and MLCP regulate the activity of NMII, it has been reported that they also govern domain-specific spatial control of actin-based motility in the growth cone. Specifically, MLCK activity is essential for arc translocation and retrograde flow within the P domain, while MLCP appears to specifically modulate arc movement and associated myosin II contractility in the T zone and C domain (Ref). Therefore, it is proposed that the regulatory mechanisms of MLCK and MLCP are highly complex during the process of axon growth. 

[Ref]：Xiao-Feng Zhang, Andrew W Schaefer, Dylan T Burnette, Vincent T Schoonderwoert, Paul Forscher. Rho-dependent contractile responses in the neuronal growth cone are independent of classical peripheral retrograde actin flow. Neuron. 2003 Dec 4;40(5):931-44.

What follows is a discussion of the merits and limitations of different claims of the manuscript in light of the evidence presented.

(1) Using western blot and immunohistochemical analyses, authors first show that MLCK expression is increased in DRG sensory neurons following peripheral axotomy, concomitant to an increase in MLC phosphorylation, suggesting a causal effect (Figure 1). The authors claim that it is common that axon growth-promoting genes are upregulated. It would have been interesting at this point to study in this scenario the regulation of MLCP.

We thank Reviewer for the positive comment on our manuscript.

(2) Using DRG cultures and sciatic nerve crush in the context of MLCK inhibition (ML-7) and down-regulation, authors conclude that MLCK activity is required for mammalian peripheral axon regeneration both in vitro and in vivo (Figure 2). In parallel, the authors show that these treatments affect as expected the phosphorylation levels of MLC.

The in vitro evidence is of standard methods and convincing. However, here, as well as in all other experiments using siRNAs, no Control siRNAs were used. Authors do show that the target protein is downregulated, and they can follow transfected cells with GFP. Still, it should be noted that the standard control for these experiments has not been done.

Thank you for your comments. We utilized scrambled siRNA as a control. I sincerely apologize for the oversight in the manuscript; although we mentioned that scrambled siRNA was used as a control in the figure legends, we failed to clearly articulate this important information in the methods section. We have revised the manuscript accordingly. (line 87, line 549, line, line 562, line 568).

(3) The authors then examined the role of the phosphatase MLCP in axon growth during regeneration. The authors first use a known MLCP blocker, phorbol 12,13-dibutyrate (PDBu), to show that is able to increase the levels of p-MLC, with a concomitant increase in the extent of axon regrowth of DRG neurons, both in permissive as well as non-permissive substrates. The authors repeat the experiments using the knockdown of MYPT1, a key component of the MLC-phosphatase, and again can observe a growth-promoting effect (Figure 3).

The authors further show evidence for the growth-enhancing effect in vivo, in nerve crush experiments. The evidence in vivo deserves more evidence and experimental details (see comment 2). A key weakness of the data was mentioned previously: no control siARN was used.

Thank you for your comments. As mentioned above, we used scramble siRNA as control in vivo experiment as well.

(4) In the next set of experiments (presented in Figure 4) authors extend the previous observations in primary cultures from the CNS. For that, they use cortical and hippocampal cultures, and pharmacological and genetic loss-of-function using the above-mentioned strategies. The expected results were obtained in both CNS neurons: inhibition or knockdown of the kinase decreases axon growth, whereas inhibition or knockdown of the phosphatase increases growth. A main weakness in this set is that drugs were used from the beginning of the experiment, and hence, they would also affect axon specification. As pointed in Materials and Method (lines 143-145) authors counted as "axons" neurites longer than twice the diameter of the cell soma, and hence would not affect the variable measured. In any case, to be sure one is only affecting axon extension in these cells, the drugs should have been used after axon specification and maturation, which occurs at least after 5 DIV.

Thank you for your comments. We acknowledge that the early administration of drugs can lead to unintended effects on neuronal polarization and axon formation. However, in line with our previous publication, we focused exclusively on measuring the longest length of the axon. To quantify axon length, we selected neurons exhibiting an axonal process exceeding twice the diameter of their cell body and measured the longest axon from 100 neurons for each condition (Ref 1, Ref 2). Consequently, we believe that drug administration at the onset of cell culture influences axon formation; however, it does not significantly affect the drug's impact on axon length.

[Ref 1]: Chang-Mei Liu, Rui-Ying Wang, Saijilafu, Zhong-Xian Jiao, Bo-Yin Zhang, Feng-Quan Zhou. MicroRNA-138 and SIRT1 form a mutual negative feedback loop to regulate mammalian axon regeneration. Genes Dev. 2013 Jul 1;27(13):1473-83.

[Ref 2]: Eun-Mi Hur, Saijilafu, Byoung Dae Lee, Seong-Jin Kim, Wen-Lin Xu, Feng-Quan Zhou. GSK3 controls axon growth via CLASP-mediated regulation of growth cone microtubules. Genes Dev. 2011 Sep 15;25(18):1968-81.

(5) In Figure 7, the authors a local cytoskeletal action of the drug, but the evidence provided does not differentiate between a localized action of the drugs and a localized cell activity.

We appreciate the reviewer’s insightful comments and have revised our title to “MLCK/MLCP Regulates mammalian axon regeneration and redistributes growth cone F-actin.” Furthermore, we have made corresponding revisions to the manuscript (line31, line 73).

References:

(1) Eun-Mi Hur 1, In Hong Yang, Deok-Ho Kim, Justin Byun, Saijilafu, Wen-Lin Xu, Philip R Nicovich, Raymond Cheong, Andre Levchenko, Nitish Thakor, Feng-Quan Zhou. 2011. Engineering neuronal growth cones to promote axon regeneration over inhibitory molecules. Proc Natl Acad Sci U S A. 2011 Mar 22;108(12):5057-62. doi: 10.1073/pnas.1011258108.

(2) Garrido-Casado M, Asensio-Juárez G, Talayero VC, Vicente-Manzanares M. 2024. Engines of change: Nonmuscle myosin II in mechanobiology. Curr Opin Cell Biol. 2024 Apr;87:102344. doi: 10.1016/j.ceb.2024.102344.

(3) Karen A Newell-Litwa 1, Rick Horwitz 2, Marcelo L Lamers. 2015. Non-muscle myosin II in disease: mechanisms and therapeutic opportunities. Dis Model Mech. 2015 Dec;8(12):1495-515. doi: 10.1242/dmm.022103.

Reviewer #2 (Public review):

Summary:

Saijilafu et al. demonstrate that MLCK/MLCP proteins promote axonal regeneration in both the central nervous system (CNS) and peripheral nervous system (PNS) using primary cultures of adult DRG neurons, hippocampal and cortical neurons, as well as in vivo experiments involving sciatic nerve injury, spinal cord injury, and optic nerve crush. The authors show that axon regrowth is possible across different contexts through genetic and pharmacological manipulation of these proteins. Additionally, they propose that MLCK/MLCP may regulate F-actin reorganization in the growth cone, which is significant as it suggests a novel strategy for promoting axonal regeneration.

Strengths:

This manuscript presents a wide range of experimental models to address its hypothesis and biological question. Notably, the use of multiple in vivo models significantly enhances the overall validity of the study.

We thank Reviewer for the positive comment on our manuscript.

Weaknesses:

- The authors previously published that blocking myosin II activity stimulates axonal growth and that MLCK activates myosin II. The present work shows that inhibiting MLCK blocks axonal regeneration while blocking MLCP (the protein that dephosphorylates myosin II) produces the opposite effect. Although this contradiction is discussed, no new evidence has been added to the manuscript to clarify this mechanism or address the remaining questions. Critical unresolved questions include: what happens to myosin II expression when both MLCK and MLCP are inhibited? If MLCK/MLCP are acting through an independent mechanism, what would that mechanism be?

- In the discussion, the authors mention the existence of two myosin II isoforms with opposing effects on axonal growth. Still, there is no evidence in the manuscript to support this point.

- It is also unclear how MLCK/MLCP acts on the actin cytoskeleton. The authors suggest that proteins such as ADF/cofilin, Arp 2/3, Eps8, Profilin, Myosin II, and Myosin V could regulate changes in F-actin dynamics. However, this study provides no experimental evidence to determine which proteins may be involved in the mechanism.

Thank you for your comments. Axon growth is an exceptionally intricate process, facilitated by the coordinated regulation of gene expression in the soma, axonal transport along the shaft, and the assembly of cytoskeletal elements and membrane proteins at the growth cone. In this paper, our results primarily demonstrate that MLCK/MLCP plays a crucial role in regulating mammalian axon regeneration and redistributing F-actin within the growth cone; however, we did not investigate which specific proteins act downstream of MLCK/MLCP during axon regeneration.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

- A title more suitable for the evidence shown can be: MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin.

Thank you for your comments. We revised the title of our manuscript to“MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin” (line 3).

-In figure 3, It would be useful to indicate in the figure legend, that the red arrow is pointing to a suture that was performed during surgery to mark clearly the injury site.

Thank you for your comments. We revised Figure 3 legend that indicates the red arrow is pointing to a suture that was performed during surgery to mark clearly the injury site (line 571-572).

- The following is a concern raised in the previous round, and that the response by the authors was so complete and accurate that I consider it would be useful to include it in the discussion section.

Thank you for your comments. We included those contents in the discussion section of our revised manuscript (line 348-354, line 355-359).

The author combines MLCK inhibitors with Bleb (Figure 6), trying to verify if both pairs of inhibitors act on the same target/pathway. The rationale is wrong for at least two reasons.

a- Because both lines of evidence point to contrasting actions of NMII on axon growth, one approach could never "rescue" the other.

Reply by authors in R1：If MLCK regulates axon growth through the activation of Myosin, the inhibitory effect of ML-7 (an MLCK inhibitor) on axon growth might be influenced by Bleb, a NMII inhibitor. However, our findings reveal that the combination of Bleb and ML-7 does not alter the rate of axon outgrowth compared to ML-7 alone. This suggests that the roles of ML-7 and Bleb in axon growth are independent. It means MLCK may regulate axon growth independent of NMII activity.

b- Because the approaches target different steps on NMII activation, one could never "prevent" or rescue the other. For example, for Bleb to provide a phenotype, it should find any p-MLC, because it is only that form of MLC that is capable of inhibiting its ATPase site. In light of this, it is not surprising that Bleb is unable to exert any action in a situation where there is no p-MLC (ML-7, which by inhibiting the kinase drives the levels of p-MLC to zero, Figure 4A). Hence, the results are not possible to validate in the current general interpretation of the authors. (See 'major concern').

Reply by authors in R1: The reported mechanism of blebbistatin is not through competition with the ATP binding site of myosin. Instead, it selectively binds to the ATPase intermediate state associated with ADP and inorganic phosphate, which decelerates the phosphate release. Importantly, blebbistatin does not impede myosin's interaction with actin or the ATP-triggered disassociation of actomyosin. It rather inhibits the myosin head when it forms a product complex with a reduced affinity for actin. This indicates that blebbistatin functions by stabilizing a particular myosin intermediate state that is independent of the phosphorylation status of myosin light chain (MLC).

[Ref] Kovács M, Tóth J et al. Mechanism of blebbistatin inhibition of myosin II. J Biol Chem. 2004 Aug 20;279(34):35557-63.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Liu et al., present an immersion objective adapter design called RIM-Deep, which can be utilized for enhancing axial resolution and reducing spherical aberrations during inverted confocal microscopy of thick cleared tissue.

Strengths:

RI mismatches present a significant challenge to deep tissue imaging, and developing a robust immersion method is valuable in preventing losses in resolution. Liu et al., present data showing that RIM-Deep is suitable for tissue cleared with two different clearing techniques, demonstrating the adaptability and versatility of the approach.

Greetings, we greatly appreciate your feedback. In truth, we have utilized three distinct clearing techniques, including iDISCO, CUBIC, and MACS, to substantiate the adaptability and multifunctionality of the RIM-Deep adapter.

Weaknesses:

Liu et al., claim to have developed a useful technique for deep tissue imaging, but in its current form, the paper does not provide sufficient evidence that their technique performs better than existing ones.

We are in complete agreement with your recommendation, and the additional experiments will conduct a thorough comparison of the efficacy between the RIM-deep adapter and the official adapter in the context of fluorescence bead experiments, along with their performance in cubic and MASC tissue clearing techniques.

Reviewer #1 (Recommendations for the authors):

Suggestions for improvement:

Major revisions:

(1) For the bead experiment, the comparison was made to a 10X dry objective instead of an immersion objective, please make a comparison to the standard immersion objective.

Thank you for your suggestion. We fully agree with your suggestion to make a comparison with the standard immersion objective. We plan to conduct this comparison in future experiments and will thoroughly analyze the imaging differences between the official adapter and the RIM-deep adapter.

(2) It is unclear if an accurate comparison of objectives (same NA etc) is being made in Fig 1G-J, since the official adapter image appears to be of lower resolution even at the surface. At the very least, progressive 2D slices of the reconstruction must be shown for both adapters instead of just the RIM-Deep adapter.

Thank you for your suggestion. We strictly controlled the numerical aperture (NA) of the objectives in Fig 1G-J to ensure the accuracy of the comparison. However, the imaging resolution of the official adapter is consistent with that of the RIM-deep adapter. We agree that showing progressive 2D slices of the reconstruction would provide a more comprehensive comparison of the two adapters.

(3) Similarly, since there already exists an official adapter, it would be useful to see that RIM-Deep performs better even in the mouse tissue, since the clearing method was different.

Thank you for your suggestion. We will investigate the imaging performance of the two additional tissue clearing protocols using both the official adapter and the RIM-deep adapter.

(4) The movies need legends, as it is unclear if they even show 2-D slices very deep into the tissue.

Thank you for your suggestion. We will add figure legends to each movie.

(5) The purpose of Supplementary Figure 3 in its current form is unclear, as is the statement in the text related to it : "The effectiveness and utility of this adapter configuration have been substantiated through a comprehensive series of experimental validations".

Thank you for your suggestion. We will revise the statement to: "We validated the effectiveness and utility of this adapter configuration through a series of experiments."

(6) The system is variably referred to as RIM-Deep or DepthView Enhancer in the text and figures, it would be beneficial to the readers if the authors stuck to one name.

Thank you for your suggestion. We will choose RIM-Deep as the sole name.

Minor revisions

Figures

(1) “Confocal" is incorrectly spelled as "confocol" in Figure 1, "media" is misspelled in multiple places.

Thank you. We will correct these errors.

(2) The camera is misplaced in the Figure 1 A drawing

Thank you. We will fix this issue.

(3) It would be useful to have actual pictures of the immersion objective setup (both RIM-Deep and the pre-existing adapter) since the diagrams are not very clear.

Thank you. We will include actual pictures of both the RIM-Deep and the pre-existing adapter in the supplementary materials.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

This study by Popli et al. evaluated the function of Atg14, an autophagy protein, in reproductive function using a conditional knockout mouse model. The authors showed that female mice lacking Atg14 were infertile partly due to defective embryo transport function of the oviduct and faulty uterine receptivity and decidualization using PgrCre/+;Atg14f/f mice. The findings from this work are exciting and novel. The authors demonstrated that a loss of Atg14 led to an excessive pyroptosis in the oviductal epithelial cells that compromises cellular integrity and structure, impeding the transport function of the oviduct. In addition, the authors use both genetic and pharmacological approaches to test the hypothesis. Therefore, the findings from this study are high-impact and likely reproducible. However, there are multiple major concerns that need to be addressed to improve the quality of the work.

Thank you for the additional data that solidified the conclusion of this study. The authors addressed almost all of my previous concerns in this revised manuscript. However, some key points wording still need to be addressed.

Comments on revisions:

In Fig. 2A, please ensure that these are 5.0 dpc samples since implantation has already occurred at this point. However, the embryo appeared free-floating adjacent to the luminal epithelial cells (LE), even in control.

We understand the reviewer’s concern. We have now replaced the previous H & E image with a clearer, higher-quality section that shows a fully attached embryo within a closed uterine lumen representing a typical implantation morphology at the D5 stage of pregnancy. (Revised Figure 2A)

Fig. 3A-B: "Approximately 80-90% of blastocysts" contradicts the quantification in Figure 3C, which showed a percentage of blastocysts below 50%. Please clarify and correct as needed.

In Fig. 3A-B, we mean to say approximately 80-90% embryos. We have now corrected the statement in the revised manuscript (Line no: 349-351).  

The authors showed that Acetylated a-tubulin was present in the ampulla region of cKO (Fig. 4A). However, the revised manuscript still stated that (lines 397-399) ...there was a substantial loss of the ciliary epithelial cells (indicated by fewer a-tubulin and FOXJ1-positive cells) (Fig. 4B, left panel and Fig. S3)... So, the authors may want to tone down their conclusion regarding a "substantial loss" of ciliated epithelial cells if the quantification of ciliated cell number is not performed.

We thank the reviewer for this suggestion. To avoid redundancy and ambiguity, we have revised the statement as below (Line no: 391-395):

“As shown in Fig. 4A, normal ciliary structures were observed in the ampulla of both control and cKO oviducts. However, in the isthmus of cKO oviducts, we observed a reduction in both the FOXJ1- and PAX8-expressing cells (Fig. 4B, and Fig. S3).”

Fig. 4C - the areas with red inset boxes labeled for isthmus are not really isthmus (in both control and cKO). The zoomed-in images (Fig. 4C - The far-right panel for both control and cKO, images are the transitional zone from the ampulla to the isthmus. The isthmus areas should have a thick muscle layer with almost no ciliated cells - see Fig. 4B cKO - those are true isthmus areas.

We thank the reviewer for noting this. We have corrected the label accordingly. Since ciliary epithelial cells predominantly reside in the ampulla, we have included high-resolution images specifically for the ampulla regions.

• Fig. 3A and 3C, it appears that the images were taken at different magnifications, but the scale bars are the same at 200 um. The authors, please double-check the scale bars.

We thank the reviewer for noting this. We have double-checked all the figures to ensure the scale bars are correctly displayed and aligned with the resolution.

• Fig. 6D - why polyphillin-treated samples did not sum to 100%? - please double-check.

Since approximately 50% of the embryos were retained in the oviduct following polyphyllin treatment (Figure 6C, upper bar), the bar in Figure 6D represents this percentage (50% retained) rather than 100%.

Reviewer #2 (Public review)

In this manuscript, Popli et al investigated the roles of autophagy-related gene, Atg14, in the female reproductive tract (FRT) using conditional knockout mouse models. By ablation of Atg14 in both oviduct and uterus with PR-Cre (Atg14 cKO), authors discovered that such females are completely infertile. They went on to show that Atg14 cKO females have impaired embryo implantation as well as embryo transport from oviduct to uterus. Further analysis showed that Atg14 cKO leads to increased pyroptosis in oviduct, which disrupts oviduct epithelial integrity and leads to obstructive oviduct lumen and impaired embryo transport. The authors concluded that Atg14 is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable proper embryo transport.

The authors have barely addressed most of my concerns in this revised version with a few minor issues remaining to be addressed:

(1) The authors tried to address my first concern regarding the statement that "autophagy is critical for maintaining the oviduct homeostasis". The revised statement in Lines 53-54 "we report that Atg14-dependent autophagy plays a crucial role in maintaining..." is still not correct. It should be corrected as " we report that autophagy-related protein Atg14 plays a crucial role in maintaining...".

We thank the reviewer for this nice suggestion. We have now modified the statement as suggested (Line no: 54).

(2) Line 349-351 described 80-90% of blastocysts retrieved from oviducts of cKO mice, which is in consistent with Figure 3B (showing more than 98%).

We thank the reviewer for noting this. We have now corrected the statement as: “Unexpectedly, oviduct flushing from cKO mice resulted in the retrieval of approximately 90% of embryos, suggesting their potential entrapment within the oviducts, impeding their transit to the uterus”. (Line No: 349-351).

(3) Line 447, "Fig. 5E" should be Fig. 6A. In addition, grammar error in the next sentence.

We have corrected the figure number and addressed the grammatical error.

(4) In Figure 6D, why the composition of blastocysts in chemical treated group do not add up to 100%.

As explained in Reviewer 1 responses, the bar in Figure 6D represents the 50% retained embryos from Figure 6C upper bar the full count.

Reviewer #3 (Public review):

Summary:

The manuscript by Pooja Popli and co-authors tested the importance of Atg14 in the female reproductive tract by conditionally deleting Atg14 use PrCre and also Foxj1cre. The authors showed that loss of Atg14 leads to infertility due to the retention of embryos within the oviduct. The authors further concluded that the retention of embryos within the oviduct is due to pyroptosis in oviduct cells leading to defective cellular integrity. The revised manuscript has included new experimental data (Figs. S2B, 5B, 5C, and S3) that satisfied the concerns of this reviewer. The manuscript should provide important advancement to the field.

We sincerely thank the reviewer for the thoughtful evaluation of our manuscript and appreciate your constructive feedback.

Author response:

We appreciate the reviewers thoughtful consideration of our manuscript, and their recognition of the variety of experimental and computational approaches we have brought to bear in probing the very challenging question of uncoupled proton leak through EmrE.

We did record SSME measurements with MeTPP+, a small molecule substrate at two different protein:lipid ratios. These experiments report the rate of net flux when both proton-coupled substrate antiport and substrate-gated proton leak are possible. We will add this data to the revision, including data acquired with different lipid:protein ratio that confirms we are detecting transport rather than binding. In brief, this data shows that the net flux is highly dependent on both proton concentration (pH) and drug-substrate concentration, as predicted by our mechanistic model. This demonstrates that both types of transport contribute to net flux when small molecule substrates are present.

In the absence of drug-substrate, proton leak is the only possible transport pathway. The pyranine assay directly assesses proton leak under these conditions and unambiguously shows faster proton entry into proteoliposomes through the ∆107-EmrE mutant than through WT EmrE, with the rate of proton entry into ∆107-EmrE proteoliposomes matching the rate of proton entry achieved by the protonophore CCCP. We have revised the text to more clearly emphasize how this directly measures proton leak independently of any other type of transport activity. The SSME experiments with a proton gradient only (no small molecule substrate present) provide additional data on shorter timescales that is consistent with the pyranine data. The consistency of the data across multiple LPRs and comparison of transport to proton leak in the SSME assays  further strengthens the importance of the C-terminal tail in determining the rate of flux.

None of the current structural models have good resolution (crystallography, EM) or sufficient restraints (NMR) to define the loop and tail conformations sufficiently for comparison with this work. We are in the process of refining an experimental structure of EmrE with better resolution of the loop and tail regions implicated in proton-entry and leak. Direct assessment of structural interactions via mutagenesis is complicated because of the antiparallel homodimer structure of EmrE. Any point mutation necessarily affects both subunits of the dimer, and mutations designed to probe the hydrophobic gate on the more open face of the transporter also have the potential to disrupt closure on the opposite face, particularly in the absence of sufficient resolution in the available structures. Thus, mutagenesis to test specific predicted structural features is deferred until our structure is complete so that we can appropriately interpret the results.

In our simulation setup, the MD results can be considered representative and meaningful for two reasons. First, the C-terminal tail, not present in the prior structure and thus modeled by us, is only 4 residues long. We will show in the revision and detailed response that the system will lose memory of its previous conformation very quickly, such that velocity initialization alone is enough for a diverse starting point. Second, our simulation is more like simulated annealing, starting from a high free energy state to show that, given such random initialization, the tail conformation we get in the end is consistent with what we reported. It is also difficult to sample back-and-forth tail motion within a realistic MD timescale. Therefore, it can be unconclusive to causally infer the allosteric motions with unbiased MD of the wildtype alone. The best viable way is to look at the equilibrium statistics of the most stable states between WT- and ∆107-EmrE and compare the differences.

Author response:

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

eLife Assessment

This descriptive manuscript builds on prior research showing that the elimination of Origin Recognition Complex (ORC) subunits does not halt DNA replication. The authors use various methods to genetically remove one or two ORC subunits from specific tissues and observe continued replication, though it may be incomplete. The replication appears to be primarily endoreduplication, indicating that ORC-independent replication may promote genome reduplication without mitosis. Despite similar findings in previous studies, the paper provides convincing genetic evidence in mice that liver cells can replicate and undergo endoreduplication even with severely depleted ORC levels. While the mechanism behind this ORC-independent replication remains unclear, the study lays the groundwork for future research to explore how cells compensate for the absence of ORC and to develop functional approaches to investigate this process. The reviewers agree that this valuable paper would be strengthened significantly if the authors could delve a bit deeper into the nature of replication initiation, potentially using an origin mapping experiment. Such an exciting contribution would help explain the nature of the proposed new type of Mcm loading, thereby increasing the impact of this study for the field at large.

We appreciate the reviewers’ suggestion. Till now we know of only one paper that mapped origins of replication in regenerating mouse liver, and that was published two months back in Cell (PMID: 39293447).  We want to adopt this method, but we do not need it to answer the question asked.  We have mapped origins of replication in ORC-deleted cancer cell lines and compared to wild-type cells in Shibata et al., BioRXiv (PMID: 39554186) (it is under review).  We report the following:  Mapping of origins in cancer cell lines that are wild type or engineered to delete three of the subunits, ORC1, ORC2 or ORC5 shows that specific origins are still used and are mostly at the same sites in the genome as in wild type cells. Of the 30,197 origins in wild type cells (with ORC), only 2,466 (8%) are not used in any of the three ORC deleted cells and 18,319 (60%) are common between the four cell types. Despite the lack of ORC, excess MCM2-7 is still loaded at comparable rates in G1 phase to license reserve origins and is also repeatedly loaded in the same S phase to permit re-replication. 

Citation: Specific origin selection and excess functional MCM2-7 loading in ORC-deficient cells. Yoshiyuki Shibata, Mihaela Peycheva, Etsuko Shibata, Daniel Malzl, Rushad Pavri, Anindya Dutta. bioRxiv 2024.10.30.621095; doi: https://doi.org/10.1101/2024.10.30.621095 (PMID: 39554186)

We have now included this in the discussion.

Public Reviews:

Reviewer #1 (Public review):

The origin recognition complex (ORC) is an essential loading factor for the replicative Mcm2-7 helicase complex. Despite ORC's critical role in DNA replication, there have been instances where the loss of specific ORC subunits has still seemingly supported DNA replication in cancer cells, endocycling hepatocytes, and Drosophila polyploid cells. Critically, all tested ORC subunits are essential for development and proliferation in normal cells. This presents a challenge, as conditional knockouts need to be generated, and a skeptic can always claim that there were limiting but sufficient ORC levels for helicase loading and replication in polyploid or transformed cells. That being said, the authors have consistently pushed the system to demonstrate replication in the absence or extreme depletion of ORC subunits.

Here, the authors generate conditional ORC2 mutants to counter a potential argument with prior conditional ORC1 mutants that Cdc6 may substitute for ORC1 function based on homology. They also generate a double ORC1 and ORC2 mutant, which is still capable of DNA replication in polyploid hepatocytes. While this manuscript provides significantly more support for the ability of select cells to replicate in the absence or near absence of select ORC subunits, it does not shed light on a potential mechanism.

The strengths of this manuscript are the mouse genetics and the generation of conditional alleles of ORC2 and the rigorous assessment of phenotypes resulting from limiting amounts of specific ORC subunits. It also builds on prior work with ORC1 to rule out Cdc6 complementing the loss of ORC1.

The weakness is that it is a very hard task to resolve the fundamental question of how much ORC is enough for replication in cancer cells or hepatocytes. Clearly, there is a marked reduction in specific ORC subunits that is sufficient to impact replication during development and in fibroblasts, but the devil's advocate can always claim minimal levels of ORC remaining in these specialized cells.

The significance of the work is that the authors keep improving their conditional alleles (and combining them), thus making it harder and harder (but not impossible) to invoke limiting but sufficient levels of ORC. This work lays the foundation for future functional screens to identify other factors that may modulate the response to the loss of ORC subunits.

This work will be of interest to the DNA replication, polyploidy, and genome stability communities.

Thank you.

Reviewer #2 (Public review):

This manuscript proposes that primary hepatocytes can replicate their DNA without the six-subunit ORC. This follows previous studies that examined mice that did not express ORC1 in the liver. In this study, the authors suppressed expression of ORC2 or ORC1 plus ORC2 in the liver.

Comments:

(1) I find the conclusion of the authors somewhat hard to accept. Biochemically, ORC without the ORC1 or ORC2 subunits cannot load the MCM helicase on DNA. The question arises whether the deletion in the ORC1 and ORC2 genes by Cre is not very tight, allowing some cells to replicate their DNA and allow the liver to develop, or whether the replication of DNA proceeds via non-canonical mechanisms, such as break-induced replication. The increase in the number of polyploid cells in the mice expressing Cre supports the first mechanism, because it is consistent with few cells retaining the capacity to replicate their DNA, at least for some time during development.

In our study, we used EYFP as a marker for Cre recombinase activity. ~98% of the hepatocytes in tissue sections and cells in culture express EYFP, suggesting that the majority of hepatocytes successfully expressed the Cre protein to delete the ORC1 or ORC2 genes. To assess deletion efficiency, we employed sensitive genotyping and Western blotting techniques to confirm the deletion of ORC1 and ORC2 in hepatocytes isolated from Alb-Cre mice. Results in Fig. 2C and Fig. 6D demonstrate the near-complete absence of ORC2 and ORC1 proteins, respectively, in these hepatocytes.

The mutant hepatocytes underwent at least 15–18 divisions during development. The inherited ORC1 or ORC2 protein present during the initial cell divisions, would be diluted to less than 1.5% of wild-type levels within six divisions, making it highly unlikely to support DNA replication, and yet we observe hepatocyte numbers that suggest there was robust cell division even after that point.

Furthermore, the EdU incorporation data confirm DNA synthesis in the absence of ORC1 and ORC2. Specifically, immunofluorescence showed that both in vitro and in vivo, EYFP-positive hepatocytes (indicating successful ORC1 and ORC2 deletion) incorporated EdU, demonstrating that DNA synthesis can occur without ORC1 and ORC2.

Finally, the Alb-ORC2f/f mice have 25-37.5% of the number of hepatocyte nuclei compared to WT mice (Table 2).  If that many cells had an undeleted ORC2 gene, that would have shown up in the genotyping PCR and in the Western blots.

(2) Fig 1H shows that 5 days post infection, there is no visible expression of ORC2 in MEFs with the ORC2 flox allele. However, at 15 days post infection, some ORC2 is visible. The authors suggest that a small number of cells that retained expression of ORC2 were selected over the cells not expressing ORC2. Could a similar scenario also happen in vivo?

This would not explain the significant incorporation of EdU in hepatocytes that are EYFP positive and do not have detectable ORC by Western blots.  Also note that for MEFs we are delivering the Cre by Adenovirus infection in vitro, so there is a finite probability that a cell will not receive the virus, the Cre and will not delete ORC2.  However, in vivo, the Alb-Cre will be expressed in every cell that turns on albumin.  There is no escaping the expression of Cre.

(3) Figs 2E-G shows decreased body weight, decreased liver weight and decreased liver to body weight in mice with recombination of the ORC2 flox allele. This means that DNA replication is compromised in the ALB-ORC2f/f mice.

It is possible that DNA replication is partially compromised or may slow down in the absence of ORC2. However, it is important to emphasize that livers with ORC2 deletion remain capable of DNA replication, so much so that liver function and life span are near normal. Therefore, some kind of DNA replication has to serve as a compensatory mechanism in the absence of ORC2 to maintain liver function and support regeneration.

(4) Figs 2I-K do not report the number of hepatocytes, but the percent of hepatocytes with different nuclear sizes. I suspect that the number of hepatocytes is lower in the ALB-ORC2f/f mice than in the ORC2f/f mice. Can the authors report the actual numbers?

We show in Table 2 that the Alb-Orc2f/f mice have about 25-37.5% of the hepatocytes compared to the WT mice.

(5) Figs 3B-G do not report the number of nuclei, but percentages, which are plotted separately for the ORC2-f/f and ALB-ORC2-f/f mice. Can the authors report the actual numbers?

In all the FACS experiments in Fig. 3B-G we collect data for a total of 10,000 nuclei (or cells).  For Fig. 3E-G we divide the 10,000 nuclei into the bottom 40% on the EYFP axis (EYFP low, which is mostly EYFP negative) as the control group, and EYFP high (top 20% on the EYFP axis) test group.  We have described this in the Methods in the revision and labeled EYFP negative and positive as EYFP low and high in the Figures and Figure legends.

(6) Fig 5 shows the response of ORC2f/f and ALB-ORC2f/f mice after partial hepatectomy. The percent of EdU+ nuclei in the ORC2-f/f (aka ALB-CRE-/-) mice in Fig 5H seems low. Based on other publications in the field it should be about 20-30%. Why is it so low here? The very low nuclear density in the ALB-ORC2-f/f mice (Fig 5F) and the large nuclei (Fig 5I) could indicate that cells fire too few origins, proceed through S phase very slowly and fail to divide.

The percentage of EdU+ nuclei in the ORC2f/f without Alb-Cre mice is 8%, while in PMID 10623657 ~10% of wild type nuclei incorporate  EdU at 42 hr post partial hepatectomy (mid-point between the 36-48 hr post hepatectomy that was used in our study).  The important result here is that in the ORC2f/f mice with Alb-Cre (+/-) we are seeing significant EdU incorporation. We have also corrected the X-axis labels in 5F, 5I, 7E and 7F to reflect that those measurements were not made at 36 hr post-resection but later (as was indicated in the schematic in Fig. 5A).

(7) Fig 6F shows that ALB-ORC1f/f-ORC2f/f mice have very severe phenotypes in terms of body weight and liver weight (about on third of wild-type!!). Fig 6H and 6I, the actual numbers should be presented, not percentages. The fact that there are EYFP negative cells, implies that CRE was not expressed in all hepatocytes.

The liver weight is very dependent on the body weight, and so we have to look at the liver to body weight ratio to determine if it is inordinately small, and the ratio is 70% of the WT.  In females the liver and body weight are low (although in proportion to each other), which maybe is what the reviewer is talking about.  However, the fact that liver weight and body weight are not as low in males, suggest that this is a gender (hormone?) specific effect and not a DNA replication defect.  We had discussed this possibility.  We have another paper also in BioRXiv (Su et al. doi.org/10.1101/2024.12.18.629220) that suggests that ORC subunits have significant effect on gene expression, so it is possible that that is what leads to this sexual dimorphism in phenotype.  We have now added this to the discussion.

The bottom 40% of nuclei on the EYFP axis in the FACS profiles (what was labeled EYFP negative but will now be called EYFP low) contains mostly non-hepatocytes that are genuinely EYFP negative.   Non-hepatocytes (bile duct cells, endothelial cells, Kupffer cells, blood cells) are a significant part of cells in the dissociated liver (as can be seen in the single cell sequencing results in PMID: 32690901).  Their presence does not mean that hepatocytes are not expressing Cre.  Hepatocytes are nearly 100% EYFP positive, as can be seen in the tissue sections (where the hepatocytes take up most of visual field) and in cells in culture.  Also if there are EYFP negative hepatocyte nuclei in the FACS, that still does not rule out EYFP presence in the cytoplasm.  The important point from the FACS is that the EYFP high nuclei (which have expressed Cre for the longest period) are polyploid relative to the EYFP low nuclei.

(8) Comparing the EdU+ cells in Fig 7G versus 5G shows very different number of EdU+ cells in the control animals. This means that one of these images is not representative. The higher fraction of EdU+ cells in the double-knockout could mean that the hepatocytes in the double-knockout take longer to complete DNA replication than the control hepatocytes. The control hepatocytes may have already completed DNA replication, which can explain why the fraction of EdU+ cells is so low in the controls. The authors may need to study mice at earlier time points after partial hepatectomy, i.e. sacrifice the mice at 30-32 hours, instead of 40-52 hours.

The apparent difference that the reviewer comments on stems from differences in nuclear density in the images in Fig. 7G and 5G (also quantitated in Fig. 7F and 5F).  The quantitation in Fig. 7H and 5H show that the % of EdU plus cells are comparable (5-8%). 

(9) Regarding the calculation of the number of cell divisions during development: the authors assume that all the hepatocytes in the adult liver are derived from hepatoblasts that express Alb. Is it possible to exclude the possibility that pre-hepatoblast cells that do not express Alb give rise to hepatocytes? For example the cells that give rise to hepatoblasts may proliferate more times than normal giving rise to a higher number of hepatoblasts than in wild-type mice.

Single cell sequencing of mouse liver at e11 shows hepatoblasts expressing hepatocyte specific markers (PMID: 32690901).  All the cells annotated from the single-cell seq analysis are differentiated cells arguing against the possibility that undifferentiated endodermal cells (what the reviewer probably means by pre-hepatoblasts) exist at e11.  We have added this citation to the paper.

Here is a review that says the hepatoblasts expressing Albumin are present before e13.  (https://www.ncbi.nlm.nih.gov/books/NBK27068/) says: “The differentiation of bi-potential hepatoblasts into hepatocytes or BECs begins around e13 of mouse development. Initially hepatoblasts express genes associated with both adult hepatocytes (Hnf4α, Albumin) ...”  Thus, we can be certain that hepatoblasts before e13 express albumin.  Our calculation of number of cell divisions in Table 2 begins from e12.

The reviewer may be suggesting that ORC deletion leads to the immediate demise of hepatoblasts (despite having inherited ORC protein from the endodermal cells) causing undifferentiated endodermal cells to persist and proliferate much longer than in normal development.  We consider it unlikely, but if true it will be very unexpected, both by suggesting that deletion of ORC immediately leads to the death of the hepatoblasts (despite a healthy reserve of inherited ORC protein) and by suggesting that there is a novel feedback mechanism from the death/depletion of hepatoblasts leading to the persistence and proliferation of undifferentiated endodermal cells. We have added the reviewer’s suggestion to the discussion.

(10) My interpretation of the data is that not all hepatocytes have the ORC1 and ORC2 genes deleted (eg EYFP-negative cells) and that these cells allow some proliferation in the livers of these mice.

Please see the reply in question #1.  Particularly relevant: “Finally, the Alb-ORC2f/f mice have 25-37.5% of the number of hepatocyte nuclei compared to WT mice (Table 2).  If that many cells had an undeleted ORC2 gene, that would have shown up in the genotyping PCR and in the Western blots.

Reviewer #3 (Public review):

Summary:

The authors address the role of ORC in DNA replication and that this protein complex is not essential for DNA replication in hepatocytes. They provide evidence that ORC subunit levels are substantially reduced in cells that have been induced to delete multiple exons of the corresponding ORC gene(s) in hepatocytes. They evaluate replication both in purified isolated hepatocytes and in mice after hepatectomy. In both cases, there is clear evidence that DNA replication does not decrease at a level that corresponds with the decrease in detectable ORC subunit and that endoreduplication is the primary type of replication observed. It remains possible that small amounts of residual ORC are responsible for the replication observed, although the authors provide arguments against this possibility. The mechanisms responsible for DNA replication in the absence of ORC are not examined.

Strengths:

The authors clearly show that there are dramatic reductions in the amount of the targeted ORC subunits in the cells that have been targeted for deletion. They also provide clear evidence that there is replication in a subset of these cells and that it is likely due to endoreduplication. Although there is no replication in MEFs derived from cells with the deletion, there is clearly DNA replication occurring in hepatocytes (both isolated in culture and in the context of the liver). Interestingly, the cells undergoing replication exhibit enlarged cell sizes and elevated ploidy indicating endoreduplication of the genome. These findings raise the interesting possibility that endoreduplication does not require ORC while normal replication does.

Weaknesses:

There are two significant weaknesses in this manuscript. The first is that although there is clearly robust reduction of the targeted ORC subunit, the authors cannot confirm that it is deleted in all cells. For example, the analysis in Fig. 4B would suggest that a substantial number of cells have not lost the targeted region of ORC2. Although the western blots show stronger effects, this type of analysis is notorious for non-linear response curves and no standards are provided. The second weakness is that there is no evaluation of the molecular nature of the replication observed. Are there changes in the amount of location of Mcm2-7 loading that is usually mediated by ORC? Does an associated change in Mcm2-7 loading lead to the endoreduplication observed? After numerous papers from this lab and others claiming that ORC is not required for eukaryotic DNA replication in a subset of cells, we still have no information about an alternative pathway that could explain this observation.

We do not see a significant deficit in MCM2-7 loading (amount and rate) in cancer cell lines where we have deleted ORC1, ORC2 or ORC5 genes separately in Shibata et al. bioRxiv 2024.10.30.621095; doi: https://doi.org/10.1101/2024.10.30.621095 (PMID: 39554186).  This is now cited in the discussion.

The authors frequently use the presence of a Cre-dependent eYFP expression as evidence that the ORC1 or ORC2 genes have been deleted. Although likely the best visual marker for this, it is not demonstrated that the presence of eYFP ensures that ORC2 has been targeted by Cre. For example, based on the data in Fig. 4B, there seems to be a substantial percentage of ORC2 genes that have not been targeted while the authors report that 100% of the cells express eYFP.

(1) The PCR reactions in Fig. 4B are still contaminated by DNA from non-hepatocyte cells:  bile duct cells, endothelial, Kupfer cells and blood cells.  Microscopy of  cultured cells idnetifies the hepatocytes unequivocally from their morphology. <2% of the hepatocyte cells in culture in Fig. 4C are EYFP-.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The authors should present the data as suggested in the review and reformulate their conclusions. If possible, mice should be examined 30-32 hours after partial hepatectomy.

Based on the Literature we chose a time that is consistent with the previous paper from us (Uchida et al., Genes & Dev).

Reviewer #3 (Recommendations for the authors):

(1) It would improve the paper to use single-cell methods (e.g. FISH) to assess the deletion of ORC subunits in the targeted cells.

This is something we will reserve for future studies.

(2) The importance of the paper would be increased dramatically by showing that the elimination of ORC changed the location of Mcm2-7 loading. This would be highly likely if the authors hypothesis that ORC is not involved is true. On the other hand, given ORC's role in origin selection, an observation that the same sites are used but less frequently would support a hypothesis that residual intact ORC is responsible for the replication observed.

Shibata et al (PMID: 39554186) has answered this question.  The loss of ORC does not change the locations of origins or even the ability to specify origins.  We argue that this is what is to be expected from our hypothesis, that although ORC is clearly important for MCM loading in yeast and in biochemical experiments, something unexpected is going on in human cells.  Either a vanishingly small amount of ORC (undetectable by commonly used methods) can load the full complement of MCM2-7 at a rate that is comparable to wild type cells, or there is an ORC-independent mechanism of MCM2-7 loading.   This is now added to the discussion.

Author response:

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

Reviewer 1:

Comments on revisions:

This manuscript is in some ways improved - mainly by toning down the conclusions - but a few major weaknesses have not been addressed. I do not agree that it is not justified to perform experiments to investigate the sterility of single CDK8 knockout mice since this could be important and given that the new data show that while there is some overlap in expression of the two prologues, there are also significant differences in the testis. At the least, it would have been interesting and easy to do to show the expression of CDK8 and CDK19 in the single cell transcriptomics, since this might help to identify the different populations.

Certainly, we tried to analyse Cdk8/Cdk19 in single cell transcriptomics. However, we were unable to draw a clear conclusion. Due to a limited sensitivity of single cell sequencing, especially for low abundant transcripts, such as transcription factors (for 10x technology used in our study) (Chuang et al., 2024), it is challenging to establish with certainty CDK8/19 positive and -negative tissues from single cell data because both transcripts are minor. Nevertheless, the majority of cell types showed some expression of CDK8/19, with maximum expression in pachytene/diplotene spermatocytes. We do not include these data to the manuscript particularly as we were successful to assess Cdk8/19 expression patterns using IF approaches.

Author response image 1.

The only definitive way of concluding a kinase-independent phenotype is to rescue with a kinase dead mutant. While I agree that the inhibitors have been well validated, since they did not have any effects, it is hard to be sure that they actually reached their targets in the tissue concerned. This could have been done by cell thermal shift assay. In the absence of any data on this, the conclusion of a kinase-independent effect is weak.

We totally agree with this point, but it takes several years to produce mice with inducible expression of KD CDK8 mice on the DKO background. These experiments are already underway in our lab, however, their results will be published in our future works.

Figure 2 legend includes (G) between (B) and (C), and appears to, in fact, refer to Fig 1E, for which the legend is missing the description.

Thank you, we corrected this.

Finally, Figure S1C appears wrong. Goblet cells are not in the crypt but on the villi (so the graph axis label is wrong), and there are normally between 5 and 15 per villus, so the iDKO figure is normal, but there are a surprisingly high number of goblet cells in the controls. And normally there are 10-15 Paneth cells/crypt, so it looks like these have been underestimated everywhere. I wonder how the counting was done - if it is from images such as those shown here then I am not surprised as the quality is insufficient for quantification. How many crypts and villi were counted? Given the difficulty in counting and the variability per crypt/villus, with quantitative differences like this it is important to do quantifications blind. I personally wouldn't conclude anything from this data and I would recommend to either improve it or not include it. If these data are shown, then data showing efficient double knockout in this tissue should also accompany it, by IF, Western or PCR. Otherwise, given a potentially strong phenotype, repopulation of the intestine by unrecombined crypts might have occurred - this is quite common (see Ganuza et al, EMBO J. 2012).

We added fig. S1C with Western blot showing presence of CDK8 and CCNC in WT intestine and  their absence in the DKO intestine. We also corrected that the part of the intestine analyzed was the duodenum, not ileum. We also replaced intestine sections photos with the ones of better quality and higher magnification (200X) and corrected Y axis legend. We apologize for the confusion, and thank the reviewer for careful analysis of our data, which allowed us to make this correction. The numbers of cells were counted on 600x magnification, and the magnification given in the article is for presentation purposes only. Our number of goblet cells was indeed calculated per villus, not crypt, and the resulting number is similar to ones reported in Dannapel et al (Dannappel et al., 2022). As for Paneth cells their numbers correspond to several articles that use the c57bl6 strain (Brischetto et al., 2021; King et al., 2013), as the number of Paneth cells differs between different part of the intestine and different mouse strains (Nakamura et al., 2020). 

Reviewer 2:

This reviewer appreciated the authors' effort in improving the quality of this manuscript during their revision. While some concerns remain, the revision is a much improved work and the authors addressed most of my major concerns.

Figure 2E CDK8 and CDK19 immunofluorescent staining images seem to show CDK8 and CDK19 location are completely distinct and in different cells, the authors need to elaborate on this results and discuss what such a distinct location means in line of their double knockout data.

We thank the reviewer for this suggestion. We had expanded the discussion in the lines 518 and 529 and included a better quality picture of the 200x magnification. Our main line of reasoning is that despite distinct expression in different cell types, high magnification show a certain level of expression of both proteins in most cells, so single knockouts will not demonstrate more than a slight phenotype, while the full knockout will have the full effect. This is especially true if our hypothesis that CCNC stabilization is important here, as both kinases can stabilize the protein.

Minor comments:

Supplemental figure 1(C) legend typo : (C) Periodic acid-Schiff stained sections of ilea of tamoxifen treated R26/Cre/ERT2 and DKO mice.

Thank you, we corrected this.

While the effort to identify and generate new antibodies is appreciated, the specificity of the antibodies used should be examined and presented if available.

The specificity of the antibodies for the western blot is confirmed in figure S1F. We added fig. S1G with IF staining of CDK19 KO testes proving our CDK19 antibody specificity.

References:

Brischetto C., Krieger K., Klotz C., et.al. 2021. NF-κB determines Paneth versus goblet cell fate decision in the small intestine. Development 148. doi:10.1242/dev.199683

Chuang H.-C., Li R., Huang H., et.al. 2024. Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3. BMC Genomics 25:1127. doi:10.1186/s12864-024-11036-0

Dannappel M.V., Zhu D., Sun X., et.al. 2022. CDK8 and CDK19 regulate intestinal differentiation and homeostasis via the chromatin remodeling complex SWI/SNF. J Clin Invest 132. doi:10.1172/JCI158593

King S.L., Mohiuddin J.J., Dekaney C.M.. 2013. Paneth cells expand from newly created and preexisting cells during repair after doxorubicin-induced damage. Am J Physiol Gastrointest Liver Physiol 305:G151–62. doi:10.1152/ajpgi.00441.2012

Nakamura K., Yokoi Y., Fukaya R., et.al. 2020. Expression and localization of Paneth cells and their α-defensins in the small intestine of adult mouse. Front Immunol 11:570296. doi:10.3389/fimmu.2020.570296

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Although the scripts are available at the github link that is shown, the Readme file is not available as a text file. Spreadsheets summarizing the RNA-seq data ought to be available for download, but these are not present. Likewise, are spreadsheets available for the data used to generate the plots in Fig. 10, so that the identities of particular, correlated genes can be viewed?

We have now included the excel sheet with all the DEGs shown in Figure 8-9 (Figure 8 – Source data 1-8). The source data include DEGs that are up- and down-regulated in gWAT, iWAT, liver, and skeletal muscle. The source data files (excel) are the standard output format. We have also updated the github (https://github.com/Leandromvelez/CTRP10-Manuscript-DEG-Sex-specific-connectivities-and-integration) to include a README file and updated the R scripts to annotate steps and processing considerations.  In addition, the README file now contains drive links to the files used the unfiltered kallisto TPM and counts at the transcript-level, as well as resulting Differential Expression results based on genotype.  Obviously, all criteria from aligned transcripts such as gene filtering and normalization are included in the scripts provided.

Several items would strengthen the work:

(1) Is a CTRP10 antibody available, and does the protein abundance correlate with the mRNA abundances that were assessed in Fig. 1?

Unfortunately, no validated antibody currently exists for CTRP10. Consequently, we were not able to assess protein abundance of CTRP10 in our study.

(2) Were there compensatory changes in the abundance of other CTRP family members? This might be observed at the protein, but not mRNA, level. It might be reasonable to test for the effects of liver, gWAT, skeletal muscle, and iWAT.

We observed no compensatory changes in other CTRP family members based on our RNA-seq data. Unfortunately, we do not have protein data for other CTRP family members.

(3) The gene expression changes shown in Fig. 9 are ranked according to z-score, but it is not clear how this is calculated. It would be helpful to indicate the log2 change in each case.

The z-score is a very commonly used method to show DEGs in studies involving RNA-seq data. We calculate the z-score based on the gene transcript source data (Fig. 8 – Source data 1-8). Z-score is defined as z = (x-μ)/σ, where x is the raw score (gene transcript level), μ is the population mean (mean of gene expression across both WT and KO samples), and σ is the population standard deviation. In essence, the z-score is the raw score minus the population mean, divided by the population standard deviation. We now included this information in Fig. 9 legend.

(4) In Fig. 6, female HFD-fed KO mice had increased glucose (and insulin) after an overnight fast, but increased glucose was not observed in the GTT data. Possibly, this is because the mice were fasted for only 6h for the GTT. This might be mentioned during the description of these data, on lines 221-224. However, this also raises the question of whether there is a difference in the rate of gluconeogenesis (or possibly glycogenolysis for the 6h data) in the KO compared to the controls. Understanding this would require the use of tracers, and is reasonably beyond the scope of this study, but might be mentioned in the discussion.

Per reviewer’s suggestion, we have included this in the “limitation section” of the discussion.

Reduced RER in the HFD-fed female mice might begin to suggest a mechanism since this suggests the mice might have decreased oxidation of carbohydrates and increased oxidation of fat compared to control animals. A glucose tracer might be used to test whether more glucose is stored and, if so, in what tissue this occurs. Possibly, this could be done ex vivo on isolated tissues or cells. Again, this is reasonably beyond the scope of the present study.

Per reviewer’s suggestion, we have included this in the “limitation section” of the discussion.

(5) The discussion includes a brief discussion of the role of estrogen and suggests that in CTRP10 KO mice there are differences in other factors that would be needed to explain the phenotype. Although it is agreed that this is likely the case, estrogen levels were not measured in the present study. It seems like this would be important to study, and might shed light on the female-specific phenotype.

We have now included serum estrogen data. No significant differences in estrogen levels were seen between WT and KO female mice fed either a low-fat diet (Fig. 4 – figure supplement 1) or a high-fat diet (Fig. 5 – figure supplement 2).

Reviewer #2 (Recommendations For The Authors):

While the concept is potentially exciting, there are major problems with the current manuscript. It lacks the mechanistic details behind MHO.

(1) There is a significant gap that was not addressed by the authors. How exactly does CTRP10 lead to the activation of proteins like Fgf1, Fgf21, Il22ra1, Ucp3, and Klf15 in Ctrp10 knockout female mice? Is it likely that CTRP10 regulates these proteins via indirect mechanisms?

We acknowledge that the lack of mechanistic understanding of how CTRP10 loss-of-function leads to changes in gene expression is a major limitation of the study. We have highlighted this limitation in the discussion section.

• The author notes that Ctrp10 knockout female mice, particularly those on a high-fat diet lack Nr1d1 and can sustain a relatively healthy metabolic state. This is supported by the demonstrated upregulation of Fgf1, Fgf21, Il22ra1, Ucp3, and Klf15 in Ctrp10 knockout female mice. However, the mechanisms through which Ctrp10 knockout influences the expression of these molecules are not elucidated.

We acknowledge that this is a major limitation of the study. We have highlighted this limitation in the discussion section. 

• How do you substantiate the role of age and a high-nutrient diet in the development of obesity in knockout female mice? However, it is still unclear whether administering a high-fat diet in >20 week age of mice can develop insulin resistance where obesity is developing in LFD.

When fed a low-fat diet, Ctrp10-KO female mice developed obesity with age and yet show little if any glucose intolerance or insulin resistance based on our glucose tolerance and insulin tolerance tests. For the HFD group, we are only comparing WT and KO mice on the same diet (not across diet). While WT mice on HFD gained significant amount of weight over time as expected, Ctrp10-KO female mice gain substantially higher amount of weight relative to WT littermates. Despite this, we did not observe a worsening of glucose tolerance and insulin resistance (based on GTT and ITT) in the KO female mice relative to WT controls as we would expect, since greater adiposity in HFD-fed mice generally correlated with worse metabolic outcomes. 

(2) The authors should add the NR1D1 dependency study in female mice if possible.

To address would require the generation of Ctrp10/Nr1d1 double KO mouse model and to carry out the entire study again in these double KO mice. Although this suggestion by the reviewer is a good one, this is beyond the scope of the present study.

(3) NR1D1 represses the set of genes that promotes lipogenesis (the author should add some data that validates this statement).

The role of NR1D1 in regulating metabolic genes are extensively documented in the published literature. NR1D1 (also known as REV-ERBα) is a constitutive transcriptional repressor (PMID: 26044300; PMID: 27445394). Many metabolic genes that are normally represses by NR1D1 is de-repressed in mice lacking NR1D1 globally or in the tissue-specific manner (PMID: 26044300; PMID: 34350828; PMID: 22562834). Among the many NR1D1 target genes involved in lipid metabolism include: CD36, Plin2, Elovl5, Acss3 (from: PMID: 26044300); as well as Scd1, Scd2, Pnpla5, Acsl1, Fasn, Hadhb, and Oxsm (from: PMID: 34350828).  We have included this information in the discussion section.

(4) The authors should study the effect of Ctrp10 overexpression in HFD-fed female mice and also with KO of CTRP10 in adult mice if possible.

The suggestion by the reviewer is a good one. However, this is beyond the scope of the study. We do not have a Ctrp10 conditional KO mouse model; as such, we could not study the effect of knocking out CTRP10 in adult mice. Overexpression studies are often considered non-physiological these days since the level of the overexpressed protein is generally much higher than the normal physiological level. For this reason, we did not attempt any overexpression study. 

Reviewer #3 (Recommendations For The Authors):

Line 114: Could you please provide definitions for "GluK2" and "GluK4" for readers unfamiliar with these terms?

We have now provided definition for these terms.

Line 140: It's stated that skeletal muscle and the pancreas express similar levels of Ctrp10 as the brain. Please double-check and clarify this assertion for accuracy.

In mice, based on our own data (Fig. 1B), Ctrp10 expression in skeletal muscle and pancreas is comparable to that in the whole brain. In human, based on publicly available data (e.g., Genotype-Tissue Expression portal; GTex), brain expresses much higher level of CTRP10 transcript relative to other peripheral tissues.

Line 141: Have you investigated whether Ctrp10 levels in plasma change after refeeding? If not, consider exploring this aspect to enhance the comprehensiveness of the study.

No validated antibody currently exists for CTRP10. As such, we could not assess plasma level of CTRP10 after refeeding. We have included this as limitation of our study in the discussion section.  

Lines 143-144: Clarify the age bracket of the animals used in the study. Additionally, have you observed similar responses, such as downregulation of Ctrp10 in response to refeeding, in both old and young mice in peripheral tissues?

We have now included the age of the mice (~10 weeks old) for the fasting refeeding study as shown in Fig. 1C in the result and method sections.  

Lines 135-149: To complement the experiments shown in Fig 1B-D, provide data pertaining to females.

Ideally, we would like to have this data as well. However, to do this for females would involve 47 mice and the collection of 120 tissues (Fig. 1B; n = 10 per tissue), 390 tissues (Fig. 1C; n = 7-8 per tissue per fast or refed state), and 528 tissues (Fig. 1D; n = 11 per tissue per HFD or LFD). This would be a total of 1038 tissue samples. The main purpose of Fig. 1B-D is to demonstrate that Ctrp10 transcript is widely expressed and that its expression is modulated by nutritional (HFD vs. LFD) and metabolic (fast vs. refeed) states. These data provided a rationale to examine the metabolic phenotype in mice lacking CTRP10.

To address the reviewer’s point, we looked at the expression levels of CTRP10/C1QL1 between males and females in the Genotype-Tissue Expression (GTEx) database portal and it does not appear that there are sex differences in CTRP10 expression patterns in normal tissues.  

Line 152: Can you provide evidence supporting the hypothesis that Ctrp10 is secreted into the circulation?

CTRP10 has a classic signal peptide sequence and the protein is secreted when expressed in HEK 293 cells (PMID: 18783346). We have shown previously that CTRP10 can be found in the FPLC-fractionated mouse serum using a polyclonal rabbit anti-mouse CTRP10 antibody we generated (PMID: 18783346); this antibody, however, does not work on tissue lysates (many non-specific bands). There is evidence in published literature to show that CTRP10/C1QL2 is clearly found circulating in human plasma. Some of the studies include: 1) Human C1QL2/CTRP10 is detected in the human plasma from UK BioBank (PMID: 37794186; C1QL2 is highlighted in page 335) and serum samples from pregnant females (PMID: 39062451; C1QL2 is highlighted in Table 2). We have included this information in the Introduction section.

Line 178: In Fig 4 D and E (and other figures in the paper), it would be more accurate to express adipocyte size in "μm²" instead of "uM2."

We have double checked and fixed this issue in the figure 4 and 7.

Line 259: Please specify the age of the animals used in the study.

In the method section, we did mention that LFD was provided for the duration of the study, beginning at 5 weeks of age; and that HFD was provided for 14 weeks, beginning at 6-7 weeks of age. Also, in Figure 2A and Figure 4A, the age of the mice is also indicated.

Lines 275-283 and 288-296: It would be more appropriate to move this content to the Discussion section for better contextualization.

We feel that the published information on NR1D1 and FGF21 should be mentioned in the result section so that the readers can immediately appreciate the significance of our data shown in Fig. 8 and 9. However, we also included similar information concerning NR1D1 in the discussion section for better contextualization as suggested.  

Line 301: The section on DEG analysis requires additional details. How was the DEG analysis conducted? Were the DEGs from "wild type and KO mice" compared with "human DEGs regulated by sex"? Also, details about the phenotype of the human subjects and their association with obesity should be included. Additionally, discuss specific genes identified by the analysis and their relevance to the Ctrp10 story and human sex-specific gene connectivity analysis.

We have updated the section on DEG analysis and, related to reviewer comments above, significantly expanded the github repository, detailing an analytical walkthrough of all computational analyses performed. To clarify the human integration analysis, we have added the following to the methods:

“To investigate the degree of conservation of CTRP-engaged pathways, we mapped the differentially expressed genes (DEGs) identified from Ctrp10 knockout (KO) versus wild-type (WT) mice to their human orthologs, including human CTRP10, in the GTEx database for transcriptional correlations. Individuals were stratified by sex to examine sex-specific gene connectivity, consisting of 210 males and 100 females to compare gene expression across tissues. Gene-connectivity analyses were performed based on population correlation significances summarized by cumulative -log10(pvalues) as previously described"

Line 330: In Fig 7L, increased oxidative stress in the liver of KO mice is shown. Please provide an explanation for the claim that Ctrp10-KO female mice resembled the WT controls.

In Fig. 7L, we did observe a modest, but significant, increase in oxidative stress in the liver based on the quantification of malondialdehyde (MDA) level, a marker of tissue oxidative stress. However, we did not see any significant differences in the expression of oxidative genes in the liver between WT and KO female mice (Fig. 7J); thus, the statement in line 330 (discussion section) that pertains to oxidative gene expression in fat and liver (Fig. 7E and 7J) is correct. 

Line 375: Could you clarify the term "adipose tissue health" and further discuss or provide evidence demonstrating compromised adipose tissue health in female KO mice following HFD?

Adipose tissue health refers to the healthy functioning of adipose tissue (based on its functionality, immune cell population and profile, and metabolic gene expression profiles). Adipose tissue releases free fatty acids in response to fasting and takes up lipids in response to refeeding. Both are these functions are preserved in KO mice as we did not observe any significant differences in free fatty acids (NEFA) and triglyceride levels in the fasted and refed states (Fig. 6AB). Also, we did not observe any significant differences in the expression of inflammatory and fibrotic genes in the adipose tissue of WT and KO female mice fed a high-fat diet (Fig. 7E). If anything, we actually observed a modest, but significant, reduction in the expression of some ER and oxidative stress genes in the KO female mice relative to WT controls (Fig. 7E). 

Line 408: Please provide data regarding estrogen levels in wild-type and KO female mice for comparison.

We have now included serum estrogen data. No significant differences in estrogen levels were seen between WT and KO female mice fed either a low-fat diet (Fig. 4 – figure supplement 1) or a high-fat diet (Fig. 5 – figure supplement 2).

Line 587: The GitHub link provided seems to be inactive or incorrect. Please verify and provide the correct link.

We have also updated the github (https://github.com/Leandromvelez/CTRP10-Manuscript-DEG-Sex-specific-connectivities-and-integration) to include a README file and updated the R scripts to annotate steps and processing considerations. 

Lines 590-599: Provide additional details about the analysis of human sex-specific genes. Including a table of the top DEGs and pathways differentially regulated by sex would be beneficial for readers' comprehension.

We have expanded the methods, results and associated github repositories to detail all reproducible parameters used in these analyses.  The new table of DEGs is included in the manuscript and github repositories.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this article, Nedbalova et al. investigate the biochemical pathway that acts in circulating immune cells to generate adenosine, a systemic signal that directs nutrients toward the immune response, and S-adenosylmethionine (SAM), a methyl donor for lipid, DNA, RNA, and protein synthetic reactions. They find that SAM is largely generated through the uptake of extracellular methionine, but that recycling of adenosine to form ATP contributes a small but important quantity of SAM in immune cells during the immune response. The authors propose that adenosine serves as a sensor of cell activity and nutrient supply, with adenosine secretion dominating in response to increased cellular activity. Their findings of impaired immune action but rescued larval developmental delay when the enzyme Ahcy is knocked down in hemocytes are interpreted as due to effects on methylation processes in hemocytes and reduced production of adenosine to regulate systemic metabolism and development, respectively. Overall this is a strong paper that uses sophisticated metabolic techniques to map the biochemical regulation of an important systemic mediator, highlighting the importance of maintaining appropriate metabolite levels in driving immune cell biology.

Strengths:

The authors deploy metabolic tracing - no easy feat in Drosophila hemocytes - to assess flux into pools of the SAM cycle. This is complemented by mass spectrometry analysis of total levels of SAM cycle metabolites to provide a clear picture of this metabolic pathway in resting and activated immune cells.

The experiments show that the recycling of adenosine to ATP, and ultimately SAM, contributes meaningfully to the ability of immune cells to control infection with wasp eggs.

This is a well-written paper, with very nice figures showing metabolic pathways under investigation. In particular, the italicized annotations, for example, "must be kept low", in Figure 1 illustrate a key point in metabolism - that cells must control levels of various intermediates to keep metabolic pathways moving in a beneficial direction.

Experiments are conducted and controlled well, reagents are tested, and findings are robust and support most of the authors' claims.

Weaknesses:

The authors posit that adenosine acts as a sensor of cellular activity, with increased release indicating active cellular metabolism and insufficient nutrient supply. It is unclear how generalizable they think this may be across different cell types or organs.

In the final part of the Discussion, we elaborate slightly more on a possible generalization of our results, while being aware of the limited space in this experimental paper and therefore intend to address this in more detail and comprehensively in a subsequent perspective article.

The authors extrapolate the findings in Figure 3 of decreased extracellular adenosine in ex vivo cultures of hemocytes with knockdown of Ahcy (panel B) to the in vivo findings of a rescue of larval developmental delay in wasp egg-infected larvae with hemocyte-specific Ahcy RNAi (panel C). This conclusion (discussed in lines 545-547) should be somewhat tempered, as a number of additional metabolic abnormalities characterize Ahcy-knockdown hemocytes, and the in vivo situation may not mimic the ex vivo situation. If adenosine (or inosine) measurements were possible in hemolymph, this would help bolster this idea. However, adenosine at least has a very short half-life.

We agree with the reviewer, and in the 4th paragraph of the Discussion we now discuss more extensively the limitations of our study in relation to ex vivo adenosine measurements and the importance of the SAM pathway on adenosine production.

Reviewer #2 (Public review):

Summary:

In this work, the authors wish to explore the metabolic support mechanisms enabling lamellocyte encapsulation, a critical antiparasitic immune response of insects. They show that S-adenosylmethionine metabolism is specifically important in this process through a combination of measurements of metabolite levels and genetic manipulations of this metabolic process.

Strengths:

The metabolite measurements and the functional analyses are generally very strong and clearly show that the metabolic process under study is important in lamellocyte immune function.

Weaknesses:

The gene expression data are a potential weakness. Not enough is explained about how the RNAseq experiments in Figures 2 and 4 were done, and the representation of the data is unclear.

The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewer that we should describe the necessary details again here. The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

The paper would also be strengthened by the inclusion of some measure of encapsulation effectiveness: the authors show that manipulation of the S-adenosylmethionine pathway in lamellocytes affects the ability of the host to survive infection, but they do not show direct effects on the ability of the host to encapsulate wasp eggs.

The reviewer is correct that wasp egg encapsulation and host survival may be different (the host can encapsulate and kill the wasp egg and still not survive) and we should also include encapsulation efficiency. This is now added to Figure 3D, which shows that encapsulation efficiency is reduced upon Ahcy-RNAi, which is consistent with the reduced number of lamellocytes.

Reviewer #3 (Public review):

Summary:

The authors of this study provide evidence that Drosophila immune cells show upregulated SAM transmethylation pathway and adenosine recycling upon wasp infection. Blocking this pathway compromises the lamellocyte formation, developmental delay, and host survival, suggesting its physiological relevance.

Strengths:

Snapshot quantification of the metabolite pool does not provide evidence that the metabolic pathway is active or not. The authors use an ex vivo isotope labelling to precisely monitor the SAM and adenosine metabolism. During infection, the methionine metabolism and adenosine recycling are upregulated, which is necessary to support the immune reaction. By combining the genetic experiment, they successfully show that the pathway is activated in immune cells.

Weaknesses:

The authors knocked down Ahcy to prove the importance of SAM methylation pathway. However, Ahcy-RNAi produces a massive accumulation of SAH, in addition to blocking adenosine production. To further validate the phenotypic causality, it is necessary to manipulate other enzymes in the pathway, such as Sam-S, Cbs, SamDC, etc.

We are aware of this weakness and have addressed it in a much more detailed discussion of the limitations of our study in the 6th paragraph of the Discussion.

The authors do not demonstrate how infection stimulates the metabolic pathway given the gene expression of metabolic enzymes is not upregulated by infection stimulus.

Although the goal of this work was to test by 13C tracing whether the SAM pathway activity is upregulated, not to analyze how its activity is regulated, we certainly agree with the reviewer that an explanation of possible regulation, especially in the context of the enzyme expressions we show, should be included in our work. Therefore, we have supplemented the data with methyltransferase expressions (Figure 2-figure supplement 3. And S3_Data) and better describe the changes in expression of some SAM pathway genes, which also support stimulation of this pathway by changes in expression. The enzymes of the SAM transmethylation pathway are highly expressed in hemocytes, and it is known that the activity of this pathway is primarily regulated by (1) increased methionine supply to the cell and (2) the actual utilization of SAM by methyltransferases. Therefore, a possible increase in SAM transmethylation pathway in our work can be suggested (1) by increased expression of 4 transporters capable of transporting methionine, (2) by decreased expression of AhcyL2 (dominant-negative regulator of Ahcy) and (3) by increased expression of 43 out of 200 methyltransferases. This was now added to the first section of Results.

Recommendations for the authors:

Reviewing Editor Comments:

In the discussion with the reviewers, two points were underlined as very important:

(1) Knocking down Ahyc and other enzymes in the SAM methylation pathway may give very distinct phenotypes. Generalising the importance of "SAM methyaltion" only by Ahcy-RNAi is a bit cautious. The authors should be aware of this issue and probably mention it in the Discussion part.

We are aware of this weakness and have addressed it in a much more detailed discussion of the limitations of our study in the 6th paragraph of the Discussion.

(2) Sample sizes should be indicated in the Figure Legends. Replicate numbers on the RNAseq are important - were these expression levels/changes seen more than once?

Sample sizes are shown as scatter plots with individual values wherever possible and all graphs are supplemented with S1_Data table with raw data. The RNAseq data have already been described in detail in our previous paper (doi.org/10.1371/journal.pbio.3002299), but we agree with the reviewers that we should describe the necessary details again here. The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

Reviewer #1 (Recommendations for the authors):

Major points:

(1) Please provide sample sizes in the legends rather than in a supplementary table.

Sample sizes are shown either as scatter plots with individual values or added to figure legends now.

(2) More details in the methods section are needed:

For hemocyte counting, are sessile and circulating hemocytes measured?

We counted circulating hemocytes (upon infection, most sessile hemocytes are released into the circulation). While for metabolomics all hemocyte types were included, for hemocyte counting we were mainly interested in lamellocytes. Therefore, we counted them 20 hours after infection, when most of the lamellocytes from the first wave are fully differentiated but still mostly in circulation, as they are just starting to adhere to the wasp egg. This was added to the Methods section.

How were levels of methionine and adenosine used in ex vivo cultures selected? This is alluded to in lines 158-159, but no references are provided.

The concentrations are based on measurements of actual hemolymph concentrations in wild-type larvae in the case of methionine, and in the case of adenosine, we used a slightly higher concentration than measured in the adgf-a mutant to have a sufficiently high concentration to allow adenosine to flow into the hemocytes. This is now added to the Methods section.

Minor points:

Response to all minor points:  Thank you, errors has now been fixed.

(1) Line 186 - spell out MTA - 5-methylthioadenosine.

(2) Lines 196-212 (and elsewhere) - spelling out cystathione rather than using the abbreviation CTH is recommended because the gene cystathione gamma-lyase (Cth) is also discussed in this paragraph. Using the full name of the metabolite will reduce confusion.

We rather used cystathionine γ-lyase as a full name since it is used only three times while CTH many more times, including figures.

(3) Figure 2 - supplement 2: please include scale bars.

(4) Line 303 - spelling error: "trabsmethylation" should be "transmethylation".

(5) Line 373 - spelling error: "higer" should be "higher".

Reviewer #2 (Recommendations for the authors):

For the RNAseq data, it's unclear whether the gene expression data in Figures 2 and 4 include biological replicates, so it's unclear how much weight we should place on them.

The replicate numbers for RNAseq data were added to figure legends, the TPM values for the selected genes shown in figures are in S1_Data and new S4_Data file with complete RNAseq data (TPM and DESeq2) was added to this revised version.

The representation of these data is also a weakness: Figure 2 shows measurements of transcripts per million, but we don't know what would be high or low expression on this scale.

We have added the actual TPM values for each cell in the RNAseq heatmaps in Figure 2, Figure 2-figure supplement 3, and Figure 4 to make them more readable. Although it is debatable what is high or low expression, to at least have something for comparison, we have added the following information to the figure legends that only 20% of the genes in the presented RNAseq data show expression higher than 15 TPM.

Figure 4 is intended to show expression changes with treatment, but expression changes should be shown on a log scale (so that increases and decreases in expression are shown symmetrically) and should be normalized to some standard level (such as uninfected lamellocytes).

The bars in Figure 4C,D show the fold change (this is now stated in the y-axis legend) compared to 0 h (=uninfected) Adk3 samples - the reason for this visualization is that we wanted to show (1) the differences in levels between Adk3 and Adk2 and in levels between Ak1 and Ak2, respectively, and at the same time (2) the differences between uninfected and infected Adk3 and Ak1. In our opinion, these fold change differences are also much more visible in normal rather than log scale.

Reviewer #3 (Recommendations for the authors):

(1) It might be interesting to test how general this finding would be. How about Bacterial or fungal infection? The authors may also try genetic activation of immune pathways, e.g. Toll, Imd, JAK/STAT.

Although we would also like to support our results in different systems, we believe that our results are already strong enough to propose the final hypothesis and publish it as soon as possible so that it can be tested by other researchers in different systems and contexts than the Drosophila immune response.

(2) How does the metabolic pathway get activated? Enzyme activity? Transporters? Please test or at least discuss the possible mechanism.

The response is already provided above in the Reviewer #3 (Public review) section.

(3) The authors might test overexpression or genetic activation of the SAM transmethylation pathway.

Although we agree that this would potentially strengthen our study, it may not be easy to increase the activity of the SAM transmethylation pathway - simply overexpressing the enzymes may not be enough, the regulation is primarily through the utilization of SAM by methyltransferases and there are hundreds of them and they affect numerous processes. 

(4) Supplementation of adenosine to the Ahcy-RNAi larvae would also support their conclusion.

Again, this is not an easy experiment, dietary supplementation would not work, direct injection of adenosine into the hemolymph would not last long enough, adenosine would be quickly removed.

(5) It is interesting to test genetically the requirement of some transporters, especially for gb, which is upregulated upon infection.

Although this would be an interesting experiment, it is beyond the scope of this study; we did not aim to study the role of the SAM transmethylation pathway itself or its regulation, only its overall activity and its role in adenosine production.

Author response:

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

Reviewer #1 (Public review):

Summary:

Wang et al. created a series of specific FLIM-FRET sensors to measure the activity of different Rab proteins in small cellular compartments. They apply the new sensors to monitor Rab activity in dendritic spines during induction of LTP. They find sustained (30 min) inactivation of Rab10 and transient (5 min) activation of Rab4 after glutamate uncaging in zero Mg. NMDAR function and CaMKII activation are required for these effects. Knockdown of Rab4 reduced spine volume change while knockdown of Rab10 boosted it and enhanced functional LTP (in KO mice). To test Rab effects on AMPA receptor exocytosis, the authors performed FRAP of fluorescently labeled GluA1 subunits in the plasma membrane. Within 2-3 min, new AMPARs appear on the surface via exocytosis. This process is accelerated by Rab10 knock-down and slowed by Rab4 knock-down. The authors conclude that CaMKII promotes AMPAR exocytosis by i) activating Rab4, the exocytosis driver and ii) inhibiting Rab10, possibly involved in AMPAR degradation.

Strengths:

The work is a technical tour de force, adding fundamental insights to our understanding of the crucial functions of different Rab proteins in promoting/preventing synaptic plasticity. The complexity of compartmentalized Ras signaling is poorly understood and this study makes substantial inroads. The new sensors are thoroughly characterized, seem to work very well, and will be quite useful for the neuroscience community and beyond (e.g. cancer research). The use of FLIM for read-out is compelling for precise activity measurements in rapidly expanding compartments (i.e., spines during LTP).

Thank you for the evaluation.

Weaknesses:

The interpretation of the FRAP experiments (Figure 5, Ext. Data Figure 13) is not straightforward as spine volume and surface area greatly expand during uncaging. I appreciate the correction for the added spine membrane shown in Extended Data Figure 14i, but shouldn't this be a correction factor (multiplication) derived from the volume increase instead of a subtraction?

We thank the reviewer for this question. The fluorescence change should reflect a subtraction of surface area, as SEP-GluA1 is only fluorescent on the cell surface, unlike cytosolic mCherry, whose fluorescence intensity is proportional to spine volume. Therefore, the overall fluorescence change (ΔF) should be the addition of the contribution from AMPAR trafficking (ΔF_t) and the change in surface area (ΔS) multiplied by the remaining SEP-GluA1 fluorescence per unit area (f):

ΔF = ΔF_t + fΔS

Since fluorescence immediately after photobleaching (before AMPAR trafficking happens), F_o, is given by fS (S is the surface area of the spine):

ΔF/F_o = ΔF_t/ F_o + fΔS / fS

\= ΔF_t/fS + ΔS/S

Assuming that the surface area change (ΔS/S) is the volume change (ΔV/V) to the power of 2/3, the contribution of the AMPAR trafficking can be calculated as:

ΔF_t/F = ΔF/F – (Δ<sup>V/V)^2/3

This is the reason that we subtracted the contribution of the spine surface area. We have discussed this in the updated method section.

Also, experiments were not conducted or analyzed blind, risking bias in the selection/exclusion of experiments for analysis. This reduces my confidence in the results.

We acknowledge the reviewer's concern regarding the lack of blinding in our experiments. However, it is challenging to conduct blinded experiments for certain types of studies, such as sensor screening for a protein family, where we do not have expected results or a specific hypothesis prior to the experiments. In these cases, our primary readout is whether the sensor indicates any activity change upon stimulation.

To address this concern, after identifying that Rab10 is inactivated during structural LTP (sLTP) and is likely important for inhibiting spine structural LTP, we performed blinded electrophysiology experiments and obtained similar results (deletion of Rab10 from Camk2a-positive neurons leads to enhanced LTP; Fig. 4k, 4l).

Reviewer #2 (Public review):

Summary:

Wang et al. developed a set of optical sensors to monitor Rab protein activity. Their investigation into Rab activity in dendritic spines during structural long-term plasticity (sLTP) revealed sustained Rab10 inactivation (>30min) and transient Rab4 activation (~5 min). Through pharmacological and genetic manipulation to constitutively activate or inhibit Rab proteins, they found that Rab10 negatively regulates sLTP and AMPA receptor insertion, while Rab4 positively influences sLTP but only in the transient phase. The optical sensors provide new tools for studying Rab activity in cells and neurobiology. However, a full understanding of the timing of Rab activity will require a detailed characterization of sensor kinetics.

Strengths:

(1) Introduction of a series of novel sensors that can address numerous questions in Rab biology.

(2) Multiple methods to manipulate Rab proteins to reveal the roles of Rab10 and rab4 in LTP.

(3) Discovery of Rab4 activation and Rab10 inhibition with different kinetics during sLTP, correlating with their functional roles in the transient (Rab4) and both transient and sustained (Rab10) phases of sLTP.

Thank you for the positive evaluation.

Weaknesses:

(1) Lack of characterization of sensor kinetics, making it difficult to determine if the observed Rab kinetics during sLTP were due to sensor behavior or actual Rab activity.

We estimated that the kinetics of the sensors for Rab4 and Rab10 are within a few minutes. For Rab4, we observed rapid increase and decrease of the activation in response to glutamate uncaging. Thus, this would be the upper limit of the ON/OFF time constants of Rab4. For Rab10, we observed a rapid dissociation of the sensor in response to sLTP induction within ~1 min. This means that the donor and acceptor molecules are quickly dissociated during the process. Thus, the off kinetics of the sensor is within the range of minute. Meanwhile, we have the on-kinetics from Rab10 activation (donor/accepter association) in response to NMDA application and again this is within a few minutes. Given these rapid sensor kinetics in neurons, our observation of the sustained inactivation of Rab10 should reflect the true behavior of Rab10, rather than just the sensor’s response.

We revised our manuscript discussion session as follows:

“Understanding the kinetics of Rab4 and Rab10 sensors is essential for interpreting their actual activity during sLTP. The Rab4 sensor exhibits a rapid rise and fall in activation (Fig. 3), indicating ON/OFF times of less than a few minutes. In contrast, the Rab10 sensor rapidly dissociates during sLTP induction (Fig. 2), with OFF kinetics occurring within one minute and fast ON kinetics in response to NMDA (Fig. 1j). Given these rapid kinetics, the observed sustained inactivation of Rab10 likely reflects its true behavior rather than sensor dynamics.”

(2) It is crucial to assess whether the overexpression of Rab proteins as reporters, affects Rab activity and cellular structure and physiology (e.g. spine number and size).

While we did not measure the effects of Rab sensor overexpression on Rab activity or cellular structure and physiology, we showed that sLTP is similar in neurons expressing sensors. This suggests that the overexpression of Rab sensors does not significantly disrupt signaling required for sLTP.

(3) The paper does not explain the apparently different results between NMDA receptor activation and glutamate uncaging. NMDA receptor activation increased Rab10 activity, while glutamate uncaging decreased it. NMDA receptor activation resulted in sustained Rab4 activation, whereas glutamate uncaging caused only brief activation of about 5 minutes. A potential explanation, ideally supported by data, is needed.

It is a long-standing question in the field why simple NMDA receptor activation by bath application of NMDA does not induce LTP, but instead induce LTD. Rab proteins are regulated by many GEFs and GAPs and identifying different mechanisms requires completely different techniques, such as molecular screening. While our manuscript provides some insights into this question by showing that they provide opposing signals for Rab10, we believe that identifying exact mechanisms would be out of the scope of this manuscript.

(4) There is a discrepancy between spine phenotype and sLTP potential with Rab10 perturbation. Rab10 perturbation affected spine density but not size, suggesting a role in spinogenesis rather than sLTP. However, glutamate uncaging affected sLTP, and spinogenesis was not examined. Explaining the discrepancy between spine size and sLTP potential is necessary. Exploring spinogenesis with glutamate uncaging would strengthen these results. Additionally, Figure 4j shows no change in synaptic transmission with Rab10 knockout, despite an increase in spine density. An explanation, ideally supported by data, is needed for the unchanged fEPSP slope despite an increase in spine density.

We thank the reviewer for raising these important questions. In our findings, shRNA-mediated knockdown of Rab10 did not alter spine size but did increase spine density in the basal state (Extended Data Fig. 11i). This suggests that Rab10 may restrict spinogenesis without affecting spine size. Conversely, sLTP induction via glutamate uncaging is an activity-dependent process that may involve different molecular mechanisms. The signal interplay between spinogenesis and sLTP and how the exact roles of Rab signaling in different modalities of plasticity would remain elusive for the future study.

The lack of change in synaptic transmission with Rab10 knockout, despite the increase in spine density from Rab10 shRNA knockdown, may be due to different preparation and developmental stages: spine density measurements were conducted with shRNA knockdown in organotypic slices (sliced at P6-8, DIV 9-13), while electrophysiological recordings were performed in knockout mice in acute slices from adult animals (P30-60).

(5) Spine volume was imaged using acceptor fluorophores (mCherry, or mCherry/Venus) at 920nm, where the two-photon cross-section of mCherry is minimal. 920nm was also used to excite the donor fluorophore, hence the spine volume measurement based on total red channel fluorescence is the sum of minimal mCherry fluorescence from direct 920nm excitation, bleed-through from the green channel, and FRET. This confounded measurement requires correction and clarification.

We assumed that the most of fluorescence is from direct excitation of mCherry at 920 nm. The contribution from the bleed-through from mEGFP-Rab (~3%) and from FRET changes (~20%) may influence the volume measurements. However, since we observed similar fluorescence changes in the green and red channels, these factors would have only a minor impact on our results (Extended Data Fig. 6a, 6d). Also, please note that the volume change in neurons expressing sensors is just to check if the volume change is normal, and not a major point of this manuscript.  We clarified this in the method section as:

“For the sensor experiments, we used mCherry as a volume indicator. We acknowledge that contributions from bleed-through from mEGFP-Rab (approximately 3%) and FRET changes (around 20%) could affect the volume measurements. However, since we observed similar fluorescence changes in both the green and red channels, we believe these factors have a minimal impact on our results (Extended Data Fig. 6a, 6d).”

Reviewer #3 (Public review):

Summary:

This study examines the roles of Rab10 and Rab4 proteins in structural long-term potentiation (sLTP) and AMPA receptor (AMPAR) trafficking in hippocampal dendritic spines using various different methods and organotypic slice cultures as the biological model.

The paper shows that Rab10 inactivation enhances AMPAR insertion and dendritic spine head volume increase during sLTP, while Rab4 supports the initial stages of these processes. The key contribution of this study is identifying Rab10 inactivation as a previously unknown facilitator of AMPAR insertion and spine growth, acting as a brake on sLTP when active. Rab4 and Rab10 seem to be playing opposing roles, suggesting a somewhat coordinated mechanism that precisely controls synaptic potentiation, with Rab4 facilitating early changes and Rab10 restricting the extent and timing of synaptic strengthening.

Strengths:

The study combines multiple techniques such as FRET/FLIM imaging, pharmacology, genetic manipulations, and electrophysiology to dissect the roles of Rab10 and Rab4 in sLTP. The authors developed highly sensitive FRET/FLIM-based sensors to monitor Rab protein activity in single dendritic spines. This allowed them to study the spatiotemporal dynamics of Rab10 and Rab4 activity during glutamate uncaging-induced sLTP. They also developed various controls to ensure the specificity of their observations. For example, they used a false acceptor sensor to verify the specificity of the Rab10 sensor response.

This study reveals previously unknown roles for Rab10 and Rab4 in synaptic plasticity, showing their opposing functions in regulating AMPAR trafficking and spine structural plasticity during LTP.

Thank you for the positive evaluation.

Weaknesses:

In sLTP, the initial volume of stimulated spines is an important determinant of induced plasticity. To address changes in initial volume and those induced by uncaging, the authors present Extended Data Figure 2. In my view, the methods of fitting, sample selection, or both may pose significant limitations for interpreting the overall results. While the initial spine size distribution for Rab10 experiments spans ~0.1-0.4 fL (with an unusually large single spine at the upper end), Rab4 spine distribution spans a broader range of ~0.1-0.9 fL. If the authors applied initial size-matched data selection or used polynomials rather than linear fitting, panels a, b, e, f, and g might display a different pattern. In that case, clustering analysis based on initial size may be necessary to enable a fair comparison between groups not only for this figure but also for main Figures 2 and 3.

We thank the reviewer for these questions. For sensor uncaging experiments, we usually uncaged glutamate at large mushroom spines because we need to have a good signal-to-noise ratio. We just happen to choose these spines with different initial sizes for Rab4 sensor and Rab10 sensor uncaging experiments.

Another limitation is the absence of in vivo validation, as the experiments were performed in organotypic hippocampal slices, which may not fully replicate the complexity of synaptic plasticity in an intact brain, where excitatory and inhibitory processes occur concurrently. High concentrations of MNI-glutamate (4 mM in this study) are known to block GABAergic responses due to its antagonistic effect on GABA-A receptors, thereby precluding the study of inhibitory network activity or connectivity [1], which is already known to be altered in organotypic slice cultures.

(1) https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/neuro.04.002.2009/full

We appreciate the reviewer's comments and would like to clarify that we have conducted experiments in acute slices for LTP using conditional Rab10 knockout (Fig. 4k, 4l), and we obtained similar results. Additionally, we have recently published findings on the behavioral deficits observed in heterozygous Rab10 knockout mice (PubMed 37156612). These studies further support our conclusions and provide additional context for our findings.

Recommendations for the authors:

From the Senior/Reviewing Editor:

I apologize that this took longer than intended. As you will see from the reviews there was some disagreement on several points. There was some disagreement among reviewers as to the strength of the evidence with some characterizing it as "compelling," "convincing," or "solid" while others felt the characterization of the sensors was "incomplete" and that this could have affected some of the conclusions. After extensive discussion, reviewers agreed that there was a valid concern that the conclusion that Rab10 activation is sustained could reflect a feature of the sensor. If Rab10/RBD dissociation rate were very low, and the affinity of binding were very high, this could lead to an incorrect estimate of the sustained binding due to sensor kinetics, not Rab10 activation. It was noted that this has been seen in other sensors previously (e.g. first generation PKA activity sensors), which the developers altered in later generations to increase reversibility and off kinetics of the sensor.

There was also discussion of how this might be addressed and we would be interested in your comments on this issue. It was suggested that it might be helpful to revise Figure 2b to show binding fraction dynamics separately for each spine (to determine whether any actually return to baseline). Subsequently, clustering of these binding dynamics into two groups could be summarized in a version of Fig. 2e for each cluster. Differences in spine volume dynamics between these clusters would provide a measure of how strongly Rab10 binding correlates with spine volume. If they never go back to baseline, some extra experiments with longer post-plasticity induction (150mins instead of 35), might show if any reversible Rab10 binding exists post-LTP induction.

An alternative suggestion was to measure the time course in the presence of a GAP or GEF, which should alter the kinetics.

Thanks for the comments. It is important that the inactivation is observed as the dissociation of the donor and acceptor of the sensor.  Thus, the fact that the sensor rapidly decreases in response to uncaging means that they have rapid off kinetics. In addition, we provide evidence of a rapid increase of Rab10 in response to NMDA application, suggesting that kinetics is also rapid. We added discussion about this in the revised manuscript as:

“Understanding the kinetics of Rab4 and Rab10 sensors is essential for interpreting their actual activity during sLTP. The Rab4 sensor exhibits a rapid rise and fall in activation (Fig. 3), indicating ON/OFF times of just a few minutes. In contrast, the Rab10 sensor rapidly dissociates during sLTP induction (Fig. 2), with OFF kinetics occurring within one minute and fast ON kinetics in response to NMDA (Fig. 1j). Given these rapid kinetics, the observed sustained inactivation of Rab10 likely reflects its true behavior rather than sensor dynamics.”

There was also further discussion of the nature of the "spine volume" signal, given the fact that the two-photon cross-section of mCherry is minimal at 920nm. It was suggested that this could be due to direct acceptor excitation rather than FRET, but there was agreement that further clarity on this issue would be valuable.

We assumed that the most of fluorescence is from direct excitation of mCherry at 920 nm. The contribution from the bleed-through from mEGFP-Rab (~3%) and from FRET changes (~20%) may influence the volume measurements. However, since we observed similar fluorescence changes in the green and red channels, these factors would have only a minor impact on our results (Extended Data Fig. 6a, 6d). Also, please note that the volume change in neurons expressing sensors is just to check if the volume change is normal, and not a major point of this manuscript.  We clarified this in the method section as:

“For the sensor experiments, we used mCherry as a volume indicator. We acknowledge that contributions from bleed-through from mEGFP-Rab (approximately 3%) and FRET changes (around 20%) could affect the volume measurements. However, since we observed similar fluorescence changes in both the green and red channels, we believe these factors have a minimal impact on our results (Extended Data Fig. 6a, 6d).”

The equations in the methods section differ from other papers by the same lab (e.g. Laviv et al, Neuron 2020, Tu et al. Sci Adv. 2023, Jain et al. Nature 2024). Please clarify which equations are correct.

Thanks for pointing this out. In fact, some of the equations in this manuscript were wrong, and we have corrected them in the method session.

Reviewer #1 (Recommendations for the authors):

The effects of Rab knockdown affect both spine volume expansion and AMPAR recovery in a very similar fashion. To explain this tight coupling, the authors suggest that the availability of membrane could be a limiting factor for spine enlargement. However, some Rabs are known to affect actin dynamics, which could also explain the dual effects on AMPAR exocytosis and spine enlargement. It is not easy to come up with an experiment to differentiate between these alternative explanations, as blocking actin polymerization would likely affect exocytosis, too. The authors should consider/discuss the possibility that all of the observed Ras effects result from altered actin dynamics and that the lipid bilayer is sufficiently fluid to form a minimal surface around the expanding cytoskeleton.

Thanks for the suggestions. We included the discussion about the potential impact on the actin cytoskeleton by Rab10.

Typos: heterougenous, compartmantalization, chemaical, ballistically/biolistically (chose one).

Thanks for pointing out these typos. We have corrected them in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Venus shows pH sensitivity, which can be significant at synapses due to pH changes. Characterizing the pH sensitivity of the sensors is essential.

Thanks for the suggestions. We did not measure pH dependence, but the PKa of these fluorophores has already been published. PKa for EGFP and Venus are both 6.0, and it is unlikely that it influenced our measurements.

(2) Presenting individual data points within all bar graphs (e.g. Fig. 2c, 2d) would enhance data transparency.

Thanks for the suggestions. We now provide individual data points in the revised main figures.

(3) In Figure 1f: Rab5 GAP expression increased the binding fraction against expectations. In addition, clarifying the color scheme in Figure 1 is needed. Are GAPs supposed to be blue/green, and GEFs red/orange? Figure 1f seems to contradict this color scheme.

Thanks for the suggestions. We clarified these issues.

(4) Quantification of the point spread function of the uncaging laser, response/settle time of the scan mirror during uncaging, and reason for changes in neighboring spines in many example images (e.g. Figure 2a, especially at 240 s; Figure 4a) would be important.

The laser is controlled by Pockels cells, which changes the laser intensity with microsecond resolution. The laser is parked for milliseconds during uncaging, much longer than the settling time of the mirror (~0.1 milliseconds). The point spread function of the uncaging laser is limited by the diffraction (~0.5 um). The uncaging spot size is mostly limited by the diffusion of uncaged glutamate, but our calcium imaging and CaMKII imaging show that the signaling is induced mostly in the stimulated spines (Lee et al., 2009; Chang et al., 2017, 2019).

(5) Please include traces for "false" sensors in stimulated spines in Figures 2b, 2e, 3b, and 3e.

The traces for the false sensors have been presented in Extended Data Fig. 3 and Extended Data Fig. 8.

(6) The traces in Figure 4k (fEPSP slope in response to theta burst stimulation, where there is a decrease in fEPSP slope followed by a gradual increase) differ from prior publications (e.g. PMID: 1359925, 3967730, 19144965, 20016099). An investigation and explanation for these differences are necessary.

We appreciate the reviewer’s comments. We performed the experiments blindly and did not try to find a condition providing control data similar to previous publications. The variations in fEPSP responses compared to prior publications may be attributed to several factors, including differences in experimental conditions such as the genetic background of the animals used, the specific protocols for theta burst stimulation, and variations in the preparation of the hippocampal slices.

(7) The title and text state that Rab10 inactivation promotes AMPAR insertion. It is unclear if this is a direct effect on AMPAR insertion or an indirect effect through membrane remodeling. Providing data to distinguish these possibilities or adjusting the title/text to reflect alternative interpretations would be beneficial.  

We appreciate the reviewer's feedback. To clarify, we have revised our terminology to use "AMPAR trafficking" instead of "AMPAR insertion", as it includes both insertion and other mechanisms of AMPAR movement within the cell.

(8) Please provide an explanation for the initial Rab10 inactivation observed in Figure 1j upon NMDA application.

The application of NMDA in Fig. 1j is similar to the commonly used chemical LTD induction protocol. We used this broad stimulation approach to test whether our sensors could report Rab activity changes in neurons upon strong stimulation. However, it is an entirely different stimulation approach from the sLTP induction protocol, thus resulting in different sensor activity changes.  We describe the phenomenon in the revised manuscript, but we believe that detailed analyses of Rab10 activation in response to NMDA application are beyond the scope of this manuscript.

(9) Please explain why the study focuses on Rab4 and Rab10 instead of other Rab proteins.

During our initial screening of sensors for various Rab proteins, we observed significant activity changes in the sensors for Rab4 and Rab10 upon sLTP induction. This suggested their potential relevance in synaptic processes, leading us to focus on understanding their specific roles in structural long-term potentiation.

Reviewer #3 (Recommendations for the authors):

(1) Although it might seem trivial, the definition of adjacent spine has not been made in the text. It would be nice to have it in the Methods section.

We included it in the Methods section as follows:

"The adjacent spine refers to the first or second spine located next to the stimulated spine, typically positioned opposite the stimulated spine. Additionally, the size of the adjacent spine must be sufficiently large for imaging."

(2) The transfection method has been mentioned as "ballistic" and "biolistic" transfection. You might want to use only one term. Additionally, you can add the equipment used (Bio-rad?) and pressure (psi) in the Methods section.

We use “biolistic” throughout the manuscript now. We also added the equipment and conditions used.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Neuronal activity spatiotemporal fine-tuning of cerebral blood flow balances metabolic demands of changing neuronal activity with blood supply. Several 'feed-forward' mechanisms have been described that contribute to activity-dependent vasodilation as well as vasoconstriction leading to a reduction in perfusion. Involved messengers are ionic (K+), gaseous (NO), peptides (e.g., NPY, VIP), and other messengers (PGE2, GABA, glutamate, norepinephrine) that target endothelial cells, smooth muscle cells, or pericytes. Contributions of the respective signaling pathways likely vary across brain regions or even within specific brain regions (e.g., across the cortex) and are likely influenced by the brain's physiological state (resting, active, sleeping) or pathological departures from normal physiology.

The manuscript "Elevated pyramidal cell firing orchestrates arteriolar vasoconstriction through COX-2derived prostaglandin E2 signaling" by B. Le Gac, et al. investigates mechanisms leading to activitydependent arteriole constriction. Here, mainly working in brain slices from mice expressing channelrhodopsin 2 (ChR2) in all excitatory neurons (Emx1-Cre; Ai32 mice), the authors show that strong optogenetic stimulation of cortical pyramidal neurons leads to constriction that is mediated through the cyclooxygenase-2 / prostaglandin E2 / EP1 and EP3 receptor pathway with contribution of NPY-releasing interneurons and astrocytes releasing 20-HETE. Specifically, using a patch clamp, the authors show that 10-s optogenetic stimulation at 10 and 20 Hz leads to vasoconstriction (Figure 1), in line with a stimulation frequency-dependent increase in somatic calcium (Figure 2). The vascular effects were abolished in the presence of TTX and significantly reduced in the presence of glutamate receptor antagonists (Figure 3). The authors further show with RT-PCR on RNA isolated from patched cells that ~50% of analyzed cells express COX-1 or -2 and other enzymes required to produce PGE2 or PGF2a (Figure 4). Further, blockade of COX-1 and -2 (indomethacin), or COX-2 (NS-398) abolishes constriction. In animals with chronic cranial windows that were anesthetized with ketamine and medetomidine, 10-s long optogenetic stimulation at 10 Hz leads to considerable constriction, which is reduced in the presence of indomethacin. Blockade of EP1 and EP3 receptors leads to a significant reduction of the constriction in slices (Figure 5). Finally, the authors show that blockade of 20-HETE synthesis caused moderate and NPY Y1 receptor blockade a complete reduction of constriction.

The mechanistic analysis of neurovascular coupling mechanisms as exemplified here will guide further in-vivo studies and has important implications for human neuroimaging in health and disease. Most of the data in this manuscript uses brain slices as an experimental model which contrasts with neurovascular imaging studies performed in awake (headfixed) animals. However, the slice preparation allows for patch clamp as well as easy drug application and removal. Further, the authors discuss their results in view of differences between brain slices and in vivo observations experiments, including the absence of vascular tone as well as blood perfusion required for metabolite (e.g., PGE2) removal, and the presence of network effects in the intact brain. The manuscript and figures present the data clearly; regarding the presented mechanism, the data supports the authors' conclusions.

We thank the reviewer for his/her supportive comments as well as for pointing out pros and cons of the brain slice preparation.

Some of the data was generated in vivo in head-fixed animals under anesthesia; in this regard, the authors should revise the introduction and discussion to include the important distinction between studies performed in slices, or in acute or chronic in-vivo preparations under anesthesia (reduced network activity and reduced or blockade of neuromodulation, or in awake animals (virtually undisturbed network and neuromodulatory activity).

We have now added a paragraph in the introduction (lines 52-64) to highlight the distinction between ex vivo and in vivo models. We now also discuss that anesthetized animals exhibit slower NVC (Line 308-309).

Further, while discussed to some extent, the authors could improve their manuscript by more clearly stating if they expect the described mechanism to contribute to CBF regulation under 'resting state conditions' (i.e., in the absence of any stimulus), during short or sustained (e.g., visual, tactile) stimulation, or if this mechanism is mainly relevant under pathological conditions; especially in the context of the optogenetic stimulation paradigm being used (10-s long stimulation of many pyramidal neurons at moderate-high frequencies) and the fact that constriction leading to undersupply in response to strongly increased neuronal activity seems counterintuitive?

We now discuss more extensively the physiological relevance (lines 422-434 and 436-439) and the conditions where the described mechanisms of neurogenic vasoconstriction may occur.

We agree with the reviewer that vasoconstriction in response to a large increase in neuronal activity is counterintuitive as it leads to undersupply despite an increased energy demand. We now discuss its potential physio/pathological role in attenuating neuronal activity by reducing energy supply (lines 453-464).

Reviewer #2 (Public review):

Summary:

The present study by Le Gac et al. investigates the vasoconstriction of cerebral arteries during neurovascular coupling. It proposes that pyramidal neurons firing at high frequency lead to prostaglandin E2 (PGE2) release and activation of arteriolar EP1 and EP3 receptors, causing smooth muscle cell contraction. The authors further claim that interneurons and astrocytes also contribute to vasoconstriction via neuropeptide Y (NPY) and 20-hydroxyeicosatetraenoic acid (20-HETE) release, respectively. The study mainly uses brain slices and pharmacological tools in combination with Emx1Cre; Ai32 transgenic mice expressing the H134R variant of channelrhodopsin-2 (ChR2) in the cortical glutamatergic neurons for precise photoactivation. Stimulation with 470 nm light using 10-second trains of 5-ms pulses at frequencies from 1-20 Hz revealed small constrictions at 10 Hz and robust constrictions at 20 Hz, which were abolished by TTX and partially inhibited by a cocktail of glutamate receptor antagonists. Inhibition of cyclooxygenase-1 (COX-1) or -2 (COX-2) by indomethacin blocked the constriction both ex vivo (slices) and in vivo (pial artery), and inhibition of EP1 and EP3 showed the same effect ex vivo. Single-cell RT-PCR from patched neurons confirmed the presence of the PGE2 synthesis pathway.

While the data are convincing, the overall experimental setting presents some limitations. How is the activation protocol comparable to physiological firing frequency? 

As also suggested by Reviewer #1 we have now discussed more extensively the physiological relevance of our observations (lines 422-434 and 436-439).

The delay (minutes) between the stimulation and the constriction appears contradictory to the proposed pathway, which would be expected to occur rapidly. The experiments are conducted in the absence of vascular "tone," which further questions the significance of the findings. 

The slow kinetics observed ex vivo are probably due to the low recording temperature and the absence of pharmacologically induced vascular tone, as already discussed (lines 312-317). Furthermore, as recommended by reviewer #1, we have presented the advantages and limitations of ex vivo and in vivo approaches (lines 52-64).

Some of the targets investigated are expressed by multiple cell types, which makes the interpretation difficult; for example, cyclooxygenases are also expressed by endothelial cells.

Under normal conditions, endothelial cells only express COX-1 and barely COX-2, whose expression is essentially observed in pyramidal cells (see Tasic et al. 2016, Zeisel et al. 2015, Lacroix et al., 2015). As pointed out by Reviewer # 1, our ex vivo pharmacological data clearly indicate that vasoconstriction is mostly due to COX-2 activity, and to a much lesser extent to COX-1. Since it is well established that the previously described vascular effects of pyramidal cells are essentially mediated by COX-2 activity (Iadecola et al., 2000; Lecrux et al., 2011; Lacroix et al., 2015), we are quite confident that vasoconstriction described here is mainly due COX-2 activity of pyramidal cells.

Finally, how is the complete inhibition of the constriction by the NPY Y1 receptor antagonist BIBP3226 consistent with a direct effect of PGE2 and 20-HETE in arterioles? 

We agree with both reviewers that the complete blockade of the constriction by the NPY Y1 receptor antagonist BIBP3226 needs to be more carefully discussed. We have now included in the discussion the possible involvement of Y1 receptors in pyramidal cells, which could promote glutamate release and possibly COX-2, thereby contributing to PGE2 and 20-HETE signaling (lines 402-409).

Overall, the manuscript is well-written with clear data, but the interpretation and physiological relevance have some limitations. However, vasoconstriction is a rather understudied phenomenon in neurovascular coupling, and the present findings may be of significance in the context of pathological brain hypoperfusion.

We thank the reviewer for his/her comment and suggestions, which have helped us to improve our manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Methods:

It is not clear if brain slices (or animals) underwent one, two, or several optogenetic stimulations - especially for experiments where 'control' is compared to 'treated' - does this data come from the same vessels (before and after treatment) or from two independent groups of vessels? If repeated stimulations are performed, do these repeated stimulations cause the same vascular response?

As indicated in the Materials and Methods section, line 543: “Only one arteriole was monitored per slice” implies that the comparisons between the ‘control’ and ‘treated’ groups were made from independent groups of vessels. To clarify this point, we have added “receiving a single optogenetic or pharmacological stimulation” to this sentence lines 543-544.

For in vivo experiments, animals underwent 10-20 optogenetic stimulations with a 5-minute interstimulus interval during an experiment lasting 2 hours for maximum. Trials from the same vessel were averaged (with a 0.1 s interpolation) for analysis, and the mean per vessels is presented in the graphics.

Figure 2:

Can the authors speculate about the cause for the slow increase in indicator fluorescence from minute 1.5 onward, which seems dependent on stimulation frequency? Is this increase also present when slices from a ChR2-negative animal undergo the same stimulation paradigm?

Rhod2 was delivered by the patch pipette as indicated in the Materials and Methods section (line 514). Although a period of “at least 15 min after passing in whole-cell configuration to allow for somatic diffusion of the dye” (line 551-552) was observed, this single-wavelength Ca2+ indicator likely continued to diffuse into the cells during the optical recording thereby, inducing a slight increase in delta F/F0, which is consistent with the positive slopes of the mean fluorescence changes observed during the 30-s control baseline (Fig. 2b).

Figure 4: Why did the authors include panel a) here? Also, do the authors observe that cells with different COX-1 or -2 expression profiles show different (electrical, morphological) properties?

The purpose of panel a) in Fig. 4 was to ensure the regular spiking electrophysiological phenotype of the pyramidal neurons whose cytoplasm was harvested for subsequent RT-PCR analysis. Despite our efforts, we found no difference in the 32 electrophysiological features between COX-1 or COX-2 positive and negative cells. This is now clearly stated in the result section (lines 210-212) and a supplementary table of electrophysiological features is now provided. Because it is difficult to determine the morphology of neurons analyzed by single-cell RT-PCR (Devienne et al. 2018), these cells were not processed for biocytin labeling.

Figure 5: (1) Maybe the authors could highlight panels b-f as in vivo experiments to emphasize that these are in-vivo observations while the other experiments (especially panels g, h) are made in slices? 

We thank the reviewer for this suggestion. A black frame is now depicted in Figure 5 to emphasize in vivo experiments.

(2) What is the power of the optogenetic stimulus in this experiment? 

The power of the optogenetic stimulus was 38 mW/mm² in ex vivo experiments (see Line 527). For in vivo experiments, 1 mW pulses of 5 ms were used, the intensity being measured at the fiber end. We now provide the information for in vivo experiments in the Methods lines 639-640.

(3) Experiments were performed with Fluorescein-Dextran at 920-nm excitation which would overlap with EYFP fluorescence from the ChR2-EYFP transgene. Did the authors encounter any issues with crosstalk between the two labels? 

Crosstalk between EYFP and fluorescein fluorescence was indeed an issue. This is why arterioles were monitored at the pial level to avoid fluorescence contamination from the cortical parenchyma. Because of the perivascular space around pial arterioles, it was possible to measure vessel diameter without pollution for the parenchyma (see Author response image 1 below). To clarify this point we added the statement “which are not compromised by the fluorescence from the ChR2-EYFP transgene in the parenchyma (Madisen et al. 2012),” Line 628-629. Note that line scan acquisitions without photoactivation stimulation did not trigger any progressive change in the vessel size or resting fluorescence.

Author response image 1.

Example of a pial arteriole filled with fluorescein dextran (cyan) in an Emx1-EYFP mouse (parenchyma labeled with YFP, in cyan). The red line represents a line scan to record the change in diameter. Due to the perivascular space surrounding the arterioles, the vessel walls are clearly identified and separated from the fluorescent parenchyma.

(4) Could the authors potentially extend the time course in panel e) to show the recovery of the preparation to the baseline? 

Because arterioles were only monitored for a 40-s period during a session of optogenetic stimulation/imaging we cannot extend panel e. Nonetheless, a 5 minutes interstimulus interval was observed to allow the full recovery of the preparation to the baseline. This now clarified line 640. Of note, the arteriole shown in panel d before indomethacin treatment fully recovered to baseline after this treatment.

Also, did the authors observe any 'abnormal' behavior of the vasculature after stimulation, such as large-amplitude oscillations? (5) 

We did not specifically investigate resting state oscillations, such as vasomotion, but the 10-s long baseline recording for each measurement indicates no long lasting, abnormal and de novo behavior with a frequency higher than 0.1-0.2 Hz.

Can the authors show in vivo data from control experiments in EYFP-expressing or WT mice that underwent the same stimulation paradigm (Supplementary Figure 1 shows data from brain slices)?

The reviewer is correct to point out this important control, as optogenetic stimulation can induce a vascular response without channel rhodopsin activation at high power (see our study on the topic, Rungta et al, Nat Com 2017). We therefore tested this potential artefact in a WT mouse using our setup, with different intensities and durations of optogenetic stimulation.

Author response image 2A shows that stimulations of 10 seconds, 10 Hz, 1 mW, 5 ms pulses, i.e. the conditions we used for the experiments in Emx1 mice, did not induce dilation or constriction. Stimulation for 5 seconds with the same number of pulses, but with a higher power (4 mW), longer duration (20 ms pulses) and at a higher frequency elicited a small dilation in 1 of 2 pial arterioles (Author response image 2B). For this reason, we used only shorter (5ms) and less intense (1 mW) optogenetic stimulation to ensure that the observed dilation was solely due to Emx1 activation and not to light-induced artefactual dilation.

Author response image 2.

Optogenetic stimulation in a wild-type mouse. A. No diameter changes upon stimulations of 10 seconds, 10 Hz, 1 mW, 5 ms pulses, i.e. the conditions we used for the experiments in Emx1 mice. B. Stimulation of higher power (4 mW), longer duration (20 ms pulses) and at a higher frequency elicited a small dilation in 1 (grey traces) of 2 pial arterioles.

Figures 6 and 7: It is surprising that blockade of NPY Y1 receptors leads to a complete loss of the constriction response. As shown in Figure 7, the authors suggest that pyramidal neuron-released PGE2 (and glutamate) initiate several cascades acting on smooth muscle directly (PGE2-EP1/EP3), through astrocytes (Glu/COX-1/PGE2 or 20-HETE), or through NPY interneurons (Glu/NPY/Y1 or PGE2/NPY/Y1). This would imply that COX-1/2 and NPY/Y1 pathways act in series (as discussed by the authors). Besides the potential effects on NPY release mentioned in the discussion, could the authors comment if both (NPY and PGE2) pathways need to be co-activated in smooth muscle cells to cause constriction?

We thank the reviewer for raising this surprising complete loss of vasoconstriction by Y1 antagonism, despite the contribution of other vasoconstrictive pathways. We now discuss (lines 402-409) the possibility that activation of the neuronal Y1 receptors in pyramidal cells may also have contributed to the vasoconstriction by promoting glutamate and possibly PGE2 release. The combined activation of vascular and neuronal Y1 receptors may explain the complete blockage of optogenetically induced vasoconstriction by BIBP3226.

Reviewer #2 (Recommendations for the authors):

The complete block of the constriction by BIBP3226 needs to be carefully considered.

We thank the reviewer for stressing this point also raised by Reviewer #1. As mentioned above we now discuss (lines 402-409) the possibility that activation of the neuronal Y1 receptors in pyramidal cells may also have contributed to the vasoconstriction by promoting glutamate and possibly PGE2 release. The combined activation of vascular and neuronal Y1 receptors may explain the complete blockage of optogenetically induced vasoconstriction by BIBP3226.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary of what the authors were trying to achieve:

In this manuscript, the authors investigated the role of β-CTF on synaptic function and memory. They report that β-CTF can trigger the loss of synapses in neurons that were transiently transfected in cultured hippocampal slices and that this synapse loss occurs independently of Aβ. They confirmed previous research (Kim et al, Molecular Psychiatry, 2016) that β-CTF-induced cellular toxicity occurs through a mechanism involving a hexapeptide domain (YENPTY) in β-CTF that induces endosomal dysfunction. Although the current study also explores the role of β-CTF in synaptic and memory function in the brain using mice chronically expressing β-CTF, the studies are inconclusive because potential effects of Aβ generated by γ-secretase cleavage of β-CTF were not considered. Based on their findings, the authors suggest developing therapies to treat Alzheimer's disease by targeting β-CTF, but did not address the lack of clinical improvement in trials of several different BACE1 inhibitors, which target β-CTF by preventing its formation.

We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

Major strengths and weaknesses of the methods and results:

The conclusions of the in vitro experiments using cultured hippocampal slices were well supported by the data, but aspects of the in vivo experiments and proteomic studies need additional clarification.

(1) In contrast to the in vitro experiments in which a γ-secretase inhibitor was used to exclude possible effects of Aβ, this possibility was not examined in in-vivo experiments assessing synapse loss and function (Figure 3) and cognitive function (Figure 4). The absence of plaque formation (Figure 4B) is not sufficient to exclude the possibility that Aβ is involved. The potential involvement of Aβ is an important consideration given the 4-month duration of protein expression in the in vivo studies.

We appreciate the reviewer for raising this question. While our current data did not exclude the potential involvement of Aβ-induced toxicity in the synaptic and cognitive dysfunction observed in mice overexpressing β-CTF, addressing this directly remains challenging. Treatment with γ-secretase inhibitors could potentially shed light on this issue. However, treatments with γ-secretase inhibitors are known to lead to brain dysfunction by itself likely due to its blockade of the γ-cleavage of other essential molecules, such as Notch[1, 2]. Therefore, this approach is unlikely to provide a clear answer, which prevents us from pursuing it further experimentally in vivo. We hope the reviewer understands this limitation. We have included additional discussion (page 14 of the revised manuscript) to highlight this question.

(2) The possibility that the results of the proteomic studies conducted in primary cultured hippocampal neurons depend in part on Aβ was also not taken into consideration.

We thank the reviewer for raising this question. In the revised manuscript, we examined the protein levels of synaptic proteins after treatment with γ-secretase inhibitors and found that the levels of certain synaptic proteins were further reduced in neurons expressing β-CTF (Supplementary figure 5A-B). These results do not support Aβ as a major contributor of the proteomic changes induced by β-CTF.

Likely impact of the work on the field, and the utility of the methods and data to the community:

The authors' use of sparse expression to examine the role of β-CTF on spine loss could be a useful general tool for examining synapses in brain tissue.

We thank the reviewer for these comments.

Additional context that might help readers interpret or understand the significance of the work:

The discovery of BACE1 stimulated an international effort to develop BACE1 inhibitors to treat Alzheimer's disease. BACE1 inhibitors block the formation of β-CTF which, in turn, prevents the formation of Aβ and other fragments. Unfortunately, BACE1 inhibitors not only did not improve cognition in patients with Alzheimer's disease, they appeared to worsen it, suggesting that producing β-CTF actually facilitates learning and memory. Therefore, it seems unlikely that the disruptive effects of β-CTF on endosomes plays a significant role in human disease. Insights from the authors that shed further light on this issue would be welcome.

Response: We would like to express our gratitude to the reviewer for raising this question. It remains puzzling why BACE1 inhibition has failed to yield benefits in AD patients, while amyloid clearance via Aβ antibodies are able to slow down disease progression. One possible explanation is that pharmacological inhibition of BACE1 may not be as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to the clearance of existing amyloid plaques[3], whereas its pharmacological inhibition prevents the formation of new plaques but does not deplete the existing ones[4]. We think the negative results of BACE1 inhibitors in clinical trials may not be sufficient to rule out the potential contribution of β-CTF to AD pathogenesis. Given that cognitive function continues to deteriorate rapidly in plaque-free patients after 1.5 years of treatment with Aβ antibodies in phase three clinical studies[5], it is important to consider the potential role of other Aβ-related fragments in AD pathogenesis, such as β-CTF. We included further discussion in the revised manuscript (page 15 of the revised manuscript) to discusss this question.

Reviewer #2 (Public Review):

Summary:

In this study, the authors investigate the potential role of other cleavage products of amyloid precursor protein (APP) in neurodegeneration. They combine in vitro and in vivo experiments, revealing that β-CTF, a product cleaved by BACE1, promotes synaptic loss independently of Aβ. Furthermore, they suggest that β-CTF may interact with Rab5, leading to endosomal dysfunction and contributing to the loss of synaptic proteins.

We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

Weaknesses:

Most experiments were conducted in vitro using overexpressed β-CTF. Additionally, the study does not elucidate the mechanisms by which β-CTF disrupts endosomal function and induces synaptic degeneration.

We would like to thank the reviewer for this comment. While a significant portion of our experiments were conducted in vitro, the main findings were also confirmed in vivo (Figure 3 and 4). Repeating all the experiments in vivo would be challenging and may not be possible because of technical difficulties. Regarding the use of overexpressed β-CTF, we acknowledge that this represents a common limitation in neurodegenerative disease studies. These diseases progress slowly over decades in patients. To model this progression in cell or mouse models within a time frame feasible for research, overexpression of certain proteins is often inevitable. Since β-CTF levels are elevated in AD patients[6], its overexpression is not a irrelevant approach to investigate its potential effects.

We did not further investigate the mechanisms by which β-CTF disrupted endosomal function because our preliminary results align with previous findings that could explain its mechanism. Kim et al. demonstrated that β-CTF recruits APPL1 (a Rab5 effector) via the YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. However, this paper did not show whether this Rab5 overactivation-induced endosomal dysfunction leads to any damages in synapses. In our study, we observed that co-expression of Rab5_S34N with β-CTF effectively mitigated β-CTF-induced spine loss in hippocampal slice cultures (Figures 6L-M), indicating that Rab5 overactivation-induced endosomal dysfunction contributed to β-CTF-induced spine loss. We included further discussion in the revised manuscript to clarify this (page 15 of the revised manuscript).

Reviewer #3 (Public Review):

Summary:

Most previous studies have focused on the contributions of Abeta and amyloid plaques in the neuronal degeneration associated with Alzheimer's disease, especially in the context of impaired synaptic transmission and plasticity which underlies the impaired cognitive functions, a hallmark in AD. But processes independent of Abeta and plaques are much less explored, and to some extent, the contributions of these processes are less well understood. Luo et all addressed this important question with an array of approaches, and their findings generally support the contribution of beta-CTF-dependent but non-Abeta-dependent process to the impaired synaptic properties in the neurons. Interestingly, the above process appears to operate in a cell-autonomous manner. This cell-autonomous effect of beta-CTF as reported here may facilitate our understanding of some potentially important cellular processes related to neurodegeneration. Although these findings are valuable, it is key to understand the probability of this process occurring in a more natural condition, such as when this process occurs in many neurons at the same time. This will put the authors' findings into a context for a better understanding of their contribution to either physiological or pathological processes, such as Alzheimer's. The experiments and results using the cell system are quite solid, but the in vivo results are incomplete and hence less convincing (see below). The mechanistic analysis is interesting but primitive and does not add much more weight to the significance. Hence, further efforts from the authors are required to clarify and solidify their results, in order to provide a complete picture and support for the authors' conclusions.

We would like to thank the reviewer for the suggestions. We have addressed the specific comments in following sections.

Strengths:

(1) The authors have addressed an interesting and potentially important question

(2) The analysis using the cell system is solid and provides strong support for the authors' major conclusions. This analysis has used various technical approaches to support the authors' conclusions from different aspects and most of these results are consistent with each other.

We would like to thank the reviewer for these comments.

Weaknesses:

(1) The relevance of the authors' major findings to the pathology, especially the Abeta-dependent processes is less clear, and hence the importance of these findings may be limited.

We would like to thank the reviewer for this question. Phase 3 clinical trial data from Aβ antibodies show that cognitive function continues to decline rapidly, even in plaque-free patients, after 1.5 years of treatment[5]. This suggests that plaque-independent mechanisms may drive AD progression. Therefore, it is crucial to consider the potential contributions of other Aβ species or related fragments, such as alternative forms of Aβ and β-CTF. While it is early to predict how much β-CTF contributes to AD progression, it is notable that β-CTF induced synaptic deficits in mice, which recapitulates a key pathological feature of AD. Ultimately, the contribution of β-CTF in AD pathogenesis can only be tested through clinical studies in the future.

(2) In vivo analysis is incomplete, with certain caveats in the experimental procedures and some of the results need to be further explored to confirm the findings.

We would like to thank the reviewer for this suggestion. We have corrected these caveats in the revised manuscript.

(3) The mechanistic analysis is rather primitive and does not add further significance.

We would like to thank the reviewer for this comment. We did not delve further into the underlying mechanisms because our analysis indicates that Rab5 overactivation-induced endosomal dysfunction underlies β-CTF-induced synaptic dysfunction, which is consistent with another study and has been addressed in our study[6]. We hope the reviewer could understand that our focus in this paper is on how β-CTF triggers synaptic deficits, which is why we did not investigate the mechanisms of β-CTF-induced endosomal dysfunction further.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data, or analyses:

(1) In Figures 4H, 4J, 4K and Supplemental Figures 3C, 3E, and 3G, it was unclear whether a repeated measures 2-way ANOVA, rather than a 2-way ANOVA, followed by appropriate post-hoc analyses was used to strengthen the conclusion that there were significant effects in the behavioral tests.

We appreciate the reviewer for raising this point and apologize for the lack of clear description in the manuscript. In those figures mentioned above, we use a repeated measures 2-way ANOVA to analyze the data by Graphpad Prism. In Figure 4H, fear conditioning tests were conducted. The same cohort of mice were used in the baseline, contextual and cued tests. Firstly, baseline freezing was tested; then these mice underwent tone and foot shock training, followed by contextual test and cued test. So, a repeated measures 2-way ANOVA is more appropriate for the experiment.

In water T maze tests (Figure 4J and K), the same cohort of mice were trained and tested each day. So, it’s also appropriate to use a repeated measures 2-way ANOVA.

In Supplementary figure 3C, 3E and 3G, OFT was conducted. In this experiment, the locomotion of the same cohort of mice were recorded. Also, it’s appropriate to use a repeated measures 2-way ANOVA.

Clearer description for these experiments has been provided in the revised manuscript.

(2) Including gender analyses would be helpful.

The mice we used in this study were all males.

Minor corrections to text and figures:

(1) Quantitative analyses in Figures 5A-C, 5H, 6G, 6H, and Supplementary Figures 4 and 5C would be helpful.

We have provided quantitative analysis of these results (Figure 5D, 5J, 6K, Supplementary figure 4D, 5F) mentioned above in the revised manuscript.

(2) Percent correct (%) in Figures 4J and 4K should be labeled as 0, 50, and 100 instead of 0.0, 0.5, and 1.0.

We would like to thank the reviewer for pointing out this. We have made corrections in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

In the study conducted by Luo et al, it was observed that the fragment of amyloid precursor protein (APP) cleaved by beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), known as β-CTF, plays a crucial role in synaptic damage. The study found increasing expression of β-CTF in neurons could induce synapse loss both in vitro and in vivo, independent of Aβ. Mechanistically, they explored how β-CTF could interfere with the endosome system by interacting with RAB5. While this study is intriguing, there are several points that warrant further investigation:

(1) The study involved overexpressing β-CTF in neurons. It would be valuable to know if the levels of β-CTF are similarly increased in Alzheimer's disease (AD) patients or AD mouse models.

We would like to thank the reviewer for the suggestion. It’s reported β-CTF levels were significantly elevated in the AD cerebral cortex[6]. Most AD mouse models are human APP transgenic mouse models with elevated β-CTF levels[7].

(2) The study noted that β-CTF in neurons is a membranal fragment, but the overexpressed β-CTF was not located in the membrane. It is important to ascertain whether the membranal β-CTF and cytoplasmic β-CTF lead to synapse loss in a similar manner.

We apologize for not clearly explaining the localization of β-CTF in the original manuscript. β-CTF is produced from APP through β-cleavage, a process that occurs in organelles such as endo-lysosomes[8]. The overexpressed β-CTF is also primarily localized in the endo-lysosomal systems (Figure 5C and Supplementary figure 4C), similar to those generated by APP cleavage.

(3) The study found a significant decrease in GluA1, a subunit of AMPA receptors, due to β-CTF. It would be beneficial to investigate whether there are systematic alterations in NMDA receptors, including GluN2A and GluN2B.

We would like to express our gratitude to the reviewer for bringing up this question. The protein levels of GluN2A and GluN2B are also reduced in neurons expressing β-CTF (Figure 6E-F)

(4) The study showed a significant decrease in the frequency of miniature excitatory postsynaptic currents (mEPSC), indicating disrupted presynaptic vesicle neurotransmitter release. It would be pertinent to test whether the expression level of the presynaptic SNARE complex, which is required for vesicle release, is altered by β-CTF.

We would like to express our gratitude to the reviewer for bringing up this question. The protein level of the presynaptic SNARE complex, such as VAMP2, is also reduced in neurons expressing β-CTF (Figure 6E, G).

(5) Since AMPA receptors are glutamate receptors, it is important to determine whether the ability of glutamate release is altered by β-CTF. In vivo studies using a glutamate sensor should be conducted to examine glutamate release.

We would like to express our gratitude to the reviewer for this suggestion. It will be interesting to use glutamate sensors to assess the ability of glutamate release in the future.

(6) The quality of immunostaining associated with Figures 4B and 4C was noted to be suboptimal.

We apologize for the suboptimal quality of these images. The immunostaining in Figures 4B and 4C were captured using the stitching function of a confocal microscope to display larger areas, including the entire hemisphere and hippocampus. We have reprocessed the images to obtain higher-quality versions.

(7) It would be insightful to investigate whether treatment with a BACE1 inhibitor in the study could reverse synaptic deficits mediated by β-CTF.

We would like to thank the reviewer for this sggestion. In Figure 1I-M, we constructed an APP mutant (APP_MV), which cannot be cleaved by BACE1 to produce β-CTF and Aβ but has no impact on β’-cleavage. When co-expressed with BACE1, APP_MV failed to induce spine loss, supporting the effect of β-CTF. We think these results domonstrate that β-CTF underlies the synaptic deficits. It would be interesting to test the effects of BACE1 inhibition in the future.

(8) Considering the potential implications for therapeutics, it is worth exploring whether extremely low levels of β-CTF have beneficial effects in regulating synaptic function or promoting synaptogenesis at a physiological level.

We would like to thank the reviewer for raising this question. We found that when the plasmid amount was reduced to 1/8 of the original dose, β-CTF no longer induced a decrease in dendritic spine density (Supplementary figure 2E-F). It’s reported APP-Swedish mutation in familial AD increased synapse numbers and synaptic transmission, whereas inhibition of BACE1 lowered synapse numbers, suppressed synaptic transmission in wild type neurons, suggesting that at physiological level, β-CTF might be synaptogenic[9].

(9) The molecular mechanism through which β-CTF interferes with Rab5 function should be elucidated.

We would like to thank the reviewer for raising this question. Kim et al have elucidated the mechanism through which β-CTF interferes with Rab5 function. β-CTF recruited APPL1 (a Rab5 effector) via YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

(10) The study could compare the role of β-CTF and Aβ in neurodegeneration in AD mouse models.

We would like to thank the reviewer for raising this point. While it is easier to dissect the role of Aβ and β-CTF in vitro, some of the critical tools are not applicabe in vivo, such as γ-secretase inhibitors, which lead to severe side effects because of their inhibition on other γ substrates[1, 2]. Therefore it will be difficult to deomonstrate their different roles in vivo. There are studies showing that β-CTF accumulation precedes Aβ deposition in model mice and mediates Aβ independent intracellular pathologies[10, 11], consistent with our results.

(11) Based on the findings, it would be valuable to discuss possible explanations for the failure of most BACE1 inhibitors in recent clinical trials for humans.

Response: We would like to express our gratitude to the reviewer for raising this recommendation. It is a big puzzle why BACE1 inhibition failed to provide beneficial effects in AD patients whereas clearance of amyloid by Aβ antibodies could slow down the AD progress. One potential answer is that pharmacological inhibition of BACE1 might be not as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to clearance of existing amyloid plaques[3], whereas pharmacological inhibition of BACE1 could not stop growth of existing plaques, although it prevents formation of new plaques[4]. The negative result of BACE1 inhibitors might not be sufficient to exclude the possibility that β-CTF could also contribute to the AD pathogenesis. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

Reviewer #3 (Recommendations For The Authors):

Major:

(1) The cell experiments were performed at DIV 9, do the authors know whether at this age, the neurons are still developing and spine density has not reached a pleated yet? If so, the observed effect may reflect the impact on development and/or maturation, rather than on the mature neurons. The authors should be more specific about this issue.

We would like to thank the reviewer for pointing out this question. These slice cultures were made from 1-week-old rats. DIV 9 is about two weeks old. These neurons are still developing and spine density has not reached a plateau yet[12]. In addition, we also investigated the effects of β-CTF on the synapses of mature neurons in two-month-old mice (Figure 3). So we think the observed effect reflects the impact on both immature and mature neurons.

(2) mEPSCs shown in Figure 3D were of small amplitudes, perhaps also indicating that these synapses are not yet mature.

In Figure 3D, the mEPSC results were obtained from pyramidal neurons in the CA1 region of two-month-old mice. At the age of two months, neurotransmitter levels and synaptic density have reached adult levels[13].

(3) There was no data on the spine density or mEPSCs in the mice OE b-CTF, hence it is unclear whether a primary impact of this manipulation (b-CTF effect) on the synaptic transmission still occurs in vivo.

In Figure 3, we examined the density of dendritic spines and mEPSCs from CA1 pyramidal neurons infected with lentivirus expressing β-CTF in mice and showed that those neurons expressing additional amount of β-CTF exhibited lower spine density and less mEPSCs, supporting that β-CTF also damaged synaptic transmission in vivo.

(4) OE of b-CTF should lead to the production of Abeta, although this may not lead to the formation of significant plaques. How do the authors know whether their findings on behavioral and cognitive impairments were not largely mediated by Abeta, which has been widely reported by previous studies?

We would like to thank the reviewer for pointing out this question. Indeed, our in vivo data could not exclude the potential involvement of Aβ in the pathology, despite the absence of amyloid plaque formation. It will be difficult to demonstrate this question in vivo because of the severe side effects from γ inhibition.

(5) Figure 4H, the freezing level in the cued fear conditioning was very high, likely saturated; this may mask a potential reduction in the b-CTF OE mice (there is a hint for that in the results). The authors should repeat the experiments using less strong footshock strength (hence resulting in less freezing, <70%).

We would like to express our gratitude to the reviewer for bringing up this question. The contextual fear conditioning test assesses hippocampal function, while the cued fear conditioning test assesses amygdala function. We hope the reviewer understands that our primary goal is to assess hippocampus-related functions in this experiment and we did see a significant difference between GFP and β-CTF groups. Therefore, we think the intensity of footshock we used was suitable to serve the primary purpose of this experiment.

(6) Why was the deficit in the Morris water maze in the b-CTF OE mice only significant in the training phase?

We would like to thank the reviewer for rasing this question and apologize for not describing the test clearly. This is a water T maze test, not Morris water maze test.

To make the behavioral paradigm of the water T maze test easier to understand, we have provided a more detailed description of the methods in the new version of the manuscript.

The acquisition phase of the Water T Maze (WTM) evaluates spatial learning and memory, where mice use spatial cues in the environment to navigate to a hidden platform and escape from water, while the reversal learning measures cognitive flexibility in which mice must learn a new location of the hidden platform[14]. In reversal learning task (Figure 4J-K), the learning curves of the two groups of mice did not show any significant differences, indicating that the expression of β-CTF only damages spatial learning and memory but not cognitive flexibility. This is consistent with a previous report using APP/PS1 mice[15].

(7) Will the altered Rab5 in the b-CTF OE condition also affect the level of other proteins?

We would like to express our gratitude to the reviewer for raising this interesting question.  Expression of Rab5_S34N in β-CTF-expressing neurons did not alter the levels of synapse-related proteins that were reduced in these neurons (Supplementary figure 5G-H), suggesting Rab5 overactivation did not contribute to these protein expression changes induced by β-CTF.

(8) How do the authors reconcile their findings with the well-established findings that Abeta affects synaptic transmission and spine density? Do they think these two processes may occur simultaneously in the neurons, or, one process may dominate in the other?

APP, Aβ, and presenilins have been extensively studied in mouse models, providing convincing evidence that high Aβ concentrations are toxic to synapses[16]. Moreover, addition of Aβ to murine cultured neurons or brain slices is toxic to synapses[17]. However, Aβ-induced synaptotoxicity was not observed in our study. A major difference between our study and others is that our study used a isolated expression system that apply Aβ only to individual neurons surrounded by neurons without excessive amount of Aβ, whereas the rest studies generally apply Aβ to all the neurons. Therefore, we predict that Aβ does not lead to synaptic deficits from individual neurons in cell autonomous manners, whereas β-CTF does. Aβ and β-CTF represent two parallel pathways of action. Additional discussion for this question has been included in the revised manuscript (page 14 of the revised manuscript).

Minor:

Fig 2F-G, "prevent" rather than "reverse"?

We would like to thank the reviewer for pointing this out. We have made corrections in the revised manuscript.

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Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

A number of modifications/additions have been made to the text which help to clarify the background and details of the study and I feel have improved the study.

NAD deficiency induced using the dietary/Haao null model showed a window of susceptibility at E7.5-10.5. Further, HAAO enymze activity data has been added at E11.5 and the minimal HAAO activity in the embryo act E11.5 supports the hypothesis that the NAD synthesis pathway from kynurenine is not functional until the liver starts to develop.

The caveat to this is that absence of expression/activity in embryonic cells at E7.5-10/5 relies on previous scRNA-seq data. Both reviewers commented that analysis of RNA and/or protein expression at these stages (E7.5-10.5) would be necessary to rule this out, and would strongly support the conclusions regarding the necessity for yolk sac activity.

There are a number of antibodies for HAAO, KNYU etc so it is surprising if none of these are specific for the mouse proteins, while an alternative approach in situ hydridisation would also be possible.

We have tested 2 anti-HAAO antibodies, 2 anti-KYNU antibodies and 1 anti-QPRT antibody on adult liver and various embryonic tissues.

Given that all tested antibodies only detected a specific band in tissues with very high expression and abundant target protein levels (adult liver), they were determined to be unsuitable to conclusively prove that these proteins of the NAD _de novo_synthesis pathway are absent in embryos prior to the development of a functional liver. They were also unsuitable for IHC experiments to determine which cell types (if any) have these proteins.

The antibodies, tested assays and samples, and the results obtained were as follows:

Anti-HAAO antibody (ab106436, Abcam, UK) 

• Was tested in western blots of liver, E11.5-E14.5 yolk sac, E14.5 placenta, and E14.5 and E16.5 embryonic liver lysates from wild-type (WT) and Haao-/- mice. The target band (32.5 KD) was visible in the WT liver samples and absent in_Haao_-/- livers, and faintly visible in E11.5-E14.5 WT yolk sac, with intensity gradually increasing in E12.5 and E13.5 WT yolk sac. Multiple strong non-specific bands occurred in all samples, requiring cutting off the >50 KD area of the blots.

• Was re-tested in western blots comparing WT, Haao-/-, and Kynu-/- E9.5-E11.5 embryo, E9.5 yolk sac, and adult liver tissues. It detected the target band faintly only in WT and Kynu-/- liver lysates. No target band could be resolved in E9.5 yolk sac or embryo lysates. Due to the low sensitivity of the antibody, it is unsuitable to conclusively determine whether HAAO is present or absent in E9.5 yolk sacs and E9.5-E11.5 embryos.

• Was tested in IHC with DAB and IF, producing non-specific staining on both WT and Haao-/- liver and kidney tissue. 

Anti-HAAO antibody (NBP1-77361, Novus Biologicals, LLC, CO, USA)

• Was tested in western blots and detected a very faint target band in WT liver lysate that was absent in Haao-/- lysate, with stronger non-specific bands occurring in both genotypes.

• Was tested in IHC with DAB, producing non-specific staining on both WT and Haao-/- liver and kidney tissue 

Anti-L-Kynurenine Hydrolase antibody (11796-1-AP, Proteintech Group, IL, USA)

• Was tested in western blots and detected a faint target band (52 KD) in E11.5, E12.5 E13.5, and E14.5 yolk sac lysates. Detected a weak band in E14.5 liver, a stronger band in E16.5 liver, but not in E14.5 placenta. The target band was only resolved with normal ECL substrate and extended exposure when the >75 KD part of the blot was cut off. 

• Was re-tested in western blots comparing WT, Haao-/-, and Kynu-/- E9.5-E11.5 embryo, E9.5 yolk sac, and adult liver tissues. It detected the target band only in WT and Haao-/- liver lysates, requiring Ultra Sensitive Substrate. No target band could be resolved in yolk sac or embryo lysates of any genotype.

Anti-L-Kynurenine Hydrolase antibody (ab236980, Abcam, UK)

• Was tested in western blots and detected a very faint target band (52 KD) in WT liver lysates and no band in Kynu-/- liver lysates. Multiple non-specific bands occurred irrespective of the Kynu genotype of the lysate.

• Was tested in IHC with DAB and IF, producing non-specific staining on both WT and Kynu-/- liver and kidney tissue 

Anti-QPRT (orb317756, Biorbyt, NC, USA)

• Was tested in western blots and detected a faint target band (31 KD) with multiple other bands between 25-75 KD and an extremely strong band around 150 KD on WT liver lysates.

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

Reviewer 1 Public Review:

The current dietary study narrows the period when deficiency can cause malformations (analysed at E18.5), and altered metabolite profiles (eg, increased 3HAA, lower NAD) are detected in the yolk sac and embryo at E10.5. However, without analysis of embryos at later stages in this experiment it is not known how long is needed for NAD synthesis to be recovered - and therefore until when the period of exposure to insufficient NAD lasts. This information would inform the understanding of the developmental origin of the observed defects.

Our previous published work (Cuny et al 2023 https://doi.org/10.1242/dmm.049647) indicates that the timing of NAD de novo synthesis pathway precursor availability and consequently the timing of NAD deficiency during organogenesis drives which organs are affected in their development. Furthermore, experimental data of another project (manuscript submitted) shows that mouse embryos (from mothers on an NAD precursor restricted diet that induces CNDD) were NAD deficient at E9.5 and E11.5, but embryo NAD levels were fully recovered at E14.5 when compared to same-stage embryos from mothers on precursor-sufficient diet. This was observed irrespective of the embryos’ Haao genotype. In the current study, NAD precursor provision was only restricted until E10.5. Thus, we expect that our embryos phenotyped at E18.5 had recovered their NAD levels back to normal by E14.5 at the latest.  More research, beyond the scope of the current manuscript, is required to spatio-temporally link embryonic NAD deficiency to the occurrence of specific defect types and elucidate the mechanistic origin of the defects. To acknowledge this, we updated the respective Discussion paragraph on page 7 and added the following statement: “This observation supports our hypothesis that the timing of NAD deficiency during organogenesis determines which organs/tissues are affected (Cuny et al., 2023), but more research is needed to fully characterise the onset and duration of embryonic NAD deficiency in dietary NAD precursor restriction mouse models.”

More importantly, there is still a question of whether in addition to the yolk sac, there is HAAO activity within the embryo itself prior to E12.5 (when it has first been assayed in the liver - Figure 1C). The prediction is that within the conceptus (embryo, chorioallantoic placenta, and visceral yok sac) the embryo is unlikely to be the site of NAD synthesis prior to liver development. Reanalysis of scRNA-seq (Fig 1B) shows expression of all the enzymes of the kynurenine pathway from E9.5 onwards. However, the expression of another available dataset at E10.5 (Fig S3) suggested that expression is 'negligible'. While the expression in Figure 1B, Figure S1 is weak this creates a lack of clarity about the possible expression of HAAO in the hepatocyte lineage, or especially elsewhere in the embryo prior to E10.5 (corresponding to the period when the authors have demonstrated that de novo NAD synthesis in the conceptus is needed). Given these questions, a direct analysis of RNA and/or protein expression in the embryos at E7.5-10.5 would be helpful. 

We now have included additional data showing that whole embryos at E11.5 and embryos with their livers removed at E14.5 have negligible HAAO enzyme activity. The observed lack of HAAO activity in the embryo at E11.5 is consistent with the absence of a functional embryonic liver at that stage. Thus, it confirms that the embryo is dependent of extraembryonic tissues (the yolk sac) for NAD de novo synthesis prior to E12.5. The additional datasets are now included in Supplementary Table S1 and as Supplementary Figure 2. The Results section on page 2 has been updated to refer to these datasets.

Reviewer #2 (Public Review): 

Page 4 and Table S4. The descriptors for malformations of organs such as the kidney and vertebrae are quite vague and uninformative. More specific details are required to convey the type and range of anomalies observed as a consequence of NAD deficiency. 

We now provide more information about the malformation types in the Results on page 4. Also, Table S4 now defines the missing vertebral, sternum, and kidney descriptors.

Can the authors define whether the role of the NAD pathway in a couple of tissue or organ systems is the same? By this I mean is the molecular or cellular effect of NAD deficiency is the same in the vertebrae and organs such as the kidney. What unifies the effects on these specific tissues and organs and are all tissues and organs affected? If some are not, can the authors explain why they escape the need for the NAD pathway? 

This is a good comment, highlighting that further research, beyond the scope of this manuscript, is needed to better understand the underlying mechanisms of CNDD causation. We have expanded the Discussion paragraph “NAD deficiency in early organogenesis is sufficient to cause CNDD” to indicate that while the timing of NAD deficiency during embryogenesis explains variability in phenotypes among the CNDD spectrum, it is unknown why other organs/tissues are seemingly not affected by NAD deficiency.

To answer the reviewer’s questions and elucidate the underlying cellular and molecular processes in individual organs affected by NAD deficiency, a multiomic approach is required. This is because NAD is involved in hundreds of molecular and cellular processes affecting gene expression, protein levels, metabolism, etc. For details of NAD functions that have relevance to embryogenesis, the reviewer may refer to our recent review article (Dunwoodie et al 2023 https://doi.org/10.1089/ars.2023.0349). 

Page 5 and Figure 6C. The expectation and conclusion for whether specific genes are expressed in particular cell types in scRNA-seq datasets depend on the number of cells sequenced, the technology (methodology) used, the depth of sequencing, and also the resolution of the analysis. It is therefore essential to perform secondary validation of the analysis of scRNA-seq data. At a minimum, the authors should perform in situ hybridization or immunostaining for Tdo2, Afmid, Kmo, Kynu, Haao, Qprt, and Nadsyn1 or some combination thereof at multiple time points during early mouse embryogenesis to truly understand the spatiotemporal dynamics of expression and NAD synthesis. 

We have tested antibodies against HAAO, KYNU, and QPRT in adult mouse liver samples (the main site of NAD de novo synthesis) but these produced non-specific bands in western blotting experiments. Therefore, immunostaining studies on embryonic tissues were not feasible. 

However, we agree that histological methods such as in situ hybridisation would provide secondary validation of the exact cell types that express these genes. To acknowledge this, we have updated a sentence on page 5 referring to the data shown in Figure 6C as follows: “While histological methods such as in situ hybridisation would be required to confirm the exact cell types expressing these genes, the available expression data indicates that the genes encoding those enzymes required to convert L-kynurenine to NAD (kynurenine pathway) are exclusively expressed in the yolk sac endoderm lineage from the onset of organogenesis (E8.0-8.5).”

Absolute functional proof of the yolk sac endoderm as being essential and required for NAD synthesis in the context of CNDD might require conditional deletion of Haao in the yolk sac versus embryo using appropriate Cre driver lines or in the absence of a conditional allele, could be performed by tetraploid embryo-ES cell complementation approaches. But temporal dietary intervention can also approximate the same thing by perturbing NAD synthesis Shen the yolk sac is the primary source versus when the liver becomes the primary source in the embryo. 

Reviewer 1 has made a similar comment about confirming that indeed NAD de novo synthesis activity is limited to extraembryonic tissues (=yolk sacs) and absent in the embryo prior to development of an embryonic liver. We now have included additional data showing that whole embryos at E11.5 and embryos with their livers removed at E14.5 have negligible HAAO enzyme activity. The observed lack of HAAO activity in the embryo at E11.5 is consistent with the absence of a functional embryonic liver at that stage. We think this provides enough proof that the embryo is dependent of extraembryonic tissues (the yolk sac) for NAD de novo synthesis prior to E12.5. The additional datasets are now included in Supplementary Table S1 and as Supplementary Figure 2. The Results section on page 2 has been updated to refer to these data.

Reviewer #1 (Recommendations For The Authors): 

(1) Introduction (page 1) introduces mouse models with defects in the kynurenine pathway "confirming that NAD de novo synthesis is required during embryogenesis ...". This requirement is revealed by the imposition of maternal dietary deficiency and more detail (or a more clear link to the following sentences) here would help the reader who is not familiar with the previous papers using the HAAO mice and dietary modulation.

We have updated this paragraph in the Introduction to better indicate that the requirement of NAD de novo synthesis for embryogenesis was confirmed in mouse models by modulating the maternal dietary NAD precursor provision during pregnancy.

(2) Discussion - throughout the introduction and results the authors refer to the NAD de novo synthesis pathway, with the study focussing on the effects of HAAO loss of function. Data implies that the kynurenine pathway is active in the yolk sac but whether de novo synthesis from L-tryptophan occurs has not been addressed. The first sub-heading of the discussion could be more accurate referring to the kynurenine pathway, or synthesis from kynurenine. 

We agree that our manuscript needed to make better distinction between NAD de novo synthesis starting from kynurenine and starting from tryptophan. We removed “from Ltryptophan” from the sub-heading in the Discussion and clarified in this paragraph which genes are required to convert tryptophan to kynurenine and which genes to convert kynurenine to NAD. We also updated two Results paragraphs (page 2, 2nd paragraph; page 5, 5th paragraph) to improve clarity.

It is worth noting that our statement in the Discussion “this is the first demonstration of NAD de novo synthesis occurring in a tissue outside of the liver and kidney.” is valid because vascular smooth muscle cells express Tdo2 and in combination with the other requisite genes expressed in endoderm cells, the yolk sac has the capability to synthesise NAD de novo from L-tryptophan.

(3) Outlook - While this section is designed to be looking ahead to the potential implications of the work, the last section on gene therapy of the yolk sac seems far removed from the paper content and highly speculative. I feel this could detract from the main points of the study and could be removed. 

We have updated the Outlook paragraph and shortened the final part to “Further research is required to better understand the mechanisms of CNDD causation and of other causes of adverse pregnancy outcomes involving the yolk sac.”

(4) In Figure 2D it would be useful to label the clusters as the colours in the legend are difficult to match to the heatmap. 

We now have labelled the clusters with lowercase letters above the heatmap to make it easier to match the clusters in Figure 2D to the colours used for designating tissues and genotypes. These labels are described in the figure’s key and the figure legend.  

Reviewer #2 (Recommendations For The Authors): 

Page 4 and Table S4. The descriptors for malformations of organs such as the kidney and vertebrae are quite vague and uninformative. More specific details are required to convey the type and range of anomalies observed as a consequence of NAD deficiency. 

We now provide more information about the malformation types in the Results on page 4. Also, Table S4 now defines the missing vertebral, sternum, and kidney descriptors.

Can the authors define whether the role of the NAD pathway in a couple of tissue or organ systems is the same? By this I mean is the molecular or cellular effect of NAD deficiency is the same in the vertebrae and organs such as the kidney. What unifies the effects on these specific tissues and organs and are all tissues and organs affected? If some are not, can the authors explain why they escape the need for the NAD pathway? 

This is a good comment, highlighting that further research, beyond the scope of this manuscript, is needed to better understand the underlying mechanisms of CNDD causation. We have expanded the Discussion paragraph “NAD deficiency in early organogenesis is sufficient to cause CNDD” to indicate that while the timing of NAD deficiency during embryogenesis explains variability in phenotypes among the CNDD spectrum, it is unknown why other organs/tissues are seemingly not affected by NAD deficiency.

To answer the reviewer’s questions and elucidate the underlying cellular and molecular processes in individual organs affected by NAD deficiency, a multiomic approach is required. This is because NAD is involved in hundreds of molecular and cellular processes affecting gene expression, protein levels, metabolism, etc. For details of NAD functions that have relevance to embryogenesis, the reviewer may refer to our recent review article (Dunwoodie et al 2023 https://doi.org/10.1089/ars.2023.0349). 

Page 5 and Figure 6C. The expectation and conclusion for whether specific genes are expressed in particular cell types in scRNA-seq datasets depend on the number of cells sequenced, the technology (methodology) used, the depth of sequencing, and also the resolution of the analysis. It is therefore essential to perform secondary validation of the analysis of scRNA-seq data. At a minimum, the authors should perform in situ hybridization or immunostaining for Tdo2, Afmid, Kmo, Kynu, Haao, Qprt, and Nadsyn1 or some combination thereof at multiple time points during early mouse embryogenesis to truly understand the spatiotemporal dynamics of expression and NAD synthesis. 

We have tested antibodies against HAAO, KYNU, and QPRT in adult mouse liver samples (the main site of NAD de novo synthesis) but these produced non-specific bands in western blotting experiments. Therefore, immunostaining studies on embryonic tissues were not feasible. 

However, we agree that histological methods such as in situ hybridisation would provide secondary validation of the exact cell types that express these genes. To acknowledge this, we have updated a sentence on page 5 referring to the data shown in Figure 6C as follows: “While histological methods such as in situ hybridisation would be required to confirm the exact cell types expressing these genes, the available expression data indicates that the genes encoding those enzymes required to convert L-kynurenine to NAD (kynurenine pathway) are exclusively expressed in the yolk sac endoderm lineage from the onset of organogenesis (E8.0-8.5).”

Author response:

Public Reviews:

Reviewer #1 (Public review):

In this paper by Brickwedde et al., the authors observe an increase in posterior alpha when anticipating auditory as opposed to visual targets. The authors also observe an enhancement in both visual and auditory steady-state sensory evoked potentials in anticipation of auditory targets, in correlation with enhanced occipital alpha. The authors conclude that alpha does not reflect inhibition of early sensory processing, but rather orchestrates signal transmission to later stages of the sensory processing stream. However, there are several major concerns that need to be addressed in order to draw this conclusion.

First, I am not convinced that the frequency tagging method and the associated analyses are adequate for dissociating visual vs auditory steady-state sensory evoked potentials.

Second, if the authors want to propose a general revision for the function of alpha, it would be important to show that alpha effects in the visual cortex for visual perception are analogous to alpha effects in the auditory cortex for auditory perception.

Third, the authors propose an alternative function for alpha - that alpha orchestrates signal transmission to later stages of the sensory processing stream. However, the supporting evidence for this alternative function is lacking. I will elaborate on these major concerns below.

(1) Potential bleed-over across frequencies in the spectral domain is a major concern for all of the results in this paper. The fact that alpha power, 36Hz and 40Hz frequency-tagged amplitude and 4Hz intermodulation frequency power is generally correlated with one another amplifies this concern. The authors are attaching specific meaning to each of these frequencies, but perhaps there is simply a broadband increase in neural activity when anticipating an auditory target compared to a visual target?

We appreciate the reviewer’s insightful comment regarding the potential bleed-over across frequencies in the spectral domain. We fully acknowledge that the trade-off between temporal and frequency resolution is a challenge, particularly given the proximity of the frequencies we are examining.

To address this concern, we performed additional analyses to investigate whether there is indeed a broadband increase in neural activity when anticipating an auditory target as compared to a visual target, as opposed to distinct frequency-specific effects. Our results show that the bleed-over between frequencies is minimal and does not significantly affect our findings. Specifically, we repeated the analyses using the same filter and processing steps for the 44 Hz frequency. At this frequency, we did not observe any significant differences between conditions.

These findings suggest that the effects we report are indeed specific to the 40 Hz frequency band and not due to a general broadband increase in neural activity. We hope this addresses the reviewer’s concern and strengthens the validity of our frequency-specific results.

Author response image 1.

Illustration of bleeding over effects over a span of 4 Hz. A, 40 Hz frequency-tagging data over the significant cluster differing between when expecting an auditory versus a visual target (identical to Fig. 9 in the manuscript). B, 44 Hz signal over the same cluster chosen for A. The analysis was identical with the analysis performed in  A, apart from the frequency for the band-pass filter.

We do, however, not specifically argue against the possibility of a broadband increase when anticipating an auditory compared to a visual target. But even a broadband-increase would directly contradict the alpha inhibition hypothesis, which poses that an increase in alpha completely disengages the whole cortex. We will clarify this point in the revised manuscript.

(2) Moreover, 36Hz visual and 40Hz auditory signals are expected to be filtered in the neocortex. Applying standard filters and Hilbert transform to estimate sensory evoked potentials appears to rely on huge assumptions that are not fully substantiated in this paper. In Figure 4, 36Hz "visual" and 40Hz "auditory" signals seem largely indistinguishable from one another, suggesting that the analysis failed to fully demix these signals.

We appreciate the reviewer’s insightful concern regarding the filtering and demixing of the 36 Hz visual and 40 Hz auditory signals, and we share the same reservations about the reliance on standard filters and the Hilbert transform method.

To address this, we would like to draw attention to Author response image 1, which demonstrates that a 4 Hz difference is sufficient to effectively demix the signals using our chosen filtering and Hilbert transform approach. We believe that the reason the 36 Hz visual and 40 Hz auditory signals show similar topographies lies not in incomplete demixing but rather in the possibility that this condition difference reflects sensory integration, rather than signal contamination.

This interpretation is further supported by our findings with the intermodulation frequency at 4 Hz, which also suggests cross-modal integration. Furthermore, source localization analysis revealed that the strongest condition differences were observed in the precuneus, an area frequently associated with sensory integration processes. We will expand on this in the discussion section to better clarify this point.

(3) The asymmetric results in the visual and auditory modalities preclude a modality-general conclusion about the function of alpha. However, much of the language seems to generalize across sensory modalities (e.g., use of the term 'sensory' rather than 'visual').

We thank the reviewer for pointing this out and agree that in some cases we have not made a good enough distinction between visual and sensory. We will make sure, that when using ‘sensory’, we either describe overall theories, which are not visual-exclusive or refer to the possibility of a broad sensory increase. However, when directly discussing our results and the interpretation thereof, we will now use ‘visual’ in the revised manuscript.

(4) In this vein, some of the conclusions would be far more convincing if there was at least a trend towards symmetry in source-localized analyses of MEG signals. For example, how does alpha power in the primary auditory cortex (A1) compare when anticipating auditory vs visual target? What do the frequency-tagged visual and auditory responses look like when just looking at the primary visual cortex (V1) or A1?

We thank the reviewer for this important suggestion and have added a virtual channel analysis. We were however, not interested in alpha power in primary auditory cortex, as we were specifically interested in the posterior alpha, which is usually increased when expecting an auditory compared to a visual target (and used to be interpreted as a blanket inhibition of the visual cortex). We will improve upon the clarity concerning this point in the manuscript.

We have however, followed the reviewer’s suggestion of a virtual channel analysis, showing that the condition differences are not observable in primary visual cortex for the 36 Hz visual signal and in primary auditory cortex for the 40 Hz auditory signal. Our data clearly shows that there is an alpha condition difference in V1, while there no condition difference for 36 Hz in V1 and for 40 Hz in Heschl’s Gyrus (see Author response image 2).

Author response image 2.

Virtual channels for V1 and Helschl’s gyrus. A, alpha power for the virtual channel created in V1 (Calcerine_L and Calcerine_R from AAL atlas; Tzourio-Mazoyer et al., 2002, NeuroImage). A cluster permutation analysis over time (between -2 and 0) revealed a significant condition difference between ~ -2 and -1.7 s (p = 0.0449). B, 36 Hz frequency-tagging signal for the virtual channel created in V1 (equivalent to the procedure in A). The same cluster permutation as performed in A revealed no significant condition differences. C, 40 Hz frequency-tagging signal for the virtual channel created in Heschl’s gryrus (Heschl_L and Heschl_R from AAL atlas; Tzourio-Mazoyer et al., 2002, NeuroImage). The same cluster permutation as performed in A revealed no significant condition differences.

(5) Blinking would have a huge impact on the subject's ability to ignore the visual distractor. The best thing to do would be to exclude from analysis all trials where the subjects blinked during the cue-to-target interval. The authors mention that in the MEG experiment, "To remove blinks, trials with very large eye-movements (> 10 degrees of visual angle) were removed from the data (See supplement Fig. 5)." This sentence needs to be clarified since eye-movements cannot be measured during blinking. In addition, it seems possible to remove putative blink trials from EEG experiments as well, since blinks can be detected in the EEG signals.

We thank the reviewer for mentioning that we were making this point confusing. From the MEG-data, we removed eyeblinks using ICA. Alone for the supplementary Fig. 5 analysis, we used the eye-tracking data to confirm that participants were in fact fixating the centre of the screen. For this analysis, we removed trials with blinks (which can be seen in the eye-tracker as huge amplitude movements or as large eye-movements in degrees of visual angle; see Author response image 3 below to show a blink in the MEG data and the according eye-tracker data in degrees of visual angle). We will clarify this in the methods section.

As for the concern closed eyes to ignore visual distractors, in both experiments we can observe highly significant distractor cost in accuracy for visual distractors, which we hope will convince the reviewer that our visual distractors were working as intended.

Author response image 3.

Illustration of eye-tracker data for a trial without and a trial with a blink. All data points recorded during this trial are plottet. A, ICA component 1, which reflects blinks and its according data trace in a trial. No blink is visible. B, eye-tracker data transformed into degrees of visual angle for the trial depicted in A. C, ICA component 1, which reflects blinks and its according data trace in a trial. A clear blink is visible. D, eye-tracker data transformed into degrees of visual angle for the trial depicted in C.

(6) It would be interesting to examine the neutral cue trials in this task. For example, comparing auditory vs visual vs neutral cue conditions would be indicative of whether alpha was actively recruited or actively suppressed. In addition, comparing spectral activity during cue-to-target period on neutral-cue auditory correct vs incorrect trials should mimic the comparison of auditory-cue vs visual-cue trials. Likewise, neutral-cue visual correct vs incorrect trials should mimic the attention-related differences in visual-cue vs auditory-cue trials.

We thank the reviewer for this suggestion. We have analysed the neutral cue trials in the EEG dataset (see suppl. Fig. 1) and will expand this figure to show all conditions. There were no significant differences to auditory or visual cues, but descriptively alpha power was higher for neutral cues compared to visual cues and lower for neutral cues compared to auditory cues. While this may suggest that for visual trials alpha is actively suppressed and for auditory trials actively recruited, we do not feel comfortable to make this claim, as the neutral condition may not reflect a completely neutral state. The neutral task can still be difficult, especially because of the uncertainty of the target modality.

As for the analysis of incorrect versus correct trials, we love the idea, but unfortunately the accuracy rate was quite high so that the number of incorrect trials would not be sufficient to perform a reliable analysis.

(7) In the abstract, the authors state that "This implies that alpha modulation does not solely regulate 'gain control' in early sensory areas but rather orchestrates signal transmission to later stages of the processing stream." However, I don't see any supporting evidence for the latter claim, that alpha orchestrates signal transmission to later stages of the processing stream. If the authors are claiming an alternative function to alpha, this claim should be strongly substantiated.

We thank the reviewer for pointing out, that we have not sufficiently explained our case. The first point refers to gain control akin to the alpha inhibition hypothesis, which claims that increases in alpha disengage a whole cortical area. Since we have confirmed the alpha increase in our data to originate from primary visual cortex through source analysis, this should lead to decreased visual processing. The increase in 36 Hz visual processing therefore directly contradicts the alpha inhibition hypothesis. We propose an alternative explanation for the functionality of alpha activity in this task. Through pulsed inhibition, information packages of relevant visual information could be transmitted down the processing stream, thereby enhancing relevant visual signal transmission. We believe the fact that the enhanced visual 36 Hz signal we found correlated with visual alpha power on a trial-by-trial basis, and did not originate from primary visual cortex, but from areas known for sensory integration supports our claim.

We will make this point clearer in our revised manuscript.

Reviewer #2 (Public review):

Brickwedde et al. investigate the role of alpha oscillations in allocating intermodal attention. A first EEG study is followed up with a MEG study that largely replicates the pattern of results (with small to be expected differences). They conclude that a brief increase in the amplitude of auditory and visual stimulus-driven continuous (steady-state) brain responses prior to the presentation of an auditory - but not visual - target speaks to the modulating role of alpha that leads them to revise a prevalent model of gating-by-inhibition.

Overall, this is an interesting study on a timely question, conducted with methods and analysis that are state-of-the-art. I am particularly impressed by the author's decision to replicate the earlier EEG experiment in MEG following the reviewer's comments on the original submission. Evidently, great care was taken to accommodate the reviewer's suggestions.

We thank the reviewer for the positive feedback and expression of interest in the topic of our manuscript.

Nevertheless, I am struggling with the report for two main reasons: It is difficult to follow the rationale of the study, due to structural issues with the narrative and missing information or justifications for design and analysis decisions, and I am not convinced that the evidence is strong, or even relevant enough for revising the mentioned alpha inhibition theory. Both points are detailed further below.

We thank the reviewer for raising this important point. We will revise our introduction and results in line with the reviewer’s suggestions, hoping that our rationale will then be easier to follow and that our evidence will be more convincing.

Strength/relevance of evidence for model revision: The main argument rests on 1) a rather sustained alpha effect following the modality cue, 2) a rather transient effect on steady-state responses just before the expected presentation of a stimulus, and 3) a correlation between those two. Wouldn't the authors expect a sustained effect on sensory processing, as measured by steady-state amplitude irrespective of which of the scenarios described in Figure 1A (original vs revised alpha inhibition theory) applies? Also, doesn't this speak to the role of expectation effects due to consistent stimulus timing? An alternative explanation for the results may look like this: Modality-general increased steady-state responses prior to the expected audio stimulus onset are due to increased attention/vigilance. This effect may be exclusive (or more pronounced) in the attend-audio condition due to higher precision in temporal processing in the auditory sense or, vice versa, too smeared in time due to the inferior temporal resolution of visual processing for the attend-vision condition to be picked up consistently. As expectation effects will build up over the course of the experiment, i.e., while the participant is learning about the consistent stimulus timing, the correlation with alpha power may then be explained by a similar but potentially unrelated increase in alpha power over time.

We thank the reviewer for raising these insightful questions and suggestions.

It is true that our argument rests on a rather sustained alpha effect and a rather transient effect on steady-state responses and a correlation between the two. However, this connection would not be expected under the alpha inhibition hypothesis, which states that alpha activity would inhibit a whole cortical area (when irrelevant to the task), exerting “gain control”. This notion directly contradicts our results of the “irrelevant” visual information a) being transmitted at all and b) increasing.

However, it has been shown on many occasions that alpha activity exerts pulsed inhibition, so we proposed an alternative theory of an involvement in signal transmission. In this case, the cyclic inhibition would serve as an ordering system, which only allows for high-priority information to pass, resulting in higher signa-to-noise. We do not make a claim about how fast or when these signals are transmitted in relation to alpha power. For instance, it could be that alpha power increases as a preparatory state even before signal is actually transmitted.  Zhigalov (2020 Hum. Brain M.) has shown that in V1, frequency-tagging responses were up-and down regulated with attention – independent of alpha activity.

But we do believe that the fact that visual alpha power correlates on a trial-by-trial level with visual 36 Hz frequency-tagging increases and (a relationship which has not been found in V1, see Zhigalov 2020, Hum. Brain Mapp.) suggest a strong connection. Furthermore, the fact that the alpha modulation originates from early visual areas and occurs prior to any frequency-tagging changes, while the increase in frequency-tagging can be observed in areas which are later in the processing stream (such as the precuneus) is strongly indicative for an involvement of alpha power in the transmission of this signal. We cannot fully exclude alternative accounts and mechanisms which effect both alpha power and frequency-tagging responses. 

We do believe that the alternative account described by the reviewer does not contradict our theory, as we do believe that the alpha power modulation may reflect an expectation effect (and the idea that it could be related to the resolution of auditory versus visual processing is very interesting!). It is also possible that this expectation is, as the reviewer suggests, related to attention/vigilance and might result in a modality-general signal increase. And indeed, we can observe an increase in the frequency-tagging response in sensory integration areas. Accordingly, we believe that the alternative explanation provided by the reviewer contradicts the alpha inhibition hypothesis, but not necessarily our alternative theory.

We will revise the discussion, which we hope will make our case stronger and easier to follow. Additionally, we will mention the possibility for alternative explanations as well as the possibility, that alpha networks fulfil different roles in different locations/task environments.

Structural issues with the narrative and missing information: Here, I am mostly concerned with how this makes the research difficult to access for the reader. I list the major points below:

In the introduction the authors pit the original idea about alpha's role in gating against some recent contradictory results. If it's the aim of the study to provide evidence for either/or, predictions for the results from each perspective are missing. Also, it remains unclear how this relates to the distinction between original vs revised alpha inhibition theory (Fig. 1A). Relatedly if this revision is an outcome rather than a postulation for this study, it shouldn't be featured in the first figure.

We agree with the reviewer that we have not sufficiently clarified our goal as well as how different functionalities of alpha oscillations would lead to different outcomes. We will revise the introduction and restructure the results and hope that it will be easier to follow.

The analysis of the intermodulation frequency makes a surprise entrance at the end of the Results section without an introduction as to its relevance for the study. This is provided only in the discussion, but with reference to multisensory integration, whereas the main focus of the study is focussed attention on one sense. (Relatedly, the reference to "theta oscillations" in this sections seems unclear without a reference to the overlapping frequency range, and potentially more explanation.) Overall, if there's no immediate relevance to this analysis, I would suggest removing it.

We thank the reviewer for pointing this out and will add information about this frequency to the introduction part. We believe that the intermodulation frequency analysis is important, as it potentially supports the notion that condition differences in the visual-frequency tagging response are related to downstream processing rather than overall visual information processing in V1. We would therefore prefer to leave this analysis in the manuscript.

Reviewer #3 (Public review):

Brickwedde et al. attempt to clarify the role of alpha in sensory gain modulation by exploring the relationship between attention-related changes in alpha and attention-related changes in sensory-evoked responses, which surprisingly few studies have examined given the prevalence of the alpha inhibition hypothesis. The authors use robust methods and provide novel evidence that alpha likely exhibits inhibitory control over later processing, as opposed to early sensory processing, by providing source-localization data in a cross-modal attention task.

This paper seems very strong, particularly given that the follow-up MEG study both (a) clarifies the task design and separates the effect of distractor stimuli into other experimental blocks, and (b) provides source-localization data to more concretely address whether alpha inhibition is occurring at or after the level of sensory processing, and (c) replicates most of the EEG study's key findings.

We are very grateful to the reviewer for their positive feedback and evaluation of our work.

There are some points that would be helpful to address to bolster the paper. First, the introduction would benefit from a somewhat deeper review of the literature, not just reviewing when the effects of alpha seem to occur, but also addressing how the effect can change depending on task and stimulus design (see review by Morrow, Elias & Samaha (2023).

We thank the reviewer for this suggestion and agree. We will add a paragraph to the introduction which refers to missing correlation studies and the impact of task design.

Additionally, the discussion could benefit from more cautionary language around the revision of the alpha inhibition account. For example, it would be helpful to address some of the possible discrepancies between alpha and SSEP measures in terms of temporal specificity, SNR, etc. (see Peylo, Hilla, & Sauseng, 2021). The authors do a good job speculating as to why they found differing results from previous cross-modal attention studies, but I'm also curious whether the authors think that alpha inhibition/modulation of sensory signals would have been different had the distractors been within the same modality or whether the cues indicated target location, rather than just modality, as has been the case in so much prior work?

We thank the reviewer for suggesting these interesting discussion points and will include a paragraph in our discussion which goes deeper into these topics.

Overall, the analyses and discussion are quite comprehensive, and I believe this paper to be an excellent contribution to the alpha-inhibition literature.

Author response:

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

General Response to Public Reviews

We thank the three reviewers for their positive evaluation of our work, which presents the first molecular characterization of type-II NB lineages in an insect outside the fly Drosophila. They seem convinced of our finding of an additional type-II NB and increased proliferation during embryogenesis in the red flour beetle. The reviewers expressed hesitations on our interpretation that the observed quantitative differences of embryonic lineages can directly be linked to the embryonic development of the central complex in Tribolium. While we still believe that a connection of both observations is a valid and likely hypothesis, we acknowledge that due the lack of functional experiments and lineage tracing a causal link has not directly been shown. We have therefore changed the manuscript to an even more careful wording that on one hand describes the correlation between increased embryonic proliferation with the earlier development of the Cx but on the other hand also stresses the need for additional functional and lineage tracing experiments to test this hypothesis. We have also strengthened the discussion on alternative explanations of the increased lineage size and emphasize the less disputed elements like presence and conservation of type-II NB lineages. 

While our manuscript could in conclusion not directly show that the reason of the heterochronic shift lies in the progenitor behaviour, we still provide a first approach to answering the question of the developmental basis of this shift and testable hypotheses directly emerge from our work. We agree with reviewer#1 that functional work is best suited to test our hypothesis and we are planning to do so. However, we believe that the presented work is already rich in novel data and significantly advances our understanding on the conservation and divergence of type-II NBs in insects. We would also like to stress that most transgenic tools for which genome-wide collections exist for Drosophila have to be created for Tribolium and doing so can be quite time consuming. Conducting RNAi experiments is certainly possible in Tribolium but observing phenotypes in this defined cellular context will need laborious optimization. We have for example tried knocking down Tc-fez/erm but could not see any embryonic phenotype which might be due to an escaper effect in which only mildly affected or wild type-like embryos survive while the others die in early embryogenesis. Due to pleiotropic functions of the involved genes a cell-specific knockdown might be necessary and we are working towards establishing a system to do that in the red flour beetle. For the stated reasons, we see our work as an important basis to inspire future functional studies that build up on the framework that we introduced. 

In response to these common points, we have made the following changes to the manuscript

-        The title has been changed from ‘being associated’ to ‘correlate’

-        The conclusions part of the abstract has been changed

-        We deleted the statement ‘…thus providing the material for the early central complex formation…’

-        Rephrased to saying that the two observations just correlate

-        The part of the discussion ‘Divergent timing of type-II NB activity and heterochronic development of the central complex’ has been extensively rewritten and now discusses several alternative explanations that were suggested by the reviewers. It also stresses the need for further functional work and lineage tracing (line 859-862 (608-611)).

In addition, we have made numerous changes to the manuscript to account for more specific comments of the reviewers and to the recommendations for the authors.

Our responses to the individual comments can be found in the following. 

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Insects inhabit diverse environments and have neuroanatomical structures appropriate to each habitat. Although the molecular mechanism of insect neural development has been mainly studied in Drosophila, the beetle, Tribolium castaneum has been introduced as another model to understand the differences and similarities in the process of insect neural development. In this manuscript, the authors focused on the origin of the central complex. In Drosophila, type II neuroblasts have been known as the origin of the central complex. Then, the authors tried to identify those cells in the beetle brain. They established a Tribolium fez enhancer trap line to visualize putative type II neuroblasts and successfully identified 9 of those cells. In addition, they also examined expression patterns of several genes that are known to be expressed in the type II neuroblasts or their lineage in Drosophila. They concluded that the putative type II neuroblasts they identified were type II neuroblasts because those cells showed characteristics of type II neuroblasts in terms of genetic codes, cell diameter, and cell lineage. 

Strengths: 

The authors established a useful enhancer trap line to visualize type II neuroblasts in Tribolium embryos. Using this tool, they have identified that there are 9 type II neuroblasts in the brain hemisphere during embryonic development. Since the enhancer trap line also visualized the lineage of those cells, the authors found that the lineage size of the type II neuroblasts in the beetle is larger than that in the fly. They also showed that several genetic markers are also expressed in the type II neuroblasts and their lineages as observed in Drosophila. 

Weaknesses: 

I recommend the authors reconstruct the manuscript because several parts of the present version are not logical. For example, the author should first examine the expression of dpn, a well-known marker of neuroblast. Without examining the expression of at least one neuroblast marker, no one can say confidently that it is a neuroblast. The purpose of this study is to understand what makes neuroanatomical differences between insects which is appropriate to their habitats. To obtain clues to the question, I think, functional analyses are necessary as well as descriptive analyses. 

The expression of an exclusive type-II neuroblast marker would indeed have been the most convincing evidence. However, asense is absent from type-II NBs and deadpan is not specific enough as it is expressed in many other cells of the developing protocerebrum. The gene pointed, although also expressed elsewhere, emerged as the the most specific marker. Therefore, we start with pointed and fez/erm to describe the first appearance and developmental progression of the cells and then add further evidence that these cells are indeed type-II neuroblasts. Further evidence is provided in the following chapters.  We have discussed the need for functional work in the general response. 

Reviewer #2 (Public Review): 

The authors address the question of differences in the development of the central complex (Cx), a brain structure mainly controlling spatial orientation and locomotion in insects, which can be traced back to the neuroblast lineages that produce the Cx structure. The lineages are called type-II neuroblast (NB) lineages and are assumed to be conserved in insects. While Tribolium castaneum produces a functional larval Cx that only consists of one part of the adult Cx structure, the fan-shaped body, in Drosophila melanogaster a non-functional neuropile primordium is formed by neurons produced by the embryonic type-II NBs which then enter a dormant state and continue development in late larval and pupal stages. 

The authors present a meticulous study demonstrating that type-II neuroblast (NB) lineages are indeed present in the developing brain of Tribolium castaneum. In contrast to type-I NB lineages, type-II NBs produce additional intermediate progenitors. The authors generate a fluorescent enhancer trap line called fez/earmuff which prominently labels the mushroom bodies but also the intermediate progenitors (INPs) of the type-II NB lineages. This is convincingly demonstrated by high-resolution images that show cellular staining next to large pointed labelled cells, a marker for type-II NBs in Drosophila melanogaster. Using these and other markers (e.g. deadpan, asense), the authors show that the cell type composition and embryonic development of the type-II NB lineages are similar to their counterparts in Drosophila melanogaster. Furthermore, the expression of the Drosophila type-II NB lineage markers six3 and six4 in subsets of the Tribolium type-II NB lineages (anterior 1-4 and 1-6 type-II NB lineages) and the expression of the Cx marker skh in the distal part of most of the lineages provide further evidence that the identified NB lineages are equivalent to the Drosophila lineages that establish the central complex. However, in contrast to Drosophila, there are 9 instead of 8 embryonic type-II NB lineages per brain hemisphere and the lineages contain more progenitor cells compared to the Drosophila lineages. The authors argue that the higher number of dividing progenitor cells supports the earlier development of a functional Cx in Tribolium. 

While the manuscript clearly shows that type-II NB lineages similar to Drosophila exist in Tribolium, it does not considerably advance our understanding of the heterochronic development of the Cx in these insects. First of all, the contribution of these lineages to a functional larval Cx is not clear. For example, how do the described type-II NB lineages relate to the DM1-4 lineages that produce the columnar neurons of the Cx? What is the evidence that the embryonically produced type-II NB lineage neurons contribute to a functional larval Cx? The formation of functional circuits could rely on larval neurons (like in Drosophila) which would make a comparison of embryonic lineages less informative with respect to understanding the underlying variations of the developmental processes. Furthermore, the higher number of progenitors (and consequently neurons) in Tribolium could simply reflect the demand for a higher number of cells required to build the fan-shaped body compared to Drosophila. In addition, the larger lineages in Tribolium, including the higher number of INPs could be due to a greater number of NBs within the individual clusters, rather than a higher rate of proliferation of individual neuroblasts, as suggested. What is the evidence that there is only one NB per cluster? The presented schemes (Fig. 7/12) and description of the marker gene expression and classification of progenitor cells are inconsistent but indicate that NBs and immature INPs cannot be consistently distinguished. 

We thank this reviewer for pointing out the inconsistency in our classification of cells within the lineages as one central part of our manuscript. These were due to a confusion in the used terms (young vs. immature). We have corrected this mistake and have changed the naming of the INP subtypes to immature-I and immature-II. We are confident that based on the analysed markers, type-II NBs and immature INPs can actually be distinguished with confidence.

We agree that a functional link of increased proliferation to heterochronic CX development is not shown although we consider it to be likely. As stated in the general response we have changed the manuscript to saying that the two observations (higher number of progenitors and larger lineages/more INPs) correlate but that a causal link can only be hypothesized for the time being. At the same time, we have strengthened the discussion on alternative explanations.

We would like to remain with our statement of an increased number of embryonic progeny of Tribolium type-II NBs. We counted the total number of progenitor cells emerging from the anterior median cluster and divided this by the number of type II NBs in that cluster. Hence, the shown increased number of cells represents an average per NB but is not influenced by the increased number of NBs. On the same line, we have never seen indication for the presence of additional NBs within any cluster while one type-II NB is what we regularly found. Hence, we are confident that we know the number of respective NBs. The fact that the fly data included also neurons and was counted at a later stage indicates that the observed differences are actually minimum estimates.

We have discussed that based on the position and comparison to the grasshopper we believe that Tribolium type-II NB 1-4 contribute to the x, y, z and w tracts. To confirm this, lineage tracing experiments would be necessary, for which tools remain to be developed. 

We agree that the role of larvally born neurons and the fate of Tribolium neuroblasts through the transition from embryo to larva and pupa need to be further studied.

Available data suggests that the adult fan shaped body in Tribolium does not hugely differ in size from the Drosophila counterpart, although no data in terms of cell number is available. In the larva, however, no fan shaped body or protocerebral bridge can be distinguished in flies while in beetle larvae, these structures are clearly developed. Hence, we think that it is more likely that differences observed in the embryo reflect differences in the larval central complex. We discuss the need for further investigation of larval stages.

The main difference between Tribolium and Drosophila Cx development with regards to the larval functionality might be that Drosophila type-II NB lineage-derived neurons undergo quiescence at the end of embryogenesis so that the development of the Cx is halted, while a developmental arrest does not occur in Tribolium. However, this needs to be confirmed (as the authors rightly observe). 

Indeed, there is evidence that cells contributing to the CX go into quiescence in flies – hence, this certainly is one of the mechanisms. However, based on our data we would suggest that in addition, the balance of embryonic versus larval proliferation of type-II lineages is different between the two insects: The increased embryonic proliferation and development leads to a functional larval CX in beetles while in flies, postembryonic proliferation may be increased in order to catch up.

Reviewer #3 (Public Review):

Summary: 

In this paper, Rethemeier et al capitalize on their previous observation that the beetle central complex develops heterochronically compared to the fly and try to identify the developmental origin of this difference. For this reason, they use a fez enhancer trap line that they generated to study the neuronal stem cells (INPs) that give rise to the central complex. Using this line and staining against Drosophila type-II neuroblast markers, they elegantly dissect the number of developmental progression of the beetle type II neuroblasts. They show that the NBs, INPs, and GMCs have a conserved marker progression by comparing to Drosophila marker genes, although the expression of some of the lineage markers (otd, six3, and six4) is slightly different. Finally, they show that the beetle type II neuroblast lineages are likely longer than the equivalent ones in Drosophila and argue that this might be the underlying reason for the observed heterochrony. 

Strengths: 

- A very interesting study system that compares a conserved structure that, however, develops in a heterochronic manner. 

- Identification of a conserved molecular signature of type-II neuroblasts between beetles and flies. At the same time, identification of transcription factors expression differences in the neuroblasts, as well as identification of an extra neuroblast. 

- Nice detailed experiments to describe the expression of conserved and divergent marker genes, including some lineaging looking into the co-expression of progenitor (fez) and neuronal (skh) markers. 

Weaknesses: 

- Comparing between different species is difficult as one doesn't know what the equivalent developmental stages are. How do the authors know when to compare the sizes of the lineages between Drosophila and Tribolium? Moreover, the fact that the authors recover more INPs and GMCs could also mean that the progenitors divide more slowly and, therefore, there is an accumulation of progenitors who have not undergone their programmed number of divisions. 

We understand the difficulty of comparing stages between species, but we feel that our analysis is on the save side. At stages comparable with respect to overall embryonic development (retracting or retracted germband), the fly numbers are clearly smaller. To account for potential heterochronic shifts in NB activity, we have selected the stages to compare based on the criteria given: In Drosophila the number of INPs goes down after stage 16, meaning that they reach a peak at the selected stages. In Tribolium the chosen stages also reflect the phase when lineage size is larger than in all previous stages. Therefore, we believe that the conclusion that Tribolium has larger lineages and more INPs is well founded. Lineage size in Tribolium might further increase just before hatching (stage 15) but we were for technical reasons not able to look at this. As lineage size goes down in the last stage of Drosophila embryogenesis the number of INPs goes down and type-II NB enter quiescence, we think it is highly unlikely that the ratio between Tribolium and Drosophila INPs reverses at this stage, but a study of the behaviour of type-II NB in Tribolium and whether there is a stage of quiescence is still needed.

- The main conclusion that the earlier central complex development in beetles is due to the enhanced activity of the neuroblasts is very handwavy and is not the only possible conclusion from their data. 

As discussed in the general response we have made several changes to the manuscript to account for this criticism and discuss alternative explanations for the observations.

- The argument for conserved patterns of gene expression between Tribolium and Drosophila type-II NBs, INPs, and GMCs is a bit circular, as the authors use Drosophila markers to identify the Tribolium cells. 

We tested the hypothesis that in Tribolium there are type-II NBs with a molecular signature similar to flies. Our results are in line with that hypothesis. If pointed had not clearly marked cells with NB-morphology or fez/erm had not marked dividing cells adjacent to these NBs, we would have concluded that no such cells/lineages exist in the Tribolium embryo, or that central complex producing lineages exist but express different markers. Therefore, we regard this a valid scientific approach and hence find this argument not problematic.  

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: Based on the above, I believe that the authors, despite advancing significantly, fall short of identifying the reasons for the divergent timing of central complex development between beetle and fly. 

We agree that based on the available data, we cannot firmly make that link and we have changed the text accordingly.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

In addition to these descriptive analyses, functional analyses can be included. RNAi is highly effective in this beetle. 

We agree that functional analyses of some of the studied genes and possible effects of gene knockdowns on the studied cell lineages and on central complex development could be highly informative. However, when studying specific cell types or organs these experiments are less straight forward than it may seem as knockdowns often lead to pleiotropic effects, sterility or lethality. All the genes involved are expressed in additional cells and may have essential functions there. Given the systemic RNAi of Tribolium, it is challenging to unequivocally assign phenotypes to one of the cell groups. Overcoming these challenges is often possible but needs extensive optimization. Our study, though descriptive is already rich in data and is the first description of NB-II lineages in Tribolium central complex development. We see it as a basis for future studies on central complex development that will include functional experiments.

(1) Introduction 

For these reasons the beetle... 

Could you explain the differences in the habitats between Tribolium and Drosophila? or What is the biggest difference between these two species at the ecological aspect? 

We have added a short characterisation of the main differences.

The insect central complex is an anterior... 

The author should explain why they focus on the structure. 

Added

It is however not known how these temporal... 

If the authors want to get the answer to the question, they need to conduct functional analyses. 

While we agree with the importance of functional work (see above) we believe that detailed descriptions under the inclusion of molecular markers as presented here is very informative by itself for understanding developmental processes and sets the foundation for the analysis of mutant/RNAi- phenotypes in future studies.

CX - Central complex? 

We have opted to not use this abbreviation anymore for clarity.

“because intermediate cycling progenitors have also been...” 

Is the sentence correct? 

We have included ‘INPs’ in the sentence to make clear what the comparison refers to and added a comma

“However, molecular characterization of such lineage in another...” 

The authors should explain why molecular characterization is necessary. 

We have done so

(2) Results 

a) Figure 8. Could you delineate the skh/eGFP expression region? 

We have added brackets to figure 1 panel A to indicate the extent of skh and other gene expressions within the lineages.

b) This section should be reorganized for better logical flow. 

There certainly are different ways to organize this part and we have considered different structures of the results part. We eventually subjectively concluded that the chosen one is the best fit for our data (also see comment below on dpn-expression).

c) For the tables. The authors should mention what statistical analysis they have conducted. 

The tables themselves are just listing the raw numbers. They are the basis for the graph in figure 9. Statistical tests (t-test) are mentioned in the legend of that figure and now also in the Methods sections.

“We also found that the large Tc-pnt...” 

The authors could examine the mitotic index using an anti-pH3 antibody. 

We have used the anti-pH3 antibody to detect mitoses (figure 3C, table 1 and 3) but as data on mitoses based on this antibody is only a snapshot it would require a lot of image data to reliably determine an index in this specific cells. While mitotic activity over time possibly combined with live imaging might be very interesting in this system also with regards to the timing of development, for this basic study we are satisfied with the statement that the type-II NB are indeed dividing at these stages.

“Based on their position by the end of embryogenesis...” 

How can the authors conclude that they are neuroblasts without examining the expression of NB markers? 

Type-II NB do not express asense as the key marker for type I neuroblasts. To corroborate our argument that the cells are neuroblasts we have used several criteria:

- We have used the same markers that are used in Drosophila to label type-II NBs (pnt, dpn, six4). We are not aware of any other marker that would be more specific.

- We have shown that these cells are larger and have larger nuclei than neighbouring cells and they are dividing

- We have shown that these cells through their INP lineages give rise to central complex neuropile

We believe that these features taken together leave little doubt that the described cells are indeed neuroblasts. 

“We found that the cells they had assigned as...” 

How did the authors distinguish that they are really neuroblasts? 

We see the difficulty that we first describe the position and development of these cells (e.g. fig 3) and then add further evidence (cell size, additional marker dpn) that these are neuroblasts (also see above). However, without previous knowledge on position (and on pnt expression as the most specific marker) the type-II NB could not have been distinguished from other NBs based on cell size or expression of other markers.

“Conserved patterns of gene expression...” 

This must be the first (especially dpn). 

Dpn is not specific to type-II NB because it is also expressed in type-1 NBs, mature INPs and possibly other neural cells. It is therefore impossible to identify type-II NBs based on this gene alone. We therefore first used the most specific marker, pnt, in addition to adjacent fez expression to identify candidates for type-II lineages. Then we mapped expression of further genes on these lineages to support the interpretation (and show homology to the Drosophila lineages). Although of course the structure of a paper does not necessarily have to reflect the sequence in which experiments were done we would find putting dpn expression first misleading as it would not be clear why exactly a certain part of the expression should belong to type-II NB. Also, our pnt-fez expression data shows the position of the NB-II in the context of the whole head lobe whereas the other gene expressions are higher magnifications focussing on details. We therefore believe that the structure we chose best fits our data and the other reviewers seemed to find it acceptable as well.  

“As type-II NBs contribute to central...” 

Before the sentence, the author could explain differences in the central complex structure between Tribolium and Drosophila in terms of cell number and tissue size. 

We have added references on the comparisons of tissue sizes, but unfortunately there is no Tribolium data that can be directly compared to available Drosophila resources in terms of cell number.  

“We conclude that the embryonic development of...” 

How did the authors conclude? They must explain their logic. 

Actually, before this sentence, I only found the description of the comparison between Tribolium NBs and Drosophila once. 

We agree that this conclusion is not fully evident from the presented data. We have therefore changed this part to stating that there is a correlation with the earlier central complex development described in Tribolium. See also response to the general reviewer comments.

“Hence, we wondered...” 

The authors need to do a functional assessment of the genes they mentioned. 

We agree that the goals originally stated at the beginning of this paragraph can only be achieved with functional experiments. We have therefore rephrased this part.

(3) Discussion

“A beetle enhancer trap line...” 

This part should be moved elsewhere (it does not seem to be a discussion) 

In accordance with this comment and reviewer#2’s similar comment we have removed this section. We have added a statement on the importance of testing the expression of an enhancer trap line to the results part and an added the use of CRISPR-Cas9 for line generation to the introduction. 

“We have identified a total...” 

The authors emphasized that they discovered 9 type II NBs. The authors should clarify how important this it

We have added some discussion on the importance of this finding.

Dpn is a neural marker - Is this correct? 

According to Bier et al 1992 (now added as reference) dpn is a pan-neural marker. Reviewer#2 also recommended calling dpn a neural marker.

“Previous work described a heterochronic...” - reference? 

Reference have been added

“By contrast, we show that Tribolium...” 

What about the number of neurons in the central complex in Tribolium and Drosophila? 

Does the lineage size of type II NBs reflect the number? 

Unfortunately, we do not have numbers for that.  

Reviewer #2 (Recommendations For The Authors): 

I recommend using page and line numbers to make reviewing and revising less timeconsuming. 

We apologize for this oversight. We include a line numbering system into our resubmission.

(1) Abstract 

"These neural stem cells are believed to be conserved among insects, but their molecular characteristics and their role in brain development in other insect neurogenetics models, such as the beetle Tribolium castaneum have so far not been studied." 

I recommend explaining the importance of studying Tribolium with regard to the evolution of brain centres rather than just stating that data are lacking. 

We have now emphasized the importance of Tribolium as model for the evolution of brain centres.

"Intriguingly, we found 9 type-II neuroblast lineages in the Tribolium embryo while Drosophila produces only 8 per brain hemisphere." 

It should be made clear that the 9 lineages also refer to brain hemispheres. 

We have added this information

(2) Introduction 

I would remove the first paragraph of the introduction; the use of Tribolium as model representative for insects is too general. The authors should focus on the specific question, i.e. the introduction should start with paragraph 2. 

While we can relate to the preference for short and concise writing, we feel that giving some background on Tribolium might be important as we expect that many of our readers might be primarily Drosophila researchers. Keeping this paragraph also seems in line with a recommendation of reviewer#1 to add some additional information on Tribolium ecology.  

"Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared…”

The abbreviation has not been introduced. 

For clarity we have now opted to not use this abbreviation but to always spell out central complex.

"Several NBs of the anterior-most part of the neuroectoderm contribute to the CX and compared to the ventral ganglia produced by the trunk segments, it is of distinctively greater complexity..." 

Puzzling statement. Why would you compare a brain center with ventral ganglia? I recommend removing this. 

We have changed this statement to just emphasizing the complexity of the brain structure.

"The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons..."  In my opinion, this statement is too vague and unprofessional in style. Instead of "dramatically increased" use numbers. 

We have removed ‘dramatically increased’ and now give a numeric example.

"The dramatically increased number of neural cells that are produced by individual type-II lineages, and the fact that one lineage can produce different types of neurons, leads to the generation of increased neural complexity within the anterior insect brain when compared to the ventral nerve cord.." 

I assume that this statement relates to the comparison of type I and II nb lineages. However, type I NB lineages also produce different types of neurons due to GMC temporal identity, and neuronal hemi-lineage identity. 

We have rephrased and tried to make clear that the second part of the statement is not specific to type-II NB only. In line with the comment above we have also removed the reference to the ventral nerve cord.

"In addition, in Drosophila brain tumours have been induced from type-II NBs lineages [34], opening up the possibility of modelling tumorigenesis in an invertebrate brain, thus making these lineages one of the most intriguing stem cell models in invertebrates [35,36]." 

This statement is misplaced here; it should be mentioned at the start (if at all). 

We have moved this statement up.

"However, molecular characterisation of such lineages in another insect but the fly and a thorough comparison of type-II NBs lineages and their sub-cell-types between fly and beetle are still lacking" 

The background information should include what is known about type-II NB lineages in Tribolium, including marker gene expression, e.g. Farnworth et al. 

We refer to He et al 2019, Farnworth et al 2020 and Garcia-Perez 2021. All these publications speculate about a contribution of type-II NBs to Tribolium central complex development but do not show evidence of it. As we emphasize throughout the manuscript, the present work is the first description of type-II NB in Tribolium. 

"The ETS-transcription factor pointed (pnt) marks type-II NBs [40,41], which do not express the type-I NB marker asense (ase) but the pro-neural gene deadpan (dpn)"  Deadpan is considered a pan-neural gene. To avoid confusion, I would remove "proneural" throughout.

We have done so throughout the manuscript.

"We further found that, like the type-II NBs itself, the youngest Tc-pnt-positive but fezmm-eGFP-negative INPs neither express Tc-ase (Fig. 5D, pink arrowheads)."  What is the evidence that these are the youngest pnt positive cells? Position? This needs to be explained. 

We have clarified that ‘youngest pnt-positive cells’ refers to the position of these cells close to the type-II NB.

"Therefore these neural markers can be used for a classification of type II NBs (Tc-pnt+, Tcase-), young INPs (Tc-pnt+, Tc-fez/erm-, Tc-ase-), immature INPs (Tc-pnt+, Tcfez/erm+, Tcase+), mature INPs (Tc-dpn+, Tc-ase+, Tc-fez/erm+, Tc-pros+), and GMCs (Tc-ase+, Tcfez/ erm+, Tc-pros+, Tc-dpn). This classification is summarized in Fig. 7 A-B." 

This is not the best classification and not in line with the schemes in Figure 7 - the young INPs are also immature. What is the difference? It needs to be explained what "mature" means (dividing?). 

Thank you for pointing this out. We have corrected the error in this part that confused the two original groups (young and immature). To take the immaturity of both types of INPs into account we have then also changed our naming of INP subtypes into immature-I and immature-II and throughout the manuscript). Figure 7 and figure 12 were also changed accordingly. While our classification if primarily based on gene expression the available data indicates that both types of immature INPs are not dividing, whereas mature INPs are. We have added a statement on that to this part.

"In beetles a single-unit functional central complex develops during embryogenesis while in flies the structure is postembryonic." 

This statement is vague - the authors need to explain what is meant by "single-unit". The phrase "The structure is postembryonic" also needs more explanation. The Drosophila CX neuroblasts lineages originate in the embryo and the neurons form a commissural tract that becomes incorporated into the fan-shaped body of the Cx. 

We have explained single-unit central complex and have improved our summary of known differences in central complex development between fly and beetle.

"To assess the size of the embryonic type-II NBs lineages in beetles we counted the Tc- fez/erm positive (fez-mm-eGFP) cells (INPs and GMCs) associated with a Tc-pntexpressing type-II NBs of the anterior medial group (type-II NBs lineages 1-7).  It is not clear what is meant by "with a Tc-pnt-expressing type-II NBs". Is this a typo?" 

We have removed this bit.

(3) Discussion 

I would remove the first paragraph "A beetle enhancer trap lines reflects Tc-fez/earmuff expression". This is a repetition of the methods rather than a discussion. 

This part has been removed also in line with reviewer#1’s comment.

(4) Figures 

Figure 2 

To which developing structure do the strongly labelled areas in Figure 2D correspond? 

We believe that these areas from the protocerebrum including central complex, mushroom bodies and optic lobe. We have added this to the text and to the figure legend.

Figure 7 

What do A and B represent? Different stages? 

A and B show the same lineage but map the expression of different additional markers for clarity. We have added an explanation of this. 

The classification contradicts the description in the section "Conserved patterns of gene expression mark Tribolium type-II NBs, different stages of INPs and GMCs" (last sentence) where young INPs are first in the sequence and described as pnt+, erm-, ase- and immature INPs as pnt+ erm+ and ase+. 

We have corrected this mistake and changed the names of the subtypes into immatureI and immature-II (see above).

"We conclude that the evolutionary ancient six3 territory gives rise to the neuropile of the z, y, x and w tracts." 

Please clarify if six3 is also expressed in the corresponding grasshopper NB lineages or if your conclusion is based on the comparison of Drosophila and Tribolium and you assume that this is the ancestral condition. 

Six3 expression has not been studied in grasshoppers. Owing to the highly conserved nature of an anterior median six3 domain in arthropods and bilaterian animals in general, we would expect it to be expressed anterior-medially in grasshoppers as well. In Drosophila the gene is expressed in the anterior-medial embryonic region where the type-II NBs are expected to develop, but to our knowledge it has not been specifically studied which type-II NB lineages are located within this domain. We have clarified in our text that we do not claim that the origin of anterior-medial type-II NB 1-4 and the X,Y, Z and W lineages from the six3 territory is highly conserved but only the territory itself. As far as we know our work is the first to analyse the relationship of type-II lineages and the conserved head patterning genes six3 and otd. We have added some clarification of this into this part of the discussion.

(5) Methods 

The methods section should include the methods for cell counting, as well as cell and nuclei size measurements including statistics (e.g. how many embryos, how many NB lineages). The comparison of the Tribolium NB lineage cell numbers to published Drosophila data should include a brief description of the method used in Drosophila (in addition to the method used here in Tribolium) so that the reader can understand how the data compare. 

We have added a separate section on this to the Methods part which also includes the criteria used in Drosophila. We have also included some more information to the results part on the inclusion of neurons in the Drosophila counts that may only be partially included in our numbers. This does however not change the results in terms of larger numbers of progenitor cells in Tribolium.

(6) Typos and minor errors 

Abstract 

“However, little is known on the developmental processes that create this diversity” 

Change to ... little is known about

Changed.

NBs lineages 

Change to NB lineages throughout. 

We have used text search to find and replace all position where this was used erroneously,

Results 

"Schematic drawing of expression different markers in type-II NB lineages.." 

Schematic drawing of expression of different markers 

Corrected

Discussion 

"However, the type-II NB 7, which is we assigned to the anterior medial group but which..." 

.... which we assigned.... 

corrected

"......might be the one that does not have a homologue in the fly embryo The identification of more..."  Full stop missing. 

Added.

"Adult like x, y, and w tracts as well as protocerebral bridge are...." 

Change to "The adult like x, y, and w tracts as well as the protocerebral bridge are.... 

This part has been removed with the rewriting of this paragraph.  

Reviewer #3 (Recommendations For The Authors): 

(1) Suggestions for improved or additional experiments, data, or analyses: 

a) The analysis of nuclear size is wrong. The authors compare the largest cell of a cluster of cells with a number of random cells from the same brain. It is obvious that the largest cell of a cluster will be larger than the average cell of the same brain. A better control would be to compare the largest cell of the pnt+ cluster with the largest cell of a random sample of cells, although this also comes with biases. Personally, I have no doubt that the authors are looking at neuroblasts, based on the markers they are using, so I would recommend completely eliminating Figure 4.

We agree that we produced a somewhat biased and expected result when we select the largest cell of a cluster for size comparison. However, we found it important to show based on a larger sample that these cells are also statistically larger than the average cell of a brain, which we think our assessment shows. We do not claim that type-II NBs are the largest cells of a brain, or that they are larger than type-I NBs, therefore in a random sample there might be cells that are equally big (see also distribution of the control sample shown in figure 4, and we have added a note on this to the text). We are happy to hear that this reviewer has no doubts we are looking at neural stem cells. However, reviewer#1 did express some hesitations and therefore we think it is important to keep the information on cell size as part of our argument that we are indeed looking at type-II NBs (gene expression, cell size, dividing, part of a neural lineage).

b) The comparison of NB, INP, and GMC numbers between Drosophila and Trbolium (section "The Tribolium embryonic lineages of type-II NBs are larger and contain more mature INPs than those of Drosophila") compares an experiment that the authors did with published data. I would suggest that the authors repeat the Drosophila stainings and compare themselves to avoid cases of batch effects, inconsistent counting, etc.

None of the authors is a Drosophila expert or has any experience at working with this model and reassessing the lineage size would require a number of combinatorial staining. Therefore, we feel that using the published data produced by experts and which also includes repeat experiments is for us the more reliable approach.

c) In Figure 10, there are some otd+ GFP+ cells laterally. What are these? 

We believe that these cells contribute to the eye anlagen. We have added this information to the legend.

(2) Minor corrections to the text and figures: 

a) There are some typos in the text: e.g. "pattering" in the abstract. 

We have carefully checked the text for typos and hope that we have found everything.

b) The referencing of figures in the text is inconsistent (eg "Figure 5 panel A" vs "Figure 5D" on page 12). 

We have checked throughout the manuscript and made sure to always refer to a panel correctly.

c) In Figure 3C, the white staining (anti-PH3) is not indicated in the Figure. 

The label has been added in the figure.

d) Moreover, in Figure 3, green is not very visible in the images. 

We have improved the colour intensity where possible.

e) In the figures, it might be better to outline the cells with color-coded dashed circles instead of using arrows. 

We think that this would obscure some details of the stainings and create a rather artificial representation. We also feel that doing this consistently in all our images is an amount of work not justified by the degree of expected improvement to the figures

NOTE: We are submitting a revised version of the supplementary material which only contains two minor changes: a headline was added to Table S4 (Antibodies and staining reagents) and a typo was corrected in line one of table S5 (TC to Tc).

Author response:

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

First, we thank the reviewers for a thorough reading of our paper and some useful comments. A recurrent remark of the reviewers concerns the appearance of kRas-expressing cells (labelled by a nuclear blue fluorescent marker) which we attribute to the progeny of the initially induced cell. The reviewers suggest that these cells may have been obtained through activation of the Cre-recombinase in other cells by cyclofen released from light scattering, via diffusion, leakiness, etc. These remarks are perfectly reasonable from people not familiar with the cyclofen uncaging approach that we are using, but are unwarranted as we shall show below. 

We have been using cyclofen uncaging with subsequent activation of a Cre-recombinase (or some other proteins) since 2010 (see ref.34, Sinha et al., Zebrafish 7, 199-204 (2010) and our 2018 review (ref.35, Zhang et al., ChemBioChem 19,1-8 (2018)). In our experiments, the embryos are incubated in the dark in 6µM caged cyclofen (cCyc) and washed in E3 medium (and transferred to a new medium with no cCyc). In these conditions, over many years we never observed activation of the recombinase, i.e. the appearance of the associated fluorescent label in cells of embryos grown in E3 medium. Hence leakiness can be ruled out (in presence of cCyc or in its absence).

Following transfer of the embryos to new E3 medium we illuminate the embryos locally with light at 405nm. In these conditions, cCyc is only partially uncaged and results in activation of Cre-recombinase in only a few cells (1,2, 3, …) within the illuminated region only, namely in the appearance of the kRas-associated nuclear blue fluorescent label in usually one cell (and sometimes in a few more). Data and statistics are now incorporated in the revised manuscript, see Fig.2A and S7. In absence of activation of a reprogramming factor these fluorescently labelled cells disappear within a few days (either via shut-down of their promotor, apoptosis or some other mechanism). The crucial point here is that we see less and not more kRas expressing cells (i.e. with nuclear blue fluorescence) in absence of VentX activation. This observation rules out activation of Cre-recombinase in other cells days after illumination due to leakiness, cyclofen released by light or diffusing from the illumination spot.

To observe many more fluorescent cells days after activation of the initial cell, one needs to transiently activate VentX-GR by overnight incubation in dexamethasone (DEX). Injecting the embryos at 1-cell stage with VentX-GR only or incubating them in DEX (without injection of VentX-GR) does not result in the appearance of more blue fluorescent cells.  Following activation of VentX-GR, the fluorescent cells observed a couple of days after initiation are visualized in E3 medium (i.e. in absence of cyclofen) and are localized to the vicinity of the otic vesicle (the region where the initial cell was activated). In the revised manuscript we show images of these fluorescent cells taken a few days apart in the same embryo in which a single cell was initially activated (Fig.S8). Hence, we attribute these cells to the progeny of the activated cell. Obviously, single cell tracking via time-lapse microscopy would definitely nail down this issue and provide fascinating insight into the initial stages of tumor growth. Unfortunately, immobilization of embryos in the usual medium (e.g. MS222, tricaine) over 5-6 days to track the division and motion of single cells is not possible. We are considering some other possibilities (immobilization in bungarotoxin or via photo-activation of anionic channels), but these challenging experiments are for a future paper.

Reviewer #1 (Public Review): 

The authors then performed allotransplantations of allegedly single fluorescent TICs in recipient larvae and found a large number of fluorescent cells in distant locations, claiming that these cells have all originated from the single transplanted TIC and migrated away. The number of fluorescent cells showed in the recipient larve just after two days is not compatible with a normal cell cycle length and more likely represents the progeny of more than one transplanted cell.  

As mentioned in the manuscript, we measure the density of cells/nl and inject in the yolk of 2dpf Nacre embryos a volume equivalent to about 1 cell, following published protocols (S.Nicoli and M.Presta, Nat.Prot. 2,2918 (2007)). We further image the injected cell(s) by fluorescence microscopy immediately following injection, as shown in Fig.4A and Fig.S8B. We might miss a few cells but not many. With a typical cell cycle of ~10h the images of tumors in larvae at 3dpt (and not 2dpt) correspond to  ~100 cells. In any case the purpose of this experiment was to show that the progeny of the initial induced cell is capable of developing into a tumor in a naïve fish, which is the operational definition of cancer that we adopted here. 

The ability to migrate from the injection site should be documented by time-lapse microscopy. 

As stated above our purpose here is not to study tumor formation from transplanted cell(s)  but to use that assay as an operational test of cancer. Besides as mentioned earlier single cell tracking in larvea over 3-4dpt is not a trivial task.

Then, the authors conclude that "By allowing for specific and reproducible single cell malignant transformation in vivo, their optogenetic approach opens the way for a quantitative study of the initial stages of cancer at the single cell level". However, the evidence for these claims are weak and further characterization should be performed to: 

(1) Show that they are actually activating the oncogene in a single cell (the magnification is too low and it is difficult to distinguish a single nucleus, labelling of the cell membrane may help to demonstrate that they are effectively activating the oncogene in, or transplanting, a single cell)  

In the revised manuscript we provide larger magnification of the initial induced cell and show examples of oncogene activation in more than one cell. 

(2) The expression of the genes used as markers of tumorigenesis is performed in whole larvae, with only a few transformed cells in them. Changes should be confirmed in FACS sorted fluorescent cells  

When the oncogene is activated in a whole larvae all cells are fluorescent and thus FACS  is of no use for cell sorting. Sorting could be done in larvae where single cells are activated , but then the efficiency of FACS is not good enough to isolate the few fluorescent cells among the many more non-fluorescent ones. We agree that the expression change of the genes used as markers of tumorigenesis is an underestimate of their true change, but our goal at this time is not to precisely measure the change in expression level, but to show that the pattern of change was different from the controls and corresponded to what is expected in tumorigenesis.

(3) The histology of the so called "tumor masses" is not showing malignant transformation, but at the most just hyperplasia. 

The histology of the hyperplasic tissues show cellular proliferation with a higher density of nuclear material which is characteristic of tumors, Fig.S4C. Besides the increased expression of pERK in these tissues, Fig.S4A,B is also a hallmark of cancer. 

In the brain, the sections are not perfectly symmetrical and the increase of cellularity on one side of the optic tectum is compatible with this asymmetry. 

The expected T-shape formed by the sections of the tegmentum and hypothalamus are compatible with the symmetric sections shown in Fg.2D. The asymmetry in the optic tectum is a result of the hyperplasic growth.

(4) The number of fluorescent cells found dispersed in the larvae transplanted with one single TIC after 48 hours will require a very fast cell cycle to generate over 50 cells. Do we have an idea of the cell cycle features of the transplanted TICs? 

As answered above, the transplanted larvae are shown at 3dpt. With a cell cycle of about 10h, a single cell can give rise to about 100 cells in that time lapse.  

Reviewer #2 (Public Review): 

Summary: 

This paper describes a genetically tractable and modifiable system …which could be used to study an array of combinations and temporal relationships of these cancer drivers/modifiers. 

We thank this referee for its positive comments. We would also like to point out that our approach provides for the first quantitative means to estimate the probability of tumorigenesis from a single cell, an estimate which is crucial in any assessment of cancer malignancy and the effectiveness of prophylactics. 

Weaknesses: 

There is minimal quantitation of … the efficiency of activation of the Ras-TFP fusion (Fig 1) in, purportedly, a single cell. …, such information seems essential.  

We have added more images of induction of a single (or a few cells) and a plot where the probability of RAS activation in one or a few cells is specified. 

The authors indicate that a single cell is "initiated" (Fig 2) using the laser optogenetic technique, but without definitive genetic lineage tracing, it is not possible to conclude that cells expressing TFP distant from the target site near the ear are daughter cells of the claimed single "initiated" cell. A plausible alternative explanation is 1) that the optogenetic targeting is more diffuse (i.e. some of the light of the appropriate wavelength hits other cells nearby due to reflection/diffraction), so these adjacent cells are additional independent "initiated" cells or 2) that the uncaged tamoxifen analogue can diffuse to nearby cells and allow for CreER activation and recombination.  

We have addressed this point in our general comments to the reviewers’ remarks. The possibilities mentioned by this reviewer would result in cells expressing TFP in absence of VentX activation, which is NOT the case. Cells expressing TFP away from the initial site are observed DAYS after activation of the oncogene (and TFP) in a single cell and ONLY upon activation of VentX.

In Fig 2B, the claim is made that "the activated cell has divided, giving rise to two cells" - unless continuously imaged or genetically traced, this is unproven. 

We have addressed this remark previously. Tracking of larvae over many days is not possible with the usual protocol using tricaine to immobilize the larvae. Nonetheless, in the revised version we present images of an embryo imaged at various times post activation (1hpi, 3dpi, 7dpi) where proliferation and metastasis of the cells can be observed. We are pursuing other alternatives for time-lapse microscopy over many days, since besides convincing the sceptics, a single cell tracking experiment (possibly coupled with in-situ spatial transcriptomics) will shed a new and fascinating light on the initial stages of tumor growth. 

In addition, it appears that Figures S3 and S4 are showing that hyperplasia can arise in many different tissues (including intestine, pancreas, and liver, S4C) with broad Ras + Ventx activation …. This should be clarified in the manuscript). 

This is true and has been clarified in the new version. 

In Fig S7 where single cell activation and potential metastasis is discussed, similar gut tissues have TFP+ cells that are called metastatic, but this seems consistent with the possibility that multiple independent sites of initiation are occurring even when focal activation is attempted. 

As mentioned previously this is ruled out by the fact that these cells are observed days after cyclofen uncaging (and TFP activation) and IF AND ONLY IF VentX was activated during the first dpi.

Although the hyperplastic cells are transplantable (Fig 4), the use of the term "cells of origin of cancer" or metastatic cells should be viewed with care in the experiments showing TFP+ cells (Fig 1, 2, 3) in embryos with targeted activation for the reasons noted above.  

The purpose of this transplantation experiment was to show that cell in which both kRas and VentX have been activated possess the capacity to metastasize and develop a tumor mass when transplanted in a naïve zebrafish. This -  to the best of our knowledge  - is the operational definition of a malignant tumor. Notice also that transplantation of kRAS only activated cells (i.e. without subsequent activation of VentX) does NOT yield tumors, rather the transplanted cell disappears after a few days, see Fig.S10. 

Reviewer #3 (Public Review): 

Summary: 

This study employs an optogenetics approach … to examine tumorigenesis probabilities under altered tissue environments.  

We thank this reviewer for this remark, since we believe that the probability to assess the probability of tumorigenesis from a single cell is probably the most significant contribution of this work.

Weaknesses: 

Lack of Methodological Clarity: The manuscript lacks detailed descriptions of methodologies, 

We have included additional detail of our methodology and statistical analyses in the revised manuscript.

Sub-optimal Data Presentation and Quality:  

Lack of quantitative data and control condition data obtained from images of higher magnification limits the ability to robustly support the conclusions.  

We have included more images at higher magnification and quantitative data to support the main report of targeted single cell induction. 

Here are some details:  

Authors might want to provide more evidence to support their claim on the single cell KRAS activation.  

More images and a data on activation of single or few cells in the illumination field are provided as well as statistical analysis of  cell induction.  

Stability of cCYC: The manuscript does not provide information on the half-life and stability of cCYC. Understanding these properties is crucial for evaluating the system's reliability and the likelihood of leakiness, which could significantly influence the study's outcomes. 

We have been using the cCyc system for about 14 years. We refer the reader to our previous papers and reviews on this methodology. Briefly, cCyc is stable when not illuminated with light around 375nm. Typically, we incubate our embryos in the dark for about 1h before washing, transferring them into E3 medium and illuminating them. Assessing the leakiness of the system is easy as expression of a fluorescent marker is permanently turned on. We have observed none in the conditions of our experiment or in previous works.

Metastatic Dissemination claim: However, the absence of a supportive cellular compartment within the fin-fold tissue makes the presence of mTFP-positive metastatic cells there particularly puzzling. This distribution raises concerns about the spatial specificity of the optogenetic activation protocol … The unexpected locations of these signals suggest potential ectopic activation of the KRAS oncogene, 

We have addressed this remark in the introduction and above. Specifically, metastatic and proliferative mTFP-positive cells are observed IF AND ONLY IF VentX is also activated concomitant with activation of kRAS in a single cell. No proliferative cells are observed in absence of VentX activation, or in presence of VentX or Dex alone, or if kRAS has not been activated by cyclofen uncaging. 

Image Resolution Concerns: The cells depicted in Figure 3C β, which appear to be near the surface of the yolk sac and not within the digestive system as suggested in the MS, underscore the necessity for higher-resolution imaging. Without clearer images, it is challenging to ascertain the exact locations and states of these cells, thus complicating the assessment of experimental results. 

Better images are provided in the revised version.

The cell transplantation experiment is lacking protocol details:

Details are provided. We have followed regular protocols for transplantation:  S.Nicoli and M.Presta, Nat.Prot. 2,2918 (2007). 

If the cells are obtained from whole larvae with induced RAS + VX expression, it is notable and somewhat surprising that the larvae survived up to six days post-induction (6dpi) before cells were harvested for transplantation. This survival rate and the subsequent ability to obtain single cell suspensions raise questions about the heterogeneity of the RAS + VX expressing cells that transplanted. 

From Fig.S4D, about 50% of the embryos survive at 6dpi. Though an interesting question by itself we have not (yet) addressed the important issue of the heterogeneity of the outgrowth obtained from a single cell. Our purpose here was just to show that cells in which both kRAS and VentX have been activated possess the capacity to metastasize and develop a tumor mass when transplanted in a naïve zebrafish. This -  to the best of our knowledge  - is the operational definition of a malignant tumor.

Unclear Experimental Conditions in Figure S3B: …It is not specified whether the activation of KRAS was targeted to specific cells or involved whole-body exposure. 

This was whole body (global) illumination and is specified in the revised version.

Contrasting Data in Figure S3C compared to literature: The graph in Figure S3C indicates that KRAS or KRAS + DEX induction did not result in any form of hyperplastic growth. The authors should provide detailed descriptions of the conditions under which the experiments were conducted in Figure S3B and clarifying the reasons for the discrepancies observed in Figure S3C are crucial. The authors should discuss potential reasons for the deviation from previous reports. 

This discrepancy is discussed in the revised version. First the previous reports consider the development of tumors within 3-4 weeks which we have not studied in detail. Second, the expression of the oncogene in these reports might be stronger than in ours. Third, the stochastic and random appearance of tumors in these reports suggest that some other mechanism (transient stress-induced reprogramming?) might have activated the oncogene in the initial cell. 

Further comments: 

Throughout the study, KRAS-activated cell expansion and metastasis are two key phenotypes discussed that Ventx is promoting. However, the authors did not perform any experiments to directly show that KRAS+ cells proliferate only in Ventx-activated conditions.  

Yes, we did. See Fig. S1 and compare with Fig.S3B, or Fig.S10A in comparison with Fig.2A,B.

The authors also did not show any morphological features or time-lapse videos demonstrating that KRAS+ cells are motile, even though zebrafish is an excellent model for in vivo live imaging. This seems to be a missed opportunity for providing convincing evidence to support the authors' conclusions.  

Performing time-lapse microscopy on larvae over many (4-5) days is not possible with the regular tricaine protocol for immobilization. We are definitely planning such experiments, but they will require some other protocol, perhaps using bungarotoxin or some optogenetic inhibitory channels.

There were minimal experimental details provided for the qPCR data presented in the supplementary figures S5 and S6, therefore, it is hard to evaluate result obtained. 

More details are given in the revised version.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Abstract: what is the definition of tumors that they are using? I never heard of a full-blown tumor that develops in less than 6 days from a single cell!  

This is indeed surprising! We are using an operational definition of a tumor: if cells from an hyperplasic tissue can metastasize and outgrow when transplanted in a naïve zebrafish, then it is a tumor. 

Introduction: The claim that this is the first report of the induction of oncogene expression in a single cell in zebrafish is wrong as there are other reports (PMID: 27810924, PMID: 30061297) 

These other approaches are invasive (electroporation and transplantation). We have added non-invasive in the revised version. 

Figure 2: The quality of these images is too low to visualize the infiltration that they talk about, the sections are not perfectly coronal and the asymmetric distribution of cells may be confused with an infiltration. 

We have addressed this question above. 

Results, page 5: how do we know that these are metastatic cells? there could have been spurious activation in other locations, you need to prove that these cells moved from one place to the other and that they are of the same cell type as the primary tumor  

We have addressed this question extensively in the introduction and in our answers to the reviewers. We have also added a figure showing cell proliferation in the same embryos at various time post induction. Time-lapse microscopy studies of tumor initiation and growth over many days are planned, but will be the subject of an other paper.

Figure 3: not clear why they did not use anaesthetic or mounting media to take pictures of the transplanted fish  

We tried to minimally stress the larvae that are already in a perilous condition…

Results, page 6: Not clear why the authors used KRAS v12 as an oncogene and uncaged its expression in the brain, as KRAS is not a common oncogene for brain tumors. 

There are reports of kRASG12V tumors in zebrafish brain (doi: 10.1186/s12943-015-0288-2)

It is not clear what is the mechanism of Ventx -driven oncogenesis? What changes in gene expression, cell function etc are induced by Ventx in the cells that express KRASv12? The qPCR analysis performed is done on whole larvae and an analysis on single TICs and their progeny should be done following FACS sorting of fluorescent cells.  

FACS sorting of a single TIC (and its progeny) among many thousand cells in the embryo is not possible. The analysis on whole larvae provides an underestimate of the changes in gene expression following activation of kRAS and VentX.  We are looking for spatial transcriptomics as a better approach of the changes in gene expression induced in single TICs and their progeny, but that is beyond the scope of this paper. 

Nuclear staining is necessary to make sure that only 1 cell was transplanted. How is it possible that we get more than 50 cells from a single transplanted cell in less than 48 hours? What is the length of the cell cycle of these transformed cells? 

Nuclear staining is not necessary as the transplanted cell is fluorescent. Thus we can see how many cells are transplanted. With a cell-cycle of about 10h in 3dpt, a single cell will have generated as many as 100 cells. 

Reviewer #2 (Recommendations For The Authors): 

Minor grammatical change - hyperplasic more commonly called hyperplastic. 

Reviewer #3 (Recommendations For The Authors): 

Provide Detailed Methodologies: Clearly describe all experimental protocols used, particularly those for cell transplantation and photo-activation techniques. Detailed protocols will aid in replicating your findings and enhancing the manuscript's credibility.  

Done.

Provide High-Resolution Imaging data: To substantiate the claims about cell location and behaviour, provide high-resolution images where individual cells and their specific tissue contexts are clearly visible. 

Greater magnification images provided.

Quantitative Data: Incorporate quantitative analyses to strengthen the findings, particularly in experiments where cell proliferation and activation are key outcomes. 

Done.

Verify Single Cell Activation: Offer additional evidence or experimental validation to support the claim that KRASG12V activation is confined to single cells, considering the limitations mentioned about the photo-activation setup. 

Discussion, figures and statistical analysis added in manuscript.

Discuss Stability and Leakage of cCYC: Provide data on the stability and half-life of cCYC to assess the likelihood of system leakiness, which could influence the interpretation of your results.  

Reference to our previous papers and reviews added.

Clarify Metastatic Claims: Discuss the unexpected presence of mTFP-positive cells in nontraditional metastatic sites, like the fin fold, and consider additional experiments to verify whether these are cases of ectopic activation or true metastasis.

Discussion added in manuscript

Utilize time-lapse live imaging to visually document the motility and behaviour of KRAS+ cells over time, leveraging the strengths of the zebrafish model. 

Definitely interesting, but non trivial to conduct over many days and subject for a future paper.

Address Discrepancies in KRAS Activation Effects from literature: Specifically, discuss why your findings on KRAS-induced hyperplasia differ from existing literature. Consider whether experimental conditions or KRAS expression levels might have contributed to these differences.  

Discussion added in revised version

Author response:

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

Reviewer #1 (Public review):

When different groups (populations, species) are presented with similar environmental pressures, how similar are the ultimate targets (genes, pathways)? This study sought to illuminate this broader question via experimental evolution in D. simulans and quantifying gene-expression changes, specifically in the context of standing genetic variation (and not de novo mutation). Ultimately, the authors showed pleiotropy and standing-genetic variation play a significant role in the "predictability" of evolution.

The results of this manuscript look at the interplay between pleiotropy, standing genetic variation and parallelism (i.e. predictability of evolution) in gene expression. Ultimately, their results suggest that (a) pleiotropic genes typically have a smaller range in variation/expression, and (b) adaptation to similar environments tends to favor changes in pleiotropic genes, which leads to parallelism in mechanisms (though not dramatically). However, it is still uncertain how much parallelism is directly due to pleiotropy, instead of a complex interplay between them and ancestral variation.

Yes, the reviewer is correct that our results for the direct effects of pleiotropy were not consistent for both measures of pleiotropy. We highlight this in the discussion:” Only tissue specificity had a significant direct effect, which was even larger than the indirect effect (Table 2). No significant direct effect was found for network connectivity. The discrepancy between the two measures of pleiotropy is particularly interesting given their significant correlation (Supplementary Figure 1). This suggests that both measures capture aspects of pleiotropy that differ in their biological implications.”

Reviewer #2 (Public review):

Summary:

Lai and collaborators use a previously published RNAseq dataset derived from an experimental evolution set up to compare the pleiotropic properties of genes which expression evolved in response to fluctuating temperature for over 100 generations. The authors correlate gene pleiotropy with the degree of parallelisms in the experimental evolution set up to ask: are genes that evolved in multiple replicates more or less pleiotropic?

They find that, maybe counter to expectation, highly pleiotropic genes show more replicated evolution. And such effect seems to be driven by direct effects (which the authors can only speculate on) and indirect effect through low variance in pleiotropic genes (which the authors indirectly link to genetic variation underlying gene expression variance).

Weaknesses:

The results offer new insights into the evolution of gene expression and into the parameters that constrain such evolution, i.e., pleiotropy. Although the conclusions are supported by the data, I find the interpretation of the results a little bit complicated.

We are very happy to read that the reviewer finds our conclusions to be supported by the data.

Major comment:

The major point I ask the authors to address is whether the connection between polygenic adaptation and parallelism can indeed be used to interpret gene expression parallelism. If the answer is not, please rephrase the introduction and discussion, if the answer is yes, please make it explicit in the text why it is so.

Yes, we think that gene expression parallelism can be explained by polygenic adaptation.

The authors argument: parallelism in gene expression is the same as parallelism in SNP allele frequency (AFC) (see L389-383 here they don't mention that this explanation is derived from SNP parallelism and not trait parallelism, and see Fig1 b). In previous publications the authors have explained the low level of AFC parallelism using a polygenic argument. Polygenic traits can reach a new trait optimum via multiple SNPs and therefore although the trait is parallel across replicates, the SNPs are not necessarily so.

In the current paper, they seem to be exchanging SNP AFC by gene expression, and to me, those are two levels that cannot be interchanged. Gene expression is a trait, not a SNP, and therefore the fact that a gene expression doesn't replicate cannot be explained by polygenic basis, because again the trait is gene expression itself. And, actually the results of the simulations show that high polygenicity = less trait parallelism (Fig4).

We agree with the reviewer that it is important to consider different hierarchies when talking about the implications of polygenic adaptation. The lowest hierarchical level is SNP variation and the highest level is fitness. In-between these extreme hierarchical levels is gene expression. While gene expression is a trait itself, as correctly pointed out by the reviewer, it is possible that selection is not favoring a specific trait value, because selection targets a trait on a higher hierarchical level. This implies that not only SNPs, but also intermediate traits such as gene expression can exhibit redundancy. Considering a simple example of one selected trait (e.g. body size), which is affected by the expression level of two genes A and B, each regulated by SNP A1, A2 and B1, B2. It is now possible to modulate the focal trait by allele frequency changes of A1, which in turn will only affect gene A. Alternatively, SNP B2 may change, modifying the expression of gene B, leading to the same change in body size. Hence, we could have redundancy both at the SNP level as well as on the gene expression level (although higher redundancy is expected on the SNP level). Most importantly, this redundancy at intermediate hierarchical levels is not pure theory, but it is supported by empirical evidence. We have shown that redundancy exists not only for gene expression (10.1111/mec.16274) but also for metabolite concentrations (10.1093/gbe/evad098).

Now, if the authors focus on high parallel genes (present in e.g. 7 or more replicates) and they show that the eQTLs for those genes are many (highly polygenic) and the AFC of those eQTL are not parallel, then I would agree with the interpretation. But, given that here they just assess gene expression and not eQTL AFC, I do not think they can use the 'highly polygenic = low parallelism' explanation.

This is clearly an interesting proposed research project, but we doubt that it would result in the expected outcome. Since most of the adaptive gene expression changes are not having a simple genetic basis (10.1093/gbe/evae077) and most expression variation is determined by trans-regulatory effects (10.1038/s41576-020-00304-w), eQTL mapping will most likely not identify all contributing loci. Large effect loci are more easily identified, but they are also expected to be more parallel.

The interpretation of the results to me, should be limited to: genes with low variance and high pleiotropy tend to be more parallel, and the explanation might be synergistic pleiotropy.

We thank the reviewer for the suggestion, but prefer to stick to our interpretation of the data.

Comments on revisions: The authors didn't really address any of the comments made by any of the reviewers - basically nothing was changed in the main text. Therefore, I leave my original review unchanged.

We modestly disagree, in our point to point reply, we respond to all reviewers’ comments. Since, we did not identify any major problem in our manuscript, we only modified the wording in some parts where we felt that a clarification could resolve the misunderstanding of the reviewers. In response to the reviewers’ comments, we added a new paragraph in the discussion and generated a new figure.

Reviewer #3 (Public review):

The authors aim to understand how gene pleiotropy affects parallel evolutionary changes among independent replicates of adaptation to a new hot environment of a set of experimental lines of Drosophila simulans using experimental evolution. The flies were RNAsequenced after more than 100 generations of lab adaptation and the changes in average gene expression were obtained relative to ancestral expression levels from reconstructed ancestral lines. Parallelism of gene expression change among lines is evaluated as variance in differential gene expression among lines relative to error variance. Similarly, the authors ask how the standing variation in gene expression estimated from a handful of flies from a reconstructed outbred line affects parallelism. The main findings are that parallelism in gene expression responses is positively associated with pleiotropy and negatively associated with expression variation. Those results are in contradiction with theoretical predictions and empirical findings. To explain those seemingly contradictory results the authors invoke the role of synergistic pleiotropy and correlated selection, although they do not attempt to measure either.

Strengths:

The study uses highly replicated outbred laboratory lines of Drosophila simulans evolved in the lab under constant hot regime for over 100 generations. This allows for robust comparisons of evolutionary responses among lines.

The manuscript is well written and the hypotheses are clearly delineated at the onset.

The authors have run a causal analysis to understand the causal dependencies between pleiotropy and expression variation on parallelism.

The use of whole-body RNA extraction to study gene expression variation is well justified.

Weaknesses:

The accuracy of the estimate of ancestral phenotypic variation in gene expression is likely low because estimated from a small sample of 20 males from a reconstructed outbred line. It might not constitute a robust estimate of the genetic variation of the evolved lines under study.

We agree with the reviewer that variation estimates based on 20 samples are not very precise. Nevertheless, we demonstrated that the estimated variance in gene expression was highly correlated between two independent samples from the same ancestral population. Furthermore, we identified a significant correlation of expression variance with evolutionary parallelism. In other words, the biological signal has been sufficiently strong despite the variance estimate has been noisy.

There are no estimates of the standing genetic variation of expression levels of the genes under study, only estimates of their phenotypic variation. I wished the authors had been clear about that limitation and had refrained from equating phenotypic variation in expression level with standing genetic variation.

The reviewer is right that we did not estimate genetic variation of gene expression, but use expression variation as a proxy for the standing genetic variation. There are two potential problems with this approach. First, a large expression variation could be caused by a single large effect variant segregating at intermediate frequency. Such large effect variants will exhibit a highly parallel selection response-contrary to our empirical results. Since we have shown previously (10.1093/gbe/evae077) that adaptive gene expression changes are mostly polygenic we do not consider this extreme scenario to be very relevant in our study. Rather, we would like to emphasize that neither a SNP analysis of the 5’ region nor an eQTL study will provide an unbiased estimator of genetic variation of gene expression. The second problem arises if gene expression noise differs among genes, hence more noisy genes will appear to have more standing genetic variation than genes with less noise. Since, we average across many different cells and cell types, gene expression noise is expected to be levelled out- this aspect is discussed in detail in the manuscript.

In other words, despite these two potential limitations, we consider our approach superior to alternative approaches of estimating genetic variation in gene expression.

Moreover, since the phenotype studied is gene expression, its genetic basis extends beyond expressed sequences. The phenotypic variation of a gene's expression may thus likely misrepresent the genetic variation available for its evolution. The authors do not present evidence that sequence variation correlates with expression variation.

Gene expression is determined by the joint effects of cis-regulatory and trans-regulatory variation. Hence, recombination can create more extreme phenotypes than the one of the parental lines (in quantitative genetics this is called transgressive segregation). It is unclear to what extent this constitutes a problem for our analyses. Nevertheless, we would like to point out that eQTL mapping will miss many trans-acting variants and therefore we doubt that the requested empirical evidence for correlation between genetic variation (estimated by eQTL mapping) and observed expression variation is as straight forward as suggested by the reviewer.

Nevertheless, we reference an empirical study, which showed a positive correlation between expression variation and cis-regulatory variation.

The authors have not attempted to estimate synergistic pleiotropy among genes, nor how selection acts on gene expression modules. It makes their conclusion regarding the role of synergistic pleiotropy rather speculative.

The reviewer is correct that we did not demonstrate synergistic pleiotropy, but we discuss this as a possible explanation for the observed direct effects of pleiotropy.

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

Reviewer #1 (Public review):

The results of this manuscript look at the interplay between pleiotropy, standing genetic variation, and parallelism (i.e. predictability of evolution) in gene expression. Ultimately, their results suggest that (a) pleiotropic genes typically have a smaller range in variation/expression, and (b) adaptation to similar environments tends to favor changes in pleiotropic genes, which leads to parallelism in mechanisms (though not dramatically). However, it is still uncertain how much parallelism is directly due to pleiotropy, instead of a complex interplay between them and ancestral variation.

I have a few things that I was uncertain about. It may be these things are easily answered but require more discussion or clarity in the manuscript.

(1) The variation being talked about in this manuscript is expression levels, and not SNPs within coding regions (or elsewhere). The cause of any specific gene having a change in expression can obviously be varied - transcription factors, repressors, promoter region variation, etc. Is this taken into account within the "network connectivity" measurement? I understand the network connectivity is a proxy for pleiotropy - what I'm asking is, conceptually, what can be said about how/why those highly pleiotropic genes have a change (or not) in expression. This might be a question for another project/paper, but it feels like a next step worth mentioning somewhere.

In current study, we are only able to detect significant and repeatable expression changes but unable to identify the underlying causal variants. An eQTL study in the founder population in combination with genomic resequencing for both evolved and ancestral populations would be required to address this question.

(2) The authors do have a passing statement in line 361 about cis-regulatory regions. Is the assumption that genetic variation in promoter regions is the ultimate "mechanism" driving any change in expression? In the same vein, the authors bring up a potential confounding factor, though they dismiss it based on a specific citation (lines 476-481; citation 65). I'm of the mindset that in order to more confidently disregard this "issue" based on previous evidence, it requires more than one citation. Especially since the one citation is a plant. That specific point jumps out to me as needing a more careful rebuttal.

It was not our intention to claim that the expression changes in our experiment are caused by cis-regulatory variation only. We believe that the observed expression variation has both cis- and trans-genetic components, where as some studies tend to estimate much higher cisvariation for gene expression in Drosophila populations (e.g. [1, 2]). We mentioned the positive correlation between cis-regulatory polymorphism and expression variation to (1) highlight the genetic control of gene expression and (2) make the connection between polygenic adaptation and gene expression evolutionary parallelism.

(3) I feel like there isn't enough exploration of tissue specificity versus network connectivity. Tissue specificity was best explained by a model in which pleiotropy had both direct and indirect effects on parallelism; while network connectivity was best explained (by a small margin) via the model which was mostly pleiotropy having a direct effect on ancestral variation, that then had a direct effect on parallelism. When the strengths of either direct/indirect effects were quantified, tissue specificity showed a stronger direct effect, while network connectivity had none (i.e. not significant). My confusion is with the last point - if network connectivity is explained by a direct effect in the best-supported model, how does this work, since the direct effect isn't significant? Perhaps I am misunderstanding something.

To clarify, for network connectivity, there’s a significant “indirect” effect on parallelism (i.e. network connectivity affect ancestral gene expression and ancestral gene expression affect parallelism). Hence, in table 2, the direct effect of network connectivity on parallelism is weak and not significant while the indirect effect via ancestral variation is significant.

Also, network connectivity might favor the most pleiotropic genes being transcription factor hubs (or master regulators for various homeostasis pathways); while the tissue specificity metric perhaps is a kind of a space/time element. I get that a gene having expression across multiple tissues does fit the definition of pleiotropy in the broad sense, but I'm wondering if some important details are getting lost - I'm just thinking about the relative importance of what tissue specificity measurements say versus the network connectivity measurement.

We examined the statistical relationship between the two measures and found a moderate positive correlation on the basis of which we argued that the two measures may capture different aspects of pleiotropy. We appreciate the reviewer’s suggestions about the biological basis of the two estimates of pleiotropy, but we think that without further experimental insights, an extended discussion of this topic is too premature to provide meaningful insights to the readership.

Reviewer #2 (Public review):

Summary:

Lai and collaborators use a previously published RNAseq dataset derived from an experimental evolution set up to compare the pleiotropic properties of genes whose expression evolved in response to fluctuating temperature for over 100 generations. The authors correlate gene pleiotropy with the degree of parallelisms in the experimental evolution set up to ask: are genes that evolved in multiple replicates more or less pleiotropic?

They find that, maybe counter to expectation, highly pleiotropic genes show more replicated evolution. Such an effect seems to be driven by direct effects (which the authors can only speculate on) and indirect effects through low variance in pleiotropic genes (which the authors indirectly link to genetic variation underlying gene expression variance).

Weaknesses:

The results offer new insights into the evolution of gene expression and into the parameters that constrain such evolution, i.e., pleiotropy. Although the conclusions are supported by the data, I find the interpretation of the results a little bit complicated.

Major comment:

The major point I ask the authors to address is whether the connection between polygenic adaptation and parallelism can indeed be used to interpret gene expression parallelism. If the answer is not, please rephrase the introduction and discussion, if the answer is yes, please make it explicit in the text why it is so.

Our answer is yes, we interpreted gene expression parallelism (high ancestral variance -> less parallelism) using the same framework that links polygenic adaptation and parallelism (high polygenicity = less trait parallelism). We believe that our response covers several of the reviewer’s concerns.

The authors' argument: parallelism in gene expression is the same as parallelism in SNP allele frequency (AFC) (see L389-383 here they don't mention that this explanation is derived from SNP parallelism and not trait parallelism, and see Figure 1 b). In previous publications, the authors have explained the low level of AFC parallelism using a polygenic argument. Polygenic traits can reach a new trait optimum via multiple SNPs and therefore although the trait is parallel across replicates, the SNPs are not necessarily so.

Importantly, our rationale is based on the idea that gene expression is rarely the direct target of selection, but rather an intermediate trait [3]. Recently, we have specifically tested this assumption for gene expression and metabolite concentrations and our analysis showed that both traits were are redundant [4], as previously shown for DNA sequences [5]. The important implication for this manuscript is that gene expression is also redundant, so that adaptation can be achieved by distinct changes in gene expression in replicate populations adapting to the same selection pressure. This implies that we can use the same simulation framework for gene expression as for sequencing data. In our case different SNP frequencies correspond to different expression levels (averaged across individuals from a population), which in turn increases fitness by modifying the selected trait. Importantly, the selected trait in our simulations is not gene expression, but a not defined high level phenotype. A key insight from our simulations is that with increasing polygenicity the expression of a gene is more variable in the ancestral population.

In the current paper, they seem to be exchanging SNP AFC by gene expression, and to me, those are two levels that cannot be interchanged. Gene expression is a trait, not an SNP, and therefore the fact that a gene expression doesn't replicate cannot be explained by a polygenic basis, because again the trait is gene expression itself. And, actually, the results of the simulations show that high polygenicity = less trait parallelism (Figure 4).

As detailed above, because adaptation can be reached by changes in gene expression at different sets of genes, redundancy is also operating on the expression level not just on the level of SNPs. To clarify, the x-axis of Fig. 4 is the expression variation in the ancestral population.

Now, if the authors focus on high parallel genes (present in e.g. 7 or more replicates) and they show that the eQTLs for those genes are many (highly polygenic) and the AFC of those eQTLs are not parallel, then I would agree with the interpretation. But, given that here they just assess gene expression and not eQTL AFC, I do not think they can use the 'highly polygenic = low parallelism' explanation.

The interpretation of the results to me, should be limited to: genes with low variance and high pleiotropy tend to be more parallel, and the explanation might be synergistic pleiotropy.

While we understand the desire to model the full hierarchy from eQTLs to gene expression and adaptive traits, we raise caution that this would be a very challenging task. eQTLs very often underestimate the contribution of trans-acting factors, hence the understanding of gene expression evolution based on eQTLs is very likely incomplete and cannot explain the redundancy of gene expression during adaptation. Hence, we think that the focus on redundant gene expression is conceptually simpler and thus allows us to address the question of pleiotropy without the incorporation of allele frequency changes.  

Reviewer #3 (Public review):

The authors aim to understand how gene pleiotropy affects parallel evolutionary changes among independent replicates of adaptation to a new hot environment of a set of experimental lines of Drosophila simulans using experimental evolution. The flies were RNAsequenced after more than 100 generations of lab adaptation and the changes in average gene expression were obtained relative to ancestral expression levels from reconstructed ancestral lines. Parallelism of gene expression change among lines is evaluated as variance in differential gene expression among lines relative to error variance. Similarly, the authors ask how the standing variation in gene expression estimated from a handful of flies from a reconstructed outbred line affects parallelism. The main findings are that parallelism in gene expression responses is positively associated with pleiotropy and negatively associated with expression variation. Those results are in contradiction with theoretical predictions and empirical findings. To explain those seemingly contradictory results the authors invoke the role of synergistic pleiotropy and correlated selection, although they do not attempt to measure either.

Strengths:

(1) The study uses highly replicated outbred laboratory lines of Drosophila simulans evolved in the lab under a constant hot regime for over 100 generations. This allows for robust comparisons of evolutionary responses among lines.

(2) The manuscript is well written and the hypotheses are clearly delineated at the onset.

(3) The authors have run a causal analysis to understand the causal dependencies between pleiotropy and expression variation on parallelism.

(4) The use of whole-body RNA extraction to study gene expression variation is well justified.

Weaknesses:

(1) It is unclear how well phenotypic variation in gene expression of the evolved lines has been estimated by the sample of 20 males from a reconstructed outbred line not directly linked to the evolved lines under study. I see this as a general weakness of the experimental design.

Our intention was not to measure the phenotypic variance of the evolved lines, but rather to estimate the phenotypic variance at the beginning of the experiment. Hence, we measured and investigated the variation of gene expression in the ancestral population since this was the beginning of the replicated experimental evolution. Furthermore, since the ancestral population represents the natural population in Florida, the gene expression variation reflects the history of selection history acting on it.

(2) There are no estimates of standing genetic variation of expression levels of the genes under study, only phenotypic variation. I wished the authors had been clear about that limitation and had discussed the consequences of the analysis. This also constitutes a weakness of the study.

The reviewer is correct that we do not aim to estimate the standing genetic variation, which is responsible for differences in gene expression. While we agree that it could be an interesting research question to use eQTL mapping to identify the genetic basis of gene expression, we caution that trans-effects are difficult to estimate and therefore an important component of gene expression evolution will be difficult to estimate. Hence, we consider that our focus on variation in gene expression without explicit information about the genetic basis is simpler and sufficient to address the question about the role of pleiotropy.

(3) Moreover, since the phenotype studied is gene expression, its genetic basis extends beyond expressed sequences. The phenotypic variation of a gene's expression may thus likely misrepresent the genetic variation available for its evolution. The genetic variation of gene expression phenotypes could be estimated from a cross or pedigree information but since individuals were pool-sequenced (by batches of 50 males), this type of analysis is not possible in this study.

We agree with the reviewer that gene expression variation may also have a non-genetic basis, we discuss this in depth in the discussion of the manuscript.  

(4) The authors have not attempted to estimate synergistic pleiotropy among genes, nor how selection acts on gene expression modules. It makes any conclusion regarding the role of synergistic pleiotropy highly speculative.

We mentioned synergistic pleiotropy as a possible explanation for our results. A positive correlation between the fitness effect of gene expression variation would predict more replicable evolutionary changes. A similar argument has been made by [6]. 

I don't understand the reason why the analysis would be restricted to significantly differentially expressed genes only. It is then unclear whether pleiotropy, parallelism, and expression variation do play a role in adaptation because the two groups of adaptive and non-adaptive genes have not been compared. I recommend performing those comparisons to help us better understand how "adaptive" genes differentially contribute to adaptation relative to "nonadaptive" genes relative to their difference in population and genetic properties.

We agree with the reviewer that the comparison between the pleiotropy of adaptive and nonadaptive genes is interesting. We performed the analysis but omitted from the current manuscript for simplicity. Similar to the results in [6], non-adaptive genes are more pleiotropic than the adaptive genes. For adaptive genes we find a positive correlation between the level of pleiotropy and evolutionary parallelism. Thus, high pleiotropy limits the evolvability of a gene, but moderate and potentially synergistic pleiotropy increases the repeatability of adaptive evolution. We included this result in the revised manuscript and discuss it.

There is a lack of theoretical groundings on the role of so-called synergistic pleiotropy for parallel genetic evolution. The Discussion does not address this particular prediction. It could be removed from the Introduction.

We modestly disagree with the reviewer, synergistic pleiotropy is covered by theory and empirical results also support the importance of synergistic pleiotropy. 

References

(1) Genissel A, McIntyre LM, Wayne ML, Nuzhdin SV. Cis and trans regulatory effects contribute to natural variation in transcriptome of Drosophila melanogaster. Molecular biology and evolution. 2008;25(1):101-10. Epub 20071112. doi: 10.1093/molbev/msm247. PubMed PMID: 17998255.

(2) Osada N, Miyagi R, Takahashi A. Cis- and Trans-regulatory Effects on Gene Expression in a Natural Population of Drosophila melanogaster. Genetics. 2017;206(4):2139-48. Epub 20170614. doi: 10.1534/genetics.117.201459. PubMed PMID: 28615283; PubMed Central PMCID: PMCPMC5560811.

(3) Barghi N, Hermisson J, Schlötterer C. Polygenic adaptation: a unifying framework to understand positive selection. Nature reviews Genetics. 2020;21(12):769-81. Epub 2020/07/01. doi: 10.1038/s41576-020-0250-z. PubMed PMID: 32601318.

(4) Lai WY, Otte KA, Schlötterer C. Evolution of Metabolome and Transcriptome Supports a Hierarchical Organization of Adaptive Traits. Genome biology and evolution. 2023;15(6). Epub 2023/05/26. doi: 10.1093/gbe/evad098. PubMed PMID: 37232360; PubMed Central PMCID: PMCPMC10246829.

(5) Barghi N, Tobler R, Nolte V, Jaksic AM, Mallard F, Otte KA, et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS biology. 2019;17(2):e3000128. Epub 2019/02/05. doi: 10.1371/journal.pbio.3000128. PubMed PMID: 30716062.

(6) Rennison DJ, Peichel CL. Pleiotropy facilitates parallel adaptation in sticklebacks. Molecular ecology. 2022;31(5):1476-86. Epub 2022/01/09. doi: 10.1111/mec.16335. PubMed PMID: 34997980; PubMed Central PMCID: PMCPMC9306781.

Author response:

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

Reviewer 1:

Point 1 of public reviews and point 2 of recommendations to authors. 

Temporal ambiguity in credit assignment: While the current design provides clear task conditions, future studies could explore more ambiguous scenarios to further reflect real-world complexity…. The role of ambiguity is very important for the credit assignment process. However, in the current task design, the instruction of the task design almost eliminates the ambiguity of which the trial's choice should be assigned credit to. The authors claim the realworld complexity of credit assignment in this task design. However, the real-world complexity of this type of temporal credit assignment involves this type of temporal ambiguity of responsibility as causal events. I am curious about the consequence of increasing the complexity of the credit assignment process, which is closer to the complexity in the real world.

We agree that the structure of causal relationships can be more ambiguous in real-world contexts. However, we also believe that there are multiple ways in which a task might approach “real-world complexity”. One way is by increasing the ambiguity in the relationships between choices and outcomes (as done by Jocham et al., 2016). Another is by adding interim decisions that must be completed between viewing the outcome of a first choice, which mimics task structures such as the cooking tasks described in the introduction. In such tasks, the temporal structure of the actions maybe irrelevant, but the relationship between choice identities and the actions is critical to be effective in the task (e.g., it doesn’t matter whether I add spice before or after the salt, all I need to know that adding spice will result in spicy soup).  While ambiguity about either form of causal relation is clearly an important part of real-world complexity, and would make credit assignment harder, our study focuses on how links between outcomes and specific past choice identities are created at the neural level when they are known to be causal. 

We consequently felt it necessary to resolve temporal ambiguity for participants. Instructing participants on the structure of the task allowed us to make assumptions about how credit assignment for choice identities should proceed (assign credit to the choice made N trials back) and allowed us make positive predictions about the content of representations in OFC when viewing an outcome. This gave the highest power to detect multivariate information about the causal choice and the highest interpretability of such findings. 

In contrast, if we had not resolved this ambiguity, it would be difficult to tell if incorrect decoding from the classifier resulted from noise in the neural signal, or if on that trial participants were assigning credit to non-causal choices that they erroneously believed to have caused the outcome due to the perceived temporal structure. We believe this would have ultimately decreased our power to determine whether representations of the causal choice were present at the time of outcome because we would have to make assumptions about what counts as a “true” causal representation. 

We have commented on this in the discussions (p.13): 

“While our study was designed to focus on the complexity of assigning credit in tasks with different known causal structures, another important component of real-world credit assignment is temporal ambiguity. To isolate the mechanisms which create associations between specific choices and specific outcomes, we instructed participants on the causal structure of each task, removing temporal ambiguity about the causal choice.  However, our results are largely congruent with previously reported results in tasks that dissolved the typical experimental trial structure, producing temporal ambiguity, and which observed more pronounced spreading of effect, in addition to appropriate credit assignment (Jocham et al, 2016).  Namely, this study found that activation in the lOFC increased only when participants received rewards contingent on a previous action, an effect that was more pronounced in subjects whose behavior reflected more accurate credit assignment. This suggests a shared lOFC mechanism for credit assignment in different types of complex environments. Whether these mechanisms extend to situations where the temporal causal structure is completely unknown remains an important question.”

Point 2 of public reviews and point 1 of recommendations to authors

Role of task structure understanding: The difference in task comprehension between human subjects in this study and animal subjects in previous studies offers an interesting point of comparison…. The credit assignment involves the resolution of the ambiguity in which the causal responsibility of an outcome event is assigned to one of the preceding events. In the original study of Walton and his colleagues, the monkey subjects could not be instructed on the task structure defining the causal relationships of the events. Then, the authors of the original study observed the spreading of the credit assignments to the "irrelevant" events, which did not occur in the same trial of the outcome event but to the events (choices) in neighbouring trials. This aberrant pattern of the credit assignment can be due to the malfunctions of the credit assignment per se or the general confusion of the task structure on the part of the monkey subjects. In the current study design, the subjects are humans and they are not confused about the task structure. Consistently, it is well known that human subjects rarely show the same patterns of the "spreading of credit assignment". So the implicit mechanism of the credit assignment process involves the understanding of the task structure. In the current study, there are clearly demarked task conditions that almost resolve the ambiguity inherent in the credit assignment process. Yet, the focus of the current analysis stops short of elucidating the role of understanding the task structure. It would be great if the authors could comment on the general difference in the process between the conditions, whether it is behavioral or neural.

We would like to thank the reviewer for making this important point. We believe that understanding the structure of the credit-assignment problem above is quite important, at least for the type of credit assignment described here. That is, because participants know that the outcome viewed is caused by the choice they made, 0 or 1 trials into the past, they can flexibly link choice identities to the newly observed outcomes as the probabilities change. Note, however, that this is already very challenging in the 1-back condition because participants need to track the two independently changing probabilities. We believe this is critical to address the questions we aimed to answer with this experiment, as described above. 

We agree that this might be quite different from previous studies done with non-human primates, which also included many more training trials and lesions to the lOFC. Both of these aspects could manifest as difference in task performance and processing at behavioural and neural levels, respectively. Consistent with this possibility, in our task, we found no differences in credit spreading between conditions, suggesting that humans were quite precise in both, despite causal relationships being harder to track in the “indirect transition condition”. This lack of credit spreading could be because humans better understood the task-structure compared to macaques or be due to differences in functioning of the OFC and other regions. Because all participants were trained to understand, and were cued with explicit knowledge of, the task structure, it is difficult to isolate its role as we would need another condition in which they were not instructed about the task structure. This would also be an interesting study, and we leave it to future research to parse the contributions of task-structure ambiguity to credit assignment. 

Point 3 of public reviews. 

The authors used a sophisticated method of multivariate pattern analysis to find the neural correlate of the pending representation of the previous choice, which will be used for the credit assignment process in the later trials. The authors tend to use expressions that these representations are maintained throughout this intervening period. However, the analysis period is specifically at the feedback period, which is irrelevant to the credit assignment of the immediately preceding choice. This task period can interfere with the ongoing credit assignment process. Thus, rather than the passive process of maintaining the information of the previous choice, the activity of this specific period can mean the active process of protecting the information from interfering and irrelevant information. It would be great if the authors could comment on this important interpretational issue.

We agree that lFPC is likely actively protecting the pending choice representation from interference with the most recent choice for future credit assignment. This interpretation is largely congruent with the idea of “prospective memory” (e.g., Burgess, Gonen-Yaacovi, Volle, 2011), in which the lFPC can be thought of as protecting information that will be needed in the future but is not currently needed for ongoing behavior. That said, from our study alone it is difficult to make claims about whether the information maintained in frontal pole is actively protecting this information because of potentially interfering processes. Our “indirect transition condition” only contains trials where there is incoming, potentially interfering information about new outcomes, but no trials that might avoid interference (e.g., an interim choice made but there is nothing to be learned from it). We comment on this important future direction on page 14:  

“One interpretation of these results is that the lFPC actively protects information about causal choices when potentially interfering information must be processed. Future studies will be needed to determine if the lFPC’s contributions are specific to these instances of potential interference, and whether this is a passive or active process”

Point 3 of recommendation to authors 

A slightly minor, but still important issue is the interpretation of the role of lOFC. The authors compared the observed patterns of the credit assignment to the ideal patterns of credit assignment. Then, the similarity between these two matrices is used to find the associated brain region. In the assumption that lOFC is involved in the optimal credit assignment, the result seems reasonable. But as mentioned above, the current design involves the heavy role of understanding the task structure, it is debatable whether the lOFC is just involved in the credit assignment process or a more general role of representing the task structure.

We agree that this is an important distinction to make, and it is very likely that multiple regions of the OFC carry information about the task structure, and the extent to which participants understood this structure may be reflected in behavioral estimates of credit assignment or the overall patterns of the matrices (though all participants verbalized the correct structure prior to the task). However, we believe that in our task the lOFC is specifically involved in credit-assignment because of the content of the information we decoded. We demonstrated that the lOFC and HPC carry information about the causal choice during the outcome. These results cannot be explained by differences in understanding of the task structure because that understanding would have been consistent across trials where participants choose either shape identity. Thus, a classifier could not use this to separate these types of trials and would reflect chance decoding.   

One interpretation of the lOFC’s role in credit assignment is that it is particularly important when a model of the task structure has to be used to assign credit appropriately. Here, we show lOFC the reinstates specific causal representations precisely at the time credit needs to be assigned, which are appropriate to participants’ knowledge of the task structure.  These representations may exist alongside representations of the task structure, in the lOFC and other regions of the brain (Park et al., 2020; Boorman et al., 2021; Seo and Lee, 2010; Schuck et al., 2016). We have added the following sentences to clarify our perspective on this point in the discussion (p. 13):

“Our results from the “indirect transition” condition show that these patterns are not merely representations of the most recent choice but are representations of the causal choice given the current task structure, and may exist alongside representations of the task structure, in the lOFC and elsewhere (Boorman et al., 2021; Park et al., 2020; Schuck et al., 2016; Seo & Lee, 2010).”

Point 4 of public reviews and point 4 of recommendation to authors

Broader neural involvement: While the focus on specific regions of interest (ROIs) provided clear results, future studies could benefit from a whole-brain analysis approach to provide a more comprehensive understanding of the neural networks involved in credit assignment… Also, given the ROI constraint of the analysis, the other neural structure may be involved in representing the task structure but not detected in the current analysis

Given our strong a priori hypotheses about regions of interest (ROIs) in this study, we focused on these specific areas. This choice was based on theoretical and empirical grounds that guided our investigation. However, we thank the reviewer for pointing this out and agree that there could be other unexplored areas that are critical to credit-assignment which we did not examine. 

We conducted the same searchlight decoding procedure on a whole brain map and corrected for multiple comparisons using TFCE. We found no significant regions of the brain in the “direct transition condition” but did find other significant regions in our information connectivity analysis of the “indirect transition condition”. In addition to replicating the effects in lOFC and HPC, we also found a region of mOFC which showed a strong correlation with pending choice in lFPC. It’s difficult to say whether this region is involved in credit assignment per se, because we did not see this region in the “direct transition condition” and so we cannot say that it is consistently related to this process. However, the mOFC is thought to be critical to representing the current task state (Schuck et al., 2016), and the task structure (Park et al., 2020). In our task, it could be a critical region for communicating how to assign credit given the more complex task structure of the “indirect transition condition” but more evidence would be needed to support this interpretation. 

For now, we have added the results of this whole brain analysis to a new supplementary figure S7 (page 41), and all unthresholded maps have been deposited in a Neurovault repository, which is linked in the paper, for interested readers to assess.  

Minor points:

There are some missing and confusing details in the Figure reference in the main text. For example, references to Figure 3 are almost missing in the section "Pending item representations in FPl during indirect transitions predict credit assignment in lOFC". For readability, the authors should improve this point in this section and other sections.

Thank you to the reviewer for pointing this out. We have now added references to Figure 3 on page 8:

“Our analysis revealed a cluster of voxels specifically within the right lFPC ([x,y,z] = [28, 54, 8], t(19) = 3.74, pTFCE <0.05 ROI-corrected; left hemisphere all pTFCE > 0.1, Fig. 3A)”

And on page 10: 

Specifically, we found significant correlations in decoding distance between lFPC and bilateral lOFC ([x,y,z] = [-32,24, -22], t(19) = 3.81, [x,y,z] = [20, 38, -14], t(19) = 3.87, pTFCE <0.05 ROI corrected]) and bilateral HC ([x,y,z] = [-28, -10, -24], t(19) = 3.41, [x,y,z] = [22, -10, -24], t(19) = 4.21, pTFCE <0.05 ROI corrected]), Fig. 3C).

Task instructions for the two conditions (direct and indirect) play important roles in the study. If possible, please include the following parts in the figures and descriptions in the introduction and/or results sections.

We have now included a short description of the condition instructions beginning on page 5: 

“Participants were instructed about which condition they were in with a screen displaying “Your latest choice” in the direct transition condition, and “Your previous choice” in the indirect condition.”

And have modified Figure 1 to include the instructions in the title of each condition. We thought this to be the most parsimonious solution so that the choice options in the examples were not occluded. 

The subject sample size might be slightly too small in the current standards. Please give some justifications.

We originally selected the sample size for this study to be commensurate with previous studies that looked for similar behavioral and neural effects (see Boorman et al., 2016; Howard et al., 2015; Jocham et al., 2016). This has been mentioned in the “methods” section on page 24.  

However, to be thorough, we performed a power analysis of this sample size using simulations based on an independently collected, unpublished data set. In this data set, 28 participants competed an associative learning task similar to the task in the current manuscript. We trained a classifier to decode causal choice option at the time of feedback, using the same searchlight and cross-validation procedures described in the current manuscript, for the same lateral OFC ROI. We calculated power for various sample sizes by drawing N participants with replacement 1000 times, for values of N ranging from 15 to 25. After sampling the participants, we tested for significant decoding for the causal choice within the subset of data, using smallvolume TFCE correction to correct for multiple comparisons. Finally, we calculated the proportion of these samples that were significant at a level of pTFCE <.05.  

The results of this procedure show that an N of 20 would result in 84.2% power, which is slightly above the typically acceptable level of 80%. We have added the following sentences to the methods section on page 25: 

“Using an independent, unpublished data set, we conducted a power analysis for the desire neural effect in lOFC. We found that this number of participants had 84% power to detect this effect (Fig. S8).” 

We also added the following figure to the supplemental figures page (42):

Reviewer 2:

I have several concerns regarding the causality analyses in this study. While Multivariate analyses of information connectivity between regions are interesting and appear rigorous, they make some assumptions about the nature of the input data. It is unclear if fMRI with its poor temporal resolution (in addition to possible region-specific heterogeneity in the readouts), can be coupled with these casual analysis methods to meaningfully study dynamics on a decision task where temporal dynamics is a core component (i.e., delay). It would be helpful to include more information/justification on the methods for inferring relationships across regions from fMRI data. Along this line, discussing the reported findings in light of these limitations would be essential.

We agree that fMRI is limited for capturing fast neural dynamics, and that it can be difficult to separate events that occur within a few seconds. However, we designed the information connectivity analysis to maximally separate the events in question – the representations of the causal choice being held in a pending state, and the representation of the causal choice during credit assignment. These events were separated by at least 10 seconds and by 15 seconds on average, which is commensurate with recommended intervals for disentangling information in such analysis (Mumford et al., 2012, 2014, also see van Loon et al., 2018, eLife; as example of fluctuations in decodability over time). This feature of our task design may not have been clear because information connectivity analyses are typically performed in the same task period. We clarify this point on page 32:

“Note that the decoding fidelity metric at each time point represents the decodability of the same choice at different phases of the task. These phases were separated by at least 10 seconds and 15 seconds on average, which can be sufficient for disentangling unique activity (Mumford et al., 2012, 2014).”

However, we agree with the reviewer that the limitations of fMRI make it difficult to precisely determine how roles of the OFC and lFPC might change over time, and whether other regions may contribute to information transfer at times scales which cannot be detected by fMRI. Further, we do not wish to imply causality between lFPC and lOFC (something we believe we do not claim in the paper), only that information strength in lFPC predicts subsequent strength of the same information in the OFC and HC. We have clarified this limitation on page 14:

“Although we show evidence that lFPC is involved in maintaining specific content about causal choices during interim choices, the limited temporal resolution of fMRI makes it difficult to tell if other regions may be supporting the learning processes at timescales not detectable in the BOLD response. Thus, it is possible that the network of regions supporting credit assignment in complex tasks may be much larger. Our results provide a critical first stem in discerning the nature of interactions between cognitive subsystems that make different contributions to the learning process in these complex tasks.”

Reviewer 3:  

Point 1 of public reviews:

They do find (not surprisingly) that the one-back task is harder. It would be good to ensure that the reason that they had more trouble detecting direct HC & lOFC effects on the harder task was not because the task is harder and thus that there are more learning failures on the harder oneback task. (I suspect their explanation that it is mediated by FPl is likely to be correct. But it would be nice to do some subsampling of the zero-back task [matched to the success rate of the one-back task] to ensure that they still see the direct HC and lOFC there).

We would like to thank the reviewer for this comment and agree that the “indirect transition condition” is more difficult than the direct transition condition. However, in this task it is difficult to have an explicit measure of learning failures per se because the “correctness” of a choice is to some extent subjective (i.e., based on the gift card preference and the computational model). We could infer when learning failures occur through the computational model by looking at trials in which participants made choices that the model would consider improbable, (i.e., non-reward maximizing) while accounting for outcome preference. However, there are also a myriad of other possible explanations for these choices, such as exploratory/confirmatory strategies, lapses in attention etc. Thus, we could not guarantee that the two conditions would be uniquely matched in difficulty with specific regard to learning even if we subsampled these trials. We feel it would be better left to future experiments which can specifically compare learning failures to tackle this issue. We have now addressed this point when discussing the model on page 31:  

“Note that learning failures are not trivial to identify in our paradigm and model, because every choice is based on a participant’s preference between gift card outcomes, and the ability of the computational model to accurately estimate participants’ beliefs in the stimulus-outcome transition probabilities.”

Point 2 of public reviews:

The evidence that they present in the main text (Figure 3) that the HC and lOFC are mediated by FPl is a correlation. I found the evidence presented in Supplemental Figure 7 to be much more convincing. As I understand it, what they are showing in SF7 is that when FPl decodes the cue, then (and only then) HC and lOFC decode the cue. If my understanding is correct, then this is a much cleaner explanation for what is going on than the secondary correlation analysis. If my understanding here is incorrect, then they should provide a better explanation of what is going on so as to not confuse the reader.

SF7 (now Figures 3C and 3D) does show that positive decoding in the HC and lOFC are more likely to occur when there is positive decoding in lFPC. However, the analysis shown in these figures are only meant to be control analysis to further characterise what is being captured, but not necessarily implied, by the information connectivity analysis. For example, in principle the classifier might never correctly decode a choice label in the lOFC or HC while still getting closer to the hyperplane when the lFPC patterns are correctly decoded. This would lead to a positive correlation, but a difficult to interpret result since patterns in lOFC and HPC are incorrect. Figure SF7A (now Fig. 3C) shows that this is not the case. Lateral OFC and HC have higher than chance positive decoding when lFPC has positive decoding. Figure SF7B (now Fig. 3D) shows that we can decode that information even if a new hyperplane is constructed. However, both cases have less information about the relationship between these regions because they do not include the trials where lOFC/HC and lFPC classifiers were incorrect at the same time. The correlation in Figure 3B includes these failures, giving a more wholistic picture of the data. We therefore try to concisely clarify this point on page 10:

“These signed distances allow us to relate both success in decoding information, as well as failures, between regions.”

And here on page 10: 

“Subsequent analyses confirmed that this effect was due to these regions showing a significant increase in positive (correct) decoding in trials where pending information could be positively (correctly) decoded in lFPC, and not simply due to a reduction in incorrect information fidelity (see Fig. 3C & 3D).”

And have integrated these figures on page 9:

Point 3 of public reviews:

I like the idea of "credit spreading" across trials (Figure 1E). I think that credit spreading in each direction (into the past [lower left] and into the future [upper right]) is not equivalent. This can be seen in Figure 1D, where the two tasks show credit spreading differently. I think a lot more could be studied here. Does credit spreading in each of these directions decode in interesting ways in different places in the brain?

We agree that this an interesting question because each component of the off diagonal (upper and lower triangles) may reflect qualitatively different processes of credit spreading. However, we believe this analysis is difficult to carry out with the current dataset for two reasons. First, we designed this study to ask specifically about the information represented in key credit assignment regions during precise credit assignment, meaning we did not optimize the task to induce credit spreading at any point. Indeed, our efforts to train participants on the task were to ensure they would correctly assign credit as much as possible. Figure 1F shows that the regression coefficients representing credit spreading in each condition are near zero (in the negative direction), with little individual differences compared to the credit assignment coefficients. Thus, any analysis aiming to test for credit spreading would unfortunately be poorly powered. Studies such as Jocham et al. (2016), with more variability in causal structures, or studies with ambiguity about the causal structure by dissolving the typical trial structure would be better suited to address this interesting question. The second reason why such an analysis would be challenging is that due to our design, it is difficult to intuitively determine what kind of information should be coded by neural regions when credit spreads to the upper diagonal, since these cells reflect current outcomes that are being linked to future choices. 

Replace all the FPl with LFPC (lateral frontal polar cortex)

We have no replace “FPl” with “LFPC” throughout the text and figures

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Comment of Review of Revised Version:

Although the authors have partly corrected the manuscript by removing the mislabeling in their Co-IP experiments, my primary concern on the actual functional connotations and direct interaction between PA28y and C1QBP still remains unaddressed. As already mentioned in my previous review, since the core idea of the work is PA28y's direct interaction with C1QBP, stabilizing it, the same should be demonstrated in a more convincing manner.

My other observation on the detection of C1QBP as a doublet has been addressed by usage of anti-C1QBP Monoclonal antibody against the polyclonal one used before. C1QBP doublets have not been observed in the present case.

The authors have also worked on the presentation of the background by suitably modifying the statements and incorporating appropriate citations.

However, the authors are requested to follow the recommendations provided to them by the reviewers to address the major concerns.

Thank you very much for your comments. We appreciate your concerns regarding the need for more direct evidence to support the stabilizing interaction between PA28γ and C1QBP. In response to your feedback, we have taken additional steps to provide more convincing evidence of this interaction.

To complement our existing pull-down and Co-IP experiments, we utilized AlphaFold 3 to predict the three-dimensional structure of the PA28γ-C1QBP complex. The predicted model reveals specific residues and interfaces that are likely involved in the direct interaction between PA28γ and C1QBP. Our analysis indicates that this interaction may depend on amino acids 1-167 and 1-213 of C1QBP (Revised Appendix Figure 1E-H). Furthermore, aspartate (ASP), as the 177th amino acids of PA28γ, was predicted to interact with the 76th amino acid threonine (THR) and the 78th amino acid glycine (GLY) of C1QBP (Revised Appendix Figure 1I). This structural insight was further validated by our immunoprecipitation experiments (Revised Figure 1J). These findings provide a molecular basis for the observed stabilizing effect and suggest potential mechanisms by which PA28γ influences C1QBP stability. Specifically, the identified interaction sites offer clues into how PA28γ may stabilize C1QBP at the molecular level.

Furthermore, we performed proximity ligation assays (PLA) to detect in situ interactions between PA28γ and C1QBP at the single-cell level. PLA results clearly demonstrate the presence of PA28γ-C1QBP complexes within cells, providing direct evidence of their physical interaction (Revised Figure 1D). This approach overcomes some of the limitations associated with traditional IP experiments and confirms the direct nature of the interaction.

In summary, the integration of AlphaFold 3 predictions, PLA data, and our previous Pull-down and Co-IP experiments provides robust and direct evidence for a stable interaction between PA28γ and C1QBP. We believe that these additional findings significantly reinforce our conclusions and effectively address the concerns raised by the reviewers. Once again, thank you for your valuable feedback, which has been instrumental in refining and enhancing our study.

Reviewer #2 (Public review):

Comment of Review of Revised Version:

Weaknesses:

Many data sets are shown in figures that cannot be understood without more descriptions either in the text or the legend, e.g., Fig. 1A. Similarly, many abbreviations are not defined.

The revision addressed these issues.

Some of the pull-down and coimmunoprecipitation data do not support the conclusion about the PA28g-C1QBP interaction. For example, in Appendix Fig. 1B the Flag-C1QBP was detected in the Myc beads pull-down when the protein was expressed in the 293T cells without the Myc-PA28g, suggesting that the pull-down was not due to the interaction of the C1QBP and PA28g proteins. In Appendix Fig. 1C, assume the SFB stands for a biotin tag, then the SFB-PA28g should be detected in the cells expressing this protein after pull-down by streptavidin; however, it was not. The Western blot data in Fig. 1E and many other figures must be quantified before any conclusions about the levels of proteins can be drawn.

The revision addressed these problems.

The immunoprecipitation method is flawed as it is described. The antigen (PA28g or C1QBP) should bind to the respective antibody that in turn should binds to Protein G beads. The resulting immunocomplex should end up in the pellet fraction after centrifugation, and analyzed further by Western blot for coprecipitates. However, the method in the Appendix states that the supernatant was used for the Western blot.

The revision corrected this method.

To conclude that PA28g stabilizes C1QBP through their physical interaction in the cells, one must show whether a protease inhibitor can substitute PA28q and prevent C1QBP degradation, and also show whether a mutation that disrupt the PA28g-C1QBP interaction can reduce the stability of C1QBP. In Fig. 1F, all cells expressed Myc-PA28g. Therefore, the conclusion that PA28g prevented C1QBP degradation cannot be reached. Instead, since more Myc-PA28g was detected in the cells expressing Flag-C1QBP compared to the cells not expressing this protein, a conclusion would be that the C1QBP stabilized the PA28g. Fig. 1G is a quantification of a Western blot data that should be shown.

The binding site for PA28g in C1QBP was mapped to the N-terminal 167 residues using truncated proteins. One caveat would be that some truncated proteins did not fold correctly in the absence of the sequence that was removed. Thus, the C-terminal region of the C1QBP with residues 168-283 may still bind to the PA29g in the context of full-length protein. In Fig. 1I, more Flag-C1QBP 1-167 was pull-down by Myc-PA28g than the full-length protein or the Flag-C1QBP 1-213. Why?

The interaction site in PA28g for C1QBP was not mapped, which prevents further analysis of the interaction. Also, if the interaction domain can be determined, structural modeling of the complex would be feasible using AlphaFold2 or other programs. Then, it is possible to test point mutations that may disrupt the interaction and if so, the functional effect.

The revision added AlphaFold models for the protein interaction. However, the models were not analyzed and potential mutations that would disrupt the interact were not predicted, made and tested. The revision did not addressed the request for the protease inhibitor.

Thank you for your insightful comments regarding the binding site of PA28γ in C1QBP. We appreciate your concern about the potential misfolding of truncated proteins and the possible interaction between the C-terminal region (residues 168-283) of C1QBP and PA28γ in the context of full-length protein.

To address these concerns, we have conducted additional analyses and experiments to provide a more comprehensive understanding of the interaction between PA28γ and C1QBP. Using AlphaFold 3, we predicted the three-dimensional structure of the PA28γ-C1QBP complex. The model reveals specific residues and interfaces that are likely involved in the direct interaction between PA28γ and C1QBP. Notably, our structural analysis indicates that the interaction may primarily depend on amino acids 1-167 and 1-213 of C1QBP (Revised Appendix Figure 1E-H). Furthermore, aspartate (ASP), as the 177th amino acids of PA28γ, was predicted to interact with the 76th amino acid threonine (THR) and the 78th amino acid glycine (GLY) of C1QBP (Revised Appendix Figure 1I). This prediction supports the idea that the N-terminal region of C1QBP is crucial for its interaction with PA28γ. Regarding the observation in old Figure 1I (Revised Figure 1J), where more Flag-C1QBP 1-167 was pulled down by Myc-PA28γ compared to the full-length protein or Flag-C1QBP 1-213, we believe this can be explained by several factors:

A. The truncation of C1QBP to residues 1-167 may expose key interaction sites that are partially obscured in the full-length protein. This enhanced accessibility could lead to stronger binding affinity and higher pull-down efficiency.

B. While it is possible that some truncated proteins do not fold correctly, our data suggest that the N-terminal fragment (1-167) retains sufficient structural integrity to interact effectively with PA28γ. The increased pull-down of this fragment suggests that it captures the essential elements required for binding.

C. The C-terminal region (168-283) might exert steric hindrance or allosteric effects on the N-terminal binding site in the context of the full-length protein. This interference could reduce the overall binding efficiency, leading to less pull-down of full-length C1QBP compared to the truncated version.

Compared with the control group, the presence of Myc-PA28γ significantly increased the expression level of Flag-C1QBP (r Revised Figure 1G). Gray value analysis showed that in cells transfected with Myc-PA28γ, the decay rate of Flag-C1QBP was significantly slower than that of the control group (Revised Figure 1H), suggesting that PA28γ can delay the protein degradation of C1QBP and stabilize its protein level. This indicates that an increase in the level of PA28γ protein can significantly enhance the expression level of C1QBP protein, while PA28γ can slow down the degradation rate of C1QBP and improve its stability. In addition, our western blot analysis also proved that PA28γ could still prevent the degradation of C1QBP under the action of proteasome inhibitor MG-132 (Revised Appendix Figure 1D). Moreover, PA28γ could not stabilize the mutation of C-terminus of C1QBP (amino acids 94-282), which was not the interaction domain of PA28γ-C1QBP (Revised Figure 1K).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Barlow and coauthors utilized the high-parameter imaging platform of CODEX to characterize the cellular composition of immune cells in situ from tissues obtained from organ donors with type 1 diabetes, subjects presented with autoantibodies who are at elevated risk, or non-diabetic organ donor controls. The panels used in this important study were based on prior publications using this technology, as well as a priori and domain-specific knowledge of the field by the investigators. Thus, there was some bias in the markers selected for analysis. The authors acknowledge that these types of experiments may be complemented moving forward with the inclusion of unbiased tissue analysis platforms that are emerging that can conduct a more comprehensive analysis of pathological signatures employing emerging technologies for both high-parameter protein imaging and spatial transcriptomics.

Strengths:

In terms of major findings, the authors provide important confirmatory observations regarding a number of autoimmune-associated signatures reported previously. The high parameter staining now increases the resolution for linking these features with specific cellular subsets using machine learning algorithms. These signatures include a robust signature indicative of IFN-driven responses that would be expected to induce a cytotoxic T-cell-mediated immune response within the pancreas. Notable findings include the upregulation of indolamine 2,3-dioxygenase-1 in the islet microvasculature. Furthermore, the authors provide key insights as to the cell:cell interactions within organ donors, again supporting a previously reported interaction between presumably autoreactive T and B cells.

Weaknesses:

These studies also highlight a number of molecular pathways that will require additional validation studies to more completely understand whether they are potentially causal for pathology, or rather, epiphenomenon associated with increased innate inflammation within the pancreas of T1D subjects. Given the limitations noted above, the study does present a rich and integrated dataset for analysis of enriched immune markers that can be segmented and annotated within distinct cellular networks. This enabled the authors to analyze distinct cellular subsets and phenotypes in situ, including within islets that peri-islet infiltration and/or intra-islet insulitis.

Despite the many technical challenges and unique organ donor cohort utilized, the data are still limited in terms of subject numbers - a challenge in a disease characterized by extensive heterogeneity in terms of age of onset and clinical and histopathological presentation. Therefore, these studies cannot adequately account for all of the potential covariates that may drive variability and alterations in the histopathologies observed (such as age of onset, background genetics, and organ donor conditions). In this study, the manuscript and figures could be improved in terms of clarifying how variable the observed signatures were across each individual donor, with the clear notion that non-diabetic donors will present with some similar challenges and variability.

Thank you to all reviewers and editors for their thoughtful and constructive engagement with our manuscript. We agree that patient heterogeneity and the sample size limited the impact of this study. In the future, more cases with insulitis will become available and spatial technologies will become more scalable.

Given these constraints, we have made a significant effort to illustrate the individual heterogeneity of the disease by using the same color for each nPOD case ID throughout the manuscript and showing individual donors whenever feasible (e.g. Figures 1D-E, 2C, 2I, 3E, 3G, 4B-C, 5C, and 5F). For figures related to insulitis, we do not typically include non-T1D controls since they did not have any insulitis (Figure 2C). We also explicitly discuss the differences in the two autoantibody-positive, non-T1D cases: one closely resembled the T1D cases with respect to multiple features and the other more closely resembled the non-T1D, autoantibody-negative controls.

Reviewer #2 (Public review):

Summary:

The authors aimed to characterize the cellular phenotype and spatial relationship of cell types infiltrating the islets of Langerhans in human T1D using CODEX, a multiplexed examination of cellular markers

Strengths:

Major strengths of this study are the use of pancreas tissue from well-characterized tissue donors, and the use of CODEX, a state-of-the-art detection technique of extensive characterization and spatial characterization of cell types and cellular interactions. The authors have achieved their aims with the identification of the heterogeneity of the CD8+ T cell populations in insulitis, the identification of a vasculature phenotype and other markers that may mark insulitis-prone islets, and the characterization of tertiary lymphoid structures in the acinar tissue of the pancreas. These findings are very likely to have a positive impact on our understanding (conceptual advance) of the cellular factors involved in T1D pathogenesis which the field requires to make progress in therapeutics.

Weaknesses:

A major limitation of the study is the cohort size, which the authors directly state. However, this study provides avenues of inquiry for researchers to gain further understanding of the pathological process in human T1D.

Thank you for your analysis. We point the reader to our above description of our efforts to faithfully report the patient variability despite the small sample size.

Reviewer #3 (Public review):

Summary:

The authors applied an innovative approach (CO-Detection by indEXing - CODEX) together with sophisticated computational analyses to image pancreas tissues from rare organ donors with type 1 diabetes. They aimed to assess key features of inflammation in both islet and extra-islet tissue areas; they reported that the extra-islet space of lobules with extensive islet infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. The study also identifies four sub-states of inflamed islets characterized by the activation profiles of CD8+T cells enriched in islets relative to the surrounding tissue. Lymphoid structures are identified in the pancreas tissue away from islets, and these were enriched in CD45RA+ T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.

Strengths:

The analysis of tissue from well-characterized organ donors, provided by the Network for the Pancreatic Organ Donor with Diabetes, adds strength to the validity of the findings.

By using their innovative imaging/computation approaches, key known features of islet autoimmunity were confirmed, providing validation of the methodology.

The detection of IDO+ vasculature in inflamed islets - but not in normal islets or islets that have lost insulin-expression links this expression to the islet inflammation, and it is a novel observation. IDO expression in the vasculature may be induced by inflammation and may be lost as disease progresses, and it may provide a potential therapeutic avenue.

The high-dimensional spatial phenotyping of CD8+T cells in T1D islets confirmed that most T cells were antigen-experienced. Some additional subsets were noted: a small population of T cells expressing CD45RA and CD69, possibly naive or TEMRA cells, and cells expressing Lag-3, Granzyme-B, and ICOS.

While much attention has been devoted to the study of the insulitis lesion in T1D, our current knowledge is quite limited; the description of four sub-clusters characterized by the activation profile of the islet-infiltrating CD8+T cells is novel. Their presence in all T1D donors indicates that the disease process is asynchronous and is not at the same stage across all islets. Although this concept is not novel, this appears to be the most advanced characterization of insulitis stages.

When examining together both the exocrine and islet areas, which is rarely done, authors report that pancreatic lobules affected by insulitis are characterized by distinct tissue markers. Their data support the concept that disease progression may require crosstalk between cells in the islet and extra-islet compartments. Lobules enriched in β-cell-depleted islets were also enriched in nerves, vasculature, and Granzyme-B+/CD3- cells, which may be natural killer cells.

Lastly, authors report that immature tertiary lymphoid structures (TLS) exist both near and away from islets, where CD45RA+ CD8+T cells aggregate, and also observed an inflamed islet-subcluster characterized by an abundance of CD45RA+/CD8+ T cells. These TLS may represent a point of entry for T cells and this study further supports their role in islet autoimmunity.

Weaknesses:

As the authors themselves acknowledge, the major limitation is that the number of donors examined is limited as those satisfying study criteria are rare. Thus, it is not possible to examine disease heterogeneity and the impact of age at diagnosis. Of 8 T1D donors examined, 4 would be considered newly diagnosed (less than 3 months from onset) and 4 had longer disease durations (2, 2, 5, and 6 years). It was unclear if disease duration impacted the results in this small cohort. In the introduction, the authors discuss that most of the pancreata from nPOD donors with T1D lack insulitis. This is correct, yet it is a function of time from diagnosis. Donors with shorter duration will be more likely to have insulitis. A related point is that the proportion of islets with insulitis is low even near diagnosis, Finally, only one donor was examined that while not diagnosed with T1D, was likely in the preclinical disease stage and had autoantibodies and insulitis. This is a critically important disease stage where the methodology developed by the investigators could be applied in future efforts.

While this was not the focus of this investigation, it appears that the approach was very much immune-focused and there could be value in examining islet cells in greater depth using the methodology the authors developed.

Additional comments:

Overall, the authors were able to study pancreas tissues from T1D donors and perform sophisticated imaging and computational analysis that reproduce and importantly extend our understanding of inflammation in T1D. Despite the limitations associated with the small sample size, the results appear robust, and the claims well-supported.

The study expands the conceptual framework of inflammation and islet autoimmunity, especially by the definition of different clusters (stages) of insulitis and by the characterization of immune cells in and outside the islets.

Thank you for your feedback. We agree that it would be very informative to expand on our analysis of autoantibody-positive cases and look at additional non-immune features. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Do any of the observed cellular or structural features correlate with age of onset or disease duration? While numbers of subjects are low, considering these as continuous variables may clarify some of the findings.

Thank you for the suggestion. In Supplemental Figure 5B-C, we plotted the key immune signatures from the manuscript against the diabetes duration and age of onset.

(2) The IDO is an interesting observation and has prior support in the literature. The authors speculate this may be induced as a feature of IFNg expressed by lymphocytes in the local microenvironment. Can any of these concepts be further validated by staining for transcription factors or surrogate downstream markers associated with Th1 skewing (e.g., Tbet, CXCR3, etc)?

The only other interferon-stimulated gene in our panel is HLA-ABC. We updated Supplemental Figure 2F to include HLA-ABC expression in IDO- and IDO+ islets (within the “Inflamed” group). Consistent with the hypothesis that IDO is stimulated by interferon, HLA-ABC is also significantly higher in IDO+ islets than IDO- islets. PDL1, another interferon-stimulated gene. was included in the panel but we did not detect any signal. This antibody was very weak during testing in the tonsil, so we couldn’t confidently claim that PDL1 was not expressed.

(3) The authors discuss the potential that CD45RA may be expressed in Temra populations. This could use additional clarification and a distinction from Tscm if possible.

Unfortunately, we did not have the appropriate markers to distinguish naïve, TEMRA, or Tscm cells from each other. We updated the text in the discussion to include this consideration (Line 432).

(4) Supplemental Figure 5 is not informative in the current display.

Thank you, we replotted these data.

(5) Supplemental Table 1 could be expanded with additional metadata of interest, including the genetic features of the donors (e.g, class II diplotype and GRS2 values) that are published and available in the nPOD program.

Some genetic data are only available to nPOD investigators. We think it is more appropriate to request the data directly from them.

Reviewer #2 (Recommendations for the authors):

(1) I had only a few specific comments. I think the statement in Lines 317 and 318 is too strong. It implies that each lobe is always homogeneous for having all islets with insulitis or not having insulitis. Some lobes are certainly enriched for islets with insulitis but insulin+ islets without insulitis in some lobes in some T1D donors are seen. Please soften that statement.

We apologize for our lack of clarity. We have edited the text (line 305-309) to better articulate that organ donors fall on a spectrum. Thank you for raising this point as we think the motivation for our analysis is much clearer after these revisions.

(2) Please cite and discuss In't Veld Diabetes 20210 PMID: 20413508. While the main point of the paper is that there is beta cell replication after prolonged life support, another observation is that there is a correlation between prolonged life support and CD45+ cells in the pancreas parenchyma. This might indicate that not all immune cells in the parenchyma are T1D associated in donors with T1D.

Thank you, we have added this citation to our discussion of the importance of duration of stay in the ICU (Line 471).

(3) Can you rule out that CD46RA+/CD69+ CD8+ T cells in the islets are not TSCM?

(See above)

Reviewer #3 (Recommendations for the authors):

Similar studies in experimental models may afford increased opportunity to evaluate the significance of these findings and model their potential relevance for disease staging and therapeutic targeting.

We agree that the lack of experimental data limits the ability to interpret and validate the significance of our findings. We hope that our study motivates and helps inform such experiments.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

For the colony analysis, it is unclear from the methods and main text whether the initial individual sorted colonies were split and subject to different conditions to support the claim of bi-potency. The finding that 40% of colonies displayed tenogenic differentiation, may instead suggest heterogeneity of the sorted progenitor population. The methods as currently described, suggest that two different plates were subject to different induction conditions. It is therefore difficult to assess the strength of the claim of bi-potency.

Thanks for your valuable comment. We are sorry for the confusing illustration of colony assay. In fact, we first obtained CD29+/CD56+ myogenic progenitors by FACs. Then these freshly isolated cells were randomly seeded to 96-well plate with density of 1 cell/well. Subsequently, the single cell in each plate was cultured with growth medium to form colonies for ten days. Then myogenic induction was performed in three 96-well plates and tenogenic induction was performed in another three 96-well plates for subsequent analyses. We agree with your point that the sorted cell population could be heterogeneous myogenic progenitors. The result showed over 95% colonies successfully differentiated into myotubes, while 40% of colonies displayed tenogenic differentiation (Fig. 2g). Since the freshly obtained CD29+/CD56+ myogenic progenitors were randomly seeded for tenogenic induction or myogenic induction, the undifferentiated cells in each group were considered as the same sample. Furthermore, the optimal tenogenic differentiation condition for these cells was still waiting for investigation. Thus, we believe the colony analysis combined with the data in Figure 1 and Figure 2 could indicate the bi-potency for human CD29+/CD56+ myogenic progenitors.

This group uses the well-established CD56+/CD29+ sorting strategy to isolate muscle progenitor cells, however recent work has identified transcriptional heterogeneity within these human satellite cells (ie Barruet et al, eLife 2020). Given that they identify a tenocyte population in their human muscle biopsy in Figure 1a, it is critical to understand the heterogeneity contained within the population of human progenitors captured by the authors' FACS strategy and whether tenocytes contained within the muscle biopsy are also CD56+/CD29+.

Thanks for your constructive suggestion. We have included more samples to perform scRNA-seq and reanalyzed the data. The scRNA-seq data revealed that all the CD29+/CD56+ cells were myogenic progenitors, which occupied 19.3% of all the myogenic progenitors (Fig. 1e). However, there existed no tenocytes with CD29+/CD56+ (Fig. 1d), and tenocytes made up only a small percentage (0.06%) of all the mononuclear cells. Thus, human CD29+/CD56+ cells are myogenic progenitors, and tenocytes contained within the muscle biopsy are not CD56+/CD29+. In addition, both published research and our results indicated the heterogeneity of CD29+/CD56+ myogenic progenitors. Since the main purpose of current study was to investigate the tenogenic differentiation potential of CD29+/CD56+ myogenic progenitors, the heterogeneity in CD29+/CD56+ myogenic progenitors should be investigated in the further study.

The bulk RNA sequencing data presented in Figure 3 to contrast the expression of progenitor cells under different differentiation conditions are not sufficiently convincing. In particular, it is unclear whether more than one sample was used for the RNAseq analyses shown in Figure 3. The volcano plots have many genes aligned on distinct curves suggesting that there are few replicates or low expression. There is also a concern that the sorted cells may contain tenocytes as tendon genes SCX, MKX, and THBS4 were among the genes upregulated in the myogenic differentiation conditions (shown in Figure 3b).

Thanks for your comment. Each group consisted of three samples for RNAseq analyses. We are sorry there existed a minor analysis mistake in Fig. 3b and Fig. 3c, which have been reanalyzed in the revised version. There was no significantly difference of tendon related marker genes after myogenic differentiation (Fig. 3b), while these tenogenic genes were significantly up-regulated after tenogenic induction (Fig. 3c). As for contamination of tenocytes, scRNA-seq data showed there were no tenocytes with both CD29 and CD56 positive (please see response to Comment 2). And almost all the obtained cells highly expressed myogenic progenitors markers PAX7/MYOD1/MYF5 (Fig. 1f-g). Low expression levels of tendon markers were identified in these cells (Fig. 2a-c). Furthermore, although tendon genes slightly upregulated in myogenic differentiation conditions, these markers dramatically upregulated in tenogenic differentiation conditions (Fig. 2c). Thus, we believe the bulk RNA sequencing data could add the evidence of tenogenic differentiation ability of human CD29+/CD56+ myogenic progenitors.

Reviewer #2 (Public Review):

scRNAseq assay using total mononuclear cell population did not provide meaningful insight that enriched knowledge on CD56+/CD29+ cell population. CD56+/CD29+ cells information may have been lost due to the minority identity of these cells in the total skeletal muscle mononuclear population, especially given the total cell number used for scRNAseq was very low and no information on participant number and repeat sample number used for this assay. Using this data to claim a stem cell lineage relationship for MuSCs and tenocytes may not convincing, as seeing both cell types in the total muscle mononuclear population does not establish a lineage connection between them.

Thanks for your constructive suggestion. We have included more samples to perform scRNA-seq and reanalyzed the data. Three samples with a total of 57,193 cells were included for analysis. As you can see in Fig. 1d and 1e, the joint expression analysis revealed that all the CD29+/CD56+ cells were myogenic progenitors, which occupied 19.3% of all the myogenic progenitors.  In addition, we agree with your comment that the pseudotime analysis could be a bit misleading as the nature of computational biology with pseudotime plots, so we deleted this assay.

The TGF-b pathway assay uses a small molecular inhibitor of TGF-b to probe Smad2/3. The assay conclusion regarding Smad2/3 pathway responsible for tenocyte differentiation may be overinterpretation without Smad2/3 specific inhibitors being applied in the experiments.

Thanks for your comment. We agree with your comment and we have revised it in the revision version (Figure 7, Line 306-326).

Reviewer #3 (Public Review):

This dual differentiation capability was not observed in mouse muscle stem cells.

Thanks for your comment. We have explored the tenogenic differentiation potential of mouse MuSCs both in vivo and in vitro. However, low tenogenic differentiation ability was revealed (Figure 4), which might be due to species diversity. Maybe it is more demanding for humans to maintain the homeostasis of the locomotion system and the whole organism locomotion ability in much longer life span and bigger body size. Thus, the current study also indicated that anima studies may not clinically relevant when investigating human diseases.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

The methods section contained insufficient details for sample tissue for many methods, including the single cell analysis, RNA FISH, and for in vivo cardiotoxin treatment. ie. how were the samples subclustered for the monocle pseudotime analysis; how many cells were counted in the FISH shown in Fig 1e/f, does the n=5 refer to tissue sections or biological replicates?; for the double injury, what was the cardiotoxin dose?

Thanks for your comment. Three samples and a total 57,193 cells were analyzed in single cell analysis (Line 464). We deleted RNA FISH assay data because it provided limited information to prove bipotential ability of human CD29+/CD56+ myogenic progenitors. In addition, since the pseudotime analysis could be a bit misleading as the nature of computational biology with pseudotime plots, we also deleted this assay. For the double injury, 15μl of 10μM cardiotoxin was used for lineage tracing (Line 533).

Additionally, the RNA sequencing datasets are not currently publicly available under the accession numbers provided.

The raw data of RNA sequencing has been uploaded in NCBI (accession number: PRJNA1178160, PRJNA1012476 and PRJNA1012828), and these data will be released immediately after publication.

The poor resolution of 1d makes it impossible to read any of the gene names or interpret the expression profiles of their proposed trajectories.

Since the pseudotime analysis could be a bit misleading as the nature of computational biology with pseudotime plots, we deleted this assay.

What does the color key for 3a refer to? It is not indicated in the figure or legend.

Thanks for your comment. The color key for 3a refer to “Scaled expression values”, which has been added in the revised version.

scRNAseq of the sorted CD29/56+ population could help uncover possible cell heterogeneity within these muscle progenitors and which sub-populations of myogenic progenitor cells have tenogenic potential.

Thanks for your valuable suggestion. We included more cells from three biological repetitions to perform scRNA-seq and found that CD29/CD56+ cells were absolutely from myogenic progenitors (Fig. 1d and 1e). We agree with you that additional scRNAseq will be helpful to clarify the possible cell heterogeneity within these muscle progenitors. Since the main scope of current study is to investigate the biopotential of CD29/CD56+ myogenic progenitors, analysis of scRNAseq of the sorted CD29/56+ population would be performed in the further study for further exploration.

Typos: Line 459 sored cells... preparasion with Chromium Single Cell 3' Reagent Kits (10X genomics, cat# 1000121-1000157). Figure 4E - typo in the word tamoxifen.

Thanks for your valuable suggestion. We are sorry for the typos and have revised these typos (Line 459 and Fig. 4e).

Reviewer #2 (Recommendations For The Authors):

(1) scRNAseq is performed in total mononuclear cells isolated from human skeletal muscle. The cell number (around 15000 cells) seems very low for this assay, given the CD56+/CD29+ cells are a minority population in this sequencing, the data does not seem to provide meaningful insight into the MuSC cell identities. No information on sample numbers and number of patient participants can be found in the paper.

Thanks for your comment. We added more cells to reanalyze the data in the revised manuscript. Three samples with a total of 57,193 cells were analyzed (Line 464). The joint expression analysis revealed that all the CD29+/CD56+ cells were myogenic progenitors, which occupied 19.3% of all the myogenic progenitors (Fig. 1d and 1e). These scRNA-seq data combined with functional experiment confirmed the MuSC cell identity of CD29+/CD56+ cells from mononuclear cells.

In this regard, the paragraph starts with "To confirm the single cell analysis results, we first isolated myogenic progenitor cells from human muscle biopsy using FACS as described previously" which is misleading as the seRNAseq is not the result of the sorted cells. Please reword this paragraph to clarify.

The related paragraph has been reworded (Line 84-95).

Similarly, the existence of myocytes and tenocytes in scRNAseq does not necessarily prove a stem cell and mature cell lineage relationship. Please edit the wording to avoid overinterpretation.

Thanks for your reminding. Since the pseudotime analysis could be a bit misleading as the nature of computational biology with pseudotime plots, we deleted this assay.

(2) The in vitro differentiation assays are well performed, which included bulk culture and clonal culture. The efficiencies of those two assays seem to have discrepancies which may need clarification. Again, no sample numbers and repeats have been informed.

Since the tendon differentiation period for bulk culture was 12 days, those myotubes fused by CD29+/CD56+ myogenic progenitors with only myogenic differentiation potential will be no longer alive. Thus, the efficiency of bulk culture seemed higher than that in clonal culture. As stated in statistical analysis, at least three biological replicates and technical repeats were performed in each experimental group (Line 577).

In these paragraphs, terminologies including MuSCs, myogenic progenitors, CD56+/CD29+, and Pax7+ are interchangeably used, which generates confusion while reading. It is probably best to consistently use the cell sorting markers markers to address this cell population, throughout the paper.

Thanks for your constructive suggestion. The cell population was consistently named as CD29+/CD56+ myogenic progenitors throughout the paper.

Information on the proliferation rate and expansion of the MuSCs would be useful but not provided.

Thanks for your comment. The analysis of cell proliferation was added in Figure 1 (Fig. 1h).

The murine cell differentiation assays are not as convincing as the human study. The assay regarding "mouse muscle CD29+/CD56+ cells were isolated for tenogenic induction. However, very few mouse muscle CD29+/CD56+ cells expressed myogenic progenitor cell marker Pax7, MyoD1 and Vcam1" does not add any value to the work as those markers are not mouse MuSC markers to start with.

Thanks for your comment. The experiments concerning mouse muscle CD29+/CD56+ cells have been deleted to avoid misleading.

The Pax7-cre-TdTomato assay was also not convincing, as a negative finding may not be the best proof of absence.

Thanks for your comment. Pax7 positive cells could consistently express TdTomato for lineage tracing. In current study, large amount of tdTomato+ myofibers were observed after muscle injury (SFig. 2c-d), suggesting that the tracing system works well. However, less than 0.2% tendon cells originated from TdTomato+ MuSCs were observed even four months after tendon removal (Fig. 4f-g). When comparing in vivo data between murine MuSCs and human CD29+/CD56+ myogenic progenitors, we believe these data could indicate the poor tendon differentiation abilities of murine MuSCs.

(5) TGFb as a pathway of smad2/3 mediated tenocyte differentiation assays were well done albeit not novel. Using TGFb universal inhibitor may not accurately state the pathways were due to SMAD2/3 inhibition either.

We agree with your comment and the conclusion concerning SMAD2/3 has been deleted throughout the manuscript.

The paper also needs thorough proofreading. Currently, typographic, grammatical, and logical sequences of writing do not lend the paper to easy reading.

(1) Figure 1K and 1I have similar legends but presumably K is referring to MuSC and I is referring to differentiated cells.

(2) Tenogenic and myogenic induction should be changed to tenogenic/myogenic differentiation as they are the cells at the end of differentiation.

(3) Figure 6, it is not clear how the "human cells" are calculated in this assay.

Thanks for your constructive comment. (1) The figure legends in Figure1 have been revised (Line 797-804).  (2) Tenogenic and myogenic induction have been changed to tenogenic/myogenic differentiation manuscript when they are referring to cells at the end of differentiation (Fig.1, Fig.2, Fig.3, Fig.4, Fig.7 and SFig.1). (3) In Figure 6, “human cells” is referring to those injured tendons with transplantation of human CD29+/CD56+ myogenic progenitors. To evaluate the function of human CD29+/CD56+ myogenic progenitors, PBS group was set as negative control and uninjured group was set as normal control.

Reviewer #3 (Recommendations For The Authors):

(1) The full extent of the differentiation potential of CD29+/CD56+ stem/progenitor cells has not been thoroughly evaluated. There can also exist heterotopic ossification in injured tendon sites. Thus, it remains unclear whether these cells are truly bipotent as the authors claim, or can they differentiate into chondrocytes and osteoblasts.

Thanks for your comment. The current study focused on the tenogenic differentiation potential of CD29+/CD56+ myogenic progenitors, so the research priority was the bipotential ability of CD29+/CD56+ myogenic progenitors. We agree with you that chondrogenic and osteogenic ability of CD29+/CD56+ myogenic progenitors is also important and would investigate it in the further study.

(2) In Figure 3, the GO analysis also shows increased enrichment of muscle-related terms including muscle contraction and filament. Please clarify it.

The tenogenic differentiation efficiency of CD29+/CD56+ myogenic progenitors was about 40% in clonal assay. Some cells would myogenically differentiated under this tenogenic induction system. Thus, the GO analysis could also enrich muscle related terms including muscle contraction and filament.

(3) The authors use TNC staining to evaluate cell transplantation. My concern is whether the TNC expression is specific to the tendon site, or do engrafted human cells also express TNC in other sites such as muscle?

TNC is one of a well-known tendon-related markers. As you can see in Figure 6b and Figure 6c, although some human cells (labeled by Lamin A/C) were engrafted in muscle tissue area (labeled by MyHC), these engrafted human cells didn’t express TNC in muscle. In addition, we also used tendon related markers SCX and TNMD to confirm the tenogenic differentiation ability of engrafted human cells in vivo (SFig. 3a and 3b).

(4) The authors demonstrate that CD29+/CD56+ human stem/progenitor cells could efficiently transplant and contribute to myofiber regeneration in vivo. However, why were only a few transplanted human cells differentiating into myofiber (labeled by MyHC) in the tenon injury model even with CTX injection?

Thanks for your comment. Since skeletal muscle is able to regenerate with in situ muscle progenitor cells, regeneration of injured muscle by CTX injection was dependent on not only CD29+/CD56+ myogenic progenitors, but also native murine MuSCs. Thus, it is reasonable that there were only a few transplanted human cells differentiating into myofiber (labeled by MyHC) in the tenon injury model even with CTX injection.

(5) Figure 7 shows the crucial role of TGFB/SMAD signaling for the tenogenesis of human CD29+/CD56+ stem/progenitor cells. However, can TGFB/SMAD signaling activation facilitate the tenogenic differentiation of mouse MuSCs? This point is crucial to clarify the difference of MuSCs between different species.

Thanks for your valuable suggestion. We did a series of pilot assays to investigate the effect of TGFβ signaling activation to facilitate tenogenic differentiation of mouse MuSCs (Author response image 1). As you can see, activating TGFβ by SRI-011381 could slightly increase the expression of tenogenic markers of murine MuSCs. It’s an interesting topic and we would investigate it in the further study.

Author response image 1.

TGFβ signaling pathway slightly elevated tenogenic differentiation ability of murine MuSCs (a) Immunofluorescence staining of tendon marker Scx and Tnc in murine MuSCs induced for tenogenic differentiation with or without TGFβ signaling pathway agonist SRI-011381, respectively. Scale bars, 50 µm. (b) Quantification of Scx and Tnc fluorescent intensity in murine MuSCs undergone tenogenic induction with or without TGFβ signaling pathway agonist SRI-011381, respectively. Error bars indicated standard deviation (n=5). (c) Protein levels of Tnc and Scx. Murine MuSCs were induced towards tenogenic differentiation with or without TGFβ signaling pathway agonist SRI-011381. Total protein was extracted from cells before and after differentiation and subjected for Tnc and Scx immunoblotting. GAPDH was served as loading control.

(6) Please quantify the WB blot data throughout the manuscript.

Thanks for your comment. The WB blot data has been quantified throughout the manuscript.

(7) The data of RT-qPCR should indicate what the fold changes in relative to throughout the manuscript.

Thanks for your comment. The sentence “GAPDH was served as reference gene” was added in the figure legends to illustrate RT-qPCR results.

Author response:

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

eLife Assessment<br /> …. While intuitive, the model's underlying issue is grouping many factors under "variance in reproductive success" without explicitly modeling the molecular processes. This limitation, …, provides incomplete support for the authors' claim that the observed paradoxical patterns in rRNA genes can largely be explained by homogenizing processes, such as gene conversion, unequal crossover and replication slippage.

This second paper addresses the genetic drift in multi-copy gene systems using rRNA genes as an example. Note that genetic drift happens in two stages here – within individuals and between individuals while the drift mechanisms are very different between the two stages. We now reply to the editors’ decision that it would be more rigorous to model each molecular process, than to lump all stochastic forces into V(K).  We respond to this criticism on three fronts.

First, for molecular evolutionists, there is NO NEED to model the detailed molecular processes.  This is because we are only interested in knowing the totality of the stochastic variations.  Interesting biological forces such as selection and meiotic drive are masked by such random forces. Our objective is precisely to lump all noises into a quantity that can be estimated.

Second, the homogenization process is the bulk, if not the totality of the within-individual random forces (i.e,, genetic drift). The criticism of incomplete support for drift as a sufficient account of the observations is curious because we did conclude that genetic drift is an insufficient explanation of the human data.  Since drift only influences fixation time, which can have a significant effect in short-term evolution (as shown in Fig 2), but it does not affect fixation rate itself. In contrast, selection influences the both. Thus, we can define the limitation of drift in evolutionary process. Even if the speed of drift-driven fixation is only a few generations, it is still too little for the human-chimpanzee divergence comparisons. In contrast, the speed of genetic drift in mice, as extrapolated from the polymorphism data, is sufficient to drive the divergence between M. m. domesticus and Mus spretus. The criticism appears to be that unbiased gene conversion, unequal crossover and replication slippage together may be insufficient to account for the observations. Since the contribution of each of these three forces is not central to our goal of filtering out the total contributions, we only conclude that the totality of within-individual drift in mice is sufficient to explain the data.

Third, even if we really want to dissect the molecular processes, previous attempts by prominent theorists like Tom Nagylaki and Tomoko Ohta could only model a small subset of such processes.  In fact, Ohta often lumps a few of these forces into one process. More importantly, if we want to tackle other systems like viruses and mitochondria, we will have to develop a new set of theories for each molecular process.  V(K) can take care of all such diverse systemes.  In short, genetic drift is just noises and our goal is to quantify them in total across diverse systmes.  By filtering out noises, we will be able to move on to something more important.

We now briefly comment on the WF models in relation to multi-gene systems. For example, in the case SARS-CoV-2, there are millions of virions in each patient among millions of patients. It is not possible to know what Ne acaully means in the WF modesl. Also, the rDNA population in each individual is not the sub-populations of the WF models.  After all, the mechanisms of genetic drift within individuals by the homogenization processes are entirely different from the genetic drift between individuals.  For a comparison, we published several papers (cited in #2) using the Haldane model to estimate the strength of genetic drift. It is also important to note that the parameters and assumptions of WF model cannot fully capture the evolutionary dynamics of the multi-copy genes.

… ., along with insufficient consideration of technical challenges in alignment and variants calling, provides incomplete support for the authors' claim …

Before delving into the technical details, we would like to summarize our defense. First, all rRNA gene copies belong in a pseudo-population, due to the homogenization process. The concept of specific locus with specific variants does not apply. Second, the levels of within-individual and within-species variation is so low that sequence alignment is not a problem at all. Third, thanks to the large number of sequence reads, occasional sequence errors (rarely encountered) should have minimal effects on the analyses.  Now the technical details:

Regarding the concerns about the alignment and variant calling, we would like to clarify our methodology. While we acknowledge the technical challenges inherent in alignment and variant calling, particularly with respect to orthologous alignments to distinguish different copies, it is important to note that rDNA copies are subject to homogenization processes, meaning that there is no orthology among rDNA copies. Due to the high sequence similarity and frequent genetic exchange among rDNA units within species, we used the species-specific rDNA reference sequence for variant calling. We directly utilized the raw read depth from all rDNA copies within individuals to calculate the site frequency. For each site, we focused on the frequency of the major allele to calculate nucleotide diversity using the 2p(1-p), where p represents the frequency of the major allele. This approach helps capture genetic variation while minimizing the impact of alignment or variant calling errors, which primarily affect low-frequency variants (e.g., 0.800A, 0.199T, 0.001C, with A being the major allele). As for the divergence sites between species, we defined  FST = 0.8 as a cutoff (roughly, when a mutant is > 0.95 in frequency in one species and < 0.05 in the other, FST would be > 0.80.),  which is less likely to be influenced by low-frequency polymorphic sites within species.We believe this method is more appropriate for estimating genetic diversity at rDNA than traditional variant calling pipelines designed to detect homozygotes and heterozygotes.

Author response:

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

eLife Assessment (divided into 3 parts)

This study presents a useful modification of a standard model of genetic drift by incorporating variance in reproductive success, claiming to address several paradoxes in molecular evolution. ……

It is crucial to emphasize that our model is NOT a modification of the standard model. The Haldane model, which is generalized here for population regulation, is based on the branching process. The Haldane model and the WF model which is based on population sampling are fundamentally different. We referred to our model as the integrated WF-H model because the results obtained from the WF model over the last 90 years are often (but not always) good approximations for the Haldane model. The analogy would be the comparisons between the Diffusion model and the Coalescence model. Obviously, the results from one model are often good approximations for the other.  But it is not right to say that one is a useful modification of the other.

We realize that it is a mistake to call our model the integrated WFH model, thus causing confusions over two entirely different models. Clearly, the word “integrated” did not help. We have now revised the paper by using the more accurate name for the model – the Generalized Haldane (GH) model. The text explains clerarly that the original Haldane model is a special case of the GH model.

Furthermore, we present the paradoxes and resolve them by the GH model.  We indeed overreached by claiming that WF models could not resolve them. Whether the WF models have done enough to resolve the paradoxes or at least will be able to resolve them should not be a central point of our study. Here is what we state at the end of this study.:

“We understand that further modifications of the WF models may account for some or all of these paradoxes. However, such modifications have to be biologically feasible and, if possible, intuitively straightforward. Such possible elaborations of WF models are beyond the scope of this study. We are only suggesting that the Haldane model can be extensively generalized to be an alternative approach to genetic drift. The GH model attempts to integrate population genetics and ecology and, thus, can be applied to genetic systems far more complex than those studied before. The companion study is one such example.”

….. However, some of the claimed "paradoxes" seem to be overstatements, as previous literature has pointed out the limitations of the standard model and proposed more advanced models to address those limitations….

As stated in the last paragraph of the paper, it is outside of the scope of our study to comment on whether the earlier WF models can resolve these paradoxes.  So, all such statements have been removed or at least drastically toned down in the formal presentation.  That said, editors and reviewers may ask whether we are re-inventing the wheels.  The answers are as follows:

First, two entirely different models reaching the same conclusion are NOT the re-invention of wheels. The coalescence theory does not merely rediscover the results obtained by the diffusion models. The process of obtaining the results is itself a new invention.  This would lead to the next question: is the new process more rigorous and more efficient?  I think the Haldane model is indeed more efficient in comparisons with the very complex modifications of the WF models. 

Second, we are not sure that the paradoxes have been resolved, or even can be resolved.  Note that these skepticisms have been purged from the formal presentation. Thefore, I am presenting the arguments outside of the paper for a purely intellectual discourse. Below, please allow us to address the assertions that the WF models can resolve the paradoxes. 

The first paradox is that the drift strength in relation to N is often opposite of the WF model predictions.  Since the WF models (standard or modified) do not generate N from within the model, how can it resolve the paradox?  In contrast, the Generalized Haldane model generates N within the model. It is the regulation of N near the carrying capacity that creates the paradox – When N increases, drift also increases.

The second paradox that the same locus experiences different drifts in males and females is accepted by the reviewers.  Nevertheless, we would like to point out that this second paradox echoed the first one as newly stated in the Discussion section “The second paradox of sex-dependent drift is about different V(K)’s between sexes (generally Vm > Vf) but the same E(K) between them. In the conventional models of sampling, it is not clear what sort of biological sampling scheme could yield V(K) ≠ E(K), let alone two separate V(K)’s with one single E(K). Mathematically, given separate K distributions for males and females, it is unlikely that E(K) for the whole population could be 1, hence, the population would either explode in size or decline to zero. In short, N regulation has to be built into the genetic drift model as the GH model does to avoid this paradox.”

The third paradox stems from the fact that drift is operating even for genes under selection. But then the drift strength, 2s/V(K) for an advantage of s, is indepenent of N or Ne. Since the determinant of drift strength in the WF model is ALWAYS Ne, how is Paradox 3 not a paradox for the WF model?

The 4th paradox about multi-copy gene systems is the subject of the companion paper (Wang et al.). Note that the WF model cannot handle systems of evolution that experience totally different sorts of drift within vs. between hosts (viruses, rDNAs etc).  This paradox can be understood by the GH model and and will be addressed in the next paper.

While the modified model presented in this paper yields some intriguing theoretical predictions, the analysis and simulations presented are incomplete to support the authors' strong claims, and it is unclear how much the model helps explain empirical observations.

The objections appear to be that our claims of “paradox resolution” being too strong.  We interpret this objection is based on the view (which we agree) that these paradoxes are intrisicallly difficult to resolve by the WF models. Since our model has been perceived to be a modified WF model, the claim of resolution is clearly too strong.  However, the GH model is conceptually and operationally entirely different from the WF models as we have emphasized above. In case our reading of the editorial comments is incorrect, would it be possible for some clarifications on the nature of “incomplete support”?  We would be grateful for the help.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing the identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify unknown clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomic datasets generated by others.

Strengths:

The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas. 

We thank this reviewer for appreciating the quality of our spatial data. We do not know what caused the technical problem (grayscale version of PDF) for this reviewer (the PDF figures are in color on the eLife website and on bioRxiv). We are surprised that the eLife discussion session did not resolve this issue.

Weaknesses:

Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as in neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells. 

The small size of an individual fly is one of the most challenging aspects of performing spatial transcriptomics. While the resolution of Molecular Cartography is rather high (< 200 nm), in the brain challenges remain as noted by the reviewer. Drosophila neuronal nuclei are notoriously small and cannot be easily resolved with the current imaging techniques. We agree that for a full atlas either expansion microscopy, 3D techniques or other super-resolution techniques will be required. 

Reviewer #2 (Public Review):

Summary:

The landmark publication of the "Fly Atlas" in 2022 provided a single cell/nuclear transcriptomic dataset from 15 individually dissected tissues, the entire head, and the body of male and female flies. These data led to the annotation of more than 250 cell types. While certainly a powerful and datarich approach, a significant step forward relies on mapping these data back to the organism in time and space. The goal of this manuscript is to map 150 transcripts defined by the Fly Atlas by FISH and in doing so, provide, for the first time, a spatial transcriptomic dataset of the adult fly. Using this approach (Molecular Cartography with Resolve Biosciences), the authors, furthermore, distinguish different RNA localizations within a cell type. In addition, they seek to use this approach to define previously unannotated clusters found in the Fly Atlas. As a resource for the community at large interested in the computational aspects of their pipeline, the authors compare the strengths and weaknesses of their approach to others currently being performed in the field.

Strengths:

(1) The authors use Resolve Biosciences and a novel bioinformatics approach to generate a FISHbased spatial transcriptomics map. To achieve this map, they selected 150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset and were used in the 2022 paper to annotate specific cell types; moreover, the authors chose several highly expressed genes characteristic of unannotated cell types. Together, the approach and generated data are important next steps in translating the transcriptomic data to spatial data in the organism.

We thank the reviewer for this comment, as it reminded us that we need to be clearer in the text, about how we chose the genes to investigate. The statement that we selected “150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset” is not correct. We have chosen genes with widely differing expression levels (log-scale range of 3.95 in body, 5.76 in head, we show this now in the new Figure 1 – figure fupplement 1B, D). Many of the chosen genes are also transcription factors. In fact, the here introduced method is more sensitive than the single cell atlas: the tinman positive cells were readily located (even non-heart cells were found to express tinman), whereas in the single cell FCA data tinman expression is often not detected in the cardiomyocytes (tinman is detected in 273 cells in the entire FCA (mean expression of 1.44 UMI in positive cells), and in 71 cells out of 273 cardiac cells (26%)). 

(2) Working with Resolve, the authors developed a relatively high throughput approach to analyze the location of transcripts in Drosophila adults. This approach confirmed the identification of particular cell types suggested by the FlyAtlas as well as revealed interesting subcellular locations of the transcripts within the cell/tissue type. In addition, the authors used co-expression of different RNAs to unbiasedly identify "new cell types". This pipeline and data provide a roadmap for additional analyses of other time points, female flies, specific mutants, etc.

(3) The authors show that their approach reveals interesting patterns of mRNA distribution (e.g alpha- and beta-Trypsin in apical and basal regions of gut enterocytes or striped patterns of different sarcomeric proteins in body muscle). These observations are novel and reveal unexpected patterns. Likewise, the authors use their more extensive head database to identify the location of cells in the brain. They report the resolution of 23 clusters suggested by the single-cell sequencing data, given their unsupervised clustering approach. This identification supports the use of spatial cell transcriptomics to characterize cell types (or cell states).

(4) Lastly, the authors compare three different approaches --- their own described in this manuscript, Tangram, and SpaGE - which allow integration of single cell/nuclear RNA-seq data with spatial localization FISH. This was a very helpful section as the authors compared the advantages and disadvantages (including practical issues, like computational time).

Weaknesses:

(1) Experimental setup. It is not clear how many and, for some of the data, the sex of the flies that were analyzed. It appears that for the body data, only one male was analyzed. For the heads, methods say male and female heads, but nothing is annotated in the figures. As such, it remains unclear how robust these data are, given such a limited sample from one sex. As such, the claims of a spatial atlas of the entire fly body and its head ("a rosetta stone") are overstated. Also, the authors should clearly state in the main text and figure legends the sex, the age, how many flies, and how many replicates contributed to the data presented (not just the methods). What also adds to the confusion is the use of "n" in para 2 of the results. " ... we performed coronal sections at different depths in the head (n=13)..." 13 sections in total from 1 head or sections from 13 heads? Based on the body and what is shown in the figure, one assumes 13 sections from one head. Please clarify.

While we agree that sex differences present indeed an interesting opportunity to study with spatial transcriptomics, our goal was not to define male/female differences but rather to establish the technology to go into this detail if wanted in the future. In the revised version, we have provided an additional supplementary table with a more detailed description of the head sections (Table S3). We have added the number of animals (12 for the head sections, mixed sex; and 1 male for the body sections) to the main text. We would like to point out that we verified the specificity of our MC method on all the 5 body sections (Figure 2A, TpnC4 & Act88F and text) and not only on one. Furthermore, we also would like to state that the idea of “a Rosetta stone” was mentioned as a future prospect that clearly goes beyond our presented work. We have rewritten the discussion to make this clearer and to any avoid overstatements.

(2) Probes selected: Information from the methods section should be put into the main text so that it is clear what and why the gene lists were selected. The current main text is confusing. If the authors want others to use their approach, then some testing or, at the very least, some discussion of lower expressed genes should be added. How useful will this approach be if only highly expressed genes can be resolved? In addition, while it is understood that the company has a propriety design algorithm for the probes, the authors should comment on whether the probes for individual genes detect all isoforms or subsets (exons and introns?), given the high level of splicing in tissues such as muscle.

As stated above, while there is a slight bias to higher expressed genes (as expected for marker genes), we have also used low expressed genes like salm, CG32121, tinman (body) or sens (head). This is now shown in new Figure 1 – figure Supplement 1B, D. This shows that our method is more sensitive than single-cell data, as all cardiomyocytes can be identified by tinman expression and not only some are positive, as is the case in the FCA data. In fact, the method cannot resolve too highly expressed genes due to optical crowding of the signal leading to a worse quantification. For this reason, ninaE was removed from the analysis (as mentioned in Spatial transcriptomics allows the localization of cell types in the head and brain and in Methods).

As mentioned in the Methods, the probes are designed on gene level targeting all isoforms, but favoring principal isoforms (weighted by APPRIS level). The high level of splicing is indeed interesting and we expect that in the future spatial transcriptomics can help to generate more insight into this by designing isoform-specific probes.

(3) Imaging: it isn't clear from the text whether the repeated rounds of imaging impacted data collection. In many of what appear to be "stitched" images, there are gradients of signal (eg, figure 2F); please comment. Also, since this a new technique, could a before and after comparison of the original images and the segmented images be shown in the supplemental data so that the reader can better appreciate how the authors assessed/chose/thresholded their data? More discussion of the accuracy of spot detection would be helpful. 

High-resolution imaging (pixel size = 138 nm) of a large field of view (>1mm) for spatial transcriptomics uses a stitching method to combine several individual images to reconstruct a large field of view. This does not generate signal gradients, apart from lower signal at the extreme edges of each of the individual images, as seen in our images, too. The spot detection algorithm was written and used by Resolve Biosciences and benchmarked for human (Hela) and mouse (NIH-3T3) cell lines in Groiss et al. 2021 (Highly resolved spatial transcriptomics for detection of rare events in cells, bioR xiv). The specificity of the decoded probes was found to lie between 99.45 and 99.9% here, matching the results we found for specific detection of TpnC4 and Act88F (99.4 and 99.8%).

(4) The authors comment on how many RNAs they detected (first paragraph of results). How do these numbers compare to the total mRNA present as detected by single-cell or single-nuclear sequencing?

We can compare the numbers, but the different methodologies make the interpretation of such a comparison difficult. FCA used single nucleus sequencing, so only nuclear pre-mRNAs are detected. The total amount of counts per single cell sample strongly depends on how many cells were sequenced in an experiment. MC detects all mRNAs present in the section. Here, the size of the sample and hence the size or the number of cells analyzed determines how many mRNAs are detected. In Author response image 1, we have compared our MC results versus FCA data, comparing the genes investigated here in MC per section vs per sequencing experiment. Numbers for MC are slightly lower for the brain (not all cell types are on all sections) and much higher for the larger body samples. However, we feel a direct comparison is questionable, so we prefer to not include this figure in our manuscript.

Author response image 1:

Barplots showing total number of mRNA molecules detected in Molecular Cartography (MC, Resolve, spatial spots) and in snRNA-seq data from the Fly Cell Atlas (10x Genomics, UMIs). Individual black dots show individual experiments, counts are only shown for the chosen gene panel for each sample. Bar shows the mean, with error bars representing the standard error.

(5) Using this higher throughput method of spatial transcriptomics, the authors discern different cell types and different localization patterns within a tissue/cell type.

a. The authors should comment on the resolution provided by this approach, in terms of the detection of populations of mRNAs detected by low throughput methods, for example, in glia, motor neuron axons, and trachea that populate muscle tissue. Are these found in the images? Please show.

We did not add any markers for trachea in our gene panel, but we do detect sparse spots of repo (glia) and elav/VGlut in the muscle tissues (Gad1/VAChT are hardly detected in the muscle tissue). This is consistent with the glutamatergic nature of motor neurons in Drosophila as described previously (Schuster CM (2006), Glutamatergic synapses of Drosophila neuromuscular junctions: a high-resolution model for the analysis of experience-dependent potentiation. Cell Tissue Res 326:

287–299.). We have present these new data in new Figure 2 – figure supplement 1.

b.The authors show interesting localization patterns in muscle tissue for different sarcomere proteincoding mRNAs, including enrichment of sls in muscle nuclei located near the muscle-tendon attachment sites. As this high throughput approach is newly being applied to the adult fly, it would increase confidence in these data, if the authors would confirm these data using a low throughput FISH technique. For example, do the authors detect such alternating "stripes" ( Act 88F, TpnC4, and Mhc) or enriched localization (sls) using FISH that doesn't rely on the repeated colorization, imaging, decolorization of the probes? 

We thank the reviewer for the interest in the localization patterns in muscle tissue. We show that Act88F, TpnC4 are not detected outside of flight muscle cells (99.4% and 99.8% of the single molecular signal in flight muscles only), giving us confidence in the specificity of the MC method. Following the suggestion of the reviewer, we have adapted an HCR-FISH method to Drosophila adult body sections for the revised version of the manuscript. Using this method, we were able to confirm the higher expression/localization of sls transcripts to and around the adult flight muscle nuclei, with an enrichment in nuclei close to the muscle-tendon attachment sites (new Figure 4D-F and new Figure 4 – figure supplement 1). We have also been able to confirm some complementarity in the localization patterns of Act88F and TpnC4 in longitudinal stripes in adult flight muscles, however for Mhc we could not confirm this pattern with HCR-FISH (new Figure 5C-F and new Figure 5 – figure supplement 1). While we could confirm most of the pattern seen, we do not know the exact reason for the slight discrepancies. Thus, we now recommend that insights found with SRT should be confirmed with more classical FISH methods.

(6) The authors developed an unbiased method to identify "new cell types" which relies on coexpression of different transcripts. Are these new cell types or a cell state? While expression is a helpful first step, without any functional data, the significance of what the authors found is diminished. The authors need to soften their statements.

The term “new cell types” only appeared in the old title. We agree that with the current spatial map we cannot be sure to have found “new cell types”, instead we show where unannotated/uncharacterized clusters from the scRNA-seq atlas are located, based on their gene expression. Therefore, we have updated the title in the revised version (Spatial transcriptomics in the adult Drosophila brain and body) and thank the reviewer for this valuable suggestion.

Appraisal:

The authors' goal is to map single cell/nuclear RNAseq data described in the 2022 Fly Atlas paper spatially within an organism to achieve a spatial transcriptomic map of the adult fly; no doubt, this is a critical next step in our use of 'omics approaches. While this manuscript does the hard work of trying to take this next step, including developing and testing a new pipeline for high throughput FISH and its analysis, it falls short, in its present form, in achieving this goal. The authors discuss creating a robust spatial map, based on one male fly. Moreover, they do not reveal principles of mRNA localization, as stated in the abstract; they show us patterns, but nothing about the logic or function of these patterns. This same criticism can be said of the identification of "new cell types, just based on RNA colocalization. In both cases (mRNA subcellular localization or cell type identification), further data in the form of validation with traditional low throughput FISH and genetic manipulations to assess the relation to cell function are required for the authors to make such claims. 

We have indeed used one male fly for the adult male body data. This is mainly due to the cost of the sample processing. We used 12 individuals for the head samples (from 1 individual we acquired 2 sections, a total of 13 sections). We show that the body samples show a high correlation with each other, while the head samples cover multiple depths of the head. Still, even in the head, we find that sections at similar depths show a high similarity to each other in terms of gene-gene coexpression and expression patterns. Although obtaining sections from more animals would be valuable, we do not believe it to be necessary for our current goals. Additional replicates beyond the ones we already provide would require significant amounts of extra time and budget, while they would very likely produce similar results as we already show. Following the reviewer’s suggestion, we have tested several genes with HCR-FISH and could readily confirm the localization pattern of sls mRNA close to the terminal nuclei of the flight muscles. This pattern is likely due to a higher expression of sls in these nuclei, as a large amount of sls mRNA signal is detected within the nuclei (Figure 4). A detailed dissection of the mechanism that establishes this pattern is beyond the scope of this manuscript, which is the first one on applying spatial transcriptomics to adult Drosophila.

The usage of the term “new cell types” was indeed ambiguous and we removed this from the revised version. We now clarified that we map the spatial location of unannotated clusters in the brain. This may or may not include uncharacterized cell types. We now further specify that we have only inferred the location of the nuclei; thus, neuronal function or the location of their axonal processes are still unknown. As such, our data provides a starting point to identify uncharacterized cell types, since their marker genes and nuclear location are now determined. The next step to identify “new cell types” would indeed be to acquire genetic access to these cell types and characterize them in more detail. This is beyond the scope of this manuscript, and therefore we have toned down the title in the revised version and thank the reviewer for this valuable suggestion. 

Discussion of likely impact:

If revised, these data, and importantly the approach, would impact those working on Drosophila adults as well as those working in other model systems where single cell/nuclear sequencing is being translated to the spatial localization within the organism. The subcellular localization data - for example, the size of transcripts and how that relates to localization or the patterns of sarcomeric protein localization in muscle - are intriguing, and would likely impact our thinking on RNA localization, transport, etc if confirmed. Lastly, the authors compare their computational approaches to those available in the field; this is valuable as this is a rapidly evolving field and such considerations are critical for those wishing to use this type of approach.

We thank this reviewer for appreciating the impact of our findings and approach to the Drosophila field and beyond. We here provide the groundwork for a full Drosophila adult spatial atlas, similar to how early scRNA-seq datasets provided a framework for the Fly Cell Atlas. In the manuscript we provide both experimental information on how to successfully perform spatial transcriptomics (treating slides for optimal attachment) and the data serves as a benchmark for future experiments to improve upon (similar to how early Drop-seq datasets were compared to later 10x datasets in single-cell transcriptomics). In addition, it also provides proof of principle methods on how to integrate the FCA data with these spatial data and it identifies localized mRNA species in large adult muscle cells, showing the complementarity of spatial techniques with single-cell RNA-seq. For a small number of genes, we have confirmed the mRNA patterns using HCR-FISH in the revised version of this manuscript. To conclude, this is the first spatial adult Drosophila transcriptomics paper, locating 150 mRNA species with easy data access in our user portal (https://spatialfly.aertslab.org/).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) All figures in the manuscript were in grayscale, which made it difficult to interpret the results because the data could only be interpreted by distinguishing different colors to visualize different transcripts. This is likely a technical problem. The manuscript must contain colored images.

We apologize to the reviewer for this technical issue. The manuscript was uploaded in color to bioRxiv and to eLife. We therefore do not understand to reason for this problem. We are surprised that this issue was not resolved in the reviewers’ discussion since color is obviously essential to appreciate the beauty of this manuscript.

(2) In Figure 2a, the authors comment on the subcellular localization of trypsin isoforms, but the figure does not indicate the cell borders or the apical and basal regions of the cell. These must be indicated in the figure to help readers understand the results. 

We thank the reviewer for pointing this out; we have now indicated the outlines of the single-cell layer epithelium on the figure. While we have no marker for cell borders, we have a nuclear marker showing that it is a single cell layer. We hope this allows the reader to appreciate the subcellular localization of the trypsin isoforms.

(3) All figures (including the data on the authors' website) contain background staining, which I assume is labeling nuclei. This is not indicated in the manuscript, and should be clarified.

We again thank the reviewer for pointing this out; the background staining indeed labels nuclei (using DAPI). We have indicated this better in the revised version.

(4) In Figure 5c, the authors claim that neuronal and muscular genes are grouped into the same cluster, but they do not indicate which transcripts are neuronal and which ones are muscular. This must be indicated in the figure.

We thank the reviewer for this comment. Indeed, there was only one gene, acj6, present in the muscle cluster. So, we decided to delete this statement in the revised version.

(5) The authors utilized and compared three different approaches to integrate single nuclei sequencing data from the Fly Cell Atlas to their spatially resolved transcriptomics (SRT) data. I was wondering if it is possible to generate a virtual expression explorer using this integrated data, similar to the dataset published in the 2017 Science article by Karaiskos et al., where they combined publicly available in situ hybridization data of fly embryos and their single-cell sequencing data. This virtual expression explorer would be useful to visualize the expression pattern of transcripts that the authors of this paper did not use for their SRT.

We thank the reviewer for this interesting comment. Using Tangram, we indeed infer gene expression for all genes from the Fly Cell Atlas. To make this visible we have created a Scope session (https://scope.aertslab.org/#/Spatial_Fly/*/welcome), with which users can browse inferred gene expression levels (note that this is on a segmented cell level). We do notice that the inferred gene expression levels contain many false positives and should therefore be used with caution. The spatial data themselves can be browsed through the spatial portal at https://spatialfly.aertslab.org/ .

Reviewer #2 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data, or analyses:

The authors have used a new high throughput approach to examine the location of 150 RNAs in adult Drosophila heads or one body. It is unclear whether the fixation/repeated imaging etc is accurately reflecting the patterns of expression in vivo. The authors should confirm these data using low throughput established techniques for the RNA patterns in muscle for example.

The authors should clarify their experimental approaches and include additional samples if they indeed want to establish the rosetta stone of fly adults. These data are from only a male fly (and as such is not a complete analysis of the adult fly). To be a map of the adult fly, data from both sexes need to be included.

Unless functional data that complement the descriptive data shown here are included, the authors have to soften their conclusions. For example, while spatial transcriptomics has mapped RNA expression to a location, without some functional data, it is difficult to conclude that these are indeed "new cell types". Same with the RNA localization principles.

Recommendations for improving the writing and presentation:

(1) The manuscript should be heavily revised: in many places, important details are left out or should be moved from the methods to the main text. In addition, the authors often overstate their findings throughout the manuscript. As an example, it appears that the data presented is only from 1 fly, so this doesn't increase the reader's confidence in the data or the applicability of the approach. Also, it isn't clear how many flies were analyzed for the heads (one male fly too?) nor what variability is present from fly to fly. For the approach and data to be used by others, this is important to know.

We moved some text from the methods section to the main text to be clearer. We now also state how many animals were used for the MC method. While the data for the body has been generated from 1 male only, the data for the head was generated from 12 flies; for both cases, similar slices show very similar gene expression patterns. Furthermore, in the body we used widely known and published marker genes that all showed expected expression patterns, indicating robustness. We agree that this is not a full spatial atlas of the fly, this was also not our goal and we have removed such general statements from the revised version: we aimed to generate a spatial transcriptomics dataset, covering the entire fly (head and body) as a proof-of-principle, tackling data generation and analysis, and highlighting challenges in both.

(2) The grammar and word choice throughout are challenging often making the text difficult to follow. This reads like an early draft of the paper.

We apologize to the reviewer for any difficulties. We have revised the text and hope it is now easier to read, while still being accurate on the technical details of the various methods used in our manuscript.

Minor corrections to the text and figures.

See the weaknesses mentioned above. Also:

Figure S1 is unreadable.

There is no simple way to describe the expression values of 100 genes in 100 cell types on a single page. The resolution of the PDF is high enough that after zooming in, all the information can be read easily.

Figure S2, in a, please include the axes so that the reader can better understand the sections shown.

In b, it is unclear what the pink boxes mean. In c, the labels are barely legible.

In Figure 1 – figure supplement 2 (head sections), we have ordered the head sections from anterior to posterior. The boxes in (B) represent boxplots. We have updated this plot for clarity to better display the number of mRNA molecules detected for each gene. We have increased the font size in (C).

Figure S3, in a, please include axes. In b, the meaning of the pink box

In Figure 1 – figure supplement 3 (the body sections) we have added the anterior to posterior and dorso-ventral axis, and ordered the sections that stem from the same animal. The boxes in (B) represent boxplots. We have updated this plot for clarity to better display the number of mRNA molecules detected for each gene. We have added an explanation to the figure legend.  

Figure S4, the text in the axes of the heatmap should have a darker typeface

We have changed it to black, thanks.

Figure S5c, are the colors in the dendrogram supposed to match the spatial location on the right?

The purple of the muscles is barely visible.

Yes, they do match. Colors were modified in the revised version for better visibility.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The significance of Notch in liver cancer has been inconsistently described to date. The authors conduct a PDX screen using JAG1 ab and identify 2 sensitive tumor models. Further characterization with bulk RNA seq, scRNA seq, and ATAC seq of these tumors was performed.

Strengths:

The reliance on an extensive panel of PDXs makes this study more definitive than prior studies.

Gene expression analyses seem robust.

Identification of a JAG1-dependent signature associated with hepatocyte differentiation is interesting.

Weaknesses:

The introduction is rather lengthy and not entirely accurate. HCC is a single cancer type/histology. There may be variants of histology (allusion to "mixed-lineage" is inaccurate as combined HCC-CCa are not conventionally considered HCC and are not treated as HCC in clinical practice as they are even excluded from HCC trials), but any cancer type can have differences in differentiation. Just state there are multiple molecular subtypes of this disease.

We will shorten the Introduction, in part by eliminating the discussion of histological variation in HCC and focusing on the molecular classifications.

There is minimal data on the PDXs, despite this being highlighted throughout the text. Clinical and possibly some molecular characterization of these cancers should be provided. It is also odd that the authors include only 35 HCC and then a varied sort of cancer histologies, which is peculiar given their prior statements regarding the heterogeneity of HCC.

We agree that clinical and molecular characterizations of the PDX models would be helpful and will follow up with the relevant contract research organization to determine what characterization is available.

Regarding the liver cancer PDX panel, we suggest that a major strength of the manuscript is the large number of HCC models that were tested (the reviewer also notes the importance of the “extensive” panel); thus, we are a bit confused by the reference to “only 35 HCC”.  To clarify the choice of models in the PDX screen, it may help to put the screen in historical perspective as the project unfolded.  In retrospect, our preliminary efficacy studies using only two HCC models were fortunate to identify the highly sensitive model, LIV78.  To go beyond the simple diagnostic hypothesis that focused on Jag1, Notch2 and Hes1 expression, we took an unbiased approach to discover features linked to Notch dependence.  This approach meant running an efficacy screen in all liver cancer models that were up and running at our chosen research organization, without biased selection criteria.  That set of models is what is represented in the “pre-clinical screen” in Fig. 1B

"super-responder" is not a meaningful term, I would eliminate this use as it has no clinical or scientific convention that I am aware of.

We were aware of the interchangeable terms of “exceptional-“ or “super-responder” and prefer to leave this language in the text.  Some references are as follows: 

● Prasad et al., Characteristics of exceptional or super responders to cancer drugs. Mayo Clinic Proceedings, 2015. 

● NCI Press Release 2020:  https://www.cancer.gov/news-events/press-releases/2020/cancer-exceptional-responders-study-genetic-alterations-may-contribute

● NIH Info:  https://www.nih.gov/news-events/nih-research-matters/understanding-exceptional-responders-cancer-treatment

● “What is a Super Responder?  Bradley Jones, Cancer Today, June 26, 2020.

● “What is a Super Responder?”  AACR.  https://www.aacr.org/patients-caregivers/progress-against-cancer/what-is-a-super-responder/

The "expansion" of the PDX screen is poorly described. Why weren't these PDXs included in the first screen? This is quite odd as the responses in the initial screen were underwhelming. What was the denominator number of all PDXs that were assessed for JAG1 and NOTCH2 expression? This is important as it clarifies how relevant JAG1 inhibition would be to an unselected HCC population.

We will revise the writing here to clarify as requested.  For now, we can hopefully clarify by building on the historical context described above.  As the reviewer notes and as we describe in the text, the in vivo screen revealed only a modest JAG1 dependence.  The screen also highlighted that LIV78 was exceptional, and we wanted to understand why.  Hypothesizing that the expression of progenitor markers in LIV78 were important for understanding its JAG1 dependence, we identified four additional models at other contract research organizations.  It is this set of four that comprises the “expansion” cohort.

Was there some kind of determination of the optimal dose or dose dependency for the JAG1 ab? The original description of the JAG1 ab was in mouse lungs, not malignant or liver cells. In addition, supplementary Figure 2D is missing. There needs to be data provided on the specificity of the human-specific JAG1 ab and the anti-NOTCH2 ab. I'm not familiar with these ab, and if they are not publicly accessible reagents, more transparency on this is needed. In addition, given the reliance of the entire paper on these antibodies, I would recommend orthogonal approaches (either chemical or genetic) to confirm the sensitivity and insensitivity of select PDXs to Notch inhibition.

First, we note that the anti-human/mouse Jagged1 and Notch2 blocking antibodies used in our study have been extensively characterized as potent and selective and have been widely used outside of our group by the Notch research community (for the human/mouse cross-reactive antibodies, see Wu et al., Nature, 2010 for anti-NOTCH2 and Lafkas et al., Nature 2015 for anti-JAG1). As noted, the antibodies have been used in studies of normal mouse lungs (Lafkas et al.).  Please note that the characterization also includes mouse models of primary liver cancer that formed the foundation for the current work (please refer to Huntzicker et al, 2015).

While we show dose responses in Figures 1A and 1D, we have not optimized dosing, for example by determining the minimal drug exposures needed for pharmacodynamic changes (pathway inhibition) and efficacy.  For the purposes of this study, we erred on the side of dosing at high concentrations to minimize the risk of false negative responses.

Regarding the specificity of the human-specific anti-JAG1 antibody, which is revealed here for the first time, we apologize that we incorrectly provided a text reference to Supplementary Figure 2D instead of Supplementary Figure 1D.  We will revise accordingly.  Fig. 1D shows results from a reporter assay demonstrating that the antibody blocks signaling induced by human but not mouse JAG1.

We appreciate the value of orthogonal methods in establishing the credibility of a novel finding.  We note that genetic approaches are technically highly challenging in PDX models.  Chemically, we could have tested y-secretase inhibitors (GSIs). Our position is that such inhibitors are poor substitutes for the selective antibodies that we employed, at least for addressing the questions that are relevant in this study.   Although commonly used to perturb Notch signaling, GSIs target numerous proteins and signaling cascades independent of Notch.  Moreover, their use in vivo leads to intestinal and other toxicities, limiting exposure. 

scRNA-seq data seems to add little to the paper and there is no follow-up of the findings. Are the low-expressing JAG1 cells eventually enriched in treated tumors contributing to disease recurrence?

We respectfully disagree with this sentiment. The single-cell RNA sequencing dataset revealed the enrichment of hepatocyte-like tumor cells following Notch inhibition. Importantly, this dataset also allowed us to identify transcription factor activities regulating different cell states, which we could not have done otherwise. This understanding in turn was fundamental to develop our hypothesis that Notch inhibition, through derepressing CEBPA expression, allows chromatin engagement of HNF4A and CEPBA and thereby promotes a hepatocyte differentiation program that is not compatible with tumor maintenance.  

The discussion should be tempered. The finding of only 2 PDXs that are sensitive out of 45+ tumors treated or selected for indicates that JAG1/NOTCH2 inhibition is likely only effective in rare HCC.

We agree that strong responses to Notch inhibition in the PDX models are rare (~5%) and state as much in both the Results and Discussion sections. We maintain that it is important to put this PDX response frequency into a larger context.  First, establishing PDX models---human tumor samples that grow on the flanks of immunocompromised mice---represents a strong selective pressure.  In other words, we don’t know precisely how the frequency of responses in this selected set of PDX models may compare to the frequency that would be observed in human patient populations. Second, the magnitude of the response points to important and hitherto unappreciated biology, with blocking JAG1 or NOTCH2 reproducibly inducing regressions in the most sensitive models.  Our hope is that the field can build from this study to generate diagnostic tools that identify sensitive patient tumors, define the true frequency of this patient group within the larger HCC population (even though likely rare), and direct the relevant Notch-based therapeutics to these patients.  Within this context, and while noting the rarity of PDX responses, we hope that we have not overstated the case.

Reviewer #2 (Public review):

Summary:

The authors used a large panel of hepatocellular carcinoma patient-derived xenograft models to test the hypothesis that the developmental dependence of the liver on Jagged1-Notch2 signaling is retained in at least a subset of hepatocellular carcinomas. This led to the identification of two models that were extraordinarily sensitive to well-characterized, specific inhibitory antibodies against Jagged1 or Notch2. Based on additional analyses in these in vivo models, the authors provide compelling evidence that the response is due to the inhibition of human Notch2 and human Jagged1 on tumor cells and that this inhibition leads to a change in gene expression from a progenitor-like state to a hepatocyte-like state accompanied by cell cycle arrest. This change in cell state is associated with up-regulation of HNF4a and CEBPB and increased accessibility of predicted HNF4a and CEBPB genomic binding sites, accompanied by loss of accessibility to sequences predicted to bind TFs linked to multipotent liver progenitors. The authors put forth a plausible model in which inhibition of Notch2 downregulates transcriptional repressors of the Hairy/Enhancer of Split family, leading to increased expression of CEBPB and changes in gene expression that drive hepatocyte differentiation.

Strengths:

The strengths of the paper include the breadth of the preclinical screen in PDX models (which may be of an unprecedented size as preclinical trials go), the high quality of the well-characterized antibodies used as therapeutics and as biological perturbagens, the quality of the data and data analysis, and the authors balanced discussion of the strengths and weaknesses of their findings.

Weaknesses:

The principal weakness is the inability to clearly demonstrate the "translatability" of the PDX findings to primary human hepatocellular carcinoma.

We agree that translatability has not been fully addressed.  As noted in our response to Reviewer 1, our hope is that the field can build from this study to generate diagnostic tools that identify sensitive patient tumors, define the true frequency of this patient group within the larger HCC population, and direct the relevant Notch-based therapeutics to these patients.  We remain encouraged by the strength of the response in the sensitive models.

Additional Comments:

Hepatocellular carcinoma is increasing in frequency and is difficult to treat; cure is only possible through early diagnosis and surgery, often in the form of liver transplantation. It is also a common cancer, and so even if only 5% of tumors (a value based on the frequency of super-responders in this preclinical trial) fall into the Jagged1-Notch2 group defined by Seidel et al., the development of an effective therapy for this subgroup would be a very important advance. The chief limitation of their work is that it stops short of identifying primary human hepatocellular carcinomas that correspond to the super-responder PDX models. It can be hoped that their intriguing observations will spur work aimed at filling this gap.

There are several other loose ends. An unusual feature of this model is that both Jagged 1 and Notch2 are expressed in the same cells, and even in the same individual cells. In developmental systems, the expression of ligands and receptors in the same cell generally produces receptor inhibition rather than activation, a phenomenon described as cis inhibition. Their super-responder tumor models appear to break this rule, and how and why this is so remains to be understood. A follow-up question is what explains the observed heterogeneity in tumor cells, both at the level of Notch2 activation and scRNAseq clustering, and whether these different cell states are static or interchangeable.

We enthusiastically agree that these are fascinating questions, worthy of further study.  As noted, the majority of tumor cells express both ligand and receptor and seem to be “on” for Notch signaling.  We have not been able to determine whether the signal is induced in a cell autonomous or non-autonomous manner (or both).  As the reviewer notes, the HCC features we observe are inconsistent with the dogma that has arisen from studies on Notch signaling in developmental contexts.

We do not yet have the experimental data to fully address the second question of what causes the heterogeneity of Notch2 activation and scRNAseq clustering.  We speculate that the cell states may be dynamic, which would be consistent with the changes in cell populations observed after antibody treatment.

Another unanswered issue pertains to the nature of the tumor response to Notch signaling blockade, which appears to be mainly cell cycle arrest. There are a number of human tumors with cell autonomous Notch signaling due to gain of function Notch receptor mutations that also respond to Notch blockade with cell cycle arrest, such as T cell acute lymphoblastic leukemia (T-ALL). In general, clinical trials of pan-Notch inhibitors such as gamma-secretase inhibitors have been disappointing in such tumors, perhaps reflecting a limitation of treatments with significant toxicity that do not kill tumor cells directly. It could be argued that this limitation will be mitigated by the apparently excellent safety profile of Notch2 blocking antibody, which perhaps could be administered for a sustained period, akin to the use of tyrosine kinase inhibitors in chronic myeloid leukemia---but this remains to be determined.

We agree that a full understanding of the tumor response warrants further investigation.  Like the reviewer, we speculate that the improved safety profile of selective antibodies relative to pan-Notch inhibitors may enable greater and sustained therapeutic coverage of Notch inhibition than has been feasible in T-ALL trials.  Given that in the sensitive PDX models we observe rapid tumor regressions, not just stasis, it would seem to follow that the mechanism underpinning the tumor response involves more than just cell cycle blockade.  Whether tumor shrinkage reflects additional cell death mechanisms or simply tumor cell turnover after cell cycle arrest remains to be determined. 

A minor comment is reserved for the statement in the discussion that "In chronic myelomonocytic leukemia, which results from an inactivating mutation in the y-secretase complex component nicastrin, Notch signaling has a tumor suppressive function, that is mediated through direct repression of CEBPA and PU.1 by HES1 (Klinakis et al., 2011)". Thousands of cases of CMML and related myeloid tumors have been subjected to whole exome and even whole genome sequencing without the identification of Notch signaling pathway mutations. Thus, an important tumor suppressive role for Notch-mediated through HES1 in myeloid tumors is not proven.

We agree that our sentence about Notch and CMML does not fit well with the prevalent paradigm established by genome wide sequencing and other methods.  We will edit this paragraph accordingly, focusing on Hes1 negative regulation of CEBPA in myeloid fate control and how that shapes our thinking on molecular mechanisms in the Notch-dependent HCCs.

Reviewer #3 (Public review):

Summary:

Notch is active in HCC, but generally not mutated. The authors use a JAG1-selective blocking antibody in a large panel of liver cancer patient-derived xenograft models. They find JAG-dependent HCCs, and these are aggressive and proliferative. Notch inhibition induces cycle arrest and promotes hepatocyte differentiation, through upregulation of CEBPA expression and activation of existing HNF4A, mimicking normal developmental programs.

The authors use aJ1.b70, a potent and selective therapeutic antibody that inhibits JAG1 against PDX models. They tested over 40 PDX models and found a handful of super-responders to single-agent inhibition. In LIV78 and Li1035 cancer cells, NOTCH2 was expressed and required, in contrast to NOTCH1. RNA-seq showed that the responsive HCCs resembled the S2 transcriptional class of HCCs, which were enriched for Notch-dependent models. They conclude that these dependent tumors have transcriptomes that resemble a hybrid progenitor cell expressing FGF9 and GAS7. Inhibition was able to induce hepatocyte differentiation away from a NOTCH-driven progenitor program. scRNA-seq analysis showed a large population of NOTCH-JAG expressing cells but also showed that there are cells that did not. Not surprisingly, NOTCH2 inhibition leads to increased CEBPA and HNF4A transcriptional activity, which are standard TFs in hepatocytes.

Strengths:

The paper provides useful information about the frequency of HCCs and CCA that respond to NOTCH inhibition and could allow us to anticipate the super-responder rate if these antibodies were actually used in the clinic. The inhibitor tools are highly specific, and provide useful information about NOTCH activities in liver cancers. The large number of PDXs and the careful transcriptomic analyses were positives about the study.

Weaknesses:

The paper is mostly descriptive.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This article investigates the phenotype of macrophages with a pathogenic role in arthritis, particularly focusing on arthritis induced by immune checkpoint inhibitor (ICI) therapy.

Building on prior data from monocyte-macrophage coculture with fibroblasts, the authors hypothesized a unique role for the combined actions of prostaglandin PGE2 and TNF. The authors studied this combined state using an in vitro model with macrophages derived from monocytes of healthy donors. They complemented this with single-cell transcriptomic and epigenetic data from patients with ICI-RA, specifically, macrophages sorted out of synovial fluid and tissue samples. The study addressed critical questions regarding the regulation of PGE2 and TNF: Are their actions co-regulated or antagonistic? How do they interact with IFN-γ in shaping macrophage responses?

This study is the first to specifically investigate a macrophage subset responsive to the PGE2 and TNF combination in the context of ICI-RA, describes a new and easily reproducible in vitro model, and studies the role of IFNgamma regulation of this particular Mф subset.

Strengths:

Methodological quality: The authors employed a robust combination of approaches, including validation of bulk RNA-seq findings through complementary methods. The methods description is excellent and allows for reproducible research. Importantly, the authors compared their in vitro model with ex vivo single-cell data, demonstrating that their model accurately reflects the molecular mechanisms driving the pathogenicity of this macrophage subset.

Weaknesses:

Introduction: The introduction lacks a paragraph providing an overview of ICI-induced arthritis pathogenesis and a comparison with other types of arthritis. Including this would help contextualize the study for a broader audience.

Thank you for this suggestion, we will add a paragraph on ICI-arthritis to intro.

Results Section: At the beginning of the results section, the experimental setup should be described in greater detail to make an easier transition into the results for the reader, rather than relying just on references to Figure 1 captions.

We will clarify the experimental setup.

There is insufficient comparison between single-cell RNA-seq data from ICI-induced arthritis and previously published single-cell RA datasets. Such a comparison may include DEGs and GSEA, pathway analysis comparison for similar subsets of cells. Ideally, an integration with previous datasets with RA-tissue-derived primary monocytes would allow for a direct comparison of subsets and their transcriptomic features.

This is a great idea, we will integrate the data sets and if batch correction is successful will present this analysis.

While it's understandable that arthritis samples are limited in numbers and myeloid cell numbers, it would still be interesting to see the results of PGE2+TNF in vitro stimulation on the primary RA or ICI-RA macrophages. It would be valuable to see RNA-Seq signatures of patient cell reactivation in comparison to primary stimulation of healthy donor-derived monocytes.

We agree that this would be interesting but given limited samples and distribution of samples amongst many studies and investigators this is beyond the scope of the current study. 

Discussion: Prior single-cell studies of RA and RA macrophage subpopulations from 2019, 2020, 2023 publications deserve more discussion. A thorough comparison with these datasets would place the study in a broader scientific context.

Creating an integrated RA myeloid cell atlas that combines ICI-RA data into the RA landscape would be ideal to add value to the field.

As one of the next research goals, TNF blockade data in RA and ICI-RA patients would be interesting to add to such an integrated atlas. Combining responders and non-responders to TNF blockade would help to understand patient stratification with the myeloid pathogenic phenotypes. It would be great to read the authors' opinion on this in the Discussion section.

We will be happy to improve the discussion by including these topics.

Conclusion: The authors demonstrated that while PGE2 maintains the inflammatory profile of macrophages, it also induces a distinct phenotype in simultaneous PGE2 and TNF treatment. The study of this specific subset in single-cell data from ICI-RA patients sheds light on the pathogenic mechanisms underlying this condition, however, how it compares with conventional RA is not clear from the manuscript.

Given the substantial incidence of ICI-induced autoimmune arthritis, understanding the unique macrophage subsets involved for future targeting them therapeutically is an important challenge. The findings are significant for immunologists, cancer researchers, and specialists in autoimmune diseases, making the study relevant to a broad scientific audience.

Reviewer #2 (Public review):

Summary/Significance of the findings:

The authors have done a great job by extensively carrying out transcriptomic and epigenomic analyses in the primary human/mouse monocytes/macrophages to investigate TNF-PGE2 (TP) crosstalk and their regulation by IFN-γ in the Rheumatoid arthritis (RA) synovial macrophages. They proposed that TP induces inflammatory genes via a novel regulatory axis whereby IFN-γ and PGE2 oppose each other to determine the balance between two distinct TNF-induced inflammatory gene expression programs relevant to RA and ICI-arthritis.

Strengths:

The authors have done a great job on RT-qPCR analysis of gene expression in primary human monocytes stimulated with TNF and showing the selective agonists of PGE2 receptors EP2 and EP4 22 that signal predominantly via cAMP. They have beautifully shown IFN-γ opposes the effects of PGE2 on TNF-induced gene expression. They found that TP signature genes are activated by cooperation of PGE2-induced AP-1, CEBP, and NR4A with TNF-induced NF-κB activity. On the other hand, they found that IFN-γ suppressed induction of AP-1, CEBP, and NR4A activity to ablate induction of IL-1, Notch, and neutrophil chemokine genes but promoted expression of distinct inflammatory genes such as TNF and T cell chemokines like CXCL10 indicating that TP induces inflammatory genes via IFN-γ in the RA and ICI-arthritis.

Weaknesses:

(1) The authors carried out most of the assays in the monocytes/macrophages. How do APC-cells like Dendritic cells behave with respect to this TP treatment similar dosing?

We agree that this is an interesting topic especially as TNF + PGE2 is one of the standard methods of maturing in vitro generated human DCs. As DC maturation is quite different from monocyte activation this would represent an entire new study and is beyond the scope of the current manuscript. We will instead describe and cite the literature on DC maturation by TNF + PGE2 including one of our older papers (PMID: 18678606; 2008)

(2) The authors studied 3h and 24h post-treatment transcriptomic and epigenomic. What happens to TP induce inflammatory genes post-treatment 12h, 36h, 48h, 72h. It is critical to see the upregulated/downregulated genes get normalised or stay the same throughout the innate immune response.

We will clarify that the gene response is mostly subsiding at the 24 hour time point, which is in line with in vitro stimulation of primary monocytes in other systems.

(3) The authors showed IL1-axis in response to the TP-treatment. Do other cytokine axes get modulated? If yes, then how do they cooperate to reduce/induce inflammatory responses along this proposed axis?

We will analyze the data for other pathways that are modulated.

Overall, the data looks good and acceptable but I need to confirm the above-mentioned criticisms.

Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript presents evidence of ’vocal style’ in sperm whale vocal clans. Vocal style was defined as specific patterns in the way that rhythmic codas were produced, providing a fine-scale means of comparing coda variations. Vocal style effectively distinguished clans similar to the way in which vocal repertoires are typically employed. For non-identity codas, vocal style was found to be more similar among clans with more geographic overlap. This suggests the presence of social transmission across sympatric clans while maintaining clan vocal identity.

Strengths:

This is a well-executed study that contributes exciting new insights into cultural vocal learning in sperm whales. The methodology is sound and appropriate for the research question, building on previous work and ground-truthing much of their theories. The use of the Dominica dataset to validate their method lends strength to the concept of vocal style and its application more broadly to the Pacific dataset. The results are framed well in the context of previous works and clearly explain what novel insights the results provide to the current understanding of sperm whale vocal clans. The discussion does an overall great job of outlining why horizontal social learning is the best explanation for the results found.

Weaknesses:

The primary issues with the manuscript are in the technical nature of the writing and a lack of clarity at times with certain terminology. For example, several tree figures are presented and ’distance’ between trees is key to the results, yet ’distance’ is not clearly defined in a way for someone unfamiliar with Markov chains to understand. However, these are issues that can easily be dealt with through minor revisions with a view towards making the manuscript more accessible to a general audience.

I also feel that the discussion could focus a bit more on the broader implications - specifically what the developed methods and results might imply about cultural transmission in other species. This is specifically mentioned in the abstract but not really delved into in detail during the discussion.

We are grateful for the Reviewer’s recognition of the study’s contributions to understanding cultural vocal learning in sperm whales. In response to the concerns regarding clarity and accessibility, we have revised the manuscript to improve the definition of key concepts, such as the notion of “distance” between subcoda trees. This adjustment ensures clarity for readers unfamiliar with the technical details of Markov chains. Additionally, we have expanded the discussion to highlight broader implications of our findings, particularly their relevance to understanding cultural transmission in other species, as suggested.

Reviewer #2 (Public review):

Summary:

The current article presents a new type of analytical approach to the sequential organisation of whale coda units.

Strengths:

The detailed description of the internal temporal structure of whale codas is something that has been thus far lacking.

Weaknesses:

It is unclear how the insight gained from these analyses differs or adds to the voluminous available literature on how codas varies between whale groups and populations. It provides new details, but what new aspects have been learned, or what features of variation seem to be only revealed by this new approach? The theoretical basis and concepts of the paper are problematical and indeed, hamper potentially the insights into whale communication that the methods could offer. Some aspects of the results are also overstated.

We appreciate the Reviewer’s acknowledgment of the novelty in describing the internal temporal structure of whale codas. Regarding the concern about the unique contributions of this approach, we have further emphasized in the revised manuscript how our methodology reveals previously uncharacterized dimensions of coda structure. Specifically, our work highlights how non-identity codas, which have received limited attention, play a significant role in inter-clan acoustic interactions. By leveraging Variable Length Markov Chains, we provide a nuanced understanding of coda subunits that complements existing studies and demonstrates the value of this analytical approach.

Reviewer #3 (Public review):

Summary:

The study presented by Leitao et al., represents an important advancement in comprehending the social learning processes of sperm whales across various communicative and socio-cultural contexts. The authors introduce the concept of ”vocal style” as an addition to the previously established notion of ”vocal repertoire,” thereby enhancing our understanding of sperm whale vocal identity.

Strengths:

A key finding of this research is the correlation between the similarity of clan vocal styles for non-ID codas and spatial overlap (while no change occurs for ID codas), suggesting that social learning plays a crucial role in shaping symbolic cultural boundaries among sperm whale populations. This work holds great appeal for researchers interested in animal cultures and communication. It is poised to attract a broad audience, including scholars studying animal communication and social learning processes across diverse species, particularly cetaceans.

Weaknesses:

In terms of terminology, while the authors use the term ”saying” to describe whale vocalizations, it may be more conservative to employ terms like ”vocalize” or ”whale speech” throughout the manuscript. This approach aligns with the distinction between human speech and other forms of animal communication, as outlined in prior research (Hockett, 1960; Cheney & Seyfarth, 1998; Hauser et al., 2002; Pinker & Jackendoff, 2005; Tomasello, 2010).

We thank the Reviewer for recognizing the importance of our findings and their appeal to broader audiences interested in animal cultures and communication. In response to the suggestion regarding terminology, we have adopted a more conservative language to align with distinctions between human and non-human communication systems. For example, terms like “vocalize” and “vocal repertoire” are used in place of anthropomorphic terms such as “saying”. This ensures consistency with established conventions while maintaining clarity for a broad readership.

Reviewer #1 (Recommendations):

Comment 1

Lines 11-13: As mentioned above, the implications for comparing communication systems and cultural transmission in other species isn’t really discussed much and I think it’s a really interesting component of the study’s broader implications.

Thank you for the comment.

Action - We added a few more sentences to the discussion regarding this.

Comment 2

Figure 1: More information on the figure of these trees would help. What do the connecting lines represent? What do the plain black dots and the black dot with the white dot represent? Especially since the ”distance between trees” is a key result, it’s important that someone unfamiliar with Markov chains can understand the basics of how this is calculated and what it represents. It is explained in the methods, but a brief explanation here would make the results and the figure a lot clearer since the methods are the last section of the manuscript.

These were omitted as we believed that attempting to introduce the mathematical structure and the methodology to compare two instances, in a figure caption, would have caused more ambiguity than necessary.

Action - Added an informal introduction to these concepts on the figure caption. Also added a pointer to the Supplementary Materials.

Comment 3

Table 1: A definition of dICIs should be included here.

Added the definition of discrete ICI to the table.

Comment 4

Figure 2: The placement of the figures is a bit confusing because they are quite far from the text that references them.

We thank the reviewer for pointing this out, we tried to edit the manuscript to improve this issue, but this part of the editing is more within the journal’s powers than our own.

Action - Moved images closes to the corresponding text in manuscript.

Comment 5

Line 117: Probabilistic distance needs to be briefly explained earlier when you first mention distance (see Lines 11-13 comments).

Action - Clarifications added in the caption of figure 1. as per comment on Lines 11-13

Comment 6

Figure 4: Is order considered in these pairwise comparisons? It looks like there are two dots for each pairwise comparison. Additionally, why is the overlap different in these two comparisons? For example, short:four-plus has an overlap of 0.6, while four-plus:short has an overlap of 0.95.

The x-axis of the plots in Figure 4 is geographical clan overlap. This is calculated as per (Hersh et al., 2022) and is described in our Methods (see “Measuring clan overlap” section). Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger ( 0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

Action - We have now added a reference to that section of the Methods in our Figure 4 caption and include the clan spatial overlap matrix as a supplemental table (Table S5).

Comment 7

Figure 4: I think the reference should be Hersh et al. [11].

Thank you for catching this.

Action - Reference corrected

Comment 8

Line 227: What aspect of your analysis looked at how often codas were produced? You mention coda frequency, but it is unclear how this was incorporated into your analysis. If this is included in the methods, the language is a bit too technical to easily parse it out.

Indeed here we are referencing the results of the paper mentioned in the previous line. We do not look at coda production frequency.

Action - Added citation to paper that actually performs this analysis.

Comment 9

Lines 253-255: I think you could dig into this a little more, as ”there is currently no evidence” is not the most convincing argument that something is not a driver. Perhaps expanding on the latter sentence that clans are recognizable across oceans basins would be helpful. Does this suggest that clans with similar geographic overlap experience diverse environmental conditions across ocean basins? If so, this might better strengthen your argument against environmental drivers.

Thank you for pointing this out. We feel that the next sentence highlights that clans are recognizable across environmental variation from one side to the other of the ocean basin, which supports the inductive reasoning that codas do not vary systematically with environment. However, we have edited these sentences for clarity.

Comment 10

Lines 311-314: It would also be interesting to look at vocal style across non-ID coda types. Are some more similar to each other across clans than others? Perhaps vocal style can further distinguish types of non-ID codas.

In supplementary Materials 3.4.2 and 3.5 we highlight our results when the codas are separated by coda type summarized in Table S4. We do compare the vocal style across non-ID coda types across clans and within the same clan. The results however are aggregated to highlight the differences in style between the clans and a a coda type-only comparison is not shown.

Comment 11

Lines 390-392: I’m assuming this is why pairwise comparisons were directional (i.e., there was both an A:B and a B:A comparison)? Can you speak to why A:B and B:A comparisons can have such different overlap values?

Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger (0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

Action - We now include the clan spatial overlap matrix as a supplemental table (Table S5).

Comment 13

Line 56: Can you briefly explain what memory means in the context of Markov chains?

We provide an explanation of the meaning of memory in the Methods section on ”Variable length Markov Chains”. Briefly, the memory in this case means how many states in the past of the Markov chain’s current state are required to predict the next transition of the chain itself. Standard Markov chains “look” back only one time step, while k-th order Markov chains look back k steps. In our case, there was no reason to assume that the memory required to predict different sequences of states (interclick intervals) should be the same across all sequences, and thus we adopted the formalism of variable length Markov chains, that allow for different levels of memory across the system.

Comment 14

Supplementary Figure S3: Like in the main manuscript, briefly explain or remind us what the blank nodes and the yellow nodes are.

Action - Clarified that the orange node represents the root of the tree in the figures.

Comment 15

Supplementary Figure S7: Put the letters before the dataset name.

Action - Done.

Comment 16

Supplementary Figure S10: Unclear what ’inner vs outer’ means.

One specifies comparisons across clans (outer) and the other within the same clan (inner)

Action - Added clarification on the caption of Figure S10

Comment 17

Supplementary Figure S14: Include a-c labels in the figure itself.

Action - Labels added to figure

Comment 18

Supplementary Figure S14: The information about the nodes is what needs to be included earlier and in the main body when discussing the trees.

Action - Added the explanation earlier in the text and in the main body

Reviewer #2 (Recommendations):

Comment 19

Line 22: ”Symbolic” and ”Arbitrary” are not synonyms. Please see the comment above.

We agree. Here, we make the point that the evolution of symbolic markers of group identity can be explained from what are initially arbitrary, and meaningless, signals (see [L1, L2]). Our point being that any vocalization, any coda, could have become selected for as an identity coda, and to become symbolic, and evolve to play a key role in cultural group formation and in-group favoritism because they enable a community of individuals to solve the problem of with whom to collaborate. The specific coda itself does not affect collaborative pay offs, but group specific differences in behavior can, as such the coda is arguably symbolic; as it is observable and recognizable, and can serve as a means for social assortment even when the behavioural differences are not. This can explain the means by which the social segregation which is observed among behaviorally distinct clans of sperm whales. However, in this manuscript, we do not extend this discussion of existing literature and have attempted to concisely describe this in a couple of lines, which clearly do a disservice to the large body of literature on the evolution of symbolic markers and human ethnic groups. We have added some citations to this section so that the reader may follow up should they disagree with out brief introductory statements.

Action - Added citations and pointers to the literature.

Comment 20

Line 24: The authors’ terminology around ”markers”, ”arbitrary”, ”symbolic” is unnecessarily confusing and mystifying, giving the impression these terms are interchangeable. They are not. These terms are an integral and long-established part of key definitions in signal theory. Term use should be followed accordingly. The observation that whale vocal signals vary per population does not necessarily mean that they function as a social tag. The word ”dog” varies per population but its use relates to an animal, not the population that utters the word. ”Dog” is not ”symbolic” of England, English-speaking populations or the English language. Furthermore, the function of whale vocal signals is extremely challenging to determine. In the best conditions, researchers can pin the signal’s context, this is distinct from signal’s function and further even for the signal’s meaning. How exactly the authors determine that whale vocal signals are arbitrary is, thus, perplexing given that this would require a detailed description and understanding of who is producing the song, when, towards whom, and how the receivers react, none of which the authors have and without which no claim on the signals’ function can be made. This terminological laxness and the sensu latu in extremis to various terms in an unjustified, unnecessary and unhelpful.

We use these terms as established in Hersh et al 2022 and the works leading up to it over the last 20 years in the study of sperm whales. These are often derived from definitions by Boyd and Richerson’s work on culture in humans and animals along with evolution of symbolic markers both in theory and in humans. We agree with the reviewer that these are difficult to establish in non-humans, whales or otherwise, but feel strongly that the accumulating evidence provides strong support for the function of these signals as symbolic markers of cultural groups, and that they likely evolved from initially arbitrary calls which were a part of the vocal repertoire (similar to the process and selective environment in Efferson et al. [L1] and McElreath et al. [L2]). We feel that we do not use these terms interchangeably here, and have inherited their use from definitions from anthropology. The work presented here uses terminology built across two decades of work in cetacean, and sperm whale, culture. And do not feel that these terms should be omitted here.

Comment 21

Lines 21-27: Overly broad and hazy paragraph.

We hope the replies above and our changes satisfy this comment and clarify the text.

Comment 22

Figure 1 legend: What are ”memory structures”? Unjustified descriptor.

The phrase was chosen to make draw some intuition on the variation of context length in variable length markov models.

Action - Re-worded from memory structures to statistical properties

Comment 23

Line 30: Omit ”finite”.

Action - Omitted.

Comment 24

Line 31: Please define and distinguish ”rhythm” and ”tempo”. Also see comment above, rhythm and tempo definitions require the use of IOIs.

We disagree with the reviewer’s claims here. In our research specifically, and for sperm whale research generally, coda inter-click intervals (ICIs) are calculated as the time between the start of the first click and the start of the subsequent click. This makes ICIs identical to inter-onset intervals (IOIs) under all definitions we are aware of. For example, Burchardt and Knornschild [L3] define IOIs as such: “In a sequence of acoustic signals, the time span between the start of an element and the next element, comprising the element duration and the following gap duration”. We now include a sentence making this point.

Regardless, we disagree on a more fundamental level with the statement that unless researchers quantify inter-onset intervals (IOIs), they cannot make any claims about rhythm. There are many studies that investigate rhythmic aspects of human and animal vocalizations without using IOIs [L4–L7]. If the duration of sound elements of interest is relatively constant (as is the case for sperm whale clicks), then rhythm analyses can still be meaningfully conducted on inter-call intervals (the silent intervals between calls).

For sperm whales, coda rhythm is defined by the relative ICIs standardized by their total duration. These can be clustered into discrete, defined rhythm types based on characteristic ICI patterns. Coda tempo is relative to the total duration of the coda itself. This can also be clustered into discrete tempo types across all coda durations as well (see [L8]).

Action - We added a sentence specifying that in this case we can use both ICIs and IOIs because of the standardized length of a single click.

Comment 25

Line 36: Are there non-vocalized codas to require the disambiguation here?

No, we have omitted for clarity.

Comment 26

Line 44: ”Higher” than which other social group class?

Sperm whales live in a multi-level social organization. Clans are a “higher” level of social organization than the social “units” which we define in line 40. Clans are made up of all units which share similar production repertoire of codas.

Action - We have added ’above social units’ on line 44 to make this clear.

Comment 27

Line 47: The use of “symbolic” continues to be enigmatic, even if authors are taking in this classification from other researchers. In signal theory (semiotics), not all biomarkers are necessarily symbols. I advise the authors to avoid the use of the term colloquially and instead adopt the definition used in the research field within which the study falls in.

There is ample examples of the use of ”symbolic” when referring to markers of in-group membership both in human and non-human cultures.Our choice to use the term “symbolic” is based on a previous study [L9] that found quantitative evidence that sperm whale identity codas function as symbolic markers of cultural identity, at least for Pacific Ocean clans. The full reasoning behind why the authors used the term “symbolic markers” is given in that paper, but briefly, they found evidence that identity coda usage becomes more distinct as clan overlap increases, while non-identity coda usage does not change. This matches theoretical and empirical work on human symbolic markers[L1, L2, L10, L11].

Action - We retain the use of the term here, as defined in the works cited, and based on its prior usage in the study of both human and non-human cultures.

Comment 28

Line 50: This statement is not technically accurate. The use of a signal as a marker by individuals can only be determined by how individuals ”interpret” and react to that signal - e.g., via playback experiments - it cannot be determined by how different populations use and produce the signals.

We respectfully disagree. While we agree that the optimal situation would be that of playback, the contextual use can provide insight into the functional use of signals; as can expected patterns of use and variation, as was tested in the papers we cite. However, this argument is not the scope nor the synthesis of this paper. These statements are supported by existing published works, as cited, and we encourage the reviewer to take exception with those papers.

Comment 29

Line 69: ”Meaningful speech characteristics”??? These terms do not logically or technically follow the previous statement. Why not stay faithful to the results and state that the method used seems to be valid and reliable because it confirms former studies and methods?

Action - Reworded to better underline the method’s results with previous studies

Comment 30

Lines 72-74: This statement doesn’t seem to accurately capture/explain/resume the difference between ID and non-ID codas.

We are not sure what the reviewer is referring to in this case. The sentence in this case was meant to explain the different relations that ID/non-ID codas have with clan sympatry.

Comment 31

Line 75: The information provided in the few previous sentences does not allow the reader to understand why these results support the notion that cultural transmission and social learning occurs between clans.

We conclude out introduction with a brief summary of our overall findings, which we then use the rest of the manuscript to support these statements.

Comment 32

Table 1: So far, the authors refer to their analyses as capturing the ”rhythm” of whale clicks. Consequently, it is not readily clear at this point why the authors rely on ”ICIs” (inter click intervals) instead of the ”universal” measure used across taxa to capture the rhythm of signal sequences - IOIs (inter onset intervals). If ICIs are the same measure as IOIs, why not use the common term, instead of creating a new term name? Alternatively, if ICIs are not equivalent to IOIs, then arguably the analyses do not capture the ”rhythm” of whale clicks, as claimed by the authors. Any rhythmic claim will need to be based on IOI measures. In animal behaviour, stereotyped is primarily used to describe pathological, dysfunctional behaviour. I suggest the use of other adjective, such as ”regular”, ”repetitive”, ”recurring”, ”predictable”. Another deviation from typical terminology: ”usage frequency” -¿ ”production rate”. Why is a clan a ”higher-order” level of social organization? This requires explanation, at least a mention, of what are the ”lower-order” levels. To the non-expert reader, there is a logical circularity/gap here: Clans are said to produce clan-specific codas, and then, it is said that codas are used to delineate clans. Either one deduces, or one infers, but not both. This raises the question, are clans confirmed by any other means than codas?

We are not creating a “new term name”: inter-click interval (ICI) is the standard terminology used in odontocete (toothed whale) research. We take the reviewer’s point that some readers will not be coming to our paper with that background, however, and now explicitly point out that ICI is synonymous with IOI for sperm whales. Please see our response to your earlier comment for more on this point.

Comment 33

Line 92: Unclear term, ”sub-sequence”. Fig. 1B doesn’t seem to readily help disambiguate the meaning of the term.

In fact reference to Fig. 1B is misplaced as it does not refer to the text. A sub-sequence is simply a contiguous subset of a coda, a subset of it.

Action - Removed ambiguous reference to Fig. 1B

Comment 34

Line 94: How does the use of ”sequence” compare here with ”sub-sequence” above?

In fact its the same situation although the previous comment highlighted a source of ambiguity.

Action - Reworded the sentence to be less confusing.

Comment 35

Line 95: Signal sequences don’t ”contain” memory, they require memory for processing.

Action - Rephrased from “sequences contain memory” to “states depend on previous sequences of varying length”.

Comment 36

Lines 95-97: The analogy with human language seems forced, combinatorics in any given species are expected to entail different transitions between unit/unit-sequences.

Thank you for the comment. Indeed, the purpose of the analogy is to illustrate how variable length Markov Chains work (which have been shown to be good at discerning even accents of the same language). We used human language as an analogy to provide the readers’ with a more intuitive understanding of the results.

Action - Revised paragraph to read: “Despite we do not have direct evidence of unitary blocks in sperm whale communication, on can imagine this effect similarly to what happens with words (e.g., a word beginning with “re” can continue in more ways than one starting with “zy”).”

Comment 37

Line 97: Unclear which possibility is this.

Action - Made the wording clearer.

Comment 38

Line 99: Invocation of memory, although common in the use of Markov chains, in inadequate here given that the research did not study how individuals perceived or processed click sequences, only how individual produced click sequences. If the authors are referring to the cognitive load imposed by producing clicks sequences, terms such as ”sequence planning” will be more accurate.

Here, we use the term “fixed-memory” in relation to the definition of a variable length Markov model. We feel that, in this section of the manuscript, the context is clear that it is a mathematical definition and in no way invokes the biological idea of memory or cognition. It is rather standard to use memory to describe the order of Markov chains. Swapping words in the definition of mathematical objects when the context is clear seems to cause unnecessary ambiguity.

Action - We clarified this in the manuscript (see comments above).

Reviewer #3 (Recommendations):

Comment 39

Line 16: Add ”broadly defined” as there are many other more restricted definitions (see for example Tomasello 1999; 2009). Tomasello M (1999) The cultural origins of human cognition. Harvard University Press, Cambridge Tomasello M (2009) The question of chimpanzee culture, plus postscript (chimpanzee culture 2009). In: Laland KN, Galef BG (eds) The question of animal culture. Harvard University Press, Cambridge, pp 198-221.

Thanks for the clarification.

Action - We added the term “broadly” and added the last reference.

Comment 40

Line 22: Is all stable social learned behavior that becomes idiosyncratic and ”distinguishable” considered symbolic markers? If not, consider adding ”potentially.”

No, but the evolution of cultural groups with differing behavior can reorganize the selective environment in such a way that it can favour an in-group bias that was not initially advantageous to individuals and lead to a preference towards others who share an overt symbolic marker that initially had no meaning and a random frequency in both populations. That is to say, even randomly assigned trivial groups can evolve arbitrary symbolic markers through in-group favouritism once behavioural differences exist even in the absence of any history of rivalry, conflict, or competition between groups. See for example [L1, L2].

Comment 41

Table 1: Identity codas are defined as a ”Subset of coda types most frequently used by a sperm whale clan; canonically used to define vocal clans.” Therefore, I infer that an identity coda is not exclusively used by a specific clan and may be utilized by other clans, albeit less frequently. If this is the case, what criteria determine the frequency of usage for a coda to be categorized as an identity or non-identity coda? Does the criteria used to differentiate between ID and non-ID codas reflect the observed differences in micro changes between the two and within clans?

The methods for this categorization are defined, discussed, and justified in previous work in [L9, L12]. We feel its outside the scope of this paper to review these details here in this manuscript. However, the differences between vocal styles discussed here and the frequency production repertoires which allow for the definition of identity codas are on different scales. The differences between identity and non-identity codas are not the observed differences in vocal style reported here.

Comment 42

Table 1: The definition of vocal style states that it ”Encodes the rhythmic variations within codas.” However, if rhythm changes, does the type of coda change as well? Typically, in musical terms, the component that maintains the structure of a rhythm is ”tempo,” not ”rhythm.” How much microvariation is acceptable to maintain the same rhythm, and when do these variations constitute a new rhythm?

Thank you for raising this important point about the relationship between rhythmic variations and coda categorization. In our definition, ”vocal style” refers to subtle, micro-level variations in the rhythmic structure of codas that do not alter their overarching categorical identity. These microvariations are akin to ”tempo” changes in musical terms, which can modify the expression of a rhythm without fundamentally altering its structure.

The threshold at which microvariations constitute a new rhythm, and thus a new coda type, remains an open question and is a limitation of current analytical approaches. In our study, we used established classification methods to group codas into types, treating variations within these groups as part of the same rhythm. Future work could refine these thresholds to better distinguish between meaningful rhythmic variation and the emergence of new coda types.

Comment 43

Table 1: Change ”say” to ”vocalize” (similarly as used in line 273 for humpback whales ”vocalizations”).

Thanks.

Action - Done.

Comment 44

Lines 33-35 and Figure 1-C: Can a lay listener discern the microvariations within each coda type by ear? Consider including sound samples of individual rhythmic microvariations for the same coda type pattern (e.g., Four plus, Palindrome, Plus One, Regular) to provide readers/listeners with an impression of their detectability. If authors considered too much or redundant Supplemental material at least give a sound sample for each the 4 subcodas modeled structures examples of 4R2 coda variations depicted in Figure 1-C so the reader can have an acoustic impression of them.

We do not think that human listeners would be able to all of the variation detected here. However, this does not mean that it is not important variation for the whales. Human observers being able to classify call variation aurally shouldn’t be seen as a bar representing important biological variation for non-human species, given that their hearing and vocal production systems have evolved independently. Importantly, ’Four Plus’,’Palindrome’, etc are names of Clans; sympatric, but socially segregated, communities of whale families, which share a distinct vocal dialect of coda types. These clans each have have distinguishable coda dialects made up of dozens of coda types (and delineated based on identity codas), these are not names/categorical coda types themselves.

Action - We now provide audio samples of all coda types listed in Figure 1B in the paper’s Github repository.

Comment 45

Line 69: As stated above, it may be confusing to refer to it as ”speech.” I suggest adding something like: ”Our method does capture one essential characteristic of human speech: phonology.” Reply 45.—Thank you for drawing our attention to this.

Action - We removed the word “speech” from the manuscript, using “communication” and/or “vocalization” depending on the context.

Comment 46

Line 111-112: Consider adding a sound sample of the variation of the 4R2 coda type that can be vocalized as BCC but also as CBB as supplementary data.

What the reviewer has correctly observed is that the traditional categorical coda type ’names’ do not capture the variation within a type by rhythm nor by tempo.

Action - We have added samples of all coda types listed in Figure 1B in the paper’s Github repo.

Comment 47

Figure 3: Include a sound sample for each of the 7 coda types in Figure 1B (”specific vocal repertoires”) to illustrate the set of coda types used and their associated usage frequencies, or at least for each of the 7 coda types in Figure 3 and tables S1 and S2.

Sperm whales in the Eastern Caribbean produce dozens of rhythm types across at least five categorical tempo types [L8, L13]. The coda types represented in Figure 1B do not demonstrate all the variability inherent in the sperm whales’ vocal dialect. Importantly, Figure 3, as well as table S1 and S2, refer to clan-level dialects not specific individual coda types.

Action - We added sound samples for each coda rhythm type listed in Figure 1B to the Github repository.

Comment 48

Lines 184-190: It is unclear what human analogy term is used for ID codas. This needs clarification.

We are not making an analogy in humans for the role of ID vs non-ID codas, but only providing the example of accents as changes in vocalization (style) without a change in the actual words used (repertoire).

Action - We tried to make it clearer in the manuscript.

Comment 49

Line 190: Change ”whale speech” to ”whale vocalizations.”

Thanks.

Action - Done.

Comment 50

Figure 4: Correct citation number Hersh ”10” to Hersh ”11.”

Thanks.

Action - Fixed the reference.

Comment 51

Lines 224-232: Clarify whether the reference to how spatial overlap affects the frequency of ID codas refers to shared ID codas between clans or the production frequency of each coda within the total repertoire of codas.

The similarity between ID coda repertoires we are referring to there is based on the ID codas of both clans.

More details on the comparison can be found in [L9].

Action - We added a sentence explaining the comparison is made using the joint set of ID codas.

Comment 52

Lines 240-241: What are non-ID codas vocal cues for?

Non-ID codas likely serve as flexible, context-dependent signals that facilitate group coordination, convey environmental or social context, and promote social learning, especially in mixed-clan or overlapping habitats. Their variability suggests multifunctional roles shaped by ecological and social pressures.

Comment 53

Lines 267-268: It’s unclear whether non-ID coda vocal styles are genetically inherited or not, as argued in lines 257-258.

We did not intend to argue that non-ID coda vocal styles are genetically inherited. Instead, we aimed to present a hypothetical consideration: if non-ID coda vocal styles were genetically inherited, one would expect a direct correlation between vocal style similarity and genetic relatedness. This hypothetical framework was introduced to strengthen our argument that the observed patterns are unlikely to be explained by genetic inheritance, as such correlations have not been observed. While we acknowledge that we lack definitive proof to rule out genetic influences entirely, the evidence available strongly suggests that social learning, rather than genetic transmission, is the more plausible mechanism.

Action - Clarified in manuscript.

Comment 54

Line 277: Can males mate with females from different clans?

Yes, genetic evidence shows that males may even switch ocean basins.

Action - We have clarified that we mean the female members of units from different clans have only rarely been observed to interact at sea between clans.

Comment 55

Lines 287-292: Consider discussing the difference between controlled/voluntary and automatic/involuntary imitation and their implications for cultural selection and social learning (see Heyes 2011; 2012). Heyes, C. (2011). Automatic imitation. Psychological bulletin, 137(3), 463. Heyes, C. (2012). What’s social about social learning?. Journal of comparative psychology, 126(2), 193.

Thank you for your insightful comment regarding this. The distinction between controlled/voluntary and automatic/involuntary imitation, as highlighted by Heyes [L14, L15], provides a potentially valuable framework for interpreting social learning mechanisms in sperm whales. Automatic imitation refers to reflexive, often unconscious mimicry driven by perceptual or motor coupling, while controlled imitation involves deliberate and goal-directed efforts to replicate behaviors. Both forms likely play complementary roles in the cultural transmission observed in sperm whales.

This dual-process perspective highlights the potential for cultural selection to act at different levels. Automatic imitation may drive convergence in shared environments, promoting acoustic homogeneity and facilitating inter-clan communication. In contrast, controlled imitation ensures the preservation of clan-specific vocal traditions, maintaining cultural diversity. This interplay between automatic and controlled processes could reflect a balancing act between cultural assimilation and differentiation, underscoring the adaptive value of these mechanisms in dynamic social and ecological contexts.

Action - We have incorporated a short discussion of this distinction and its implications for our findings in the Discussion. Additionally, we have cited [L14, L15] to provide theoretical grounding for this interpretation.

Comment 56

Methods: Consider integrating the paragraph from lines 319-321 into lines 28-35 and eliminate redundant information.

Thanks.

Action - We implemented the suggestion, removing the first paragraph of the Dataset description and integrating the information when we introduce the concepts of codas and clicks.

[L1] C. Efferson, R. Lalive, and E. Fehr, Science 321, 1844 (2008).

[L2] R. McElreath, R. Boyd, and P. Richerson, Curr. Anthropol. 44, 122 (2003).

[L3] L. S. Burchardt and M. Knornschild, PLoS Computational Biology 16, e1007755 (2020).

[L4] A. Ravignani and K. de Reus, Evolutionary Bioinformatics 15, 1176934318823558 (2019).

[L5] C. T. Kello, S. D. Bella, B. Med´ e, and R. Balasubramaniam, Journal of the Royal Society Interface 14, 20170231 (2017).

[L6] D. Gerhard, Canadian Acoustics 31, 22 (2003).

[L7] N. Mathevon, C. Casey, C. Reichmuth, and I. Charrier, Current Biology 27, 2352 (2017).

[L8] P. Sharma, S. Gero, R. Payne, D. F. Gruber, D. Rus, A. Torralba, and J. Andreas, Nature Communications 15, 3617 (2024).

[L9] T. A. Hersh, S. Gero, L. Rendell, M. Cantor, L. Weilgart, M. Amano, S. M. Dawson, E. Slooten, C. M. Johnson, I. Kerr, et al., Proc. Natl. Acad. Sci. 119, e2201692119 (2022).

[L10] R. Boyd and P. J. Richerson, Cult Anthropol 2, 65 (1987). [L11] E. Cohen, Curr. Anthropol. 53, 588 (2012).

[L12] T. A. Hersh, S. Gero, L. Rendell, and H. Whitehead, Methods Ecol. Evol. 12, 1668 (2021), ISSN 2041-210X, 2041-210X.

[L13] S. Gero, A. Bøttcher, H. Whitehead, and P. T. Madsen, R. Soc. Open Sci. 3, 160061 (2016).

[L14] C. Heyes, Psychological Bulletin 137, 463 (2011).

[L15] C. Heyes, Journal of Comparative Psychology 126, 193 (2012).

Author response:

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

Reviewer #1 (Public review):

Weiler, Teichert, and Margrie systematically analyzed long-range cortical connectivity, using a retrograde viral tracing strategy to identify layer and region-specific cortical projections onto the primary visual, primary somatosensory, and primary motor cortices. Their analysis revealed several hundred thousand inputs into each region, with inputs originating from almost all cortical regions but dominated in number by connections within cortical sub-networks (e.g. anatomical modules). Generally, the relative areal distribution of contralateral inputs followed the distribution of corresponding ipsilateral inputs. The largest proportion of inputs originated from layer 6a cells, and this layer 6 dominance was more pronounced for contralateral than ipsilateral inputs, which suggests that these connections provide predominantly feedback inputs. The hierarchical organization of input regions was similar between ipsi- and contralateral regions, except for within-module connections, where ipsilateral connections were much more feed-forward than contralateral. These results contrast earlier studies which suggested that contralateral inputs only come from the same region (e.g. V1 to V1) and from L2/3 neurons. Thus, these results provide valuable data supporting a view of interhemispheric connectivity in which layer 6 neurons play an important role in providing modulatory feedback.

The conclusions of this paper are mostly well-supported by the data and analysis, but additional consideration of possible experimental biases is needed.

We thank the reviewer for their positive feedback on our manuscript.

Further discussion or analysis is needed about possible biases in uptake efficiency for different cell types. Is it possible that the nuclear retro-AAV has a tropism for layer 6 axons? Quantitative comparisons with results obtained with alternative methods such as rabies virus (Yao et al., 2023) or anterograde tracing (Harris et al., 2019) may be helpful for this.

We appreciate this technical comment. For the reasons indicated below we are confident that our AAV approach successfully and rather comprehensively labels inputs to the three target areas. Firstly, in the brains in which we injected our retrograde nuclear-AAV tracer into VISp, SSp-bfd or MOp we found several instances where layer 5 and/or layer 2/3 as was the dominant cortical projection layer (please see e.g. Figure 3 heatmaps). This was true for both ipsilateral and contralateral projection. 

Secondly, by way of comparison Yao et al., 2023 injected rabies virus into VISp (but not in SSp-bfd or MOp) and their results show notable similarities to ours: 1) They show that contralateral inputs to VISp (and higher visual areas) were mainly located in Layers 5 and 6. 2) Retrogradely labelled neurons in higher visual areas revealed anatomical hierarchy that reflects the known functional hierarchy of the mouse cortical visual system and that shown by our retro-AAV approach. Thus, as AAV and rabies based tracing lead to similar results, this is further evidence against bias via tropism of our AAV tracer. That said, direct comparisons of the results between our study and the Yao et al., 2023 study should be viewed with some caution since Yao et. al.  injected rabies virus into specific Cre-driver lines in which the rabies virus targets individual genetically defined cell types in specific layers. Importantly, because of the lack of a specific cre-driver line, L6 cortico-cortical (L6 CC) cells could not be targeted by their approach. Thus, the dataset in Yao et al., overlook the contribution of L6 CCs due to the lack of available Cre-lines. 

Thirdly, in a recent study (Weiler et al., 2024) we found that in a specific pathway (SSp-bfd→ VISp) both retro-AAV and the more traditional non-viral tracer cholera toxin subunit B (CTB) identified neurons in Layer 6 as the main source of projection neurons. The same results for the same pathway was shown by Bieler et al., 2019 (Bieler et al., 2017) using Fluorogold for retrograde tracing. Thus, the described dominance of Layer 6 projection neurons in specific pathways is likely not the result of a tropism of retro-AAV tracers. 

Please also see that we have now further extended the summary of these points in our revised manuscript in the discussion section (e.g. lines 457-463): 

Quantitative analysis of the injection sites should be included to account for possible biases. For example, L6 neurons are known to be the main target of contralateral inputs into the visual cortex (Yao et al., 2023). Thus, if the injections are biased towards or against layer 6 neurons, this may change the layer distribution of retrogradely labeled input cells. Comparison across biological replicates may help reveal sensitivity to particular characteristics of the injections.

In response to the reviewers' feedback, please see we have now quantified the injection volume per cortical layer, as shown in the revised Fig. S3D. Our results indicate that the injections were not biased toward Layer 6. Instead, the injected tracer volumes in Layers 1, 4, 5, and 6 were similar across all animals and injected areas. However, we observed that the injected tracer volume in Layer 2/3 tended to be higher than in other layers. Although the tracer volumes in Layers 2/3 appeared to be higher, the proportion of input neurons located in Layers 2/3 for most of the cortical projection areas was consistently lower than that from Layer 6. These findings provide strong evidence against injection bias towards L6 inputs.

The possibility of labelling axons of passage within the white matter should be addressed. This could potentially lead to false positive connections, contributing to the broad connectivity from most cortical regions that were observed.

For clarification, please see Fig.S2B in our revised manuscript. In this panel we plot the average percentage volume of the viral boli in the target areas and in all other nearby structures including the white matter. The percentage of virus injected into the white matter (WM) was 0.0824 ± 0.0759% for VISp and 0.0650 ± 0.0481 for SSp-bfd injections. Notably, injections into MOp showed no leakage into white matter (0%). These minimal volumes of virus in the white matter are unlikely to significantly influence the observed profile of widespread connectivity. Please see we have added a sentence to the Results section (lines 84-86) where we state that we only used brains that had a transduction of the white matter below 0.1%.

Reviewer #2 (Public review):

Summary:

Weiler et al use retrograde tracers, two-photon tomography, and automatic cell detection to provide a detailed quantitative description of the laminar and area sources of ipsi- and contralateral cortico-cortical inputs to two primary sensory areas and a primary motor area. They found considerable bilateral symmetry in the areas providing cortico-cortical inputs. However, although the same regions in both hemispheres tended to supply inputs, a larger proportion of inputs from contralateral areas originated from deeper layers (L5 and L6).

Strengths:

The study applies state-of-the-art anatomical methods, and the data is very effectively presented and carefully analyzed. The results provide many novel insights into the similarities and differences of inputs from the two hemispheres. While over the past decade there have been many studies quantitatively and comprehensively describing cortico-cortical connections, by directly comparing inputs from the ipsi and contralateral hemispheres, this study fills in an important gap in the field. It should be of great utility and an important reference for future studies on inter-hemispheric interactions.

We thank the reviewer for this encouraging feedback on our manuscript.

Weaknesses:

Overall, I do not find any major weakness in the analyses or their interpretation. However, one must keep in mind that the study only analyses inputs projecting to three areas. This is not an inherent flaw of the study; however, it warrants caution when extrapolating the results to callosal projections terminating in other areas. As inputs to two primary sensory areas and one is the primary motor cortex are studied, some of the conclusions could potentially be different for inputs terminating in high-order sensory and motor areas. Given that primary areas were injected, there are few instances of feedforward connections sampled in the ipsilateral hemisphere. The study finds that while ipsi-projections from the visual cortex to the barrel cortex are feedforward given its fILN values, those from the contralateral visual cortex are feedback instead. One is left to wonder whether this is due to the cross-modal nature of these particular inputs and whether the same rule (that contralateral inputs consistently exhibit feedback characteristics regardless of the hierarchical relationship of their ipsilateral counterparts with the target area,) would also apply to feedforward inputs within the same sensory cortices.

We acknowledge that what we find for primary sensory and motor target areas may not hold for other functionally different areas such as anterior cingulate cortex, retrosplenial cortex or frontal lobe that might be expected to receive strong feedforward cortical input. To begin to understand the organization of the global cortical input we have however first explored with primary sensory and motor areas. Please see that we have now added a sentence to the Discussion section of our manuscript that highlights the importance of investigating the hierarchical organization of intra and interhemispheric input onto higher cortical areas or within subregions of a given sensory area.

Another issue that is left unexplored is that, in the current analyses the barrel and primary visual cortex are analyzed as a uniform structure. It is well established that both the laminar sources of callosal inputs and their terminations differ in the monocular and binocular areas of the visual cortex (border with V2L). Similarly, callosal projections differ when terminating the border of S1 (a row of whiskers), and then in other parts of S1. Thus, some of the conclusions regarding the laminar sources of callosal inputs might depend on whether one is analyzing inputs terminating or originating in these border regions.

The aim of the present study was to analyse the global projectome to the VISp, SSp-bfd and MOp, irrespective of which subregions were included. Importantly, we purposely injected rather large bolus volumes to achieve large sample sizes of target neurons in each cortical layer.  For SSp-bfd, we utilised our previously reconstructed barrel map (Weiler et al., 2024) to precisely map our viral injection sites onto the barrels (Author response image 1). Analysis revealed that the six injection sites consistently encompassed 7–13 barrels (Author response image 1, three exemplary injection sites). Additionally, we determined the centres of mass for each injection site and mapped them onto the barrel map. Four of the injection sites were located in the lateral part of SSp-bfd, two in the central region, and none in the medial part. Notably, the injection sites within SSp-bfd exhibited significant overlap. As a result, a selective analysis of callosal projections targeting these injection sites would likely not yield distinct projection patterns, as the projectomes would inevitably include projections to surrounding barrels, leading to contamination.

Author response image 1.

Left: exemplary Injection sites mapped onto the 3D barrel map of SSp-bfd within the Mouse Allen Brain Atlas. Barrels were reconstructed using a specialized software as described previously (Weiler et al., 2024) Right: Centres of mass of all SSp-bfd injection sites mapped onto the 3D barrel map.

Due to the fact we covered a significant proportion of the respective target primary sensory area any further subdivision of these data is not possible and requires more tailored injections into clearly defined subareas. Investigating the separate projectomes onto these subregions (e.g. onto V1M and V1B) remains an important interesting research question that we, at least in part, will address in a future study.

Finally, while the paper emphasizes that projections from L6 "dominate" intra and contralateral cortico-cortical inputs, the data shows a more nuanced scenario. While it is true that the areas for which L6 neurons are the most common source of cortico-cortical projections are the most abundant, the picture becomes less clear when considering the number of neurons sending these connections. In fact, inputs from L2/3 and L5 combined are more abundant than those from L6 (Figure 3B), challenging the view that projections from L6 dominate ipsi- and contralateral projecting cortico-cortical inputs.

We agree in the case of the barrel cortex, layer 5 significantly contributes in terms of the number of brain areas projecting from within the ipsilateral and contralateral hemispheres. Please see we have replaced the term “dominates” in the title, abstract and in the manuscript where relevant.

Recommendations for the authors:  

Reviewer #1 (Recommendations for the authors):

The sections analyzing the role of L6 towards feedback (pg. 11-13, Figure 6) were a bit verbose and confusing to me. Three possible models are proposed:

(1) a decrease in L23 projections, (2) an increase in L56 projections, or (3) both.

However, what is being quantified appears to be the fractions inputs, with L23. L5, and L6 summing to 1. Thus, a decrease in L23 would necessarily result in an increase in L56 projections. It seems like it would make more sense to quantify the percent change in the total number of inputs (rather than fractional) from each layer so that the 3 models are actually independent possibilities.

The issue with the suggested analysis is that, with one exception (one area projecting to MOp), the number of projection neurons in contralateral areas is always ~60-80% lower compared to their ipsilateral counterparts. Consequently, this is also true for the number of projection neurons in the different cortical layers. Thus, quantifying the percentage change from the ipsilateral to the contralateral hemisphere in the total number of inputs from each layer will always result in negative values. 

Nevertheless, we addressed the reviewer’s issue by calculating the preservation index (1(ipsi-contra)/(ipsi+contra)) for the sensory-motor areas independently for the absolute number of neurons within L2/3, 5 and 6 for the cortical areas projecting to VISp, SSp-bfd and MOp (see Author response image 2). When analysing the shift from the ipsilateral to the contralateral hemisphere, we observed that significantly more projection neurons were preserved in L6 compared to L2/3 for VISp and SSp-bfd. This shows that the number of L6 projection neurons declines less from the ipsilateral to the contralateral hemisphere compared to L2/3. However, our focus was on the fraction of projection neurons within each layer relative to the other layers per hemisphere (see Fig.6 of our manuscript). This measure is critical for distinguishing between feedforward and feedback connectivity. Calculating the change for each layer independently unfortunately does not provide insights into this comparison, as it does not capture the relative distribution of projection neurons across layers, which is central to our analysis. Therefore, we chose to present the data as layer fractions normalised within each hemisphere separately, enabling a comparison of relative changes between hemispheres, as shown in Fig.6 in the manuscript. We agree that with our approach a decrease in the fraction of L2/3 neurons would necessarily lead to an increase in the fraction of L5+6 neurons. However, as we analysed the fractional change for L5 and L6 separately, we found that the fraction of projection neurons in L5 generally showed only minor changes, while the fraction of L6 projection neurons increased substantially (Fig.6C). In addition, excluding L5 from the ipsi- or contralateral default network had significant effects on the fILN in only a relatively small number of projection areas. Excluding L6 resulted in significant changes in many more projection areas than layer 5.

Author response image 2.

Preservation index for L2/3, L5 and L6 of the 24 sensory-motor areas projecting onto the three target areas VISp, SSp-bfd and MOp.

Reviewer #2 (Recommendations for the authors):

I feel that there are a few conclusions that could be strengthened in the paper:

(1) The laminar sources of callosal inputs and their terminations differ in the monocular and binocular areas of the visual cortex (border with V2L. Similarly, callosal inputs are different close to the border of S1 with S2 than in the rest of the barrel cortex. From the methods sections and Figure S2, it seems that some injections targeted the V1 binocular zone while others were aimed at the monocular zone. Thus, it would be of interest to compare the laminar distribution and fILM of the contra inputs in inputs to the binocular and monocular zones (and S1 border vs the rest, if possible within this dataset).

Please see the answer for the reviewer’s second point in the public review (above).

(2) The results are currently a bit unclear on whether the contra inputs reflect the cortical hierarchy. Figure 4E-F makes it clear that the ipsi and contra fILMs do not always match. However, it seems from the plots in Figure 4D and Figure S6 that, while the contra fILM values are always higher, there might be a correlation between the ipsi and contra fILM. This could be addressed by directly plotting contra vs ipsi fILM.

Similarly, it would be useful to directly address if there is any hint of the visual hierarchy, as calculated in Figure S5 for the contra inputs.

Regarding the first point of the reviewer: We appreciate this comment. We do indeed find a positive correlation between the fILN ipsilateral and fILN contralateral across the individual cortical areas for all three targets. (please see Author response image 3 below). This is indeed an interesting observation that indicates a high degree of preservation concerning the rank order of the anatomical hierarchy within the input arising from both hemispheres. Please see we have included this new figure 4F into the manuscript and added a sentence in the results (lines 282-288): 

Regarding the second point of the reviewer: For visual hierarchy, although weaker, we find that the hierarchical ranking of the higher cortical visual areas is preserved for the contralateral hemisphere (see Author response image 3 below). 

Author response image 3.

Rank ordered average fILN values (± sem) of higher visual cortical areas of the ventral and dorsal visual stream for the ipsilateral and contralateral hemisphere.

(3) I find the emphasis in the title and other parts of the paper on Layer 6 corticocortical cells dominating the anatomical organization of intra and interhemispheric feedback a bit of an overstatement. While it is true that the areas for which L6 is the most abundant source of cortico-cortical projections are the most abundant (Figure 3C), when just focusing on the number of neurons sending corticocortical connections (Figure 3B), this is less clear. Ipsi connections are roughly divided 1/3, 1/3 , 1/3 between L2/3 , L5 and L6. In the contra, while projections from L6 neurons are the most abundant, there are not a majority and are less than those of L2/3 and L5 together. I suggest revising the statement about L6 cells dominating cortico-cortical connections to more accurately reflect these nuances.

(4) The observations from Figure 3 discussed above suggest that L6 inputs dominate in areas with less abundant projections to the injected areas. Is this the case? Is the fraction of L6 inputs inversely correlated with the number of inputs from that area?

Please see the following correlation plots for the total number of inputs versus the fraction of L6 inputs per area for both the ipsilateral and contralateral hemisphere. We do find on the ipsilateral hemisphere a negative correlation between the total number of inputs and the L6 input fraction for VISp and to a lesser degree for SSp-bfd. Interestingly, we find the opposite correlation for the ipsilateral MOp, contralateral VISp, SSp-bfd and MOp (Author response image 4, Author response table 1). While this is an interesting finding, the correlations often appeared to be weak and often absent within the individual animals and across the three target areas (Author response table 1). Thus, these correlations are seemingly not a general feature of cortical connectivity.

Author response image 4.

Total number of cells versus fraction of cells within L6 per cortical brain area (average across animals) for the ipsilateral (top) and contralateral (bottom) hemisphere for the three target areas VISp, SSp-bfd and MOp.

Author response table 1: Respective correlations between total numbers of cells and fraction of cells within L6 per cortical brain area for the ipsilateral and contralateral hemisphere for the three target areas (significant correlations highlighted with green).

Minor issues:

(5) Where was the mouse in Figure 3A injected?

In this exemplary mouse the retrograde tracer was injected into VISp. We added this information in the Figure legend of Figure 3A1. 

(6) Clarify in panel 4F that the position of the circle corresponds to the area location.

Done as suggested. 

References

Bieler M, Sieben K, Cichon N, Schildt S, Röder B, Hanganu-Opatz IL. 2017. Rate and Temporal Coding Convey Multisensory Information in Primary Sensory Cortices. eNeuro 4. doi:10.1523/ENEURO.0037-17.2017

Weiler S, Rahmati V, Isstas M, Wutke J, Stark AW, Franke C, Graf J, Geis C, Witte OW, Hübener M, Bolz J, Margrie TW, Holthoff K, Teichert M. 2024. A primary sensory cortical interareal feedforward inhibitory circuit for tacto-visual integration. Nat Commun 15:3081. doi:10.1038/s41467-024-47459-2

Yao S, Wang Q, Hirokawa KE, Ouellette B, Ahmed R, Bomben J, Brouner K, Casal L, Caldejon S, Cho A, Dotson NI, Daigle TL, Egdorf T, Enstrom R, Gary A, Gelfand E, Gorham M, Griffin F, Gu H, Hancock N, Howard R, Kuan L, Lambert S, Lee EK, Luviano J, Mace K, Maxwell M, Mortrud MT, Naeemi M, Nayan C, Ngo N-K, Nguyen T, North K, Ransford S, Ruiz A, Seid S, Swapp J, Taormina MJ, Wakeman W, Zhou T, Nicovich PR, Williford A, Potekhina L, McGraw M, Ng L, Groblewski PA, Tasic B, Mihalas S, Harris JA, Cetin A, Zeng H. 2023. A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex. Nat Neurosci 26:350–364. doi:10.1038/s41593-022-01219-x

Author response:

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

Reviewer #1 (Public review):

Summary:

Lejeune et al. demonstrated sex-dependent differences in the susceptibility to MRSA infection. The authors demonstrated the role of the microbiota and sex hormones as potential determinants of susceptibility. Moreover, the authors showed that Th17 cells and neutrophils contribute to sex hormone-dependent protection in female mice.

Strengths:

The role of microbiota was examined in various models (gnotobiotic, co-housing, microbiota transplantation). The identification of responsible immune cells was achieved using several genetic knockouts and cell-specific depletion models. The involvement of sex hormones was clarified using ovariectomy and the FCG model.

Weaknesses:

The mechanisms by which specific microbiota confer female-specific protection remain unclear.

We thank the reviewer for highlighting the strengths of the manuscript including the models and techniques we employ. We agree that the relationship between the microbiota and sex-dependent protection is less developed compared with other aspects of the study. As detailed below, we are attempting to identify specific microbes that confer femalespecific protection and links with sex hormones. We have promising but preliminary results. Thus, in our revised manuscript, we added new data on the host response as suggested by the detailed comments from the Reviewers. We also elaborate on the potential role of the microbiota in the discussion section.

Reviewer #1 (Recommendations for the authors):

(1) The authors nicely showed that the transfer of the protective phenotype by FMT requires the female sex in recipients (Figure 2E). However, it remains unclear whether the female sex is required to develop protective microbiota in donor mice, as only the female NYU donor-male Jax recipient combination was tested. What happens if the microbiota from male NYU mice is transplanted into female Jax mice? If sex hormones act only on the downstream of the microbiota, such mice would show the protective phenotype. However, if sex hormones are required to establish a protective microbiota, the transplantation of microbiota from male NYU mice will not confer protection in recipient female Jax mice.

The Reviewer’s comment is well taken. We have not conducted the suggested experiment of FMT from male NYU mice to JAX female mice yet because we are pursuing an in vitro approach that we hope will eventually provide a more definitive answer. We observed that stool from female NYU mice and not JAX mice inhibits MRSA when cultured under anaerobic conditions, and this inhibitory activity is eliminated by filtration (Author response image 1A). We also observed that stool from male NYU mice inhibits MRSA growth to a similar extent as stool from female NYU mice (Author response image 1B). This result suggests that the protective role of sex hormones is downstream of the microbiota. We are in the process of identifying the specific microbiota member to support this conclusion.

Author response image 1.

Stool from NYU mice inhibits MRSA growth in vitro. (A) MRSA CFU/mL in media (TSB) following culture with unfiltered or filtered stool homogenate from female NYU or JAX mice. Stool homogenate or TSB alone was added in a 1:1 ratio to 1x106 CFU/mL MRSA and cultured anaerobically for up to 24 hours. (B) MRSA CFU/mL in TSB following culture with unfiltered stool homogenate from NYU male or female mice. Stool homogenate or TSB alone was added in a 1:1 ratio to 1x106 CFU/mL MRSA. 3 experimental replicates performed; stool taken from 6 individual mice per condition. Mean MRSA burden ± SEM. Area under the curve analysis + One way ANOVA with Sidak’s multiple comparisons test. ns: not significant.

(2) The results clearly showed the involvement of the specific microbiota in NYU mice in the sex-dependent bias in susceptibility to MRSA. However, the mechanisms by which specific microbiota promotes female sex-mediated protection need to be better described. Is this simply attributed to the different Th17 cell numbers in NYU and Jax mice (i.e., increased commensalspecific Th17 cells in NYU like Taconic mice)? Or is it possible that NYU microbiota impacts the regulation of sex hormones or their downstream signaling? What about the level of sex hormones in NYU and Jax mice? Are these levels equivalent or different? Do NYU and Jax microbiotas regulate the expression of sex hormone receptors in immune cells differently?

These are great questions. We do not observe baseline differences in Th17 cells like JAX versus Taconic mice (Figure 5B), suggesting that the mechanism is different. However, it is quite possible that an antigen-specific T cells, or Th17 cell specifically, is present at low levels and expands rapidly upon MRSA colonization. We have added this possibility to the discussion in the revised manuscript. To address the Reviewer’s question about the effect of the microbiota on sex hormones, we first sought to determine which sex hormone is necessary. Using estrogen receptor knockouts (Esr1^-/-), we were able to implicate estrogen and have added this important finding to the manuscript (Fig 6C). Then, we measured levels of estradiol in stool samples but did not observe a difference between NYU and JAX female mice (Author response image 2). We provide the results below but did not add it to the revised manuscript because we found it difficult to draw a conclusion without more extensive profiling as well as quantification of the receptor on specific immune cell subsets and cell-type specific knockouts. Also, see our response to Reviewer #3 regarding receptor expression. Although we have yet to explain the role of the microbiota, we hope the Reviewer agrees that we have promising yet preliminary results and that the new experiments we added to the manuscript have further strengthened the mechanism on the host-side. 

Author response image 2.

Estradiol levels in stool samples prior to MRSA inoculation. (A) Estradiol levels in stool samples collected prior to MRSA inoculation in male and female mice bred at NYU or purchased from Jackson Labs. Frozen stool samples were normalized by weight and processed using the DetectX® Estradiol ELISA Kit (Arbor Assays).

(3) The authors claimed that Th17-mediated recruitment of neutrophils likely promotes the clearance of MRSA in female NYU mice. However, the experimental evidence supporting this claim could be stronger. The authors should show the neutrophil recruitment in the gut mucosa in female and male NYU mice. Also, the levels of neutrophils between NYU and Jax female mice should be examined. To further strengthen the link between Th17 and neutrophils, it would be ideal to analyze neutrophil recruitment in mice lacking Th17 cells (i.e., Rag2-/-, anti-CD4 treated, Rorgt-/- mice).

We agree and now include a more detailed analyses of neutrophils. We found that the number of neutrophils in the intestine were not higher in NYU female mice compared with NYU male mice, with or without MRSA. Instead, we show that neutrophils in NYU female mice display higher levels of surface CD11b, a sign of activation, compared to males following inoculation with MRSA . We have added these findings to the revised manuscript (Fig5 H and I). IL-17 can activate neutrophils and increase their antimicrobial activity. Consistent with this possibility, we now show that female mice lacking the IL-17 receptor lose the enhanced colonization resistance. Based on these findings, we have modified this aspect of the conclusion, and thank the reviewer for the helpful suggestion.

Reviewer #2 (Public review):

The current study by Lejeune et al. investigates factors that allow for persistent MRSA infection in the GI tract. They developed an intriguing model of intestinal MRSA infection that does not use the traditional antibiotic approach, thereby allowing for a more natural infection that includes the normal intestinal microbiota. This model is more akin to what might be expected to be observed in a healthy human host. They find that biological sex plays a clear role in bacterial persistence during infection but only in mice bred at an NYU Facility and not those acquired from Jackson Labs. This clearly indicates a role for the intestinal microbiome in affecting female bacterial persistence but not male persistence which was unaffected by the origin of the mice and thus the microbiome. Through a series of clever microbiome-specific transfer experiments, they determine that the NYU-specific microbiome plays a role in this sexual dimorphism but is not solely responsible. Additional experiments indicate that Th17 cells, estrogen, and neutrophils also participate in the resistance to persistent infection. Notably, they assess the role of sex chromosomes (X/Y) using the established four core genotype model and find that these chromosomes appear to play little role in bacterial persistence.

Overall, the paper nicely adds to the growing body of literature investigating how biological sex impacts the immune system and the burden of infectious disease. The conclusions are mostly supported by the data although there are some aspects of the data that could be better addressed and clarified.

We thank the Reviewer for appreciating our contribution and these supportive comments. We have added several experiments to fill-in gaps and text revisions to increase clarity and acknowledge limitations. 

(1) There is something of a disconnect between the initial microbiome data and the later data that analyzes sex hormones and chromosomes. While there are clearly differences in microbial species across the two sites (NYU and JAX) how these bacterial species might directly interact with immune cells to induce female-specific responses is left unexplored. At the very least it would help to try and link these two distinct pieces of data to try and inform the reader how the microbiome is regulating the sex-specific response. Indeed, the reader is left with no clear exploration of the microbiota's role in the persistence of the infection and thus is left wanting.

We agree. This comment is similar to Reviewer #1’s feedback. As mentioned above, we are attempting to clarify the association between sex differences and the microbiota and have included preliminary results for the Reviewers. However, addressing this disconnect will require substantially more investigation. Instead, we have added insightful new data that elaborate on aspects of the host response.  We hope the Reviewer agrees that revised manuscript is stronger and that further delineation of the microbiota can be addressed by future studies.

(2) While the authors make a reasonable case that Th17 T cells are important for controlling infection (using RORgt knockout mice that cannot produce Th17 cells), it is not clear how these cells even arise during infection since the authors make most of the observations 2 days postinfection which is longer before a normal adaptive immune response would be expected to arise. The authors acknowledge this, but their explanation is incomplete. The increase in Th17 cells they observe is predicated on mitogenic stimulation, so they are not specific (at least in this study) for MRSA. It would be helpful to see a specific restimulation of these cells with MRSA antigens to determine if there are pre-existing, cross-reactive Th17 cells specific for MRSA and microbiota species which could then link these two as mentioned above.

We acknowledge that this is a limitation of our study. Although an experiment demonstrating pre-existing, cross-reactive T cells would help support our conclusion, aspects of MRSA biology may make the results of this experiment difficult to interpret. We have consulted with an expert on MRSA virulence factors, co-lead author Dr. Victor Torres, about the feasibility of this experiment. MRSA possess superantigens, such as Staphylococcal enterotoxin B, which bind directly to specific Vβ regions of T-cell receptors (TCR) and major histocompatibility complex (MHC) class II on antigen-presenting cells, resulting in hyperactivation of T lymphocytes and monocytes/macrophages. Additionally, other MRSA virulence factors, such as α-hemolysin and LukED, induce cell death of lymphocytes. MRSA’s enterotoxins are heat stable, so heat-inactivation of the bacterium may not help in this matter.  For these reasons, it is unlikely that we can perform a simple restimulation of lymphocytes with MRSA antigens. 

A study by Shao et al. provides an example of a host commensal species inducing Th17 cells with cross-reactivity against MRSA. Upon intestinal colonization, the intestinal fungus Candida albicans influences T cell polarization towards a Th17 phenotype in the spleen and peripheral lymph nodes which provided protection to the host against systemic candidemia. Interestingly, this induction of protective Th17 cells, increased IL-17 and responsiveness in circulating Ly6G+ neutrophils also protected mice from intravenous infection with MRSA, indicating that T cell activation and polarization by intestinal C. albicans leads to non-specific protective responses against extracellular pathogens.

Shao TY, Ang WXG, Jiang TT, Huang FS, Andersen H, Kinder JM, Pham G, Burg AR, Ruff B, Gonzalez T, Khurana Hershey GK, Haslam DB, Way SS. Commensal Candida albicans Positively Calibrates Systemic Th17 Immunological Responses. Cell Host & Microbe. 2019 Mar 13;25(3):404-417.e6. doi: 10.1016/j.chom.2019.02.004. PMID: 30870622; PMCID: PMC6419754.

We have added a brief version of the above discussion in the revised manuscript. Also, as mentioned earlier, we have added new data strengthening the axis between Th17 and neutrophils, including showing that IL-17 receptor is necessary and that neutrophils display signs of heightened activation in female mice during MRSA colonization.   

(3) The ovariectomy experiment demonstrates a role for ovarian hormones; however, it lacks a control of adding back ovarian hormones (or at least estrogen) so it is not entirely obvious what is causing the persistence in this experiment. This is especially important considering the experiments demonstrating no role for sex chromosomes thus demonstrating that hormonal effects are highly important. Here it leaves the reader without a conclusive outcome as to the exact hormonal mechanism.

This is a great suggestion. Rather than adding back ovarian hormones, we performed the more direct experiment and tested whether the estrogen receptor (ERα, encoded by Esr1) is necessary for the enhanced colonization resistance. Indeed, we observed that Esr1^-/- female mice have increased MRSA burden compared to Esr1^+/- littermates. We have added this new result (Figure 6C) and thank the Reviewer for their guidance. 

4) The discussion is underdeveloped and is mostly a rehash of the results. It would greatly enhance the manuscript if the authors would more carefully place the results in the context of the current state of the field including a more enhanced discussion of the role of estrogen, microbiome, and T cells and how the field might predict these all interact and how they might be interacting in the current study as well.

Author response: We thank the Reviewer for their feedback in improving the scholarship on the manuscript. We have expanded on the literature and the mechanistic model in both the discussion section and other parts to provide better context for our findings. 

Reviewer #3 (Public review):

Summary:

Using a mouse model of Staphylococcus aureus gut colonization, Lejeune et al. demonstrate that the microbiome, immune system, and sex are important contributing factors for whether this important human pathogen persists in the gut. The work begins by describing differential gut clearance of S. aureus in female B6 mice bred at NYU compared to those from Jackson Laboratories (JAX). NYU female mice cleared S. aureus from the gut but NYU male mice and mice of both sexes from JAX exhibited persistent gut colonization. Further experimentation demonstrated that differences between staphylococcal gut clearance in NYU and JAX female mice were attributed to the microbiome. However, NYU male and female mice harbor similar microbiomes, supporting the conclusion that the microbiome cannot account for the observed sex-dependent clearance of S. aureus gut colonization. To identify factors responsible for female clearance of S. aureus, the authors performed RNAseq on intestinal epithelial cells and cells enriched within the lamina propria. This analysis revealed sexdependent transcriptional responses in both tissues. Genes associated with immune cell function and migration were distinctly expressed between the sexes. To determine which immune cell types contribute to S. aureus clearance Lejeune et al employed genetic and antibody-mediated immune cell depletion. This experiment demonstrated that CD4+ IL17+ cells and neutrophils promote the elimination of S. aureus from the gut. Subsequent experiments, including the use of the 'four core genotype model' were conducted to discern between the roles of sex chromosomes and sex hormones. This work demonstrated that sex-chromosome-linked genes are not responsible for clearance, increasing the likelihood that hormones play a dominant role in controlling S. aureus gut colonization.

Strengths:

A strength of the work is the rigorous experimental design. Appropriate controls were executed and, in most cases, multiple approaches were conducted to strengthen the authors' conclusions. The conclusions are supported by the data.

The following suggestions are offered to improve an already strong piece of scholarship.

Weaknesses:

The correlation between female sex hormones and the elimination of S. aureus from the gut could be further validated by quantifying sex hormones produced in the four core genotype mice in response to colonization. Additionally, and this may not be feasible, but according to the proposed model administering female sex hormones to male mice should decrease colonization. Finally, knowing whether the quantity of IL-17a CD4+ cells change in the OVX mice has the potential to discern whether abundance/migration of the cells or their activation is promoted by female sex hormones.

In the Discussion, the authors highlight previous work establishing a link between immune cells and sex hormone receptors, but whether the estrogen (and progesterone) receptor is differentially expressed in response to S. aureus colonization could be assessed in the RNAseq dataset. Differential expression of known X and Y chromosome-linked genes were discussed but specific sex hormones or sex hormone receptors, like the estrogen receptor, were not. This potential result could be highlighted.

We appreciate the comment on the scholarship and thank the Reviewer for the insightful suggestions to improve this manuscript. We apologize for not including references that address some of the Reviewer’s questions. Other research groups have compared the levels of hormones between XX and XY males and females in the four core genotypes model and have found similar levels of circulating testosterone in adult XX and XY males. No difference was found in circulating estradiol levels in XX vs XY- females when tested at 4-6 or 79 months of age. 

Karen M. Palaszynski, Deborah L. Smith, Shana Kamrava, Paul S. Burgoyne, Arthur P. Arnold, Rhonda R. Voskuhl, A Yin-Yang Effect between Sex Chromosome Complement and Sex Hormones on the Immune Response. Endocrinology, Volume 146, Issue 8, 1 August 2005, Pages 3280–3285, https://doi.org/10.1210/en.2005-0284

Sasidhar MV, Itoh N, Gold SM, Lawson GW, Voskuhl RR. The XX sex chromosome complement in mice is associated with increased spontaneous lupus compared with XY. Ann Rheum Dis. 2012 Aug;71(8):1418-22. doi: 10.1136/annrheumdis-2011-201246. Epub 2012 May 12. PMID: 22580585; PMCID: PMC4452281.

Administering female sex hormones to males is a good idea. We did not observe an effect of injecting males with estrogen on MRSA colonization (data not shown), perhaps due to the dose or timing, or because it is not sufficient (i.e., additional hormones and factors may be required). Therefore, we analyzed the necessity of estrogen signaling and found that Esr1^-/- female mice impairs colonization resistance to MRSA. We have added this new experiment to the revised manuscript (Fig6 C).

Examination of the levels of estrogen, progesterone, and androgen receptors in our cecalcolonic lamina propria RNA-seq dataset is an excellent idea. We observed a significant increase in the G-protein coupled estrogen receptor 1 (Gper1) and a non-significant increase in Estrogen receptor alpha (Esr1) following MRSA inoculation in the immune cell compartment. This analysis has been added to the revised manuscript (Supplemental Fig6).

Reviewer #3 (Recommendations for the authors)

Minor editing issues:

The topic sentence of the last paragraph in the Results section states - 'male sex defining gene sex determining region Y (Sry) has been moved from the Y chromosome to an autosome'. 'Sex defining gene' and sex-determining region seems redundant in this context. A sex-defining gene would presumably be located within a sex-determining region.

Bold the letter 'F' in the Figure 5 legend.

It's not clear from the Figure 6E legend when the IL-17A+ CD4+ cells were quantified, 2 dpi?

In the third sentence of the second paragraph of the Discussion, the two references are merged together.

We thank the Reviewer for pointing out these editing issues. They have been addressed in the revised manuscript.