Author response:

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

Reviewer #1 (Public Review):

(1) The authors' findings are primarily rooted in a series of well-conducted in vitro experiments using two CML cell lines, K562 and MEG-01. While the findings are interesting and novel, further work to corroborate these findings in primary CML samples would have greatly strengthened the potential real-world relevance of these discoveries. The authors appear to have some PBMCs from primary CML patients and a BM sample from a Ph+ ALL in which they performed western blot analyses (Fig 1). Couldn't these samples have been used to at least confirm some of the key discoveries? For example, the neddylation of BCR-ABL, or; sensitivity of primary leukemic cells to RAPSYN knockdown, and/or; phosphorylation of RAPSYN by SRC?

We agree with your points and really appreciate your comments. To demonstrate the clinical relevance, we have conducted a series of experiments to address your concerns.

(1) after a thorough optimization on the transduction process, we have managed to show that shRNA-mediated gene silencing of RAPSYN impaired the growth of primary CML samples. These additional data are presented as Figure 1D in the revised manuscript with its corresponding figure legend and description, lines 136-141.

(2) we have invested tremendous time and effort to deal with “key discoveries” regardless of the almost impossible task with a great technical difficulty. With 5 mL (ethical approval) of PBMCs on hands, we have finally managed to confirm BCR-ABL neddylation by IP from two newly acquired CML patients. The results are as presented in Figure 2F in the revised manuscript with its corresponding figure legend and description, lines 186-187.

(2) The authors initially interrogated a fairly dated (circa 2009) microarray-based primary dataset to show that the increase in RAPSYN is primarily a post-transcriptional event, as mRNA levels are not different between healthy and CML samples. It would be interesting to see whether differences might be more readily seen in more recent RNA-seq datasets from CML patients, given the well-known differences in sensitivity between the two platforms. Additionally, I wonder if there would be transcriptional signatures of increased NEDDylation (or RAPSYN-induced NEDDylation) that could be interrogated in primary samples? Furthermore, there are proteomics datasets of CML cells made resistant to TKIs (through in vitro selection experiments) that could be interrogated for independent validation of the authors' discoveries. For example: from K562 cells, PMID: 30730747 or PMID: 34922009).

Thank you very much for your constructive comments. Based on your suggestion, we have 1) analyzed mRNA level of RAPSYN in RNA-seq datasets GSE13159 (2009), GSE138883 (2020) and GSE140385 (2020), indicating no difference between CML patients and healthy donors. We have included the results in Figure1-figure supplementary 1A and in the revised manuscript (lines 123-127); 2) examined the RNA levels of RAPSYN-related neddylation enzymes, including E1 (NAE1), E2 (UBE2M), NEDD8 and NEDP1 in these databases, and no significant differences of these neddylation-related genes were found between CML patients and healthy donors as well (Supplementary Figure 2C, lines 168-172).

We have also analyzed the proteomics datasets from PMID: 30730747 and PMID: 34922009 according to your suggestion. Unfortunately, no information on RAPSYN expression is available in these datasets. To avoid potential negligence, we have examined all CML-related proteomics datasets from 2002 to 2024, still resulting in no information about protein expression of RAPSYN. Consequently, our finding on the higher expression of RAPSYN in the PBMCs of Ph+ patients in this study appears to be an observation for the first time. And we believe that our results should be more clinically relevant than those, if any, from the cells by in vitro selection.

Reviewer #2 (Public Review):

Most of the conclusions drawn in this paper are well supported by data, but some aspects of the data need to be clarified and extended:

(1) The authors propose that targeting RAPSYN in Ph+ leukemia could have a high therapeutic index, suggesting that inhibition of RAPSYN may lead to cytotoxicity in Ph+ leukemia with high specificity and minimal side effects. To substantiate this assertion, the authors should investigate the impact on cell viability upon RAPSYN knockdown in non-Ph leukemic cell lines or HS-5 cells (similar to Figure 1C), despite their lower RAPSYN protein levels.

We appreciate your valuable comments. When we used shRNA to knockdown the expression of RAPSYN in HS-5 cells, it did not affect the cell growth of HS-5 cells. We have included the data in Figure 1C, modified its figure legend, and added corresponding description, lines 136-141.

(2) The authors intriguingly show that the protein levels of RAPSYN are significantly enriched in Ph+ patient samples and cell lines (Figure 1A, B), even though the mRNA levels remain unchanged (Supplementary Figure 1 A-C). This observation merits a clear explanation in the context of the presented results. The data in the manuscript does imply a feedforward loop mechanism (Figure 7), where BCR-ABL activates SRC, which subsequently stabilizes RAPSYN, which in turn helps protect BCR-ABL from c-CBL-mediated degradation. If this is the working hypothesis, it would be beneficial for the reader to see supporting evidence.

Thank you very much for pointing out the issue. We have realized the inappropriateness of Figure 7, which was originally placed as a summarizing figure. To avoid potential confusion and misleading, this figure has been deleted, which does not affect the results and conclusions of this study. In addition, the differences on mRNA levels and protein expressions have been responded to Reviewer #1.

(3) The authors present compelling evidence to suggest that RAPSYN may possess direct NEDD8-ligase activity on BCR-ABL. To strengthen this claim, it may be valuable to conduct further assays involving a ligase-deficient mutant, such as C366A, beyond its use in Figure 2J. Incorporating this mutant into the in vitro assay illustrated in Figure 2K, for instance, could offer substantial validation for the claim. In addition, showing whether the ligase-deficient mutant is capable of phenocopying the phosphorylation-mutant Y336F, as showcased in Figures 5E, F, and 6D, F, would be beneficial.

We are grateful to your comments. In the manuscript, we have provided sufficient data to support the direct neddylation of BCR-ABL by RAPSYN, as you commented “The authors present compelling evidence to suggest that RAPSYN may possess direct NEDD8-ligase activity on BCR-ABL.”. Cys366 was previously demonstrated as the catalytic residue essential for E3 activity of RAPSYN (Li et al. 2016, PMID: 27839998), and the phosphorylation at Phe336 was thoroughly verified by site-directed mutagenesis and the treatments of SRC-specific inhibitor saracatinib in present cellular experiments. Therefore, while we fully respect your opinions, we do not think it would be necessary to perform tedious in vitro reactions for expected negative results, which was the reason for us not to conduct enzymatic reactions with known inactive mutants, such as C366A and Y336F, in the first place.

(4) The observations presented in Figures 6 C-G require additional clarification. Notably, there are discrepancies in relative cell viability effects in K562 cells, and to some extent in MEG-01 cells, under conditions that are indicated as being either identical or highly similar. For instance, this inconsistency is observable when comparing the left panels of Figure 6C and 6D in the case of NC overexpression + shSRC#2, and the left panels of Figure 6E and 6G with NC overexpression or shNC, respectively. Listing potential causes of these discrepancies would strengthen the overall validity of the findings and their subsequent interpretation.

Thank you for your comments and apologize for the confusion. To make a meaningful comparison, we have revised the method part “Preparation of stable RAPSYNWT, RAPSYNY336F or SRC expression cell lines” (lines 625-627) and reorganized Figure 6 to reflect the differences on the negative controls. In fact, we first used LV6 (EF-1a/Puro; OE-NC1) vector for the overexpression of RAPSYNWT and SRC. Due to low expression level with LV6 and long period of time for subsequent selection, we switched to LV18 (CMV/Puro; OE-NC2) for the overexpression of RAPSYNY336F. Since the sensitivities of K562/MEG01-OE-NC cells to shSRC transduction in Figure 6C (now revised to K562/MEG01-OE-NC1) and 6D (now revised to K562/MEG01-OE-NC2) were noticeably different, we have separated RAPSYNWT and RAPSYNY336F cells as 6C and 6D with their own corresponding empty vector as negative control, instead of merging the results into a single figure with one negative control of OE-NC. In addition, given the fact that K562/MEG01 cells reacted differently upon saracatinib treatments after transduction with the empty vector, we have also distinguished the negative controls as OE-NC1 in Figure 6E, OE-NC2 in Figure 6F and shNC in Figure 6G. Afterall, the transduction of K562/MEG01 cells with different expression vectors and viral particles caused the discrepancies in the experiments of cell viability, which has been clarified by reorganizing Figure 6 in the revision.

(5) Throughout the manuscript, immunoblots which showcase immunoprecipitations of BCR-ABL or His-BCR-ABL depict poly-neddylation (e.g. Figures 2E-M, 3D-G, and 5A-E) and poly-ubiquitination (e.g. Figures 3D-G) patterns/smears where these patterns seem to extend below the molecular weight of BCR-ABL. To enhance clarity, it would be valuable for the authors to provide an explanation in the text or the figure legend for this observation. Is it reflective of potential degradation of BCR-ABL or is there another explanation behind it?

Thank you for your valuable comments. After carefully checking original immunoblots, we have ascertained that the protein band of BCR-ABL was at 250 KDa and the smear bands appeared to be higher than 250 KDa were likely caused by the conjugation of NEDD8 (neddylation) or Ubiquitin (ubiquitination) onto BCR-ABL. Regarding the molecular weight of modified BCR-ABL lower than expected, whether it is a common feature as previously reported (Mao, J., et al, 2010, PMID: 21118980) or possible degradation during the modification process or sample preparation requires further investigation. We have corrected the labeling of figures in the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

(1) It would really nail the real-world relevance of these nice findings if the authors are able to confirm some aspects of their cell line-based discoveries in publicly available 'omics datasets generated from primary CML samples. I have suggested some of these in the public review as well.

Alternatively, if they are able to investigate samples from murine CML models (eg. BALB/c CML models), it would represent a step towards real-world relevance.

Thank you very much for your constructive comments. According to your suggestion, we have examined and analyzed RAPSYN mRNA and protein in updated and publicly available datasets as replied in the public response.

(2) The Discussion repeats some of the information already presented in the Introduction (for example, lines 311-327 of the merged document, or lines 349-358). I would urge the authors to instead expand more about how RAPSYN might be upregulated at the post-transcriptional level, or its potential post-translational regulation by SRC-mediated phosphorylation.

Thanks for your constructive suggestion. We have re-written this part according to your suggestion and marked in red color in the revised manuscript, lines 319-325 and lines 351-378.

(3) There are instances of clunky phrases/grammatical mistakes in the manuscript which detract from its readability (eg: lines 142-143: "...empty body transduced shRAPSN#3 or K562 cells into...."; lines 163-164: "Despite AChR subunits α7, M2, M3, and M4 were expressed in all tested cells, no change..."; line 178: "Preeminent BCR-ABL neddylation was detected in..."). A closer proof-reading of the final manuscript is advisable.

We appreciate the valuable comments. We have made changes for improvement, which is marked in red color in the revised manuscript, lines 145-147, lines 166-168 and line 185.

(4) The western blot in Fig 5C (particularly the control "OE-NC" of K562) looks drastically different from the corresponding control lanes in Figs 5A and 5B. Similarly, the cell viability curves presented in Fig 6D and 6F (for both K562 and MEG-01, control conditions) look very different from the corresponding curves in Figs 6A and 6B.

We appreciate for your valuable comments. Because we accidently used the imagines with different exposure time, the western blots in Fig 5C (particularly the control "OE-NC" of K562) look very different from corresponding control lanes in Figs 5A and 5B. We have replaced images with the same exposure time in the revised manuscript.

For readers to clearly understand, we have revised the method part “Preparation of stable RAPSYNWT, RAPSYNY336F or SRC expression cell lines” (lines 625-627) and related figure legends to reflect the differences.

We have publicly responded the discrepancy on cell viability.

Reviewer #2 (Recommendations For The Authors):

In reviewing your study, I must insist that the completeness and robustness of your work would significantly benefit from a more exhaustive listing of the antibodies used for immunoblotting and immunoprecipitation within the Materials and Methods section. A number of antibodies have been accounted for, however, crucial ones targeting BCR-ABL, c-CBL, Ubiquitin, NEDD8, HA, Myc, and others appear to be omitted. To maintain rigorous scientific standards, I strongly encourage you to include these.

We appreciate your comments. We have carefully checked the section of Methods and added detailed information of antibodies for Immunoblotting and Immunoprecipitation in the revised manuscript, lines 502-516.

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

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Reply to the reviewers

We are very grateful to the reviewers for their positive appraisal of the manuscript and for their useful comments and suggestions. Below are our answers and corresponding modifications of the manuscript.

Reviewer #1

1 - Figures 1&4 focus on JU1264 as the primary double-sensitive strain. However, the authors built their RILs with HK104 by crossing with JU1498 in Figures 7&8. In the results section and/or methods, the authors should provide some justification for this strain switch. Alternatively, the equivalent analysis of Figure 1 focusing on JU1498 would be valuable to demonstrate that the effects of both viruses on fitness are similar to JU1264. I am not recommending that the JU1264xHK104 crosses be performed or that Figures 7&8 be repeated with JU1264xHK104 lines, but that more explanation for strain selection for RIL generation should be provided.

JU1264 and JU1498 are the strains where SANTV and LEBV were found, respectively. The experiments were performed over the years by different authors and were designed to answer different questions. JU1264 was the strain where the first virus was found and was used as a doubly sensitive strain in Figure 1 and the small RNA experiment. The main reason we chose JU1498 for genetic crosses to discover the genetic basis of LEBV sensitivity is that LEBV was detected and isolated from JU1498. Note that the JU1264 and JU1498 strains come from France and are in the same isotype group at CaeNDR (see also Figure 3) so the two strains may be interchangeable (although we cannot be sure).

We added in the text concerning the RIL construction: "We chose to use JU1498 as the LEBV-sensitive strain as it was the original strain in which LEBV was discovered."

2-The authors reasonably claim that the resistance of tropical strains like AF16 could be due to blocking viral entry or early inhibition of replication before the small RNA response is activated. Could the authors test this by directly microinjecting virus (in combination with a dye as a control for successful injection) into the intestine? I understand this could not be done on a scale that would allow for small RNA sequencing, but one could perform small-scale FISH to determine if LEBV or SANTV are replication-competent if the entry barrier is artificially overcome. Such an experiment may require considerable technical development. It may be beyond the scope/timing of this specific study, but it is worth considering to gain some insight into the possible resistance mechanisms observed.

Although the suggested experiment is in principle a great approach, it is difficult to perform without losing animals during the FISH staining. In addition, in this manuscript we are not particularly searching for the resistance mechanisms of AF16 but trying to present a wider perspective concerning viral infections of C. briggsae and their specificity. We performed small RNA analysis for AF16 together with the sensitive strains and therefore we commented on the lack of small RNA response in AF16 comparing to the sensitive strains. We thus consider that setting up intestinal injections at this point is arduous and beyond the scope of this manuscript.

Minor Comments: Line 78 - provide the full genus name for Caenorhabditis elegans at first appearance, as done for Caenorhabditis briggsae

This was modified. Line 117 - The description of cul-6 could also reference Bakowski et al. 2014. This study is referenced more generally as a player in proteostasis a few lines below but could be more explicitly tied to cul-6-mediated resistance to ORV (Bakowski et al. 2014 - see Fig. 7A) This section focus on the use of natural polymorphisms but we added this reference, which is indeed key for the effect of cul-6 knockdown on viral infection in C. elegans. Line 197-198 - The authors could consider adding sequences for FISH probes as part of Table S2. This information could add value to the present study even if previously listed in Frézal et al. We actually removed them from an earlier version since these sequences are already published: here and in further work, it seems preferable to refer to the primary study where these probes were designed, Line 263 - Were embryos obtained by bleaching of gravid adults, or was an egg lay performed, and the embryos were collected from plates? This is potentially an important distinction and should be clarified briefly in the methods. In the section “Preparation of small RNA libraries”, we obtained embryos by bleaching gravid adults.

We changed the first sentence to “Gravid hermaphrodites from uninfected cultures (AF16, HK104 and JU1264) were harvested using M9 solution, then bleached and washed twice using nuclease-free water. Embryo concentrations were estimated by counting embryos under the dissecting microscope and diluted to 2 embryos per mL of nuclease-free water. 200 embryos of each strain (AF16, HK104 and JU1264) were then plated onto 55 mm NGM plates seeded with E. coli OP50.” We also added “The embryos were obtained by bleaching gravid hermaphrodites.” to the Figure S5 legend. Line 330 - Provide justification for using JU1498 to make these RILs (see comment above). We added this sentence in the Results section. "We chose to use JU1498 as the LEBV-sensitive strain as it was the original strain in which LEBV was discovered." Line 446-Refer to the methods section for full clarity on the role of FISH in this set of experiments or reword for improved clarity. At first read-through, this phrasing made me expect some FISH experiments associated with Fig. 1, which does not appear to be the case.

We did perform FISH experiments as control that the cultures were infected, as explained in the Methods. We removed this mention from the Results section. Line 478 - The supplementary figure callouts are misaligned with the provided documents. S2A in the text appears to refer to S3A RT-qPCR results. Changed. Line 483 - Similar to above, the text suggests serial dilutions should refer to S4, not S3. Changed. Line 498 - Modify the text to 'Figure 2C and Figure 3' for clarity. Changed. Line 531,535 - viRNAs are defined in line 535 but this should be moved to 531 above at first appearance in the text. Changed. Line 593 - Typo in 'Logarithm of Odds?' Corrected. Line 621-624 - I recommend the authors include the data for the LEBV control experiments with NIL strains, either as a supplementary table, an additional panel for Fig. 6, or represented as done in Figure 8. We removed this sentence. Line 625-632 - How many total genes are represented in the QTL on IV? The reasoning behind testing rde-11 and rsd-2 is sound, but readers might want to know other potential candidates within this region (perhaps something the authors could also speculate on in the discussion). A similar comment applies for # genes in the QTLs on II and III.

We added in Table S7 the list of detected SNPs and short indels in the chromosome IV region and now indicate in the text "among them over 2700 SNPs and short indels (Table S7)." We added Table S11 with the polymorphisms in the chromosome II QTL region. We note that these tables do not include possible structural variants. The chromosome III QTL being weak, we abstained for this one but the data can now be found using CaeNDR.

Line 991-992 - Figure 1B - LEBV, SANTV, and co-infection effects on body size are mentioned but not quantified. Has this phenotype been quantified elsewhere? If so, the authors should reference it in the results section or Fig. 1 legend. Alternatively, body size could be quantified as part of this study and added to Fig. 1.

Because we do not have a large amount of data on body size, we removed "Body size quantification” from Figure 1B legend. Line 1001 - There is a typo in the first sentence; the period after LEBV should be removed. Small suggestion: Figure 2A - While described in the methods, I recommend that the authors briefly reiterate in the figure legend that the white/yellow boxes are intended to indicate serial chunking for clarity.

We removed the typo and explained the agar chunk representation in the figure legend: "The transfer by chunking a piece of agar is indicated by beige rectangles cut out from one plate and transferred to the next plate." Line 1034 - Small formatting note for Figure 4B - percentages of reads mapping to RNA1 and RNA2 appear underneath gridlines for the graph which obscures visibility and is inconsistent with the other graphs presented.

This was modified and is indeed clearer. Line 1094 - Figure S1 - this analysis could be strengthened by RT-qPCR represented as fold change in viral load instead of, or in addition to, the agarose gel image (like Fig. S3). Doing so would also allow for the normalization of eft-2 control across individual samples (e.g.: particularly low eft-2 amplification in ED3073). However, these results are sufficiently convincing that LEBV does not replicate in C. elegans, but a more quantitative approach is recommended if feasible for the authors. Alternatively, an additional figure panel and/or repeat of this analysis with C. elegans infected with ORV would also be beneficial as an additional control.

We do not understand how we can estimate a viral load by a ratio when we do not seem to see any significant amplification. Of course, a RT-qPCR would provide a finite Ct value and a ratio but they are likely to be meaningless. The ED3073 sample did not amplify for eft-2 either and calculating a ratio of high Ct values in a RT-qPCR would be misleading. We could remove the two ED3073 lanes but prefer to leave them.

Line 1112 - "Experiments using RNA2 primers gave similar results" - if this data isn't included in the study, this text should be removed.

Removed. Line 1141 - Figure S6 - For full transparency, the authors could consider including HK104 infected with LEBV to show minimal (zero) reads align to the RNA1/RNA2 segments using scales consistent with JU1264 infected with LEBV (S6C) The proportion of reads mapping (0%) are provided in Figure 4A and supplementary tables. We do not show the distribution of antisense 22G and sense 23nt along the LEBV genome for the HK104 (co)infections for the following reasons. 0% of these reads map to LEBV in HK104 monoinfection, and only 0.02% antisense 22G in coinfection. Moreover, the 23nt reads mapping to LEBV-RNA2 in the HK104 coinfection (16.54%;1931 reads) correspond to a 41 bp region with 85% nucleotide similarity between SANTV-RNA2 and LEBV-RNA2. Overall, the few 23nt (+) reads mapping to LEBV in HK104 coinfection are most likely a spillover of the HK104 antiviral response to JUv1264 entry into the intestinal cells.

Reviewer #2

Main points: 1. In figure 1C and D, is more than 1 biological replicate performed? Ideally multiple independent infections would be performed which would increase confidence in these experiments, but minimally the authors should make clear that this data was from an experiment only performed once. The conclusion from the life span assays is unlikely to change, but given the variance of the brood size assays within replicates, the conclusions that LEBV infection reduces the brood size is weakly supported.

We added “Panels C-D correspond to a single experiment (see Methods).” to the legend of Figure 1. We changed the wording to "LEBV and especially the co-infection appeared to lower brood size." We do not have data for independent experiments.

If the authors want to claim that there is a defect in viral entry in the resistant strains, they should perform infections experiments at an earlier time point that could capture viral invasion. In C. elegans with Orsay virus these experiments have been done as early as 18 hours by FISH. https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1011120 The way the assays are currently set up, if the infection was cleared it wouldn't be observed.

The strongest point that indicates that the virus does not replicate is the small RNA experiment, in which the animals were collected on the initial plate inoculated with the virus. We think that our wording was careful:

We further amended it:

• in Results " The animals were collected for sRNA sequencing on the plates onto which the viral inoculate was added and where they were constantly exposed to the virus".

• in Discussion " Indeed, as we did not assay viral entry by sensitive FISH or RT-PCR at early timepoints, it is possible that the viruses are cleared without production of small RNAs."

The evidence that the region on chromosome III contributes to susceptibility is weak. The analysis in figure 5B does not identify this region and it is not clear to me how to read the scale in figure 5C to determine that a region on chromosome III is significant.

We added in the Figure legend: "with a LOD score of 10.5, above the threshold calculated by simulations (see Methods)." and detailed the method in the Methods section (see reply to Reviewer 3 below).

In figure 6 using a more appropriate statistical test such as one way ANOVA with multiple hypothesis testing is necessary to determine if there is a difference between JU2832 and JU2916. It would be helpful if the authors could add more discussion of the evidence that they feel that supports this region being involved in susceptibility.

We do not think that an ANOVA is appropriate to analyze these proportions which cannot have normal distributions of residuals, therefore we used a generalized linear model, taking genotype and block (day of experiment) into account. This was only explained in the legend and is now explained in the Methods section as well. Maybe the reviewer suggests us to us a global analysis with strain as a factor. We could do this but we do not think that it applies well to this situation: here we test for a specific hypothesis for each one-QTL strain. We have corrected for multiple testing as explained next. The legend now reads: " The significance p values were obtained in a generalized linear model (glm) taking independent experimental blocks and infection replicates into account, testing NILs against their relevant background parent. The p values using the two strains testing for the QTL on chromosome IV and those using the two-QTL strain JU2832 are corrected for multiple testing." In addition, we now provide p values rather than three stars, which reinforce the point (they are very low).

Minor points 1. In figure 1B it would be helpful to provide more information on the animals chosen to display. Are these representative examples or extreme examples?

These are representative examples. This detail was added in the legend.

In figure 2B, adding a legend for the colored dots would be helpful.

We had indicated: "Dots are replicates within a block, with 100 animals scored per replicate (see Table S4 for the detailed results and Figure S2 and Methods for the experimental design). Experimental blocks are represented by colors and the bar indicates the grand mean of the blocks." 3. In figure 2C, the definitions for a strain to be labeled as belonging to each category should be provided.

The categorization method is now explained in the Methods section. In addition, Figure 2C legend now refers to Table S4 for the category of each strain. 4. Could the data in figure 2 be used for genome-wide association mapping and compared to the RIL QTL experiments? Adding comment on this would be helpful to understanding the usefulness of this data.

There are too few strains here to test genome-wide for association. If we had the causative SNP, it would be interesting to assess its frequency but this is beyond the focus and scope of this work, which focused on the outlier phenotype of the HK104 strain. 5. In figure 4b, in HK104 LRBV the numbers in top right corner are not defined.

We added to the legend of Figure 4B: “For the HK104 infection with LEBV, the number of read counts is provided in the top right corner to signal their rarity compared to ca. 107 in the other conditions. See Table S5 for all read counts. ” 6. Line 1001 remove period from "LEBV.of" and add period after isolates. Removed.

Reviewer #3 Major comments • The authors provide most data in both a processed and raw format, which is helpful. In two cases (data from 3 DPI, line 492 and LEBV infections in the AF16xHK104 NILs, line 621), the authors state their results, but the data seems not to be provided in the document (at least no direct reference is provided). These are supporting results and do not affect the main conclusions, nevertheless providing the data in form of a table or supplementary figure would be required. Generally, it may help to include a data availability statement to have a combined overview of where data can be found.

As noted by the reviewer, we tried to provide the data in raw format, but did not judge it necessary when the experiment had two datapoints that are provided in the text. We added the number of animals in the instance where it was missing.

Minor comments • Line 97-126: Here the manuscript fully focuses on the work in C. elegans. It would be interesting to make clear links to the work in C. briggsae (e.g. mention if homologs are present). The paragraph in line 127 clarifies advantages of studying viral infection in C. briggsae compared to C. elegans. It may be logical to place this information early in the text.

We added a sentence to link the C. elegans work and C. briggsae. • Line 166 and results from this experiment: Is the LEBV-SANTV mixture consisting of 50uL of both viruses or a total of 50uL (so 25uL of both)? This is also important for the interpretation of results.

To clarify, we changed to: “50 l ... of an equivolume mix of SANTV and LEBV”. • Line 167: The text says the culture is maintain for 4 days, but then also mentions day 5. Figure 2 clarifies the experimental setup later, but the text could be clearer here.

Thank you for noticing this. We changed the 4 to 7. • Line 172: What are the nine starter cultures?

The nine starting cultures were those obtained as described in the paragraph preceding this line in the manuscript. From a plate of infected animals (five L4 larvae), we propagated the infected population by chunking over 3 plates (day 3) and 3*3 plates (day 5). To make this point clear, we have added above: "to generate for the following experiments nine starter cultures for each of the four conditions " • Line 185: 'Infection of the set of C. briggsae natural isolates'. From the text it is not clear what set the authors refer to.

We changed to "a set" and refer to Figure 2B and Table S4 in the sentence below for the list of natural isolates. • Line 223: 'The proportion of infected animals were overall higher in Batch3 but the qualitative results are similar'. It is unclear why this statement is here instead of in the result section and it is also not clear what the authors mean by the second part of the sentence.

We moved the sentence to Results and changed it to: " The proportion of infected animals were overall higher in Batch 3 but the relative results of the different strains were similar for the three batches." • Line 326: Is 'the same method as above' using FISH or RT-qPCR?

Changed to "using FISH as above". • Line 382: What do the authors mean by 'two cross directions'?

We removed this mention as the method is better explained in the next sentence.

• Line 454-458: The data presented here does not appear well integrated in the storyline. It does not fit under the subheading. Perhaps it would be a better fit under the subheading of line 462? We moved it below the subheading. • Line 478: Reference to Fig S2 should be reference to Fig S3

Changed. • Line 483: Reference to Fig S3 should be reference to Fig S4

Changed. • Line 540-544: The sentence reads as a contradiction (C. elegans defends itself using RNAi, C. briggsae blocks viral infection during entry). As a result, the sentence reads as if RNAi is not of much antiviral importance in C. briggsae, but that cannot be concluded from this data. I am not sure if this is what the authors aim to suggest, but another word choice (e.g. changing 'whereas' and 'this does not seem the case for C. briggsae') may be considered.

We changed the wording to " whereas the C. elegans N2 reference strain allows for viral entry and defends itself against ORV via its small RNA response (Félix et al. 2011; Ashe et al. 2013; Shirayama et al. 2014; Coffman et al. 2017), in the tested resistant C. briggsae strains, the viruses appeared to be blocked at entry or at early steps of the viral cycle." • Line 585 and 592: There are two QTL approaches being applied and referred to as 'the one- and two-QTL analyses'. The description in this part is rather technical and the terminology is not clear. As a result, for readers not familiar with QTL mapping, the biological interpretation may become obscured.

We now explain in Methods: " ...scanning each pair of positions for several models, including single-QTL, full, additive and epistatic. The significance threshold LOD score of each model was estimated via 1,000 permutation tests with a coefficient of risk a=0.05. The threshold was 4.91 for the additive model and 6.09 for the full model. The LOD score of each pair of position is represented by a color scale in Figure 5C). The combination of the chromosomes III and IV QTLs had a LOD score of 10.5 in the full and additive models. No epistatic interaction was detected. The LOD score of the single-QTL model comparison was below the threshold."

• Line 659: The authors end the section about natural genetic variation in the response to SANTV with candidate genes and a CRISPR experiment. As the authors identify a small genetic region associated with LEBV susceptibility, it would be interesting to hear about any candidate genes in this region. There are still many genes and more importantly, many polymorphisms in this region (ca. 700 single-nucleotide polymorphisms and short indels). Because structural variants are difficult to call (long-read sequencing has not been performed on the parents), we had preferred to abstain to provide a list of polymorphisms that would be incomplete and preferentially point towards SNPs. However, because of the reviewer's query, we now provide it in Table S11.

• Line 674: The authors make use of HK104 strain in this study as it is the exception in their dataset that provides resistance against LEBV, but not SANTV. Possibly, the genetic variation linked to viral susceptibility uncovered using HK104 may therefore be relatively uncommon in C. briggsae. The implications of this choice and option for other studies using different genotypes could be interesting to discuss in this short paragraph. The aim in here is to discover why HK104 is specifically resistant to one virus and not the other. There is a possibility of uncovering a specific mechanism that is present in only two or three strains of our 40-strain dataset but we find this specificity particularly

interesting, regardless of its prevalence. We explore in the Discussion which of the two crosses may reveal the specificity.

• Line 774: The IPR is already described on abbreviated in line 742. As a reader, we prefer having the abbreviation explained twice than not understanding it. • Overall, to reach a broader audience, the manuscript can expand explanations in the discussion. E.g. statements in line 695 and 773, refer to previous observations, but do not explain them in enough detail to understand parallels between this and previous studies without prior knowledge.

We added some explanations, specifically for lines 695 and 773 (of previous version). • Figure 2: Only HK104 is labelled in the figure, it would be useful to also see HK105 as this strain is also explicitly mentioned in the text.

We now included HK105 and strains that are used in further experiments.

• Figure 2: It is not clear from the results or methods how strains as designated into a certain class. The figure legend says variability in the data is taken into account and that is why some strains are close to each other, yet distinct in class, but how this works is not described. We now explain our criteria. See above in the response to Reviewer 2. • Figure S3: The strain JU1264 and JU1498 are mentioned thrice (as '2', 'rep' and 'ref'). These annotations should be clarified.

These explanations were indeed missing. We now explain them in the figure legend. • Figure S4: The figure would benefit from a division in panels per strain to facilitate comparisons across strains.

Indeed. We now added a division in panels per strain. • Figure S4: Have the authors correlated viral loads with the number of infected animals? This could result in addition information if not all individuals are infected equally.

We have not done so in this precise experiment but preferred to use the number of infected animals in most other experiments, in particular because it is less subject to outlier effects. • Figure S4: Could the authors clarify the meaning of JU1264 Rep?

It is explained in the legend: "The undiluted viral preparations on JU1264 are used to normalize and are indicated as "JU1264 1/1". A separate replicate was performed and indicated as "JU1264 Rep"."

• Figure 8: The meaning of the stars in this figure is a bit confusing and the description of these stars in the legend is not clear. Indeed. We changed the legend to: " ***: p<0.001 comparing JU4034 with its parent strain HK104 using a generalized linear model."

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, Qiu and colleagues examined the effects of preovulatory (i.e., proestrous or late follicular phase) levels of circulating estradiol on multiple calcium and potassium channel conductances in arcuate nucleus kisspeptin neurons. Although these cells are strongly linked to a role as the "GnRH pulse generator," the goal here was to examine the physiological properties of these cells in a hormonal milieu mimicking late proestrus, the time of the preovulatory GnRH-LH surge. Computational modeling is used to manipulate multiple conductances simultaneously and support a role for certain calcium channels in facilitating a switch in firing mode from tonic to bursting. CRISPR knockdown of the TRPC5 channel reduced overall excitability, but this was only examined in cells from ovariectomized mice without estradiol treatment. The patch clamp experiments are comprehensive and overall solid but a direct demonstration of the role of these conductances in being necessary for surge generation (or at least having a direct physiological consequence on surge properties) is lacking, substantially reducing the impact of the findings.

Strengths:

(1) Examination of multiple types of calcium and potassium currents, both through electrophysiology and molecular biology.

(2) Focus on arcuate kisspeptin neurons during the surge is relatively conceptually novel as the anteroventral periventricular nucleus (AVPV) kisspeptin neurons have received much more attention as the "surge generator" population.

(3) The modeling studies allow for direct examination of manipulation of single and multiple conductances, whereas the electrophysiology studies necessarily require examination of each current in isolation. The construction of an arcuate kisspeptin neuron model promises to be of value to the reproductive neuroendocrinology field.

We thank the reviewer for recognizing our comprehensive examination of Kiss-ARH neurons through electrophysiological, molecular and computational modeling of their activity during the preovulatory surge, which as the reviewer pointed out is “conceptually novel.” We will bolster our argument that Kiss1-ARH neurons transition from synchronized firing to burst firing with the E2-mediated regulation of channel expression with the addition of new experiments. We will address the weaknesses as follows:

Weaknesses:

(1) The novelty of some of the experiments needs to be clarified. This reviewer's understanding is that prior experiments largely used a different OVX+E2 treatment paradigm mimicking periods of low estradiol levels, whereas the present work used a "high E2" treatment model. However, Figures 10C and D are repeated from a previous publication by the same group, according to the figure legend. Findings from "high" vs. "low" E2 treatment regimens should be labeled and clearly separated in the text. It would also help to have direct comparisons between results from low E2 and high E2 treatment conditions.

We will revise Figures 10C and 10D to include new findings on Tac2 and Vglut2 expression in OVX and E2-treated Kiss1ARH. We did show the previously published data (Qiu, eLife 2018) to contrast with Figures 10E, F showing the downregulation of TRPC5 and GIRK2 channels following E2 treatment. Most importantly, our E2 treatment regime is clearly stated in the Methods and is exactly the same that was used previously (Qiu, eLife 2016 and Qiu, eLife 2018) for the induction of the LH surge in OVX mice (Bosch, Molecular and Cellular Endocrinology 2013) .

(2) In multiple places, links are made between the changes in conductances and the transition from peptidergic to glutamatergic neurotransmission. However, this relationship is never directly assessed. The data that come closest are the qPCR results showing reduced Tac2 and increased Vglut2 mRNA, but in the figure legend, it appears that these results are from a prior publication using a different E2 treatment regimen.

In the revised Figure 1, we will now include a clear depiction of the transition from synchronized firing driven by NKB signaling in OVX females to burst firing driven by glutamate in E2-treated females. We have used the same E2 treatment paradigm as previously published (Qiu, eLife 2018).

(3) Similarly, no recordings of arcuate-AVPV glutamatergic transmission are made so the statements that Kiss1ARH neurons facilitate the GnRH surge via this connection are still only conjecture and not supported by the present experiments.

Using a horizontal hypothalamic slice preparation, we have shown that Kiss1-ARH neurons excite GnRH neurons via Kiss1ARH glutaminergic input to Kiss1AvPV neurons (summarized in Fig. 12, Qiu, eLife 2016). We do not think that it is necessary to repeat these experiments in the current manuscript.

(4) Figure 1 is not described in the Results section and is only tenuously connected to the statement in the introduction in which it is cited. The relevance of panels C and D is not clear. In this regard, much is made of the burst firing pattern that arises after E2 treatment in the model, but this burst firing pattern is not demonstrated directly in the slice electrophysiology examples.

We will revised Figure 1 to include new whole-cell, current clamp recordings documenting the burst firing in response to glutamate in E2-treated, OVX females.

(5) In Figure 3, it would be preferable to see the raw values for R1 and R2 in each cell, to confirm that all cells were starting from a similar baseline. In addition, it is unclear why the data for TTA-P2 is not shown, or how many cells were recorded to provide this finding.

Before initiating photo-stimulation for each Kiss1-ARH neuron, we adjust the resting membrane potential to -70 mV, as noted in each panel in Figure 3, through current injections. We will include new findings on the effects of the T-channel blocker TTA-P2 on slow EPSP in the revised Figure 3. The number of cells tested with each calcium channel blocker is depicted in each of the bar graphs summarizing the effects of the blockers.

(6) In Figure 5, panel C lists 11 cells in the E2 condition but panel E lists data from 37 cells. The reason for this discrepancy is not clear.

In Figure 5E, we measured the L-, N-, P/Q and R channel currents after pretreatment with TTA-P2 to block the T-type current, whereas in Figure 5C, we measured the current without TTA-P2.

(7) In all histogram figures, it would be preferable to have the data for individual cells superimposed on the mean and SEM.

In all revised Figures we will include the individual data points for the individual neurons.

(8) The CRISPR experiments were only performed in OVX mice, substantially limiting interpretation with respect to potential roles for TRPC5 in shaping arcuate kisspeptin neuron function during the preovulatory surge.

The TRPC5 channels are most important for generating slow EPSPs when expression of NKB is high in the OVX state. Conversely, the glutamatergic response becomes more significant when the expression of NKB and TRPC5 channel are muted. Therefore, the CRISPR experiments were specifically conducted in OVX mice to maximize the effects.

(9) Furthermore, there are no demonstrations that the CRISPR manipulations impair or alter the LH surge.

In this manuscript, our focus is on the cellular electrophysiological activity of the Kiss1ARH neurons in ovx and E2-treated females. Exploration of CRISPR manipulations related to the LH surge is certainly slated for future experiments, but these in vivo experiments are beyond the scope of these comprehensive cellular electrophysiological and molecular studies.

(10) The time of day of slice preparation and recording needs to be specified in the Methods.

We will provide the times of slice preparation and recordings in the revised Methods and Materials.

Reviewer #2 (Public Review):

Summary:

Kisspeptin neurons of the arcuate nucleus (ARC) are thought to be responsible for the pulsatile GnRH secretory pattern and to mediate feedback regulation of GnRH secretion by estradiol (E2). Evidence in the literature, including the work of the authors, indicates that ARC kisspeptin coordinate their activity through reciprocal synaptic interactions and the release of glutamate and of neuropeptide neurokinin B (NKB), which they co-express. The authors show here that E2 regulates the expression of genes encoding different voltage-dependent calcium channels, calcium-dependent potassium channels, and canonical transient receptor potential (TRPC5) channels and of the corresponding ionic currents in ARC kisspeptin neurons. Using computer simulations of the electrical activity of ARC kisspeptin neurons, the authors also provide evidence of what these changes translate into in terms of these cells' firing patterns. The experiments reveal that E2 upregulates various voltage-gated calcium currents as well as 2 subtypes of calcium-dependent potassium currents while decreasing TRPC5 expression (an ion channel downstream of NKB receptor activation), the slow excitatory synaptic potentials (slow EPSP) elicited in ARC kisspeptin neurons by NKB release and expression of the G protein-associated inward-rectifying potassium channel (GIRK). Based on these results, and on those of computer simulations, the authors propose that E2 promotes a functional transition of ARC kisspeptin neurons from neuropeptide-mediated sustained firing that supports coordinated activity for pulsatile GnRH secretion to a less intense firing in glutamatergic burst-like firing pattern that could favor glutamate release from ARC kisspeptin. The authors suggest that the latter might be important for the generation of the preovulatory surge in females.

Strengths:

The authors combined multiple approaches in vitro and in silico to gain insights into the impact of E2 on the electrical activity of ARC kisspeptin neurons. These include patch-clamp electrophysiology combined with selective optogenetic stimulation of ARC kisspeptin neurons, reverse transcriptase quantitative PCR, pharmacology, and CRIPR-Cas9-mediated knockdown of the Trpc5 gene. The addition of computer simulations for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.

The authors add interesting information on the complement of ionic currents in ARC kisspeptin neurons and on their regulation by E2 to what was already known in the literature. Pharmacological and electrophysiological experiments appear of the highest standards. Robust statistical analyses are provided throughout, although some experiments (illustrated in Figures 7 and 8) do have rather low sample numbers.

The impact of E2 on calcium and potassium currents is compelling. Likewise, the results of Trpc5 gene knockdown do provide good evidence that the TRPC5 channel plays a key role in mediating the NKB-mediated slow EPSP. Surprisingly, this also revealed an unsuspected role for this channel in regulating the membrane potential and excitability of ARC kisspeptin neurons.

We thank the reviewer for recognizing that the “pharmacological and electrophysiological experiments appear of the highest standards” and “the addition of the computer modeling for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength. However, we agree with the reviewer that we need to provide a direct demonstration of “burst-like” firing of Kiss1-ARH neurons. We will address the weaknesses as follows:

Weaknesses:

The manuscript also has weaknesses that obscure some of the conclusions drawn by the authors.

One has to do with the fact that "burst-like" firing that the authors postulate ARC kisspeptin neurons transition to after E2 replacement is only seen in computer simulations, and not in slice patch-clamp recordings. A more direct demonstration of the existence of this firing pattern, and of its prominence over neuropeptide-dependent sustained firing under conditions of high E2 would make a more convincing case for the authors' hypothesis.

We will provide a more direct demonstration of the existence of this firing pattern in the whole-cell current clamp experiments in the revised Figure 1.

In addition, and quite importantly, the authors compare here two conditions, OVX versus OVX replaced with high E2, that may not reflect the physiological conditions (the diestrous [low E2] and proestrous [high E2] stages of the estrous cycle) under which the proposed transition between neuropeptide-dependent sustained firing and less intense burst firing might take place. This is an important caveat to keep in mind when interpreting the authors' findings. Indeed, that E2 alters certain ionic currents when added back to OVX females, does not mean that the magnitude of these ionic currents will vary during the estrous cycle.

We have published that the magnitude of the slow EPSP, which is TRPC5 channel mediated, varies throughout the estrous cycle and the similarity to that found in OVX compared to E2-treated, OVX females (Figure 2, Qiu, eLife 2016). Moreover, TRPC5 channel mRNA expression, similar to the peptides, is downregulated by an E2 treatment (Figure 10 this manuscript) that mimics proestrus levels of the steroid (Bosch, Mol Cell Endocrinology 2013). Furthermore, the magnitude of ionic currents is directly proportional to the number of ion channels expressed in the plasma membrane, which we have found correlates with mRNA expression. Therefore, it is likely that the magnitude of these ionic currents will vary during the estrous cycle.

Lastly, the results of some of the pharmacological and genetic experiments may be difficult to interpret as presented. For example, in Figure 3, although it is possible that blockade of individual calcium channel subtypes suppresses the slow EPSP through decreased calcium entry at the somato-dendritic compartment to sustain TRPC5 activation and the slow depolarization (as the authors imply), a reasonable alternative interpretation would be that at least some of the effects on the amplitude of the slow EPSP result from suppression of presynaptic calcium influx and, thus, decreased neurotransmitter and neuropeptide secretion. Along the same lines, in Figure 12, one possible interpretation of the observed smaller slow EPSPs seen in mice with mutant TRPC5 could be that at least some of the effect is due to decreased neurotransmitter and neuropeptide release due to the decreased excitability associated with TRPC5 knockdown.

The reviewer raises a good point, but our previous findings clearly demonstrate that chelating intracellular calcium with BAPTA in whole-cell current clamp recordings abolishes the slow EPSP and persistent firing (Qiu, J. Neurosci 2021), which we have noted is the rationale for dissecting out the contribution of T, R, N, L and P/Q calcium channels to the slow EPSP in our current studies (revised Figure 3 will include the effects of T-channel blocker).

However, to further bolster the argument for the post-synaptic contribution of the calcium channels to the slow EPSP and eliminate the potential presynaptic effects of calcium channel blockers on the postsynaptic slow EPSP amplitude, which may result from reduced presynaptic calcium influx and subsequently decreased neurotransmitter release, we will utilized an additional strategy. Specifically, we will measure the response to the externally administered TACR3 agonist senktide under conditions in which the extracellular calcium influx, as well as neurotransmitter and neuropeptide release, are blocked (new Figure 3).

Author response:

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

eLife assessment

Following small molecule screens, this study provides convincing evidence that 7,8 dihydroxyflavone (DHF) is a competitive inhibitor of pyridoxal phosphatase. These results are important since they offer an alternative mechanism for the effects of 7,8 dihdroxyflavone in cognitive improvement in several mouse models. This paper is also significant due to the interest in the protein phosphatases and neurodegeneration fields.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zink et al set out to identify selective inhibitors of the pyridoxal phosphatase (PDXP). Previous studies had demonstrated improvements in cognition upon removal of PDXP, and here the authors reveal that this correlates with an increase in pyridoxal phosphate (PLP; PDXP substrate and an active coenzyme form of vitamin B6) with age. Since several pathologies are associated with decreased vitamin B6, the authors propose that PDXP is an attractive therapeutic target in the prevention/treatment of cognitive decline. Following high throughput and secondary small molecule screens, they identify two selective inhibitors. They follow up on 7, 8 dihydroxyflavone (DHF). Following structure-activity relationship and selectivity studies, the authors then solve a co-crystal structure of 7,8 DHF bound to the active site of PDXP, supporting a competitive mode of PDXP inhibition. Finally, they find that treating hippocampal neurons with 7,8 DHF increases PLP levels in a WT but not PDXP KO context. The authors note that 7,8 DHF has been used in numerous rodent neuropathology models to improve outcomes. 7, 8 DHF activity was previously attributed to activation of the receptor tyrosine kinase TrkB, although this appears to be controversial. The present study raises the possibility that it instead/also acts through modulation of PLP levels via PDXP, and is an important area for future work.

Strengths:

The strengths of the work are in the comprehensive, thorough, and unbiased nature of the analyses revealing the potential for therapeutic intervention in a number of pathologies.

Weaknesses:

Potential weaknesses include the poor solubility of 7,8 DHF that might limit its bioavailability given its relatively low potency (IC50= 0.8 uM), which was not improved by SAR. However, the compound has an extended residence me and the co-crystal structure could aid the design of more potent molecules and would be of interest to those in the pharmaceutical industry. The images related to crystal structure could be improved.

Reviewer #2 (Public Review):

Summary:

In this study, the authors performed a screening for PDXP inhibitors to identify compounds that could increase levels of pyridoxal 5'- phosphate (PLP), the co-enzymatically active form of vitamin B6. For the screening of inhibitors, they first evaluated a library of about 42,000 compounds for activators and inhibitors of PDXP and secondly, they validated the inhibitor compounds with a counter-screening against PGP, a close PDXP relative. The final narrowing down to 7,8-DHF was done using PLP as a substrate and confirmed the efficacy of this flavonoid as an inhibitor of PDXP function. Physiologically, the authors show that, by acutely treating isolated wild-type hippocampal neurons with 7,8-DHF they could detect an increase in the ratio of PLP/PL compared to control cultures. This effect was not seen in PDXP KO neurons.

Strengths:

The screening and validation of the PDXP inhibitors have been done very well because the authors have performed crystallographic analysis, a counter screening, and mutation analysis. This is very important because such rigor has not been applied to the original report of 7,8 DHF as an agonist for TrkB. Which is why there is so much controversy on this finding.

Weaknesses:

As mentioned in the summary report the study may benefit from some in vivo analysis of PLP levels following 7,8-DHF treatment, although I acknowledge that it may be challenging because of the working out of the dosage and timing of the procedure.

Reviewer #3 (Public Review):

This is interesting biology. Vitamin B6 deficiency has been linked to cognitive impairment. It is not clear whether supplements are effective in restoring functional B6 levels. Vitamin B6 is composed of pyridoxal compounds and their phosphorylated forms, with pyridoxal 5-phosphate (PLP) being of particular importance. The levels of PLP are determined by the balance between pyridoxal kinase and phosphatase activities. The authors are testing the hypothesis that inhibition of pyridoxal phosphatase (PDXP) would arrest the age-dependent decline in PLP, offering an alternative therapeutic strategy to supplements. Published data illustrating that ablation of the Pdxp gene in mice led to increases in PLP levels and improvement in learning and memory trials are consistent with this hypothesis.

In this report, the authors conduct a screen of a library of ~40k small molecules and identify 7,8dihydroxyflavone (DHF) as a candidate PDXP inhibitor. They present an initial characterization of this micromolar inhibitor, including a co-crystal structure of PDXP and 7,8-DHF. In addition, they demonstrate that treatment of cells with 7,8 DHP increases PLP levels. Overall, this study provides further validation of PDXP as a therapeutic target for the treatment of disorders associated with vitamin B6 deficiency and provides proof-of-concept for inhibition of the target with small-molecule drug candidates.

Strengths include the biological context, the focus on an interesting and under-studied class of protein phosphatases that includes several potential therapeutic targets, and the identification of a small molecule inhibitor that provides proof-of-concept for a new therapeutic strategy. Overall, the study has the potential to be an important development for the phosphatase field in general.

Weaknesses include the fact that the compound is very much an early-stage screening hit. It is an inhibitor with micromolar potency for which mechanisms of action other than inhibition of PDXP have been reported. Extensive further development will be required to demonstrate convincingly the extent to which its effects in cells are due to on-target inhibition of PDXP.

Recommendations for the authors:

There is general agreement that the study represents an advance regarding the mechanisms of pyridoxal phosphatase and 7,8 DHF. From the reviewers' comments, several major questions and considerations are raised, followed by their detailed remarks:

(1) More analysis of the solubility and dose of 7,8 DHF with regard to the 50% inhibition and the salt bridge of the B protomer, as raised by the reviewers.

(2) Is there a possible involvement of another phosphatase?

(3) Does 7,8 DHF cause an effect upon TrkB tyrosine phosphorylation?

We thank the Reviewers and Editors for their fair and constructive comments and suggestions. We have performed additional experiments to address these questions and considerations. In addition, we have generated two new high-resoling (1.5 Å) crystal structures of human PDXP in complex with 7,8-DHF that substantially expand our understanding of 7,8-DHF-mediated PDXP inhibition. The scientist who performed this work for the revision of our manuscript has been added as an author (shared first authorship).

We believe that the insights gained from these new data have further strengthened and improved the quality of our manuscript. Together, our data provide compelling evidence that 7,8-dihydroxyflavone is a direct and competitive inhibitor of pyridoxal phosphatase.

Please find our point-by-point responses to the Public Reviews that are not addressed in the Recommendations for the Authors, and the Recommendations for the Authors below.

Reviewer #2:

As mentioned in the summary report the study may benefit from some in vivo analysis of PLP levels following 7,8-DHF treatment, although I acknowledge that it may be challenging because of the working out of the dosage and timing of the procedure.

We agree that an in vivo analysis of PLP levels following 7,8-DHF treatment could be informative for the further evaluation of a possible mechanistic link between the reported effects of this compound and PDXP/vitamin B6. However, we currently do not have a corresponding animal experimentation permission in place and are unlikely to obtain such a permit within a reasonable me frame for this revision.

Recommendations For The Authors:

Reviewer #1:

The work is already well-written, comprehensive, and convincing.

Suggestions that could improve the manuscript.

(1) Include a protein tyrosine phosphatase (PTP) in the selectivity analysis. One possibility is that 7,8 DHF acts on a PTP (such as PTP1B), leading to TrkB activation by preventing dephosphorylation. I note that a previous study has looked at SAR for flavones with PTP1B (PMID: 29175190), which is worth discussion.

We thank the reviewer for bringing this interesting possibility to our attention. We were not aware of the SAR study for flavonoids with PTP1B by Proenca et al. but have now tested the effect of 7,8-DHF on PTP1B, referring to this paper. As shown in Figure 2d, PTP1B was not inhibited by 7,8-DHF at a concentration of 5 or 10 µM. At the highest tested concentration of 40 µM, 7,8-DHF inhibited PTP1B merely by ~20%. For comparison, compound C13 (3-hydroxy-7,8-dihydroxybenzylflavone-3’,4’dihydroxymethyl-phenyl), which emerged as the most active flavonoid in the SAR study by Proenca et al. inhibited PTP1B with an IC50 of 10 µM. Consistent with the results of these authors, our finding confirms that less polar substituents, such as O-benzyl groups at positions 7 and 8, and O-methyl groups at positions 3’ and 4’ of the flavone scaffold, are important for the ability of flavonoids to effectively inhibit PTP1B. We conclude that PTP1B inhibition by 7,8-DHF is unlikely to be a primary contributor to the reported cellular and in vivo effects of this flavone.

In addition to PTP1B, we have now additionally tested the effect of 7,8-DHF on the serine/threonine protein phosphatase calcineurin/PP2B, the DNA/RNA-directed alkaline phosphatase CIP, and three other metabolite-directed HAD phosphatases, namely NANP, NT5C1A and PNKP. PP2B, CIP and NANP were not inhibited by 7,8-DHF. Similar to PTP1B, PNKP activity was attenuated (~30%) only at 40 µM 7,8-DHF. In contrast, 7,8-DHF effectively inhibited NT5C1A (IC50 ~10 µM). NT5C1A is an AMP hydrolase expressed in skeletal muscle and heart. To our knowledge, a role of NT5C1A in the brain has not been reported. Based on currently available information, the inhibition of NT5C1A therefore appears unlikely to contribute to 7,8-DHF effects in the brain.

The results of these experiments are shown in the revised Figure 2d. Taken together, the extended selectivity analysis of 7,8-DHF on a total of 12 structurally and functionally diverse protein- and nonprotein-directed phosphatases supports our initial conclusion that 7,8-DHF preferentially inhibits PDXP.

(2) Line 144: It is unclear how fig 2c supports the statement here. Remove call out for clarity.

Our intention was to highlight the fact that 7,8-DHF concentrations >12.5 µM could not be tested in the BLI assay (shown in Figure 2c) due to 7,8-DHF solubility issues under these experimental conditions. However, since this is discussed in the text, but not directly visible in Figure 2c, we agree with the Reviewer and have removed this call out.

(3) Figure 3a. It is difficult to see the pink 7,8 DHF on top of the pink ribbon backbone. A better combination of colours could be used. Likewise in Figure 3b it is pink on pink again.

We have improved the combination of colors to enhance the visibility of 7,8-DHF and have consistently color-coded murine and the new human PDXP structures throughout the manuscript.

(4) Figure 3c and d. These are the two protomers I believe, but the colour coding is not present in 3c where the ribbon is now gray. Please choose colours that can be used to encode protomers throughout the figure.

Please see response to point 3 above.

(5) Figure 3f. I think this is the same protomer as 3c but a 180-degree rotation. Could this be indicated, or somehow lined up between the two figures for clarity? It would also be useful to have 3e in the same orientation as 3f, to better visualise the overlap with PLP binding. PLP and 7,8 DHF could be labelled similarly to the amino acids in 3f (the colour coding here is helpful).

Please see response to point 3 above. We have substantially revised the structural figures and have used consistent color coding and the same perspective of 7,8-DHF in the PDXP active sites.

(6) Figure 3g. The colours of the bars relating to specific mutations do not quite match the colours in Figure 3f, which I think was the aim and is very helpful.

We have adapted the colours of the residues in Figure 3f (now Fig. 3b and additionally Fig. 3 – figure supplement 1e) so that they exactly match the colours of the bars in Figure 3g (now Fig. 3d).

Reviewer #2:

No further comments.

Reviewer #3:

Page 4: The authors describe 7,8DHF as a "selective" inhibitor of PDXP - in my opinion, they do not have sufficient data to support such a strong assertion. Reports that 7,8DHF may act as a TRK-B-agonist already highlight a potential problem of off-target effects. Does 7,8DHF promote tyrosine phosphorylation of TRK-B in their hands? The selectivity panel presented in Figure 2, focusing on 5 other HAD phosphatases, is much too limited to support assertions of selectivity.

We agree with the Reviewer that our previous selectivity analysis with six HAD phosphatases was limited. To further explore the phosphatase target spectrum of 7,8-DHF, we have now analyzed six other enzymes: three other non-HAD phosphatases (the tyrosine phosphatase PTP1B, the serine/threonine protein phosphatase PP2B/calcineurin, and the DNA/RNA-directed alkaline phosphatase/CIP) and three other non-protein-directed C1/C0-type HAD phosphatases (NT5C1A, NANP, and PNKP). The C1-capped enzymes NT5C1A and NANP were chosen because we previously found them to be sensitive to small molecule inhibitors of the PDXP-related phosphoglycolate phosphatase PGP (PMID: 36369173). PNKP was chosen to increase the coverage of C0-capped HAD phosphatases (previously, only the C0-capped MDP1 was tested).

We found that calcineurin, CIP and NANP were not inhibited by up to 40 µM 7,8-DHF. The activities of PTP1B or PNKP activity were attenuated (by ~20 or 30%, respectively) only at 40 µM 7,8-DHF. In contrast, 7,8-DHF effectively inhibited NT5C1A (IC50 ~10 µM). We have previously found that NT5C1A was sensitive to small-molecule inhibitors of the PDXP paralog PGP, although these molecules are structurally unrelated to 7,8-DHF (PMID: 36369173). NT5C1A is an AMP hydrolase expressed in skeletal muscle and heart (PMID: 12947102). To our knowledge, a role of NT5C1A in the brain has not been reported. Based on currently available information, the inhibition of NT5C1A therefore appears unlikely to contribute to 7,8-DHF effects in the brain. The results of these experiments are shown in the revised Figure 2d. Taken together, the extended selectivity analysis of 7,8-DHF on a total of 12 structurally and functionally diverse protein- and non-protein-directed phosphatases supports our initial conclusion that 7,8-DHF preferentially inhibits PDXP. To nevertheless avoid any overstatement, we have now also replaced “selective” by “preferential” in this context throughout the manuscript.

We have not tested if 7,8-DHF promotes tyrosine phosphorylation of TRK-B. Being able to detect 7,8- DHF-induced TRK-B phosphorylation in our hands would not exclude an additional role for PDXP/vitamin B6-dependent processes. Not being able to detect TRK-B phosphorylation may indicate absence of evidence or evidence of absence. This would neither conclusively rule out a biological role for 7,8-DHF-induced TRK-B phosphorylation in vivo, nor contribute further insights into a possible involvement of vitamin B6-dependent processes in 7,8-DHF induced effects.

Page 6: The authors report that they obtained only two PDXP-selective inhibitor hits from their screen; 7,8DHF and something they describe as FMP-1. For the later, they state that it "was obtained from an academic donor, and its structure is undisclosed for intellectual property reasons". In my opinion, this is totally unacceptable. This is an academic research publication. If the authors wish to present data, they must do so in a manner that allows a reader to assess their significance; in the case of work with small molecules that includes the chemical structure. In my opinion, the authors should either describe the compound fully or remove mention of it altogether.

We are unable to describe “FMP-1” because its identity has not been disclosed to us. The academic donor of this molecule informed us that they were not able to permit release of any details of its structure or general structural class due to an emerging commercial interest.

We mentioned FMP-1 simply to highlight the fact that the screening campaign yielded more than one inhibitor. FMP-1 was also of interest due its complete inhibition of PDXP phosphatase activity.

Because the structure of this molecule is unknown to us, we have now removed any mention of this compound in the manuscript. For the same reason, we have removed the mention of the inhibitor hits “FMP-2” and “FMP-3” in Figure 2 – figure supplement 1 and Figure 2 – figure supplement 2. The number of PDXP inhibitor hits in the manuscript has been adapted accordingly.

Page 7: The observed plateau at 50% inhibition requires further explanation. It is not clear how poor solubility of the compound explains this observation. For example, the authors state that "due to the aforementioned poor solubility of 7,8DHF, concentrations higher than 12.5µM could not be evaluated". Yet on page 8, they describe assays against the specificity panel at concentrations of compound up to 40µM. Do the analogues of 7,8DHF (Fig 2b) result in >50% inhibition at higher concentrations? Further explanation and data on the solubility of the compounds would be of benefit.

We currently do not have a satisfactory explanation for the apparent plateau of ~50% PDXP inhibition by 7,8-DHF. Resolving this question will likely require other approaches, including computational chemistry such as molecular dynamics simulations, and we feel that this is beyond the scope of the present manuscript.

We previously speculated that the limited solubility of 7,8-DHF may counteract a complete enzyme inhibition if higher concentrations of this molecule are required. Specifically, we referred to Todd et al. who have performed HPLC-UV-based solubility assays of 7,8-DHF (ref. 35). These authors found that immediately after 7,8-DHF solubilization, nominal 7,8-DHF concentrations of 5, 20 or 50 µM resulted in 0.5, 3.0 or 13 µM of 7,8-DHF in solution of (i.e., 10, 15 or 26% of the respective nominal concentration). Seven hours later, 46, 26 or 26% of the respective nominal 7,8-DHF concentrations were found in solution. Hence, above a nominal concentration of 5 µM, 7,8-DHF solubility does not increase linearly with the input concentration, but plateaus at ~20% of the nominal concentration. This phenomenon could potentially contribute to the apparent plateau of human or murine PDXP inhibition by 7,8-DHF in vitro.

However, experiments performed during the revision of our manuscript show that they HAD phosphatase NT5C1A can be effectively inhibited by 7,8-DHF with an IC50-value of 10 µM (see revised Fig. 2). Together with the fact that the activity of the PDXP-Asn61Ser variant can be completely inhibited by 7,8-DHF (see Fig. 3d), we conclude that the reason for the observed plateau of PDXP inhibition is likely to be primarily structural, with Asn61 impeding 7,8-DHF binding. We have therefore removed the mention of the limited solubility of 7,8-DHF here. On p.14, we now say: “These data also suggest that Asn61 contributes to the limited efficacy of 7,8-mediated PDXP inhibition in vitro.”

The solubility of 7,8-DHF is dependent on the specific assay and buffer conditions. In BLI experiments, interference patterns caused by binding of 7,8-DHF in solution to biotinylated PDXP immobilized on the biosensor surface are measured. In phosphatase selectivity assays, phosphatases are in solution, and the effect of 7,8-DHF on the phosphatase activity is measured via the quantification of free inorganic phosphate.

In BLI experiments, we observed that the sensorgrams obtained with the highest tested 7,8-DHF concentration (25 µM) showed the same curve shapes as the sensorgrams obtained with 12.5 µM 7,8-DHF. This contrasts with the expected steeper slope of the curves at 25 µM vs. 12.5 µM 7,8-DHF. The same behavior was observed for the reference sensors (i.e., the SSA sensors that were not loaded with PDXP, but incubated with 7,8-DHF at all employed concentrations for referencing against nonspecific binding of 7,8-DHF to the sensors). The sensorgrams at 25 µM 7,8-DHF were therefore not included in the analysis (this is now specified in the Materials and Methods BLI section on p.27). To clarify this point, we now state that “As a result of the poor solubility of the molecule, a saturation of the binding site was not experimentally accessible” (p.7).

In contrast, the phosphatase selectivity assays described on p.8 could be performed with nominal 7,8-DHF concentrations of up to 40 µM. Although the effective 7,8-DHF concentration in solution is expected to be lower (see ref. 35 and discussed above), the limited solubility of 7,8-DHF in phosphatase assays does not prevent the quantification of free inorganic phosphate. Nevertheless, we cannot exclude some interference with this absorbance-based assay (e.g., due to turbidity caused by insoluble compound). Indeed, 5,6-dihydroxyflavone and 5,6,7-trihydroxyflavone caused an apparent increase in PDXP activity at concentrations above 10 µM (see Figure 2b), which may be related to compound solubility issues. Alternatively, these flavones may activate PDXP at higher concentrations.

We have tested the 7,8-DHF analogue 3,7,8,4’-tetrahydroxyflavone at concentrations of 70 and 100 µM. At concentrations >100 µM, the DMSO concentration required for solubilizing the flavone interferes with PDXP activity. PDXP inhibition by 3,7,8,4’-tetrahydroxyflavone was slightly increased at 70 µM compared to 40 µM (by ~18%) but plateaued between 70 and 100 µM. These results are now mentioned in the text (p.7): “The efficacy of PDXP inhibition by 3,7,8,4’-tetrahydroxyflavone was not substantially increased at concentrations >40 µM (relative PDXP activity at 40 µM: 0.46 ± 0.05; at 70 µM: 0.38 ± 0.15; at 100 µM: 0.37 ± 0.09; data are mean values ± S.D. of n=6 experiments).”

Page 9: The authors report that PDXP crystallizes as a homodimer in which 7,8DHF is bound only to one protomer. Is the second protomer active? Does that contribute to the 50% inhibition plateau? If Arg62 is mutated to break the salt bridge, does inhibition go beyond 50%?

We have no way to measure the activity of the second, inhibitor-free protomer in murine PDXP. We know that PDXP functions as a constitutive homodimer, and based on our current understanding, both protomers are active. We have previously shown that the experimental monomerization of PDXP (upon introduction of two-point mutants in the dimerization interface) strongly reduces its phosphatase activity. Specifically, PDXP homodimerization is required for an inter-protomer interaction that mediates the proper positioning of the substrate specificity loop. Thus, homodimerization is necessary for effective substrate coordination and -dephosphorylation (PMID: 24338687).

In the murine structure, we observed that 7,8-DHF binding to the second subunit (the B-protomer) is prevented by a salt bridge between Arg62 and Asp14 of a symmetry-related A-protomer in the crystal lace (i.e., this is not a salt bridge between Arg62 in the B-protomer and Asp14 in the A-protomer of a PDXP homodimer). As suggested, we have nevertheless tested the potential role of this salt bridge for the sensitivity of the PDXP homodimer to 7,8-DHF.

The mutation of Arg62 is not suitable to answer this question, because this residue is involved in the coordination of 7,8-DHF (see Figure 3b), and the PDXP-Arg62Ala mutant is inhibitor resistant (see Figure 3d). We have therefore mutated Asp14, which is not involved in 7,8-DHF coordination. As shown in the new Figure 3 – figure supplement 1d, the 7,8-DHF-mediated inhibition of PDXPAsp14Ala again reached a plateau at ~50%. This result suggests that while an Arg62-Asp14 salt bridge is stabilized in the murine crystal, it is not a determinant of the active site accessibility of protomer B in solution.

To address this important question further, we have now also generated co-crystals of human PDXP bound to 7,8-DHF, and refined two structures to 1.5 Å. We found that in human PDXP, both protomers bind 7,8-DHF. These new, higher resolution data are now shown in the revised Figure 3 and its figure supplements, and we have moved the panels referring to the previously reported murine PDXP structure to the Figure 3 – figure supplement 1. Thus, both protomers of human PDXP, but only one protomer of murine PDXP bind 7,8-DHF in the crystal structure, yet the 7,8-DHFmediated inhibition of human and murine PDXP plateaus at ~50% under the phosphatase assay conditions (see Figure 2a). We conclude that 7,8-DHF binding efficiency in the PDXP crystal does not necessarily reflect its inhibitory efficiency in solution.

Taken together, these data indicate that the apparent partial inhibition of murine and human PDXP phosphatase activity by 7,8-DHF in our in vitro assays is not explained by an exclusive binding of 7,8DHF to just one protomer of the homodimer.

Page 10-12; Is it possible to generate a mutant form of PDXP in which activity is maintained but inhibition is attenuated - an inhibitor-resistant mutant form of PDXP? Can such a mutant be used to assess on-target vs off-target effects of 7,8DHF in cells?

This is an excellent point, and we agree with the Reviewer that such an approach would provide further evidence for cellular on-target activity of 7,8-DHF. Indeed, the verification of the PDXP-7,8DHF interaction sites has led to the generation of catalytically active, inhibitor-resistant PDXP mutants, such as Tyr146Ala and Glu148Ala (Fig. 3d). However, the biochemical analysis of such mutants in primary hippocampal neurons is a very difficult task.

Primary hippocampal neurons are derived from pooled, isolated hippocampi of mouse embryos and are subsequently differentiated for 21 days in vitro. The resulting cellular yield is typically low and variable, and the viability (and contamination of the respective cultures with e.g. glial cells) varies from batch to batch. Although such cell preparations are suitable for electrophysiological or immunocytochemical experiments, they are far from ideal for biochemical studies. A meaningful experiment would require the efficient expression of a catalytically active, but inhibitor-resistant PDXP-mutant in PDXP-KO neurons. In parallel, PDXP-KO cells reconstituted with PDXP-WT (at phosphatase activity levels comparable with the PDXP mutant cells) would be needed for comparison. Unfortunately, the generation of (a) sufficient numbers of (b) viable cells that (c) efficiently express (d) functionally comparable levels of PDXP-WT or -mutant for downstream analysis (PLP/PL-levels upon inhibitor treatment) is currently not possible for us.

Human iPSC-derived (hippocampal) spheroids are at present no alternative, due to the necessity of generating PDXP-KO lines first, and the difficulties with transfecting/transducing them. Such a system would require extensive validation. We have attempted to use SH-SY5Y cells (a metastatic neuroblastoma cell line), but PDXK expression in these cells is modest and they produce too little PLP. We therefore feel that this question is beyond the scope of our current study.

Author response:

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

Public Reviews:

Reviewer #1:

Summary:

The evolution of non-shivering thermogenesis is of fundamental importance to understand. Here, in small mammals, the contractile apparatus of the muscle is shown to increase energy expenditure upon a drop in ambient temperature. Additionally, in the state of torpor, small hibernators did not show an increase in energy expenditure under the same challenge.

Strengths:

The authors have conducted a very well-planned study that has sampled the muscles of large and small hibernators from two continents. Multiple approaches were then used to identify the state of the contractile apparatus, and its energy expenditure under torpor or otherwise.

Weaknesses:

There was only one site of biopsy from the animals used (leg). It would be interesting to know if non-shivering thermogenesis is something that is regionally different in the animal, given the core body and distal limbs have different temperatures.

We thank the reviewer for their time and effort in reviewing our manuscript. Furthermore, we agree that it would be of interest to perform similar experiments upon different muscle sites in these animals. This is of particular interest as in some mammals, such as mice, distal limbs do not shiver and therefore non-shivering thermogenesis may play a more prominent role in heat regulation. A paper from Aydin et al., demonstrated that when shivering muscles (soleus) were prevented undergoing non-shivering thermogenesis via knock-out of UCP1 and were then exposed to cold temperatures, the force production of these muscles was significantly reduced due to prolonged shivering [1]. These results do suggest that even in shivering muscle, non-shivering thermogenesis plays a key role in the generation of heat for survival and for the maintenance of muscle performance. Furthermore, there is evidence from garden dormice that muscle temperature during torpor is slightly warmer than abdominal temperature and slighter cooler that heart temperature which is 7-8°C than abdominal suggesting the existence of non-shivering thermogenesis in skeletal and cardiac muscles (Giroud et al. in prep) [2]. We have added this information and reference into our discussion to reflect this important point (Discussion, paragraph 6, “As the biopsies which were used…”).

Reviewer #2:

Summary:

The authors utilized (permeabilized) fibers from muscle samples obtained from brown and black bears, squirrels, and Garden dormice, to provide interesting and valuable data regarding changes in myosin conformational states and energetics during hibernation and different types of activity in summer and winter. Assuming that myosin structure is similar between species then its role as a regulator of metabolism would be similar and not different, yet the data reveal some interesting and perplexing differences between the selected hibernating species.

Strengths:

The experiments on the permeabilized fibers are complementary, sophisticated, and well-performed, providing new information regarding the characteristics of skeletal muscle fibers between selected hibernating mammalian species under different conditions (summer, interarousal, and winter).

The studies involve complementary assessments of muscle fiber biochemistry, sarcomeric structure using X-ray diffraction, and proteomic analyses of posttranslational modifications.

Weaknesses:

It would be helpful to put these findings on permeabilized fibers into context with the other anatomical/metabolic differences between the species to determine the relative contribution of myosin energetics (with these other contributors) to overall metabolism in these different species, including factors such as fat volume/distribution.

We thank the reviewer for the time and effort they have put into reviewing our paper and are grateful for the helpful suggestions which we believe, enhances our work (please see below for detailed answers to critics).

Reviewer #3:

Summary and strengths:

The manuscript, "Remodelling of skeletal muscle myosin metabolic states in hibernating mammals", by Lewis et al, investigates whether myosin ATP activity may differ between states of hibernation and activity in both large and small mammals. The study interrogates (primarily) permeabilized muscle strips or myofibrils using several state-of-the-art assays, including the mant-ATP assay to investigate ATP utilization of myosin, X-ray diffraction of muscles, proteomics studies, metabolic tests, and computational simulations. The overall data suggests that ATP utilization of myosin during hibernation is different than in active conditions.

A clear strength of this study is the use of multiple animals that utilize two different states of hibernation or torpor. Two large animal hibernators (Eurasian Brown Bear, American Black Bear) represent large animal hibernators that typically undergo prolonged hibernation. Two small animal hibernators (Garden Dormouse, 13 Lined Ground Squirrel) undergo torpor with more substantial reductions in heart rate and body temperature, but whose torpor bouts are interrupted by short arousals that bring the animals back to near-summer-like metabolic conditions.

Especially interesting, the investigators analyze the impact that body temperature may have on myosin ATP utilization by performing assays at two different temperatures (8 and 20 degrees C, in 13 Lined Ground Squirrels).

The multiple assays utilized provide a more comprehensive set of methods with which to test their hypothesis that muscle myosins change their metabolic efficiency during hibernation.

We thank this reviewer for the effort and time they have put into carefully reviewing our manuscript and have taken on board their valuable suggestions to improve our manuscript (please see below for detailed answers to critics).

Suggestions and potential weaknesses:

While the samples and assays provide a robust and comprehensive coverage of metabolic needs and testing, the data is less categorical. Some of these may be dependent on sample size or statistical analysis while others may be dependent on interpretation.

(1) Statistical Analysis

(1a) The results of this study often cannot be assessed properly due to a lack of clarity in the statistical tests.

For example, the results related to the large animal hibernators (Figure 1) do not describe the statistical test (in the text of the results, methods, or figure legends). (Similarly for figure 6 and Supplemental Figure 1). Further, it is not clear whether or when the analysis was performed with paired samples. As the methods described, it appears that the Eurasian Brown Bear data should be paired per animal.

We thank the reviewer for these important points and have added information upon the statistical tests used where previously missing in each figure legend. Details on the statistical testing used for figure 6 are listed in the methods section, paragraph 18, “All statistical analysis of TMT derived protein expression data…”

(1b) The statistical methods state that non-parametric testing was utilized "where data was unevenly distributed". Please clarify when this was used.

We have now clariid all statistical tests used in the figure legends.

(1c) While there are two different myosin isoforms, the isoform may be considered a factor. It is unclear why a one-way ANOVA is generally used for most of the mant-ATP chase data.

The reviewer is right, in our analysis, we haven’t considered ‘myosin isoforms’ as a factor. One of the main reasons for that is because we have decided to treat fibres expressing different myosin heavy chain isoforms as totally separated entities (not interconnected).

(1d) While the technical replicates on studies such as the mant-ATP chase assay are well done, the total biological replicates are small. A consideration of the sample power should be included.

Unfortunately, obtaining additional biological samples from these unique species is challenging. Hence, we have added a statement in the Discussion section. This statement focuses on the potential benefits of increasing sample size to increase statistical power (Discussion, paragraph 2, “In contrast to our study hypothesis…”

(1e) An analysis of the biological vs statistical significance should be considered, especially for the mant-ATP chase data from the American Black Bear, where there appear to be shifts between the summer and winter data.

We agree that it is important to be careful when drawing conclusions from data only based on p-values. We agree that the modest differences observed in these data on American Black bear, whilst not significant, are worth noting and we have added these considerations into the manuscript (Discussion, paragraph 2, “In contrast to our study hypothesis…).

(2) Consistency of DRX/SRX data.

(2a) The investigators performed both mant-ATP chase and x-ray diffraction studies to investigate whether myosin heads are in an "on" or "off" state. The results of these two studies do not appear to be fully consistent with each other, which should not be a surprise. The recent work of Mohran et al (PMID 38103642) suggests that the mant-ATP-predicted SRX:DRX proportions are inconsistent with the position of the myosin heads. The discussion appears to lack a detailed assessment of this prior work and lack a substantive assessment contrasting the differing results of the two assays in the current study. i.e. why the current study's mant-ATP chase and x-ray diffraction results differ.

Prior works on skeletal muscle (observing discrepancies between Mant-ATP chase assay and X-ray diffraction) are rather scarce. Adding a comprehensive discussion about this may be beyond the scope of current study and would distract the reader from the main topic. For this reason, we have not added any section. Note that, we have other manuscripts in preparation that are specifically dedicated to the discrepancy.

(2b) The discussion of the current study's x-ray diffraction data relating to the I_1,1/I_1,0 ratio and how substantially different this is to the M6 results merits discussion. i.e. how can myosin both be more primed to contract during IBA versus torpor (according to intensity ratio), but also have less mass near the thick filament (M6).

The I1,1/I1,0 ratio indicates a subtle mass shift towards the myosin thick filament whilst the M6 spacing shows a more compliant thick filament. These results are not incompatible and rely on interpretation of the X-ray diffraction patterns. To avoid any confusion and avoid distracting the reader from the main topic, we have decided not to speculate there.

(3) Possible interactions with Heat Shock Proteins

Heat Shock Proteins (HSPs), such as HSP70, have been shown to be differential during torpor vs active states. A brief search of HSP and myosin reveals HPSs related to thick filament assembly and Heat Shock Cognate 70 interacting with myosin binding protein C. Especially given the author's discussion of protein stability and the potential interaction with myosin binding protein C and the SRX state, the limitation of not assessing HSPs should be discussed. (While HSP's relation to thick filament assembly might conceivably modify the interpretation of the M3 x-ray diffraction results, this reviewer acknowledges that possibility as a leap.)

The reviewer raises an interesting and potentially important of the potential impact of HSP and their interaction with the thick filament during hibernation. We have added a section into the discussion of this manuscript regarding this, with particular impact upon the HSP70 acting as a chaperone for myosin binding protein, however we feel that it is important to point out that HSPs have only been shown to interact with MYBPC3, a cardiac isoform of this protein which is not present in skeletal muscle [3]. (Discussion, paragraph 5, “Of potential further interest to the regulation of myosin…”).

Despite the above substantial concerns/weaknesses, this reviewer believes that this manuscript represents a valuable data set.

Other comments related to interpretation:

(4) The authors briefly mention the study by Toepfer et al [Ref 25] and that it utilizes cardiac muscles. There would benefit from increased discussion regarding the possible differences in energetics between cardiac and skeletal muscle in these states.

As this manuscript focuses solely on skeletal muscle. We believe that introducing comparisons between cardiac and skeletal muscles would confuse the reader. These types of muscles have very different regulations of SRX/DRX as an example. Note that we are preparing a manuscript focusing on cardiac muscle and hibernation.

(5) The author's analysis of temperature is somewhat limited.

(5a) First, the authors use 20 degrees C (room temperature), not 37 degrees C, a more physiologic body temperature for large mammals. While it is true that limbs are likely at a lower temperature, 20 degrees C seems substantially outside of a normal range. Thus, temperature differences may have been minimized by the author's protocol.

The authors agree that the experimental set up to perform these single fiber studies at slightly higher temperatures may have been more beneficial to replicate the physiological conditions of these hind leg muscle in the analyzed animals. However, previous work has shown that the resting myosin dynamics are in fact stable at temperatures between 20-30 degrees Celsius in type I, type II and cardiac mammalian muscle fibers [4].

(5b) Second, the authors discuss the possibility of myosin contributing to non-shivering thermogenesis. The magnitude of this impact should be discussed. The suggestion of myosin ATP utilization also implies that there is some basal muscle tone (contraction), as the myosin ATPase utilizes ATP to release from actin, before binding and hydrolyzing again. Evidence of this tone should be discussed.

The reviewer is raising an interesting point and it would indeed be interesting to assess the magnitude of the impact and whether a basal muscle tone exists. Assessing the magnitude of the impact, is not an easy task and would require very advanced simulations which we are not experts in unfortunately. As for basal muscle tone, this is difficult to say as myosin is not actually binding to actin but hydrolyzing ATP at a faster pace during hibernation. We then think that the relation between our data and basal muscle tone is unclear. Hence, we have decided not to discuss these points in the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This is a very interesting paper. I have some minor suggestions to help improve it.

Is there any way to estimate the contribution of contractile apparatus to energy expenditure in reference to what is being generated at SERCA in the resting muscle under the various states examined?

This is an interesting idea however, as far as we know, this would be challenging experimentally (in the hibernating mammals) and difficult to achieve in a reliable manner.

It is important to emphasize that while BAT has been traditionally seen to be the site of NST, the skeletal muscle is very important, especially in large mammals, where BAT is going to be a very small % of the body and unlikely to be able to adequately provide heat. The addition of the contractile apparatus to SERCA as a heat generator at rest is very important -- also, the activation of ryanodine receptor Ca2+ to increase the local [Ca2+] at SERCA to generate heat has also recently been shown and should be mentioned (Meizoso-Huesca et al 2022, PNAS; Singh et al 2023, PNAS) alongside the work of Bal et al 2012 etc...

We have included these mechanisms and references into the manuscript discussion [5, 6]. Discussion, paragraph 4, “A critical difference between the large hibernators…”

Are you able to report the likely proportion of type II fibers in the muscles you have sampled?

The fiber type breakdown for all animals used in this study is reported in supplementary table 1.

The sampling of muscle from the legs of live animals is sensible and convenient. Is it possible different muscles in the body have different levels of NST, changes in energy expenditure in torpor, and other states?

As discussed in the public review we have added to the discussion of this manuscript to reflect upon this important point of potentially different results from different muscle sites in these animals.

Reviewer #2 (Recommendations For The Authors):

Is it likely that the proportion of fast and slow myosin-heavy chains within the selected sample of myofibers from the different mammals contributes to the overall differences in the energetics of different conformational states? In living animals, how does the relative contribution of the energetics from different muscle fiber types compare with the contribution from other organs to the overall regulation of metabolism during activities in summer, winter, or periods of intermittent arousal?

Fiber types in mammals can be vastly different between species as well as having a considerable amount of plasticity to change within each species upon specific stimuli. Furthermore, some mammals also have specific myosin heavy chain isoforms which have considerable expression, for example, myosin heavy chain 2B which is expressed in rodents such as mice but not larger mammals such as humans.

In the manuscript, we demonstrate that there is no significant change in the ATP usage by myosin in resting muscle in any of the species which we examined (Fig 1 F, L; Fig 2 E, J). The relatively high mitochondrial density of type I fibers when compared to type II fibers may contribute to a higher overall requirement of energy storage primarily via lipid oxidation. However, mitochondrial respiration is heavily suppressed during hibernation, so questions remain over the overall energy demand in hibernating muscle beyond myosin [7]. The fact that myosin ATP demand is relatively preserved in hibernating muscle suggests that skeletal muscle may be a relatively energy-demanding organ even during hibernation, we speculate in the manuscript this may be due to the requirement of maintaining muscular tone and function during this period of prolonged immobilization. This may be of relevance when one considers the almost complete shutdown of organs involved with food intake and breakdown such as the stomach and liver during hibernation. Furthermore, heart rate and breathing rates are vastly suppressed. Altogether, whilst is it difficult at this point to make an accurate estimate of energy demands between the different organs of hibernators, our data points to skeletal muscle to be a relatively high energy demand organ during these periods. When considering the difference between fiber type, again our data suggests that both type I and type II fibers have relatively similar energy demands during hibernation.

The supplementary data are quite revealing as to how the myosin isoform composition is stable in some species but highly plastic in others in response to the same environmental/metabolic challenges. Why is the myosin heavy chain isoform (I and II) composition stable for brown bears but not for black bears between summer and winter? This is very interesting. For the Ground squirrel, there is remarkable plasticity between myosin heavy chain isoforms ( I and II) between summer, interbout arousal, and torpor. Yet in the Garden Dormouse, the myosin heavy chain isoform (I and II) composition is stable between these three activity states. The inconsistencies between and within species are perplexing and worthy of closer interrogation.

The measurements and role of myosin energetics in different conformational states are interesting but need to be explained in context with other metabolic regulators for these hibernating mammals, especially because some species show remarkable plasticity whereas others show remarkable stability. For example, compare brown and black bears which show differences in the response of myosin composition the activity, interbout arousal, and torpor. Ground squirrels show remarkable plasticity in myosin isoform composition between activity states (and likely metabolic differences), but the Garden Dormouse has a remarkably stable myosin isoform composition during the three metabolic/environmental challenges. What mechanisms facilitate these modifications in some but not other mammals, even those of similar size? The differences are very interesting, worthy of follow-up, and may well contribute to further understanding the significance of the energetics of different myosin conformational states.

We agree that the changes seen between these species are very interesting and worthy of further investigation. What would be of further interest would be to look at methods which would allow for even deeper phenotyping, such as single fiber proteomics, to allow for the assessment of the percentage of hybrid fibers and fibers undergoing any fiber type switch during hibernating periods. Our results do observe a modest, albeit not significant, increase in the number of type I muscle fibers in 13-lined ground squirrels and Garden dormice during torpor which is consistent with previous studies[8]. Previous studies have demonstrated that lower temperatures may promote a shift towards more oxidative type I muscle fibers in mammals[9]. This could be an explanation for why we see this specifically in the smaller hibernators, however as we demonstrate and discuss, these lower temperatures are vital for the survival of these smaller mammals during hibernation so it would be inconsistent to hypothesize that these shifts are for heat-production purposes. Further studies are warranted to understand the relevance of these shifts further, particularly those with a higher sample size. It would also be on interest to examine fiber type percentages during the progression these long hibernating periods to observe if these changes are progressive.

As for the triggers and mechanisms which facilitate these changes to myosin dynamics, this is of current investigation by the field. One which may be of particular relevance to the changes seen during hibernation would that of steroid hormones previous research has demonstrated that steroid hormone levels in make and female bears change differentially[10]. This may be of relevance as the steroid hormone estradiol has been shown to slow the resting myosin ATP turnover via the binding of myosin RLC[11]. Considering these studies, future work which looks at hibernating animals of each sex as different groups may be fruitful.

Reviewer #3 (Recommendations For The Authors):

i. PDF Pg 8- Results- 'Myosin temperature sensitivity is lost in relaxed skeletal muscles fibers of hibernating Ictidomys tridecemlineatus.': An extra comma appears to be placed between "temperature, decrease".

ii. PDF Pg 9- Results- 'Hyper-phosphorylation of Myh2 predictably stabilizes myosin backbone in hibernating Ictidomys tridecemlineatus.' (last paragraph): A parenthesis needs to be closed upon the first reference to "supplemental figures 2 and 3".

iii. PDF Pg 15- Methods- 'Samples collection and cryo-preservation'- The authors use the term "individuals" in the 2nd line. Consider using "subjects".

iv. PDF Pg 15- Methods- 'Samples collection and cryo-preservation' (2nd paragraph)- define "subadult" in approximate months or years.

v. PDF Pg 15- Methods- 'Samples collection and cryo-preservation' (2nd paragraph)- The authors state that brown bears were located in "February and again ... in late June". Was this order of operations always held? If so, a comment about how the potential ageing from the hibernation (especially if sub-adult transitions to adulthood in this period) should be included.

All samples were collected during the subadult period of the lifespan of each bear and therefore we do not think that there would be a potential aging affect observed considering the lifespan of this species to be 20-30 years.

vi. PDF Pg 15- Methods- 'Samples collection and cryo-preservation' (3rd paragraph)- The justification for deprivation of feeding of black bears 24 hours prior to euthanasia should be included. A comment on how this might impact post-translational modifications or gene expression should be included.

Animals are starved prior to prevent aspiration during euthanasia. Considering these samples are to be compared to animals which have not consumed food or water for five months the impact relative impact on PTMs and gene expression would be considered negligible.

vii. PDF Pg 17- Methods- 'Mant-ATP chase experiments' (just after normalized fluorescence equation): The "Where" may be lowercase.

viii. PDF Pg 17- Methods- 'Mant-ATP chase experiments' (last paragraph): The protocol for myosin staining, along with the antibody identification (source, catalog number) should be included.

ix. PDF Pg 18- Methods- 'Post-translational Modification Peptide mapping': Define the makeup of the acrylamide gel and/or the source and catalog number.

x. PDF Pg 18- Methods- 'Post-translational Modification Peptide mapping': The authors state that "Gel bands were washed..." Please specify which protein bands and if multiple bands (i.e. multiple isoforms) were isolated.

We thank this reviewer for their careful reading of our manuscript, we have made the changes above as relevant.

Reference list

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(2) Stickler, S., Regional body temperatures and fatty acid compositions in hibernating garden dormice: a focus on cardiac adaptions. 2022, Vienna: Vienna. p. v, 49 Seiten, Illustrationen.

(3) Glazier, A.A., et al., HSC70 is a chaperone for wild-type and mutant cardiac myosin binding protein C. JCI Insight, 2018. 3(11).

(4) Walklate, J., et al., Exploring the super-relaxed state of myosin in myofibrils from fast-twitch, slow-twitch, and cardiac muscle. Journal of Biological Chemistry, 2022. 298(3).

(5) Meizoso-Huesca, A., et al., Ca²⁺ leak through ryanodine receptor 1 regulates thermogenesis in resting skeletal muscle. Proceedings of the National Academy of Sciences, 2022. 119(4): p. e2119203119.

(6) Singh, D.P., et al., Evolutionary isolation of ryanodine receptor isoform 1 for muscle-based thermogenesis in mammals. Proceedings of the National Academy of Sciences, 2023. 120(4): p. e2117503120.

(7) Staples, J.F., K.E. Mathers, and B.M. Duffy, Mitochondrial Metabolism in Hibernation: Regulation and Implications. Physiology, 2022. 37(5): p. 260-271.

(8) Xu, R., et al., Hibernating squirrel muscle activates the endurance exercise pathway despite prolonged immobilization. Exp Neurol, 2013. 247: p. 392-401.

(9) Yu, J., et al., Effects of Cold Exposure on Performance and Skeletal Muscle Fiber in Weaned Piglets. Animals (Basel), 2021. 11(7).

(10) Frøbert, A.M., et al., Differential Changes in Circulating Steroid Hormones in Hibernating Brown Bears: Preliminary Conclusions and Caveats. Physiol Biochem Zool, 2022. 95(5): p. 365-378.

(11) Colson, B.A., et al., The myosin super-relaxed state is disrupted by estradiol deficiency. Biochemical and biophysical research communications, 2015. 456(1): p. 151-155.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review): Weaknesses:

However, the molecular mechanisms leading to NPC dysfunction and the cellular consequences of resulting compartmentalization defects are not as thoroughly explored. Results from complementary key experiments using western blot analysis are less impressive than microscopy data and do not show the same level of reduction. The antibodies recognizing multiple nucleoporins (RL1 and Mab414) could have been used to identify specific nucleoporins that are most affected, while the selection of Nup98 and Nup107 is not well explained.

The results for the Western blots are less impressive than single nuclei imaging analysis because the protocol for isolating brain nuclei is heterogeneous and includes non-neuronal cells. For this reason, we selected specific nucleoporins for Western blot studies to complement the nonspecificity of pan-NPC antibodies for which the detection is based on the glycosylated moieties. We reasoned that a combination of pan-NPC and select NUPs will give the strongest complementary validation for the mutant phenotype. We have discussed the rationale of NUP selection in discussion. In brief, we selected NUP107 as it is a major component of the Yscaffold complex and is a long-lived subunit of the NPCs (Boehmer et al., 2003; D'Angelo et al., 2009). NUP98 is a mobile nucleoporin and is associated with the central pore, nuclear basket and cytoplasmic filaments. Both NUPs have been implicated in degenerative disorders. (Eftekharzadeh et al., 2018; Wu et al., 2001).

There is also no clear hypothesis on how Aβ pathology may affect nucleoporin levels and NPC function. All functional NCT experiments are based on reporters or dyes, although one would expect widespread mislocalization of endogenous proteins, likely affecting many cellular pathways.

We agree that the interaction between Aβ pathology and the NPC remains a work in progress. We decided to rigorously characterize Aβ-mediated deficits in App KI neurons – using different approaches and in more than one animal model – before moving on to explore mechanisms in subsequent studies, which we think deserves more extensive experiments. We seek your understanding and have included in the discussion, possible mechanisms for direct and indirect Aβ-mediated disruption of NPCs. We have also included an additional study to show the disruption in the localization of an endogenous nucleocytoplasmic protein – CRTC1 (cAMP Regulated Transcriptional Coactivator), which is CREB coactivator responsive to neural activity. We observed under basal and also in tetrodotoxin-silenced conditions, there is much higher CRTC1 in the nucleus in App KI neurons relative to WT. This reflects the compromised permeability barrier that we observed via FRAP studies. (Supplementary Figure S15).

The second part of this manuscript reports that in App KI neurons, disruption in the permeability barrier and nucleocytoplasmic transport may enhance activation of key components of the necrosome complex that include receptor-interacting kinase 3 (RIPK3) and mixed lineage kinase domain1 like (MLKL) protein, resulting in an increase in TNFα-induced necroptosis. While this is of potential interest, it is not well integrated in the study. This potential disease pathway is not shown in the very simple schematic (Fig. 8) and is barely mentioned in the Discussion section, although it would deserve a more thorough examination.

The study of necroptosis is meant to showcase a single cellular pathway that requires nucleocytoplasmic transport for activation that is compromised and is relevant for AD. We agree there is much more to explore in this pathway but feel is outside the scope of this study. We have included a new illustration that models how damage to NPCs and permeability barrier results in enhanced vulnerability of App KI neurons for necroptosis (Supplemental figure S12).

Reviewer #2 (Public Review):

(1) Adding statistics and comparisons between wild-type changes at different times/ages to determine if the nuclear pore changes with time in wild-type neurons. The images show differences in the Nuclear pore in neurons from the wild-type mice, with time in culture and age. However, a rigorous statistical analysis is lacking to address the impact of age/development on NUP function. Although the authors state that nuclear pore transport is reported to be altered in normal brain aging, the authors either did not design their experiments to account for the normal aging mechanisms or overlooked the analysis of their data in this light.

All our quantifications and statistical comparisons in neuron cocultures are time-matched between WT and App KI neurons, and thus independent of age and maturity of the neurons in culture. The accelerated loss of NUP expression is evident across all time groups. However, we cannot compare across age groups in cultured neurons as the time-matched WT and App KI samples for each time point were processed and imaged separately as neurons matured over time (Fig. 1B-C). An experiment must be done simultaneously across all age groups to compare agerelated effects for WT and App KI neurons in order to account for time-dependent changes. Given the unique challenges of studying “aging” in culture systems, we opted to be more conservative in our interpretation of the results and as such, we were careful to describe the accelerated nuclear pore deficits in App KI neurons relative to time-matched WT expression and speculate its relationship to normal brain aging only in the discussion section. We seek your understanding in this matter. That said, we are able to capture the decline of the NPC in histology of brain sections and observed a statistically significant drop in WT NUP levels in animal sections across age groups where we quantified and compared the raw nuclear intensities from brain sections that were processed and imaged simultaneously across independent experiments (Fig. 1D-E). We have included a statement in the results section to highlight that point.

(2) Add experiments to assess the contribution of wild-type beta-amyloid accumulation with aging. It was described in 2012 (Guix FX, Wahle T, Vennekens K, Snellinx A, Chávez-Gutiérrez L, Ill-Raga G, Ramos-Fernandez E, Guardia-Laguarta C, Lleó A, Arimon M, Berezovska O, Muñoz FJ, Dotti CG, De Strooper B. 2012. Modification of γ-secretase by nitrosative stress links neuronal ageing to sporadic Alzheimer's disease. EMBO Mol Med 4:660-673, doi:10.1002/emmm.201200243) and 2021 (Burrinha T, Martinsson I, Gomes R, Terrasso AP, Gouras GK, Almeida CG. 2021. Upregulation of APP endocytosis by neuronal aging drives amyloid-dependent synapse loss. J Cell Sci 134. doi:10.1242/jcs.255752), 28 DIV neurons are senescent and accumulate beta-amyloid42. In addition, beta-amyloid 42 accumulates normally in the human brain (Baker-Nigh A, Vahedi S, Davis EG, Weintraub S, Bigio EH, Klein WL, Geula C. 2015. Neuronal amyloid-β accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease. Brain 138:1722-1737. doi:10.1093/brain/awv024), thus, it would be important to determine if it contributes to NUP dysfunction. Unfortunately, the authors tested the Abeta contribution at div14 when wild-type Abeta accumulation was undetected. It would enrich the paper and allow the authors to conclude about normal aging if additional experiments were performed, namely, treating 28Div neurons with DAPT and assessing if NUP is restored.

Your point is well-noted. We are intrigued at the potential contribution of WT Aβ to the decline in NUPs and NPC but decided to focus on mutant Aβ for this manuscript. We have observed negligible MOAB2-positive Aβ signals in WT neurons across all age groups (data not shown) but acknowledge the potential contributions of aging toward a reduction in NPC function. Instead, we have included a section in the discussion to highlight the aging-related expression of Aβ in WT neurons and a subset of the citations above to indicate a possible link with normal decay of NPCs.

Reviewer #3 (Public Review):

Weaknesses:

(1) It does not consider the relationship of the findings here to other published work on the intraneuronal perinuclear and nuclear accumulation of amyloid in other transgenic mouse models and in humans.

We have updated the discussion to further elaborate on intraneuronal and perinuclear accumulation of amyloid and how that relates to our NPC phenotype.

(2) It appears to presume that soluble, secreted Abeta is responsible for the effect rather than the insoluble amyloid fibrils.

At present, our data cannot fully discount the role of fibrils or other forms of Aβ causing the NPC deficits, but our studies do show that external presence of Aβ (e.g. addition of synthetic oligomeric Aβ or App KI conditioned media) leads to intracellular accumulation and NPC dysfunction. We are aware that endogenous formation of fibrils could also contribute to the NPC dysfunction but refrained from drawing any conclusions without further studies. We have stated this in the discussion.

(5) It is not clear when the alteration in NUP expression begins in the KI mice as there is no time at which there is no difference between NUP expression in KI and Wt and the earliest time shown is 2 months. If NUP expression is decreased from the earliest times at birth, then this makes the significance of the observation of the association with amyloid pathology less clear.

The phenotype we observed early in neuronal cultures and in very young animals is subtle and in all our studies, the severity of the NUP phenotypes consistently correlates with elevated intracellular Aβ. We expect that by looking at earlier/younger neurons, the deficits will not be present. However, neurons before DIV7 are immature, and hence we chose not to include those in our observations. In animals, we observed Aβ expression in neuronal soma in young mice (2 mo.), but it is not clear when the deficits manifests and how early to look. While the NUP expression is reduced at an early stage, we speculate in discussion that cellular homeostatic mechanisms can compensate for any compromised nuclear functions and to maintain viability to the point where age-dependent degradation of cellular mechanisms will eventually lead to progression of AD.

Reviewer #1 (Recommendations For The Authors):

While the App KI model is suitable for modeling one key aspect of human AD, the use of the term "AD neurons" throughout the manuscript is misleading and should be avoided when describing experiments with "App KI neurons".

Noted and corrected.

The claim that Aβ pathology causes NPC dysfunction via reduced nucleoporin protein expression would be stronger if it was better supported by biochemical evidence based on western blots (WBs) to complement the strong microscopy data. The results shown in Figure 2H show a very weak effect compared to microscopy data that does not appear to match the quantification (e.g. Lamin-B1 staining appears reduced after 2 months in WB but not the graph). It is also not clear why nuclear fractionation is required. WB analyses with RL1 and MAB414 (that recognizes multiple FG-Nupsin ICCs and WBs) would help identify Nups that are most affected by Aβ pathology.

The weaker Western blot results is due to the heterogeneity of the nuclei we isolated from the whole brain which includes non-neuronal cells. We reasoned that isolating the nuclear fraction would give us a cleaner Western blot with fewer background bands as the input lysate is more specific. We also decided to use antibodies against specific NUPs as a way to complement the pan-NPC antibodies that detect glycosylation-enriched epitopes in the nucleus. We reasoned that Western blot identification of individual subunits should provide complementary and stronger evidence for the reduction of NUPs at the peptide level. Overall, we used four different nuclear pore antibodies (RL1, Mab414, NUP98, NUP107) to demonstrate the same mutant phenotype in App KI neurons.

While the observed NCT defects are discussed in detail, the authors do not present any potential mechanisms to be tested, how intracellular Aβ may impact NPCs. Does Aβ pathology affect nucleoporin expression or stability?

We have observed the presence of Aβ adjacent to the nuclear membrane and also in the cytosol via high resolution confocal microscopy (Supplementary Figure S14). Our primary goal in this paper is to provide convincing evidence – using different assays and in more than one mouse model – for the reduction of NUPs and lower NPC counts. We feel mechanistic details of Aβdriven NPC disruption requires more extensive experimentation more suitable for subsequent publications.

The very simple schematic just represents the loss of compartmentalization, without illustrating more complex concepts. It would also be improved by representing the outer and inner nuclear membrane fusing around the NPCs with a much wider perinuclear space between the membranes. As shown now, the nuclear envelope almost looks like a single membrane, while >60kDa proteins are shown at a similar size as the 125MDa NPC.

We have updated the illustration along with a new schematic for necroptosis (Supplementary Figure S12). We have refrained from giving specific details of the damage to the nuclear pore complex because it is not yet clear the nature of these deficits.

Misspelling of "Hoechst" as "Hochest" in several figures (Fig. 1, 2, S5, S7).

Noted and corrected

Reviewer #2 (Recommendations For The Authors):

(1) Additional data analysis is required concerning the wild-type controls. The figures show clear differences in the wild-type neurons with time in culture (referring to figures 1A, 1B, 1C; 2A, 2B, 2C, 2D,6E, 6F, 6G, s4) and in different ages (2E, 2F, 2G, 5B, 5C, 5D). The data analysis is shown for knockin vs the time-matched wild-type condition. The effect of time in wild-type neurons/mice should also be analyzed. All the data is suggested to be normalized to 7 DIV/2month wild-type neurons/mice. Were these experiments done with different time points of the same culture? This would be the best to conclude on the effect of time.

We have noted a decline of NUPs in WT neurons over time in primary cultures and in animal sections. This is not surprising since the NPC and nuclear signaling pathways deteriorate with age (Liu and Hetzer, 2022; Mertens et al., 2015). However, we are unable to do a direct comparison across age groups in cultured neurons as the time-matched WT and App KI neuronal samples for each time point were processed and imaged separately as neurons matured over time (Fig. 1B-C). Hence, we perform statistical analysis for each time-matched WT and App KI neurons. To be clear, multiple independent experiments across different cultures were performed at each time point. Given the inherent challenges of studying aging in culture systems, we opted to be more conservative in our interpretation of the results and as such, we were careful to describe the accelerated nuclear pore deficits in App KI neurons relative to WT levels without inferring the effect of time and speculate its relationship to normal brain aging only in the discussion section. That said, we are able to capture the decline of the nuclear pore complex across different age groups in histology of brain sections where we observed a drop in WT NUP levels in animal sections when we quantified and compared the raw nuclear intensities from brain sections that were processed and imaged simultaneously across independent experiments (Fig. 1D-E).

Similarly, in Figure 2H, why aren't 2 months compared with 14 months? Why were these ages chosen? 2 months is a young adult, and 14 months is a middle-aged adult. To conclude, aging should have included an age between 18 and 24 months old.

As with cultures, we isolated age-matched WT and App KI animals separately. We chose 2 to 14 months as they represent young and middle-aged adults as we wanted to showcase the nuclear pore deficits induced by the presence of Aβ without drawing a conclusion on the effects of age or time. That said, we do show histology of brain sections at 18 months of age with individual NUPs. We agree that the temporal aspects of NPC loss in WT neurons is interesting, however, given our experimental parameters, we cannot draw conclusions across different age groups at the moment.

In Figure 3, statistics between wild type should have been included.

Similar to the above comment, samples were processed and imaged independently across different groups, hence we cannot compare the datapoints across time.

(4) Additional quantification: The intensity of MOAB2 at 2 and 13 months should be measured as in Figure 3C.

Intracellular Aβ signal in 2-mo. old App KI mice is diffuse throughout the soma but in older animals, they are punctate. This observation was similarly described by Lord et al. for tgAPPArcSwe mice (Lord et al., 2006). We have included a confocal micrograph of MOAB-2 immunocytochemistry of a 13-mo. App KI brain section in supplemental figures (Supplementary Figure S13). We found it challenging to differentiate whether the signal is localized intracellularly or as an extracellular aggregate. Regardless, the differences in the quality and uneven distribution of Aβ signal makes any direct comparison of soma intensity across the different age groups harder to interpret in the context of the mutant phenotype.

(5) Additional experiments: Because primary neurons differentiate, mature, and age with time in culture, they are required to control for the developmental stage of your cultures. Analyzing neuronal markers such as doublecortin for neuronal precursors, MAP2 (or Tau) for dendritic/axonal maturation, synapsin for synaptic maturation, and accumulation of senescenceassociated beta-galactosidase (SA-Beta-Gal) as an aging marker.

As part of the maintenance of cultures, we stain cultures for axodendritic markers (e.g. MAP2), glial cell distribution (e.g GFAP) and excitatory vs. inhibitory neuronal subpopulations (e.g. Gad65) and synaptic markers (e.g. PSD95) to ensure that growth, survival and viability of neurons are not compromised (data not shown). These markers for maturity are routinely tracked to ensure proper development. We also test the health of the cultures (e.g. apoptosis, necrosis) and to look for cytoskeletal disruption or fragmentation for neuronal processes.

(6) Additional methods: The quantification of Abeta intensity in Figure 3 is not clearly explained in the methods. Was the intensity measured per field, per cell body?

The quantifications for Aβ are done for each MAP2-positive cell body and have included that statement in the methods.

(7) Missing in discussion integration and references to these papers:

a. Mertens J, Paquola ACM, Ku M, Hatch E, Böhnke L, Ladjevardi S, McGrath S, Campbell B, Lee H, Herdy JR, Gonçalves JT, Toda T, Kim Y, Winkler J, Yao J, Hetzer MW, Gage FH. 2015. Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell Stem Cell 17:705-718. doi:10.1016/j.stem.2015.09.001

b. Guix FX, Wahle T, Vennekens K, Snellinx A, Chávez-Gutiérrez L, Ill-Raga G, Ramos-Fernandez E, Guardia-Laguarta C, Lleó A, Arimon M, Berezovska O, Muñoz FJ, Dotti CG, De Strooper B. 2012. Modification of γ-secretase by nitrosative stress links neuronal ageing to sporadic Alzheimer's disease. EMBO Mol Med 4:660-673. doi:10.1002/emmm.201200243

c. Burrinha T, Martinsson I, Gomes R, Terrasso AP, Gouras GK, Almeida CG. 2021. Upregulation of APP endocytosis by neuronal aging drives amyloid-dependent synapse loss. J Cell Sci 134. doi:10.1242/jcs.255752),

Neuronal amyloid-β accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease. Brain 138:1722-1737. doi:10.1093/brain/awv024).

We have cited a subset of the papers in the discussion section and also expanded the discussion to include the possibility of time-dependent changes for Aβ expression in WT neurons.

Reviewer #3 (Recommendations For The Authors):

Specific comments:

(1) Fig. 1D,E. Fig. 2E, F. This shows the change in NUP IR with time for the APP-KI, but there is also a difference between Wt and KI from the earliest time shown. How early is this difference apparent? From birth? The study should go back to the earliest time possible as the timing of the staining for NUP is important to correlate this with other events of intraneuronal Abeta and amyloid IR. Is the difference between 4 and 7-month ko mice in Figures 2G and 2F statistically significant? If not, perhaps we need a larger N to determine the timing accurately.

The point is well taken. We have not examined the WT and App KI brains before 2-mo. of age. At this early time point, the extracellular amyloid deposits are very low but intracellular Aβ can be readily detected in neuronal soma. We expect that as the animal ages, the Aβ inside cells will directly impact the NPC mutant phenotype, but it is unclear how early this phenotype manifests in animals and when we should look. To be clear, in less mature neurons (DIV7), the phenotype is very subtle and can only be observed via high resolution microscopy. The differences between 4-7 mo. old animals (Fig. 2F and G) in terms of severity of the reduction cannot be assessed as the age-matched animals for each time point were processed separately, but at each time point, we observed a significant reduction of NPC relative to WT. Nevertheless, in Figure 1E, we performed immunohistochemistry experiments with pan-NPC antibodies and quantified raw intensities to show a difference between 4/7-mo. with 13-mo. old animals.

(2) Similarly, the increase in Abeta IR is only shown for cultured neurons and only a single time point of 2 months is shown for CA1 in KI brain. Since a major point is that the decrease in NUP IR is correlated with an increase in Abeta IR, a more convincing approach would be to stain for both simultaneously in KI brain, especially since Abeta IR is quite sensitive to conformational variation between APP, Abeta, and aggregated forms and whether they are treated with denaturants for "antigen retrieval". The entire brain hemisphere should be shown as the pathology is not limited to CA1. There are many different Abeta antibodies that are specific to the amyloid state so it should be possible to come up with a set of antibodies and conditions that work for both Abeta and NUP staining.

The intracellular Aβ signal in 2-mo. old App KI mice is diffuse throughout the soma but in older animals, they are punctate. We have included a confocal micrograph of MOAB-2 immunocytochemistry of a 13-mo. App KI brain section (Supplementary Figure S13). We did not quantify Aβ as it was challenging to differentiate if the signal is intracellular Aβ or amyloid β plaques. Regardless, the differences in the quality and uneven distribution of Aβ signal makes any direct comparison of soma intensity across the different age groups much harder to interpret.

(3) Figure 3A. The staining with MOAB 2 and 82E1 appears qualitatively different with 82E1 exhibiting larger perinuclear puncta. Both antibodies appear to stain puncta inside the nucleus consistent with previously published reports of intranuclear amyloid IR. If these are flattened images, then 3D Z stacks should be shown to clarify this. Figure 3H shows what appears to be Abeta immunofluorescence quantitation in DAPT-treated cells, but the actual images are apparently not shown. The details of this experiment aren't clear or what antibody is used, but this may not be Abeta as many APP fragments that are not Abeta also react with antibodies like MOAB2.

Since 82E1 detects a larger epitope (aa1-16 as compared to 1-4 in MOAB-2), it is possible some forms of Aβ are differentially detected inside the cell. MOAB-2 is shown to detect the different forms of Aβ40 and 42, with a stronger selectivity for the latter. However, it is not known to react with APP or APP/CTFs (Youmans et al., 2012). DAPT-treated cells were processed and imaged as with other experiments in figure 3 using MOAB-2 antibodies to detect Aβ. We have included that information in the figure legends.

The way we image the cell is to collect LSM800 confocal stacks and use IMARIS software to render the nucleus in a 3D object prior to quantifying the intensity or coverage. In this way, we are capturing and quantifying the entire volume of the nucleus and not just a single plane. The majority of signal for MOAB-2 positive Aβ are punctate signals in the cytosol with a subset adjacent to the nucleus (Supplementary Figure 14; Airyscan; single plane). We also detected MOAB-2 signals coming from within the nucleus. The nature of this interaction between Aβ and the nuclear membrane/perinuclear space/nucleoplasm remains unclear.

(4) P20 L12. "We demonstrate an Aβ-driven loss of NUP expression in hippocampal neurons both in primary cocultures and in AD mouse models" It isn't clear that exogenous or extracellular Abeta drives this in living animals. All the data that demonstrate this is derived from cell culture and things may be very different (eg. Soluble Abeta concentration) in vivo. It is OK to speculate that the same thing happens in vivo, but to say it has been demonstrated in vivo is not correct.

We have rewritten the opening statement in the paragraph to narrowly define our observations in the context of App KI. We understand the caveats of our studies in primary cultures, but we have done our due diligence to study the phenomenon in different assays, using at least four different nuclear pore antibodies, and in more than one mouse model to show the deficits. We mentioned Aβ-driven loss but did not conclude which Aβ peptide (e.g. 40 vs. 42) or form (e.g. fibrillar) that drives the deficits. However, we have shown some data that oligomers and not monomers as well as extracellular Aβ can accumulate in the soma and trigger NPC deficits. We also state in the discussion that other possible mechanisms of action, mainly via indirect interactions of Aβ with the cell, could result in the deficits.

(5) P21, L21 "Inhibition of γ-secretase activity prevented cleavage of mutant APP and generation of Aβ, which led to the partial restoration of NUP levels". What the data actually shows is that treatment of the cells with DAPT led to partial restoration of NUP levels. Other studies have shown that DAPT is a gamma secretase inhibitor, so it is reasonable to suspect that the effect to gamma secretase activity, but the substrates and products are assumed rather than measured, so a little caution is a good idea here. For example, CTF alpha is also a substrate, producing P3, which is not considered abeta. The products Abeta and P3 also typically are secreted, where they can be further degraded. Abeta and P3 can also aggregate into amyloid, so whether the effect is really due to Abeta per se as a monomer or Abeta-containing aggregates isn't clear.

The point is noted. DAPT inhibition of -secretase can impact more than one substate as the complex can cleave multiple substrates. However, we have measured Aβ intensity which increases with DAPT, and while a singular experiment is insufficient to show direct Aβ involvement, we have performed other experiments that show a correlation of Aβ levels inside the soma and the degree of NPC reduction. This includes the direct application of synthetic Aβ42 oligomers. We agree the data cannot fully exclude the involvement of other -secretase cleavage products, but we feel there is strong enough evidence that Aβ – in whatever form - is at least partially if not, the main driver that promote these deficits.

(6) Discussion. The authors point to "intracellular Abeta" as a potential causative agent for decreased NUP expression and function and cite a number of papers reporting intracellular Abeta. (D'Andrea et al., 2001; Iulita et al., 2014; Kimura et al., 2003; LaFerla et al., 1997; Oddo et al., 2003b; Takahashi et al., 2004; Wirths et al., 2001). Most of these papers report immunoreactivity with Abeta antibodies and argue about whether this is really Abeta40 or 42 and not APP or APP-CTF immunoreactivity. What is missing from these papers and the discussion in this manuscript is that this is not just soluble Abeta, but Abeta amyloid of the same type that ends up in plaques because it has the same immunoreactivity with Abeta amyloid fibril-specific antibodies and even the classical anti-Abeta antibodies 6E10 and 4G8 after antigen retrieval as shown in papers by Pensalfini, et al., 2014 and Lee, et al., 2022 (1,2) who describe the evolution of neuritic plaques and their amyloid core beginning inside neurons. The term "dystrophic neurite" is a misnomer because the structures that resemble "neurites" morphologically are actually autophagic vesicles packed with Abeta and APP immunoreactive material which has the detergent insolubility properties of amyloid plaques. See (1,2). The apparent intranuclear IR of MOAB2 and 82E1 mentioned in comment 3 is relevant here. In Lee et al., the 3D serial section EM reconstruction of one of these neurons with perinuclear and nuclear amyloid shows abundant amyloid fibrils in the remnant of the nucleus. The nuclear envelope appears to break down as evidenced by the redistribution of NeuN immunoreactivity (Pensalfini et al.,) and other nuclear markers and the EM evidence (Lee et al.,). These papers are also improperly cited as evidence for a hypothetical intracellular source for soluble Abeta.

We have devoted a section of the discussion to highlight some of these findings in the context of Pensalfini et al. 2014 and Lee et al. 2022. Lee et al. tested multiple animal strains to observe the Panthos structures but did not use the App KI mouse model. Since none of our experiments directly tested their observations (e.g. perinuclear fibrils or acidity of autophagic vesicles) in App KI, we decided to take a more conservative approach in our interpretations by framing the NPC deficits without specifying the nature of the intracellular Aβ. We note in discussion that it is entirely possible that App KI animals also show the same Panthos phenotypes and the perinuclear accumulation of Aβ which results in damaged NUPs. To do that, the Panthos phenotype must first be established in App KI mice.

(7) The authors also cite the work of Ditaranto et al., 2001 and Ji et al., 2002 for Aβ-induced lysosomal leakage from these vesicular structures but overlook the original publications on Abeta-induced lysosomal leakage by Yang et al., (3) who further show that this is correlated with aggregation of Abeta42 upon internalization which also leads to the co-aggregation of APP and APP-CTFs in a detergent-insoluble form (4) and pulse-chase studies demonstrate that metabolically-labeled APP ultimately ends up as insoluble Abeta that have "ragged" N-termini (5). This work seems relevant to the results reported here as the perinuclear amyloid that the authors report here is likely to be the same insoluble, aggregated APP and APP-CTF-containing amyloid as that reported in references 1 and 2.

We have included the literature references in the discussion, highlighting the possibility of lysosomal leakage contributing to the NPC damage.

Minor points.

(1) P2, L28 "permeability barrier facilities passive" should be 'facilitates'.

(2) P7, L24 "homogenate and grounded for 5 additional strokes" One of the peculiarities of English is that the past tense of grind is ground. Grounded means something else.

(3) P8, L9 "For synthetic Aβ experiments," Abeta what? 42? 40? It makes a difference and if it is Abeta42, you should be specific in the rest of the text where it is used.

(4) P11, L14. "To determine if Aβ can trigger changes in nuclear structure and function" It seems a little early to start by presupposing that it is Abeta that triggers changes in nuclear structure and function. It sounds like you are starting out with a bias.

(5) P11, L16,17 "While Aβ pathology is robustly detected in App KIs" At some point in the manuscript, either here or in the introduction, it would be useful to include a couple of sentences about what the pathology is in these mice along with the timing of the development of the pathology to compare with the results presented here. There are several types of amyloid deposits, "neuritic" plaques, diffuse plaques, and cerebrovascular amyloid. This is important because the early "neuritic" plaques are intraneuronal at least early on before the neuron dies. See (1,2).

(6) P19, L10. "LMB is an inhibitor or CRM-1 mediated" should be of

All minor points have been addressed in the manuscript and figures.

References

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(5) Yang, A., Chandswangbhuvana, D., Shu, T., Henschen, A., and Glabe, C. G. (1999) Intracellular accumulation of insoluble, newly synthesized Aßn-42 in APP transfected cells that have been treated with Aß1-42. J. Biol. Chem. 274, 20650-20656

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Author response:

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

We would like to express our gratitude to the reviewers for their suggestions and critiques as we continually strive to enhance the quality of the manuscript. We improved it, by incorporating the reviewers’ suggestions, changing the content and numbering of figures (Figs 1, 3S1 were edited; 4 figures were moved to supplemental materials), and adding several analyses suggested by the reviewers along with accompanying figures (1S2, 1S3) and tables (1 and 2). These analyses include investigating the link between freezing behavior and 44-kHz calls as well as their sound mean power and duration. Also, we have introduced detailed information regarding the experiments performed as well as expanded the description and discussion of the results section. Finally, we added the information about 44-kHz calls reported by another group – which was inspired by our findings.

Below is the point-by-point response to the reviewers’ comments.

Reviewer #1 (Public Review):

Olszyński and colleagues present data showing variability from canonical "aversive calls", typically described as long 22 kHz calls rodents emit in aversive situations. Similarly long but higher-frequency (44 kHz) calls are presented as a distinct call type, including analyses both of their acoustic properties and animals' responses to hearing playback of these calls. While this work adds an intriguing and important reminder, namely that animal behavior is often more variable and complex than perhaps we would like it to be, there is some caution warranted in the interpretation of these data. The authors also do not provide adequate justification for the use of solely male rodents. With several reported sex differences in rat vocal behaviors this means caution should be exercised when generalizing from these findings.

We fully agree that our data should be interpreted with caution and we followed the Reviewer’s suggestions along these lines (see below). Also, we appreciate the suggestion to explore the prevalence of 44-kHz calls in female subjects, which would indeed represent an important and intriguing extension of our research. However, due to present financial constraints, we can only plan such experiments. To address the comment, we have added the sentence: “Here we are showing introductory evidence that 44-kHz vocalizations are a separate and behaviorally-relevant group of rat ultrasonic calls. These results require further confirmations and additional experiments, also in form of repetition, including research on female rat subjects.”

It is important to note that the data presented in the current manuscript originates primarily from previously conducted experiments. These earlier experiments employed male subjects only; it was due to established evidence indicating that the female estrus cycle significantly influences ultrasonic vocalization (Matochik et al., 1992). Adhering to controls for the estrus cycle would require a greater number of female subjects than males, which would not only increase animal suffering but also escalate the demands of human labor and financial costs.

Firstly, the authors argue that the shift to higher-frequency aversive calls is due to an increase in arousal (caused by the animals having received multiple aversive foot shocks towards the end of the protocols). However, it cannot be ruled out that this shift would be due to factors such as the passage of time and increase in fatigue of the animals as they make vocalizations (and other responses) for extended periods of time. In fact the gradual frequency increase reported for 22 kHz calls and the drop in 44 kHz calls the next day in testing is in line with this.

Answer: We would like to point out that the “increased-arousal” hypothesis, declared in the manuscript, is only a hypothesis – as reflected by the wording used. However, we changed the beginning of the sentence in question from “It could be argued” to “We would like to propose a hypothesis” to emphasize the speculative aspect of the proposed explanation behind the increase of 44-kHz ultrasonic emissions.

Also, we do agree that other factors could contribute to the increased emission of 44kHz calls. These factors could include: heightened fear, stress/anxiety, annoyance/anger, disgust/boredom, grief/sadness, despair/helplessness, and weariness/fatigue. We are listing these potential factors in the discussion. Also, we added: “It is not possible, at this stage, to determine which factors played a decisive role. Please note that the potential contribution of these factors is not mutually exclusive”. However, we propose a list of arguments supporting the idea that 44-kHz vocalizations communicate an increased negative emotional state. Among these arguments were the conclusions drawn from additional analyses – mostly inspired by the fatigue hypothesis proposed by the Reviewer #1. In particular, we investigated changes in the sound mean power and duration of 22-kHz and 44-kHz calls. Specifically, we showed that the mean power of 44-kHz vocalizations did not change, and was higher than that of 22-kHz vocalizations (Fig. 1S2EF).

Finally, the Reviewer #1 listed “the gradual frequency increase reported for 22 kHz calls and the drop in 44 kHz calls the next day” as arguments for the fatigue hypothesis. We do not agree that the “increase” should be interpreted as a sign of fatigue [Producing and maintaining higher frequency calls require greater effort from the vocalizer, on which we elaborated in the manuscript], also we are not sure what “drop in 44 kHz calls” the Reviewer is referring to [We assume it refers to less 44-kHz calls during testing vs. training; we suppose that the levels of arousal are lower in the test due to shorter session time and lack of shocks, which additionally contributes to fear extinction].

Secondly, regarding the analysis where calls were sorted using DBSCAN based on peak frequency and duration, it is not surprising that the calls cluster based on frequency and duration, i.e. the features that are used to define the 44 kHz calls in the first place. Thus presenting this clustering as evidence of them being truly distinct call types comes across as a circular argument.

Answer: The DBSCAN sorting results were to convey that when changing the clustering ε value, the degree of cluster separation, the 44-kHz vocalizations remained distinct from the 22-kHz and various short-call clusters that merged. In other words: 44-kHz calls remained separate from long 22-kHz, short 22-kHz and 50-kHz vocalizations, which all consolidated into one common cluster. As a result, in this mathematical analysis, 44-kHz vocalizations remained distinct without applying human biases. Additionally, frequency and duration are the two most common features used to define all types of calls (Barker et al., 2010; Silkstone & Brudzynski, 2019a, 2019b; Willey & Spear, 2013). In summary, we did not expect the analysis to isolate out the 44-kHz calls, and we were surprised by this result.

The sparsity of calls in the 30-40 kHz range (shown in the individual animal panels in Figure 2C) could in theory be explained by some bioacoustics properties of rat vocal cords, without necessarily the calls below and above that range being ethologically distinct.

Answer: We respectfully disagree with the argument regarding sparsity. It is important to note that, during prolonged fear conditioning experiments, we observed an increased incidence of 44-kHz calls (Fig. 1E-G) of up to >19% (Fig. 1S2AB) of the total ultrasonic vocalizations during specific inter-trial intervals. Also, it is possible that in observed experimental circumstances almost every fifth call could be attributed to the vocal apparatus as an artifact of its functioning (assuming we are interpreting the Reviewer’s argument correctly). While we do not believe this to be the case, we acknowledge the importance of considering such a hypothesis.

The behavioral response to call playback is intriguing, although again more in line with the hypothesis that these are not a distinct type of call but merely represent expected variation in vocalization parameters. Across the board animals respond rather similarly to hearing 22 kHz calls as they do to hearing 44 kHz calls, with occasional shifts of 44 kHz call responses to an intermediate between appetitive and aversive calls. This does raise interesting questions about how, ethologically, animals may interpret such variation and integrate this interpretation in their responses. However, the categorical approach employed here does not address these questions fully.

Answer: We are unsure of the Reviewer’s critique in this paragraph and will attempt to address it to the best of our understanding. Our finding of up to >19% of long seemingly aversive, 44-kHz calls, at a frequency in the define appetitive ultrasonic range (usually >32 kHz) is unexpected rather than “expected”. We would agree that aversive call variation is expected, but not in the appetitive frequency range.

Kindly note the findings by Saito et al. (2019), which claim that frequency band plays the main role in rat ultrasonic perception. It is possible that the higher peak frequency of 44kHz calls may be a strong factor in their perception by rats, which is, however, modified by the longer duration and the lack of modulation.

Also, from our experience, it is quite challenging to demonstrate different behavioral responses of naïve rats to pre-recorded 22-kHz (aversive) vs. 50-kHz (appetitive) vocalizations. Therefore, to demonstrate a difference in response to two distinct, potentially aversive, calls, i.e., 22-kHz vs. 44-kHz calls, to be even more difficult (as to our knowledge, a comparable experiment between short vs. long 22-kHz ultrasonic vocalizations, has not been done before).

Therefore, we do not take lightly the surprising and interesting finding that “animals respond rather similarly to hearing 22 kHz calls as they do to hearing 44 kHz calls, with occasional shifts of 44 kHz call responses to an intermediate between appetitive and aversive calls”. We would rather put this description in analogous words: “the rats responded similarly to hearing 44-kHz calls as they did to hearing aversive 22-kHz calls, especially regarding heartrate change, despite the 44-kHz calls occupying the frequency band of appetitive 50-kHz vocalizations” and “other responses to 44-kHz calls were intermediate, they fell between response levels to appetitive vs. aversive playback” – which we added to the Discussion.

Finally, we acknowledge that our findings do not present a finite and complete picture of the discussed aspects of behavioral responses to the presented ultrasonic stimuli (44-kHz vocalizations). Therefore, we have incorporated the Reviewer’s suggestion in the discussion. The added sentence reads: “Overall, these initial results raise further questions about how, ethologically, animals may interpret the variation in hearing 22-kHz vs. 44-kHz calls and integrate this interpretation in their responses.”

In sum, rather than describing the 44kHz long calls as a new call type, it may be more accurate to say that sometimes aversive calls can occur at frequencies above 22 kHz. Individual and situational variability in vocalization parameters seems to be expected, much more so than all members of a species strictly adhering to extremely non-variable behavioral outputs.

Answer: The surprising fact that there are presumably aversive calls that are beyond the commonly applied thresholds, i.e. >32 kHz, while sharing some characteristics with 22-kHz calls, is the main finding of the current publication. Whether they be finally assigned as a new type, subtype, i.e. a separate category or become a supergroup of aversive calls with 22-kHz vocalizations is of secondary importance to be discussed with other researchers of the field of study.

However, we would argue – by showing a comparison – that 22-kHz calls occur at durations of <300 ms and also >300 ms, and are, usually, referred to in literature as short and long 22-kHz vocalizations, respectively (not introduced with a description that “sometimes 22kHz calls can occur at durations below 300 ms”). These are then regarded and investigated as separate groups or classes usually referred to as two different “types” (e.g., Barker et al., 2010) or “subtypes” (e.g., Brudzynski, 2015). Analogously, 44-kHz vocalizations can also be regarded as a separate type or a subtype of 22-kHz calls. The problem with the latter is that 22-kHz vocalizations are traditionally and predominantly defined by 18–32 kHz frequency bandwidth (Araya et al., 2020; Barroso et al., 2019; Browning et al., 2011; Brudzynski et al., 1993; Hinchcliffe et al., 2022; Willey & Spear, 2013).

Reviewer #2 (Public Review):

Olszyński et al. claim that they identified a "new-type" ultrasonic vocalization around 44 kHz that occurs in response to prolonged fear conditioning (using foot-shocks of relatively high intensity, i.e. 1 mA) in rats. Typically, negative 22-kHz calls and positive 50-kHz calls are distinguished in rats, commonly by using a frequency threshold of 30 or 32 kHz. Olszyński et al. now observed so-called "44-kHz" calls in a substantial number of subjects exposed to 10 tone-shock pairings, yet call emission rate was low (according to Fig. 1G around 15%, according to the result text around 7.5%).

Answer: We are thankful for praising the strengths. Please note Figure 1G referred to 10-trial Wistar rats during delay fear conditioning session in which 44-kHz constituted 14.1% of ultrasonic vocalizations. The 7.5% number in results refers to the total of vocalizations analyzed across all animal groups used in fear conditioning experiments. These values have been updated in the current version of the manuscript. Also, please note – 44-kHz calls constituted up to 19.4% of calls, on average, in one of the ITI during fear conditioning session. However, the prevalence of aversive calls and of 44-kHz vocalizations in particular varied. It varied between individual rats; we added the text: “for n = 3 rats, 44-kHz vocalizations accounted for >95% of all calls during at least one ITI (e.g., 140 of total 142, 222 of 231, and 263 of 265 tallied 44-kHz calls), and in n = 9 rats, 44-kHz vocalizations constituted >50% of calls in more than one ITI.” See also further for the description of the array of experiments analyzed and the prevalence/percentage of 44-kHz calls encountered (Tab. 1, Fig. 1S3).

Weaknesses: I see a number of major weaknesses.

While the descriptive approach applied is useful, the findings have only focused importance and scope, given the low prevalence of "44 kHz" calls and limited attempts made to systematically manipulate factors that lead to their emission. In fact, the data presented appear to be derived from reanalyses of previously conducted studies in most cases and the main claims are only partially supported. While reading the manuscript, I got the impression that the data presented here are linked to two or three previously published studies (Olszyński et al., 2020, 2021, 2023). This is important to emphasize for two reasons:

(1) It is often difficult (if not impossible) to link the reported data to the different experiments conducted before (and the individual experimental conditions therein). While reanalyzing previously collected data can lead to important insight, it is important to describe in a clear and transparent manner what data were obtained in what experiment (and more specifically, in what exact experimental condition) to allow appropriate interpretation of the data. For example, it is said that in the "trace fear conditioning experiment" both single- and grouphoused rats were included, yet I was not able to tell what data were obtained in single- versus group-housed rats. This may sound like a side aspect, however, in my view this is not a side aspect given the fact that ultrasonic vocalizations are used for communication and communication is affected by the social housing conditions.

Answer: Preparing the current manuscript, we indeed used data collected during fear conditioning experiments which were described previously (Olszyński et al., 2021; Olszyński et al., 2022). Please note, however, that vocalization behavior during the fear conditioning itself was not the main subject of these publications. Our previous publications (Olszyński et al., 2020; Olszyński et al., 2021; Olszyński et al., 2022) present primarily ultrasonic-vocalization data from playback-part of experiments whereas here we analyze recordings obtained during fear conditioning experiments, thus we are analyzing new parts, i.e., not yet analyzed, of previously published studies. Also, we have performed additional experiments.

In the first version of the current manuscript, we did not attempt to demonstrate exactly which calls were recorded in which conditions as the focus was to demonstrate that 44-kHz calls were emitted in several different fear-conditioning experiments. Also, as the experiments were not performed simultaneously and are results from different experimental situations, we would prefer to not compare these results directly.

However, in the current version of the manuscript, we have introduced an additional reference system, based on Tab. 1, to more clearly indicate which rats have been employed in each analysis, e.g. the group of “Wistar rats that undergone 10 trials of fear conditioning” are described as “Tab. 1/Exp. 1-3/#2,4,8,13; n = 46”, i.e., these are the rats listed in rows 2, 4, 8, and 13 of Tab. 1.

We have also tried to unify the analyses, in terms of rats used, as much as possible. Finally, we have also introduced Fig. 1S3 to demonstrate the prevalence of 44-kHz calls in all experiments analyzed with the note that “the experiments were not performed in parallel”.

Regarding the Reviewer’s concerns about analyzing single- and pair-housed rats together. We have examined ultrasonic vocalizations emitted and freezing behavior in these two groups.

• Ultrasonic vocalizations; when comparing the number of vocalizations, their duration, peak frequency and latency to first occurrence, equally for all types of calls and divided into types (short 22-kHz, long 22-kHz, 44-kHz, 50-kHz), the only difference was observed in peak frequency in 50-kHz vocalizations (50.7 ± 2.8 kHz for paired vs. 61.8 ± 3.1 kHz for single rats; p = 0.0280, Mann-Whitney). Since 50-kHz calls are not the subject of the current publication, we did not investigate this difference further. Also, this difference was not observed during playback experiments (Olszyński et al., 2020, Tab. 1).

• Freezing. There were no differences between single- and pair-housed groups in freezing behavior, both in the time before first shock presentation and during fear conditioning training (Mann-Whitney).

In summary, since the two groups did not differ in relevant ultrasonic features and freezing, we decided to present the results obtained from these rats together. However, we agree with the Reviewer, and it is possible that social housing conditions may in fact affect the emission of 44-kHz vocalizations, which could be a subject of another project – involving, e.g., larger experimental groups observed under hypothesis-oriented and defined conditions.

(2) In at least two of the previously published manuscripts (Olszyński et al., 2021, 2023), emission of ultrasonic vocalizations was analyzed (Figure S1 in Olszyński et al., 2021, and Fig. 1 in Olszyński et al., 2023). This includes detailed spectrographic analyses covering the frequency range between 20 and 100 kHz, i.e. including the frequency range, where the "newtype" ultrasonic vocalization, now named "44 kHz" call, occurs, as reflected in the examples provided in Fig. 1 of Olszyński et al. (2023). In the materials and methods there, it was said: "USV were assigned to one of three categories: 50-kHz (mean peak frequency, MPF >32 kHz), short 22-kHz (MPF of 18-32 kHz, <0.3 s duration), long 22-kHz (MPF of 18-32 kHz, >0.3 s duration)". Does that mean that the "44 kHz" calls were previously included in the count for 50-kHz calls? Or were 44 kHz calls (intentionally?) left out? What does that mean for the interpretation of the previously published data? What does that mean for the current data set? In my view, there is a lack of transparency here.

Answer: As mentioned above, we indeed used data collected during fear conditioning experiments which were described previously (Olszyński et al., 2021; Olszyński et al., 2022). However, in these publications, ultrasonic vocalizations emitted during playback experiments were the main subject, while the ultrasonic calls emitted during fear conditioning (performed before the playback) were only analyzed in a preliminary way. As a result, the 44-kHz vocalizations analyzed in the current manuscript were not included in the previous analyses. In particular, in Olszyński et al. (2021), we counted the overall number of ultrasonic vocalizations before fear conditioning session to determine the basal ultrasonic emissions (Fig. S1). Then, our next article (Olszyński et al., 2022), we analyzed again the number of all ultrasonic vocalizations before fear conditioning (Fig. S1) and restricted the analysis of vocalizations during fear conditioning to 22-kHz calls (Tab. S1 and S2).

Also, we re-reviewed all the data used in our previous playback publications. Overall, 44-kHz calls were extremely rare in playback parts of the experiments. There were no 44-kHz calls in the playback data used in Olszyński et al. (2022) and Olszyński et al. (2020). In Olszyński et al. (2021), one rat produced eight 44-kHz calls. These 44-kHz calls constituted 0.03% of all vocalizations analyzed in the experiment (8/24888) and were included in the total number of calls analyzed (but not in the 50-kHz group), they were not described in further detail in that publication.

Moreover, whether the newly identified call type is indeed novel is questionable, as also mentioned by the authors in their discussion section. While they wrote in the introduction that "high-pitch (>32 kHz), long and monotonous ultrasonic vocalizations have not yet been described", they wrote in the discussion that "long (or not that long (Biały et al., 2019)), frequency-stable high-pitch vocalizations have been reported before (e.g. Sales, 1979; Shimoju et al., 2020), notably as caused by intense cholinergic stimulation (Brudzynski and Bihari, 1990) or higher shock-dose fear conditioning (Wöhr et al., 2005)" (and I wish to add that to my knowledge this list provided by the authors is incomplete). Therefore, I believe, the strong claims made in abstract ("we are the first to describe a new-type..."), introduction ("have not yet been described"), and results ("new calls") are not justified.

Answer: We would argue that 44-kHz vocalizations were indeed reported but not described. As far as we are concerned, an in-depth analysis of the properties and experimental circumstance of emission of long, high-frequency calls has not yet been performed. These researchers have observed, at least to a degree, similar calls to the ones we observed – as we mentioned in the discussion section. However, since these reported 44-kHz vocalizations were not fully described, we can only guess that they may be similar to ours. We speculate that perhaps like us, these researchers unknowingly recorded 44-kHz calls in their experiments and may also be able to describe them more extensively when re-analyzing their data as we have done here.

Possibly, it was difficult to find reports on vocalizations, similar to the 44-kHz calls that we observed, because of the canonical and accepted definitions of ultrasonic vocalization types. Biały et al. (2019) allocated them as a part of 22-kHz group, perhaps because their calls were often of a step variation having both low and high components. Shimoju et al. (2020) grouped them along with 50-kHz vocalizations because they appeared during stroking rats held vertically; this procedure was compared to tickling which usually elicits appetitive calls.

The Reviewer #2 states there are other publications to complete the list. We are aware of other articles authored by the same team as Shimoju et al. (2020) with different first authors. However, they are reporting similar findings to the cited article. Otherwise, we would gladly cite a more complete list of publications showing atypical, long, monotonous highfrequency vocalizations, similar to those observed in our experiments. Therefore, we would argue that ultrasonic vocalizations which were long, flat, high in frequency, and repeatedly occurring in a defined behavioral situation, have not been reported before. However, concerning the strong claims of novelty of our finding, we toned them down where we found this was warranted.

In general, the manuscript is not well written/ not well organized, the description of the methods is insufficient, and it is often difficult (if not impossible) to link the reported data to the experiments/ experimental conditions described in the materials and methods section.

Answer: The description of the methods has been adjusted and expanded. We added the requested link to each particular experiment as a formula “Tab. 1/Exp. nos./# nos.” which shows, each time, which experiments and experimental groups were analyzed. The list of the experiments and groups is found in the Tab. 1.

For example, I miss a clear presentation of basic information: 1) How many rats emitted "44 kHz" calls (in total, per experiment, and importantly, also per experimental condition, i.e. single- versus group-housed)?

Answer: We now clearly show which experiments were performed and how many animals were tested in each condition (Tab. 1), while the prevalence of 44-kHz calls amongst experimental conditions and animal groups is shown in Fig. 1S3. Also, we included information regarding the number of animals and treatment of each group of rats when reporting results. For example, we are stating that:

(1a) “53 of all 84 conditioned Wistar rats (Tab. 1/Exp. 1-3/#2,4,6-8,13, Figs 1B, 1E, 1S1BC) displayed” 44-kHz vocalizations – as a general assessment; these numbers are different from those in the first version of the Ms, when we are mentioning Wistar rats conditioned 6 or 10 times only.

(1b) “From this group of rats (n = 46), n = 41 (89.1%) emitted long 22-kHz calls, and 32 of them (69.6%) emitted 44-kHz calls” – this time referring only to 10-times conditioned Wistar rats as the biggest group that could be analyzed together (Figs 1F, 1G, 1S2A).

(1c) “for n = 3 rats, 44-kHz vocalizations accounted for >95% of all calls during at least one ITI (e.g., 140 of total 142, 222 of 231, and 263 of 265 tallied 44-kHz calls), and in n = 9 rats, 44kHz vocalizations constituted >50% of calls in more than one ITI.”

(2) Out of the ones emitting "44 kHz" calls, what was the prevalence of "44 kHz" calls (relative to 22- and 50-kHz calls, e.g. shown as percentage)?

Answer: The prevalence of 44-kHz vocalizations in all investigated experiments and groups is shown in Fig. 1S3CD. Also, more information regarding the percentage of 44-kHz calls was demonstrated in Fig. 1S2AB where we calculated the distribution of 44-kHz calls to 22-kHz calls in Wistar rats, in 10-trial fear conditioning, across the length of the session.

Additionally, the values are listed in the sentence regarding all Wistar rats which underwent 10 trials of fear conditioning: “these vocalizations were less frequent following the first trial (1.2 ± 0.4% of all calls), and increased in subsequent trials, particularly after the 5th (8.8 ± 2.8%), through the 9th (19.4 ± 5.5%, the highest value), and the 10th (15.5 ± 4.9%) trials, where 44-kHz calls gradually replaced 22-kHz vocalizations in some rats (Fig. 1F, 1S2B, Video 1; comp Fig. 1D vs. 1E).”

(3) How did this ratio differ between experiments and experimental conditions?

Answer: The prevalence of 44-kHz vocalizations in all experimental conditions is shown in Fig. 1S3. However, the direct comparison of results obtained in different conditions was not the goal of the present work. Also, we would argue, that such direct comparisons of results of different experiments would not be allowed. These experiments were done with different groups of animals, at different times, with different timetables of experimental manipulations.

However, we are comfortable to state that:

• There were more 44-kHz vocalizations during fear conditioning training than testing in all fear-conditioned Wistar rats;

• We observed more 44-kHz vocalizations in Wistar rats compared to SHR.

(4) Was there a link to freezing? Freezing was apparently analyzed before (Olszyński et al., 2021, 2023) and it would be important to see whether there is a correlation between "44-kHz" calls and freezing. Moreover, it would be important to know what behavior the rats are displaying while such "44-kHz" calls are emitted? (Note: Even not all 22-kHz calls are synced to freezing.) All this could help to substantiate the currently highly speculative claims made in the discussion section ("frequency increases with an increase in arousal" and "it could be argued that our prolonged fear conditioning increased the arousal of the rats with no change in the valence of the aversive stimuli"). Such more detailed analyses are also important to rule out the possibility that the "new-type" ultrasonic vocalization, the so-called "44 kHz" call, is simply associated with movement/ thorax compression.

Answer: We analyzed freezing behavior and its association with ultrasonic emissions. The emission of 44-kHz vocalizations was associated with freezing. The results are now described and presented in the manuscript, i.e., Tab. 2, its legend and the description in Results: “Freezing during the bins of 22-kHz calls only (p < 0.0001, for both groups) and during 44-kHz calls only bins (p = 0.0003) was higher than during the first 5 min baseline freezing levels of the session. Also, the freezing associated with emissions of 44-kHz calls only was higher than during bins with no ultrasonic vocalizations (p = 0.0353), and it was also 9.9 percentage points higher than during time bins with only long 22-kHz vocalizations, but the difference was not significant (p = 0.1907; all Wilcoxon)” and “To further investigate this potential difference, we measured freezing during the emission of randomly selected single 44-kHz and 22-kHz vocalizations. The minimal freezing behavior detection window was reduced to compensate for the higher resolution of the measurements (3, 5, 10, or 15 video frames were used). There was no difference in freezing during the emission of 44-kHz vs. 22-kHz vocalizations for ≥150ms-long calls (3 frames, p = 0.2054) and for ≥500-ms-long calls (5 frames, p = 0.2404; 10 frames, p = 0.4498; 15 frames, p = 0.7776; all Wilcoxon, Tab. 2B).”

Please note, that the general observation that "frequency increases with an increase in arousal" is not our claim but a general rule derived from large body of observations and proposed by the others (Briefer et al., 2012); we changed the wording of this statement to: “frequency usually increases with an increase in arousal (Briefer et al., 2012)”.

The figures currently included are purely descriptive in most cases - and many of them are just examples of individual rats (e.g. majority of Fig. 1, all of Fig. 2 to my understanding, with the exception of the time course, which in case of D is only a subset of rats ("only rats that emitted 44-kHz calls in at least seven ITI are plotted" - is there any rationale for this criterion?)), or, in fact, just representative spectrograms of calls (all of Fig. 3, with the exception of G, all of Fig. 4).

Answer: Please note, the former figures 2, 4, 6, and 8 have been now moved to supplementary figures 1S1, 2S1, 3S1, and 4S1 – to better organize the presentation of data. Figures 1, 3, 5, 7 are now 1, 2, 3, 4 respectively. In regards to presenting data from individual rats, this was to show the general patterns of ultrasonic-calls distributions observed. Showing the full data set as seen in Fig. 5A (now Fig. 3A) would obscure the readability of the graph without using mathematical clustering techniques such as DBSCAN.

Concerning the Reviewer’s #2 question regarding the criterion of “minimum seven ITI”, we selected the highest vocalizers by taking animals above the 75th percentile of the number of ITI with 44-kHz calls. However, in the current version of the manuscript, we decided to omit this part of the analysis and the accompanying part of the figure, since it did not provide any additional informative value (apart from employing questionable criterion).

Moreover, the differences between Fig. 5 and Fig. 6 are not clear to me. It seems Fig. 5B is included three times - what is the benefit of including the same figure three times?

Answer: We hope that designating Fig. 6 as supplementary to Fig. 5 (now Figs 3S1 and 3, respectively) will make interpreting them more streamlined. Fig. 6A (now Fig. 3S1A) is a more detailed look on information presented in Fig. 5B (now Fig. 3B) with spectrogram images of ultrasonic vocalizations from different areas of the plot. Also, Fig. 3B (former Fig. 5B) was removed from Fig. 3S1B (former Fig. 6B).

A systematic comparison of experimental conditions is limited to Fig. 7 and Fig. 8, the figures depicting the playback results (which led to the conclusion that "the responses to 44-kHz aversive calls presented from the speaker were either similar to 22-kHz vocalizations or in between responses to 22-kHz and 50-kHz playbacks", although it remains unclear to me why differences were seen b e f o r e the experimental manipulation, i.e. the different playback types in Fig. 8B).

Answer: There were indeed instances of such before-differences. Such differences were observed in our previous studies (Olszyński et al., 2020, Tabs S9-12; Olszyński et al., 2021, Tabs S7; Olszyński et al., 2022, Tabs S4, S9, S13, S17, S18) and were most likely due to analyzing multiple comparisons. However, we think that the carry-over effect, mentioned by the Reviewer #2 (see below), also played a role.

Related to that, I miss a clear presentation of relevant methodological aspects: 1) Why were some rats single-housed but not the others?

Answer: As stated before, data were collected from our previous experiments and the observation of 44-kHz vocalizations in fear conditioning was an emergent discovery as we decided to analyze ultrasonic recordings from fear conditioning procedures. Single-housed animals were part of our experiment comparing fear conditioning and social situation on the perception of ultrasonic playback as described in Olszyński et al. (2020). Aside from this experiment, all other rats were housed in pairs.

(2) Is the experimental design of the playback study not confounded? It is said that "one group (n = 13) heard 50-kHz appetitive vocalization playback while the other (n = 16) 22-kHz and 44kHz aversive calls". How can one compare "44 kHz" calls to 22- and 50-kHz calls when "44 kHz" calls are presented together with 22-kHz calls but not 50-kHz calls? What about carry-over effects? Hearing one type of call most likely affects the response to the other type of call. It appears likely that rats are a bit more anxious after hearing aversive 22-kHz calls, for example. Therefore, it would not be very surprising to see that the response to "44 kHz" calls is more similar to 22-kHz calls than 50-kHz calls.

Of note, in case of the other playback experiment it is just said that rats "received appetitive and aversive ultrasonic vocalization playback" but it remains unclear whether "44 kHz" calls are seen as appetitive or aversive. Later it says that "rats were presented with two 10-s-long playback sets of either 22-kHz or 44-kHz calls, followed by one 50-kHz modulated call 10-s set and another two playback sets of either 44-kHz or 22-kHz calls not previously heard" (and wonder what data set was included in the figures and how - pooled?). Again, I am worried about carry-over effects here. This does not seem to be an experimental design that allows to compare the response to the three main call types in an unbiased manner.

Answer: We apologize for being confounding and brief in our original description of the playback experiments. We wanted to avoid confusion associated with including several additional playback signals (please note some are not related to the current comparisons and include different 50-kHz ultrasonic subtypes and two different subtypes of short 22-kHz calls). We lengthened the description of these playback experiments in the current version.

In general, including more than one type of ultrasonic calls as playback has a risk of a carry-over effect as well as a habituation effect (the responses become weak). However, it greatly reduces the number of required animals. Finally, regarding the first experiment, we chose 3 playbacks to compare the rats’ reactions, as this was the most conservative choice we thought of.

We would like to highlight that we wanted to compare specifically the rats’ responses to 22-kHz vs. 44-kHz playback (as well as the effects of playback of different subtypes 50-kHz calls, which is not the subject of the current work). Therefore, we would argue, that the design of both experiments is actually unbiased regarding this key comparison (responses to 22-kHz vs. 44-kHz playback). In both experiments, 22-kHz and 44-kHz playbacks were included in the same sequences of stimuli and counterbalanced regarding their order (i.e., taking into account possible carry-over effects), and presented to the same rats. We regarded the group of rats that heard 50-kHz recordings as a baseline/control, since we know from previous playback studies what reactions to expect from rats exposed to these vocalizations (and 22-kHz playback), while in the second experiment, we reduced the 50-kHz playback to one set in order to minimize possible habituation to multiple playbacks.

We agree that the design of both experiments does not allow for full comparison of the effects of aversive playbacks to 50-kHz playback. Also, we agree that some carry-over effects could play a role. It was mentioned in the discussion: ”Please factor in potential carryover effects (resulting from hearing playbacks of the same valence in a row) in the differences between responses to 50-kHz vs. 22/44-kHz playbacks, especially, those observed before the signal (Fig. 4AB).” However, we would still argue that the observed lack of difference in heartrate response (Fig. 4A) and the differences regarding the number of 50-kHz calls emitted (e.g., Fig. 4S1F) are void of the constraints raised by the Reviewer #2.

We acknowledge that our studies do not give a complete picture of 44-kHz ultrasonic perception in relation to other ultrasonic bands and, given the possibility, we would like to perform more in-depth and focused experiments to study this aspect of 44-kHz calls in the future.

Finally, regarding the second experiment, the description of the rats now includes that they “received 22-kHz, 44-kHz, and 50-kHz ultrasonic vocalization playback”, while the description of the experiment itself includes: “Responses to the pairs of playback sets were averaged”.

Of note, what exactly is meant by "control rats" in the context of fear conditioning is also not clear to me. One can think of many different controls in a fear conditioning experiment.

More concrete information is needed.

Answer: This information was included in our previous publications. However, it was now provided in the method section of the current version of the manuscript. In general, control rats were subjected to the same procedures but did not receive electric shocks.

Literature included in the answers

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Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

Additional considerations:

The discussion of the "perfect fifth" and the proposition that this observation could be evidence of an evolutionary mechanism underlying it is rather far-fetched, especially for being presented in the Results section (with no supporting non-anecdotal evidence).

Answer: We agree with the Reviewer #1. The text was modified, the word “evolutionary” was deleted. Instead, we expended on the possible reason for prevalence of the perfect fifth in the current version of the manuscript; we added that the prevalence of the perfect fifth: “could be explained by the observation that all physical objects capable of producing tonal sounds generate harmonic vibrations, the most prominent being the octave, perfect fifth, and major third (Christensen, 1993, discussed in Bowling and Purves, 2015).”

It is not clear why Sprague-Dawleys were used as "receivers" in the playback experiment, when presumably the calls were recorded from Wistars and SHRs. While this does not critically impact the conclusions, within the species rats should be able to respond appropriately to calls made by rats of different genetic backgrounds, it adds an unnecessary source of variance.

Answer: Sprague-Dawley rats were used to test another normotensive strain of rats. Regarding the Reviewer’s main point – we beg to differ as we think that it is worth testing playback stimuli in different strains. Diverging the stimuli between different rat strains would add unnecessary variance and it seemed logical to use the same recordings to test effects in different strains. Please note that finally, in spite of this additional variance, the results of both playback experiments are, in general, similar – which may point to a universal effect of 44-kHz playback across rat strains.

It is pertinent to note that for the trace fear conditioning experiment, the rats had previously been exposed to a vocalization playback experiment. While such a pre-exposure is unlikely to be a very strong stressor, the possibility for it to influence the vocal behaviors of these rats in later experiments cannot be ruled out. It is also not clear what the control rats in this experiment experienced (home cage only?), nor what they were used for in analyses.

Answer: In the current version of the manuscript, we have described in greater detail all the experiments performed and analyzed. We would like to emphasize that both delay and trace fear conditioning experiments with radiotelemetric transmitters were not performed specifically to elicit any particular response during fear conditioning, rather that our observation of 44-kHz vocalizations emerged as a result of re-examining the audio recordings. As a result, this work summarizes our observations of 44-kHz calls from several different experiments. It is relevant to note, that 44-kHz vocalizations were observed “in rats which were exposed to vocalization playback experiment”, in rats before the playback experiments as well as in naïve rats, without transmitters implemented, trained in fear conditioning (Tab. 1/Exp. 1-3).

Our main message is that 44-kHz vocalizations were present in several experiments, with different conditions and subjects, while we are not attempting to compare in detail the results across the different experiments. In other words, we agree that pre-exposure to playback (and even more likely – transmitters implantation) could influence, but are not necessary, for 44-kHz ultrasonic emissions by the rats. To demonstrate this, we added a prolonged fear conditioning group with naïve Wistar rats (Exp. 3) to verify the emission of 44kHz calls in the absence of those experimental factors.

We modified the methods section to clarify the circumstances under which these discoveries were made, such as including the information regarding the control rats in trace fear conditioning. In particular we mention that: “Control rats were subjected to the exact same procedures but did not receive the electric shock at the end of trace periods”.

For Figure 1A-E, only example call distributions from individual rats are shown. It would perhaps be more informative to see the full data set displayed in this manner, with color/shape codes distinguishing individuals if desired.

Answer: Please note the Fig. 1S1 shows more examples of ultrasonic call distribution. Showing all the data would make it more difficult to read and interpret. The problem is partly amended in Fig. 3A.

It is not clear what is presented in Figure 2D vs. E, i.e. panel D is shown only for "selected rats" but the legend does not clarify how and why these rats were selected. It is also not clear why the legend reports p-values for both Friedman and Wilcoxon tests; the latter is appropriate for paired data which seems to be the case when the question is whether the call peak frequency alters across time, but the Friedman assumes non-paired input data.

Answer: The question refers to the current Fig. 1S2C panel (former Fig. 2E panel) and the former Fig. 2D panel. The latter was not included in the current version of the manuscript, since both reviewers opposed the presentation of “selected rats” only (see above). The full description of the Fig. 1S2C panel is now in the results section together with p-values for Friedman and Wilcoxon test. We used the latter to investigate the difference between the first and the last ITI (selected paired data), while the Friedman to investigate the presence of change within the chain of ten ITI – since it is a suitable test for a difference between two or more paired samples.

Reviewer #2 (Recommendations For The Authors):

The weaknesses listed in the public review need to be addressed.

Answer: We have done our best to address the weaknesses.

Notes: 1) Page and line numbers would have been useful.

Answer: We are including a separate manuscript version with page and line numbers.

.(2) English language needs to be improved.

Answer: The text has been checked by two native English speakers (one with a scientific background). Both only identified minor changes to improve the text which we applied.

(3) I am a bit unsure whether the comment about the Star Wars movie (1997) and the Game of Thrones series (2011) is supposed to be a joke.

Answer: These are indeed two genuine examples of the perfect fifth in human music that we hope are easily recognizable and familiar to readers. Parts of the same examples of the perfect fifth can also heard in the rat voice files provided.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

During the last decades, extensive studies (mostly neglected by the authors), using in vitro and in vivo models, have elucidated the five-step mechanism of intoxication of botulinum neurotoxins (BoNTs). The binding domain (H chain) of all serotypes of BoNTs binds polysialogangliosides and the luminal domain of a synaptic vesicle protein (which varies among serotypes). When bound to the synaptic membrane of neurons, BoNTs are rapidly internalized by synaptic vesicles (SVs) via endocytosis. Subsequently, the catalytic domain (L chain) translocates, a process triggered by the acidification of these organelles. Following translocation, the disulfide bridge connecting the H chain with the L chain is reduced by the thioredoxin reductase/thioredoxin system, and it is refolded by the chaperone Hsp90 on SV's surface. Once released into the cytosol, the L chains of different serotypes cleave distinct peptide bonds of specific SNARE proteins, thereby disrupting neurotransmission. In this study, Yeo et al. extensively revise the neuronal intoxication model, suggesting that BoNT/A follows a more complex intracellular route than previously thought. The authors propose that upon internalization, BoNT/A-containing endosomes are retro-axonally trafficked to the soma. At the level of the neuronal soma, this serotype then traffics to the endoplasmic reticulum (ER) via the Golgi apparatus. The ER SEC61 translocon complex facilitates the translocation of BoNT/A's LC from the ER lumen into the cytosol, where the thioredoxin reductase/thioredoxin system and HSP complexes release and refold the catalytic L chain. Subsequently, the L chain diffuses and cleaves SNAP25 first in the soma before reaching neurites and synapses. Strengths:

I appreciate the authors' efforts to confirm that the newly established methods somehow recapitulate aspects of the BoNTs mechanism of action, such as toxin binding and uptake occurring at the level of active synapses. Furthermore, even though I consider the SNAPR approach inadequate, the genome-wide RNAi screen has been well executed and thoroughly analyzed. It includes well-established positive and negative controls, making it a comprehensive resource not only for scientists working in the field of botulinum neurotoxins but also for cell biologists studying endocytosis more broadly. Weaknesses:

I have several concerns about the authors' main conclusions, primarily due to the lack of essential controls and validation for the newly developed methods used to assess toxin cleavage and trafficking into neurons. Furthermore, there is a significant discrepancy between the proposed intoxication model and existing studies conducted in more physiological settings. In my opinion, the authors have omitted over 20 years of work done in several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc.). I want to emphasize that I support changes in biological dogma only when these changes are supported by compelling experimental evidence, which I could not find in the present manuscript.

We thank the reviewer for his reading and comments and for pointing out the discrepancy between our proposed model and the existing model. However, we respectfully disagree with the phrase of “extensive studies have elucidated the five-steps mechanism of intoxication…”. This sentence and the following imply that the model is well-established and demonstrated. It also highlights how the reviewer is convinced about this previous model.

We contest this model for theoretical reasons and contest the strength of evidences that support it. We previously included references to previous work showing that the model is also being challenged by others. In light of the reviewer’s comments, we incluced more references in the introduction and we also explicit our main theoretical concern in the introduction:

“Arguably, the main problem of the model is its failure to propose a thermodynamically consistent explanation for the directional translocation of a polypeptidic chain across a biologial membrane. Other known instances of polypeptide membrane translocation such as the co-translational translocation into the ER indicate that it is an unfavorable process, which consumes significant energy (Alder and Theg 2003). ”

We also added the following text in the Discussion to address with the reviewer’s concerns: “Our study contradicts the long-established model of BoNT intoxication, which is described in several reviews specifically dedicated to the subject 1–4. In short, these reviews support the notion that BoNT are molecular machines able to mediate their own translocation across membranes; this notion has convinced some cell biologists interested in toxins and retrograde traffic, who describe BoNT mode of translocation in their reviews 5,6.

But is this notion well supported by data? A careful examination of the primary literature reveals that early studies indeed report that BonTs form ion channels at low pH values 7,8. These studies have been extended by the use of patch-clamp 9,10. These works and others lead to various suppositions on how the toxin forms a channel and translocate the LC 1,11 .

However, only a single study claims to reconstitute in vitro the translocation of BonT LC across membranes 12. In this paper, the authors report using a system of artificial membranes separating two aqueous compartments. They load the toxin in the cis compartment and measure the protease activity in the trans compartment after incubation. However, when the experimental conditions described are actually converted in terms of molarity, it appears that the cis compartment was loaded at 10e-8M BonT and that the reported translocated protease activity is equivalent to 10e-17 M (Figure 3D, 12). Thus, in this experiment, about 1 LC molecule in 100 millions has crossed the membrane. Such extremely low transfert rate does not tally with the extreme efficiency of intoxication in vivo, even while taking into account the difference between artificial and biological membranes.

In sum, a careful analysis of the primary literature indicate that while there is ample evidence that BoNTs have the ability to affect membranes and possibly create ion channels, there is actually no credible evidence that these channels mediate translocation of the LC. As mentioned earlier, it is not clear how such a self-translocation mechanism would function thermodynamically. By contrast, our model proposes a mechanism without a thermodynamic problem, is consistent with current knowledge about other protein toxins, such as PE, Shiga and Ricin, and can help explain previously puzzling features of BonT effects. It is worth noting that a similar self-translocation model was proposed for other protein toxins such as Pseudomonas exotoxin, which have similar molecular organisation as BonT (68). However, it has since been demonstrated that the PE toxins require cellular machinery, in particular in the ER, for intoxication (21,69,70).”

Reviewer #2 (Public Review):

Summary:

The study by Yeo and co-authors addresses a long-lasting issue about botulinum neurotoxin (BoNT) intoxication. The current view is that the toxin binds to its receptors at the axon terminus by its HCc domain and is internalized in recycled neuromediator vesicles just after the release of the neuromediators. Then, the HCn domain assists the translocation of the catalytic light chain (LC) of the toxin through the membrane of these endocytic vesicles into the cytosol of the axon terminus. There, the LC cleaves its SNARE substrate and blocks neurosecretion. However, other views involving kinetic aspects of intoxication suggest that the toxin follows the retrograde axonal transport up to the nerve cell body and then back to the nerve terminus before cleaving its substrate.

In the current study, the authors claim that the BoNT/A (isotype A of BoNT) not only progresses to the cell body but once there, follows the retrograde transport trafficking pathway in a retromer-dependent fashion, through the Golgi apparatus, until reaching the endoplasmic reticulum. Next, the LC dissociates from the HC (a process not studied here) and uses the translocon Sec61 machinery to retro-translocate into the cytosol. Only then, does the LC traffic back to the nerve terminus following the anterograde axonal transport. Once there, LC cleaves its SNARE substrate (SNAP25 in the case of BoTN/A) and blocks neurosecretion.

To reach their conclusion, Yeo and co-authors use a combination of engineered tools: a cell line able to differentiate into neurons (ReNcell VN), a reporter dual fluorescent protein derived from SNAP25, the substrate of BoNT/A (called SNAPR), the use of either native BoNT/A or a toxin to which three fragment 11 of the reporter fluorescent protein Neon Green (mNG) are fused to the N-terminus of the LC (BoNT/A-mNG11x3), and finally ReNcell VN transfected with mNG1-10 (a protein consisting of the first 10 beta strands of the mNG).

SNAPR is stably expressed all over in the ReNcell VN. SNAPR is yellow (red and green) when intact and becomes red only when cleaved by BoNT/A LC, the green tip being degraded by the cell. When the LC of BoNT/A-mNG11x3 reaches the cytosol in ReNcell VN transfected by mNG1-10, the complete mNG is reconstituted and emits a green fluorescence.

In the first experiment, the authors show that the catalytic activity of the LC appears first in the cell body of neurons where SNAPR is cleaved first. This phenomenon starts 24 hours after intoxication and progresses along the axon towards the nerve terminus during an additional 24 hours. In a second experiment, the authors intoxicate the ReNcell VN transfected by mNG1-10 using the BoNT/A-mNG11x3. The fluorescence appears also first in the soma of neurons, then diffuses in the neurites in 48 hours. The conclusion of these two experiments is that translocation occurs first in the cell body and that the LC diffuses in the cytosol of the axon in an anterograde fashion.

In the second part of the study, the authors perform a siRNA screen to identify regulators of BoNT/A intoxication. Their aim is to identify genes involved in intracellular trafficking of the toxin and translocation of the LC. Interestingly, they found positive and negative regulators of intoxication. Regulators could be regrouped according to the sequential events of intoxication.

Genes affecting binding to the cell-surface receptor (SV2) and internalization. Genes involved in intracellular trafficking. Genes involved in translocation such as reduction of the disulfide bond linking the LC to the HC and refolding in the cytosol. Genes involved in signaling such as tyrosine kinases and phosphatases. All these groups of genes may be consistent with the current view of BoNT intoxication within the nerve terminus. However, two sets of genes were particularly significant to reach the main conclusion of the work and definitely constitute an original finding important to the field. One set of genes consists of those of the retromer, and the other relates to the Sec61 translocon. This should indicate that once endocytosed, the BoNT traffics from the endosomes to the Golgi apparatus, and then to the ER. Ultimately, the LC should translocate from the ER lumen to the cytosol using the Sec61 translocon. The authors further control that the SV2 receptor for the BoNT/A traffics along the axon in a retromer-dependent fashion and that BoNT/A-mNG11x3 traverses the Golgi apparatus by fusing the mNG1-10 to a Golgi resident protein.

Strengths:

The findings in this work are convincing. The experiments are carefully done and are properly controlled. In the first part of the study, both the activity of the LC is monitored together with the physical presence of the toxin. In the second part of the work, the most relevant genes that came out of the siRNA screen are checked individually in the ReNcell VN / BoNT/A reporter system to confirm their role in BoNT/A trafficking and retro-translocation.

These findings are important to the fields of toxinology and medical treatment of neuromuscular diseases by BoNTs. They may explain some aspects of intoxication such as slow symptom onset, aggravation, and appearance of central effects.

Weaknesses:

The findings antagonize the current view of the intoxication pathway that is sustained by a vast amount of observations. The findings are certainly valid, but their generalization as the sole mechanism of BoNT intoxication should be tempered. These observations are restricted to one particular neuronal model and engineered protein tools. Other models such as isolated nerve/muscle preparations display nerve terminus paralysis within minutes rather than days. Also, the tetanus neurotoxin (TeNT), whose mechanism of action involving axonal transport to the posterior ganglia in the spinal cord is well described, takes between 5 and 15 days. It is thus possible that different intoxication mechanisms co-exist for BoNTs or even vary depending on the type of neurons.

Although the siRNA experiments are convincing, it would be nice to reach the same observations with drugs affecting the endocytic to Golgi to ER transport (such as Retro-2, golgicide or brefeldin A) and the Sec61 retrotranslocation (such as mycolactone). Then, it would be nice to check other neuronal systems for the same observations.

We thank the reviewer for the careful reading and comments of our manuscript. The reference to “a vast amount of observation” is a similar argument to the Reviewer 1 and used to suggest that our study may not be applicable as a general mechanism.

We respectfully disagree as described above and posit on the contrary that the model we propose is much more likely to be general than the model presented in current reviews for the several reasons cited (see added text in Introduction and Discussion). While we agree that more work is needed to confirm the proposed mechanisms of BonT translocation in other models, these experiments fall outside the perimeter of our study.

The fact that nerve/muscle preparations of BonT activity have relatively fast kinetics does not pose a contradiction to our model. Our model reveals primarily the requirement for trafficking to the ER membranes. This ER targeting requires trafficking through the Golgi complex, in turn explaining the requirement for trafficking to the soma of neurons in the experimental system we used. However, in neuronal cells in vivo, Golgi bodies can be found along the lenght of the axon, thus BonT may not always require trafficking to the soma of the affected cells. The time required for intoxication could thus vary greatly depending on the neuronal structural organisation.

TenT is proposed to transfer from excitatory neurons into inhibitory neurons before exerting its action. While the detailed mechanism of this fascinating mechanism remain to be explored, it clearly falls beyond the purview of this manuscript.

Regarding the use of drugs, we agree that it would be a nice addition; unfortunately we are unable to perform such experiments at this stage. Setting up a large scale siRNA screen for BonT mechanism of action is challenging as it requires a special facility with controlled access and police authorisation (in Singapore) given the high toxicity of this molecule. Unfortunately, the authorisations have now lapsed.

Reviewer #3 (Public Review): Summary:

The manuscript by Yao et al. investigates the intracellular trafficking of Botulinum neurotoxin A (BoNT/A), a potent toxin used in clinical and cosmetic applications. Contrary to the prevailing understanding of BoNT/A translocation into the cytosol, the study suggests a retrograde migration from the synapse to the soma-localized Golgi in neurons. Using a genome-wide siRNA screen in genetically engineered neurons, the researchers identified over three hundred genes involved in this process. The study employs organelle-specific split-mNG complementation, revealing that BoNT/A traffics through the Golgi in a retromer-dependent manner before moving to the endoplasmic reticulum (ER). The Sec61 complex is implicated in the retro-translocation of BoNT/A from the ER to the cytosol. Overall, the research challenges the conventional model of BoNT/A translocation, uncovering a complex route from synapse to cytosol for efficient intoxication. The findings are based on a comprehensive approach, including the introduction of a fluorescent reporter for BoNT/A catalytic activity and genetic manipulations in neuronal cell lines. The conclusions highlight the importance of retrograde trafficking and the involvement of specific genes and cellular processes in BoNT/A intoxication.

Strengths:

The major part of the experiments are convincing. They are well-controlled and the interpretation of their results is balanced and sensitive.

Weaknesses:

To my opinion, the main weakness of the paper is in the interpretation of the data equating loss of tGFP signal (when using the Red SNAPR assay) with proteolytic cleavage by the toxin. Indeed, the first step for loss of tGFP signal by degradation of the cleaved part is the actual cleavage. However, this needs to be degraded (by the proteasome, I presume), a process that could in principle be affected (in speed or extent) by the toxin.

We thank the reviewer for his comments and careful reading of our manuscript.

Regarding the read-out of the assay, we agree that the assay could be sensitive to alteration in the protein degradation pathway. We have added the following sentence in the Discussion to take it into account:

“As noted by one reviewer, the assay may be sensitive to perturbation in the general rate of protein degradation, a consideration to keep in mind when evaluating the results of large scale screens.”

While this may be valid for some hits in the general list, it is important to note that the main hits have been shown to affect toxin trafficking by an independent, orthogonal assay based on the split GFP reconstitution.

Recommendations to authors:

Reviewer #1 (Recommendations For The Authors):

(1) To assess the activity of BoNT/A in neurons, Yeo et al. have generated a neuronal stem line referred to as SNAPR. This cell line stably expresses a chimeric reporter protein that consists of SNAP25 flanked at its N-terminus with a tagRFPT and at its C-terminus with a tagGFP. After exposure to BoNT/A, SNAP25 is cleaved and, the C-terminal tGFP-containing moiety is rapidly degraded. I have many doubts about the validity of the described method. Indeed, BoNT/A activity is analysed in an indirect way by quantifying the degradation of the GFP moiety generated after toxin cleavage (Fig. 2). In this regard, the authors should consider that their approach is dependent, not only on the toxin's metalloprotease activity but also on the functionality of the proteasome in neurons. Therefore, considering the current dataset, it is impossible to rule out the possibility that the progression of GFP signal loss from the soma to the neurite terminals may be attributed to the different proteasome activity in these compartments. Is it conceivable that the GFP fragment generated upon toxin cleavage degrades more rapidly in the soma in comparison to axonal terminals? This alternative explanation could challenge the conclusion drawn in Fig. 2.

The reviewer’s alternative explanation disregards the experiments performed with the split-GFP complementation approach, which indicate translocation in the soma first. The split GFP reporter is not dependent on the proteasome activity. It also disregard the genetic data implicating many genes involved in membrane retrograde traffic, which are also not consistent with the hypothesis of the reviewer. These genes depletions not only affect SNAPR degradation but also BoNT/A-mNG11 trafficking: thus, their effect cannot be attributed to an completely hypothetical spatial heterogeneous distribution of the proteasome.

For this reason, I strongly suggest using a more physiological approach that does not depend on proteasomal degradation or on the expression of the sensor in neurons. The authors should consider performing a time course experiment following intoxication and staining BoNT/A-cleaved SNAP25 by using specific antibodies (see Antonucci F. et al., Journal of Neuroscience, 2008 or Rheaume C. et al., Toxins 2015).

For the above reason, we do not agree with the pressing importance of confirming by a third method using specific antibodies; especially considering that BonT is very difficult to detect in cells when incubated at physiological levels. By the way, the cited paper, by Antonucci F; et al. documents long distance retrograde traffic of BonT/A, which is in line with our data.

An alternative approach could involve the use of microfluidic devices that physically separate axons from cell bodies. Such a separation will allow us to test the authors' primary conclusion that SNAP25 is initially cleaved in the soma. The suggested experiments will also rule out potential overexpression artifacts that could influence the authors' conclusions when using the newly developed SNAPR approach. Without these additional experiments, the authors' main conclusion that SNAP25 is cleaved first in the neuronal soma rather than at the nerve terminal is inadequate.

As discussed above we disagree about the doubts raised by the reviewer: we present three types of evidences (SNAPR, split GFP and genetic hits) and they all point in the same direction. Thus, we respectfully doubt that a fourth approach would convince this reviewer. To note, we have attempted to use microfluidics devices as suggested by the reviewer, however, the Ren-VM neurons were not able to extend axons long enough across the device.

(2) To detect BoNT/A translocation into the cytosol, the authors have used a complementation assay by intoxicating ReNcell VM cell expressing a cytosolic HA-tagged split monomeric NeonGreen (Cyt-mNG1-10) with an engineered BoNT/A, where the catalytic domain (LC) was fused to mNG1-11. When drawing conclusions regarding the detection of cytosolic LC in the neuronal soma, the authors should highlight the limitations of this assay and explicitly describe them to the readers. Firstly, the authors need to investigate whether the addition of mNG1-11 to the LC affects the translocation process itself (by comparing with a WT, not tagged, LC).

Additionally, from the data shown in Fig. 2C, it is evident that the Cyt-mNG1-10 is predominantly expressed in the cytosol and less detected in neurites. This raises the question of whether there might be a bias for the cell soma in this assay. To address this important concern, I suggest quantifying MFI per cell (Fig. 2D) taking into consideration the amount of HA-tagged Cyt-mNG1-10. Furthermore, I strongly suggest targeting mNG1-10 to synapses and performing a similar time course experiment to observe when LC translocation occurs at nerve terminals. Alternative experiments, to prove that BoNT/A requires retrograde trafficking before it can translocate, may be done to repeat the experiments shown in Fig. 2D in the presence of inhibitors (or by KD some of the hits identified as microtubule stabilizers) that should interfere with BoNT/A trafficking to the neuronal somata. Without these additional experiments, the authors' main conclusion that the BoNT/A catalytic domain is first detected in the neuronal soma rather than at the nerve terminal is very preliminary.

Similarly as for the SNAPR assay, the reviewer is raising the level of doubt to very high levels. We respect his thoroughness and eagerness to question the new model. However, we note that a similar level of scrutiny does not apply to the prevalent competitive model. Indeed, the data supporting the self-translocation model is based on a single in vitro experiment published in one panel as we have explain din the discussion (see above).

(3) In the genome-wide RNAi screening, rather than solely assessing SV2 surface levels, it would have been beneficial to directly investigate BoNT/A binding to the neuronal membrane. For instance, this could have been achieved by using a GFP-tagged HC domain of BoNT/A. At present, the authors cannot exclude the possibility that among the 135 hits that did not affect SV2 levels, some might still inhibit BoNT/A binding to the neuronal surface. These concerns, already exemplified by B4CALT4 (which is known to be involved in the synthesis of GT1b), should be explicitly addressed in the main text.

We agree with the reviewer that perturbation of binding of BonT is possible. We added the following text:

“Network analysis reveals regulators of signaling, membrane trafficking and thioreductase redox state involved in BoNT/A intoxication

Among the positive regulators of the screen, 135 hits did not influence significantly surface SV2 levels and are thus likely to function in post-endocytic processes (Supplementary Table 2). However, we cannot formerly exclude that they could affect binding of BonT to the cell surface independently of SV2.”

(4) The authors should clearly state which reagents they have tried to use in order to explain the challenges they faced when directly testing the trafficking of BoNT/A. The accumulation of Dendra-SV2 bulbous structures at the neurite tips in VPS35-depleted cells could be interpreted as a sign of neuronal stress/death. Have the authors investigated other proteins that do not undergo retro-axonal trafficking in a retromer-dependent manner? This control is essential. In this regard, the use of a GFP-tagged HC domain of BoNT/A could prove to be quite helpful.

We tried multiple commercially available antibodies against BonT but we could not get a very good signal. The postdoc in charge of this project has now gone to greener pastures and we are not in the capacity to provide the details corresponding to these antibodies. We di dnot observe significant cell death after VPS-35 knockdown at the time of the experiment, however longe rterm treatment might result in toxicity indeed.

(5) Considering my concerns related to the SNAPR system and the complementation assay to study SNAP25 cleavage and BoNT/A trafficking, I suggest validating some of their major hits (ex. VPS34 and Sec61) by performing WB or IF analysis to examine the cleavage of endogenous SNAP25. Furthermore, the authors should test VPS35 depletion in the context of the experiments performed in Fig. 6G-H, by validating that this protein is essential for BoNT/A retrograde trafficking.

The reviewer concerns are well noted but as discussed above, the two systems we used are completely orthogonal. Thus, for the reviewer’s concerns to be valid, it would have to be two completely independent artefacts giving rise to the same result. The alternative explanation is that BonT/A translocates in the soma. The Ockham razor principle dictates that the simplest explanation is the likeliest.

(6) The introduction and the discussion section of this paper completely disregard more than 20 years of research conducted by several labs worldwide (Montecucco, Montal, Schiavo, Rummel, Binz, etc). The authors should make an effort to contextualize their data within the framework of these studies and address the significant discrepancies between their proposed intoxication model and existing research that clearly demonstrates BoNTs translocating upon the endocytic retrieval of SVs at presynaptic sites. Nevertheless, even assuming that the model proposed by the authors is accurate, numerous questions emerge. One such question is: How can the authors explain the exceptional toxicity of botulinum neurotoxin in an ex vivo neuromuscular junction preparation devoid of neuronal cell bodies (see Cesare Montecucco and Andreas Rummel's seminal studies)?

Please see above in the answer to public reviews.

(7) Scale bars should be added to all representative pictures.

This has been done. Thank you for the thorough reading of our manuscript.

Reviewer #2(Recommendations For The Authors):*

(1) The title overstates the results. It may be indicated "in differenciated ReNcell VM".

Title changed to: “Botulinum toxin intoxication requires retrograde transport and membrane translocation at the ER in RenVM neurons”

(2) In the provided manuscript there are two Figure 2 and no Figure 3. This made the reading and understanding extremely difficult and should be corrected. As a result, the Figure legends do not fit the numbering. There are also discrepancies between some Figure panels (A, B, C, etc), the text, and the Legends. All this needs to be carefully checked.

We apologize for the confusion as the manuscript as followed multiple rounds of revisions. We have carefully verified labels and legends.

(3) The BoNT/A-mNG11x3 may introduce some bias that could be discussed. Would these additional peptides block LC translocation from synaptic vesicles in the nerve termini? In addition, the mNG peptides that are unfolded before complementation may direct LC towards Sec61. These aspects should be discussed.

The comment would be valid if BoNT/A-mNG11x3 was the only approach used in the paper, however the SNAPR reporter is used with native BonT and shows data consistent with the split GFP approach.

(4) In the Figure about SV2 (Fig 3 or 4): The authors did not locate SV2. The cells seem not to have the same differentiated phenotype as in Figure 1 and Figure 2/3A.

We apologized above for the mislabeling. It is not clear what is the question here.

(5) The authors should check whether BoNT/A wt cleaves the endogeneous SNAP25 by western blot for instance in the original ReNcell VN before SNAPR engineering. This should be compared with wt SNAP25 cleavage by the BoNT/A-LC-mNG.

It is likely that BoNT/A-LC-mNG11 should have similar activity as it is only adding a small peptide at the end of the LC. At any rate, it is not clear why this is so important since both molecules translocate in the cytosol, with the same kinetics and in the same subcellular locale.

(6) Perhaps I did not understand. How can the authors exclude that what is observed is the kinetic overproduction of the reporter substrate SNAPR?

The authors could use SLO toxin (PNAS 98, 3185-3190, 2001) to permeabilize the cells all along their body and axon to introduce BoNT/A or LC (wt) and observe synchronized SNAPR cleavage throughout the cells.

The concept mentioned here is not very clear to us. The reviewer is proposing that the SNAPR is produced much more efficiently at the tips of the neurites and thus its cleavage takes longer to be detected and is apparent first in the soma?? With all due respect, this is a strange hypothesis, at odds with what we know of protein dynamics in the neurons (i.e. most proteins are largely made in the soma and transported or diffuse into the neurites).

Again, the two orthogonal approaches: split GFP and SNAPR reporter use different constructs and methods, yet converge on similar results. Perhaps, the incredulity of the reviewer might be more productively directed at the current data “demonstrating” the translocation of LC in the synaptic button?

(7) The authors could also use an essay on neurotransmitter release monitoring by electrophysiology measurements to check the functional consequences of the kinetic diffusion of LC activity along the axon. Can the authors exclude that some toxin molecules translocate from the endocytic vesicles and block neurotransmission within minutes or a few hours?

It is well established that inhibition of neurotransmission does not occur within minutes in vivo and in vitro, but rather within hours or even days. This kinetic delay is experienced by many patients and is one of the key argument against the current model of self-translocation at the synaptic vesicle level.

Minor remarks

Thank you for pointing out all these.

(1) Please check typos. There are many. Check space before the parenthesis, between numbers and h (hours), reference style etc.

Thank you. We have reviewed the text and try to eliminate all these instances.

(2) Line 90: The C of HC should be capitalized.

Fixed

(3) Line 107: add space between "neurons(Donato".

Fixed

(4) Line 109: space "72 h".

Fixed

(5) Line 115: a word is missing ? ...to show retro-axonal... ? Please clarify this sentence.

Fixed

(6) Figure 1E: does nm refer to nM (nanomolar)? Please correct. No mention of panel F.

Fixed

(7) Line 161: do you mean ~16 µm/h? Please correct.

Fixed

(8) Line 168, words are missing.

Fixed, thank you

We verified that Cyt-mNG1-10 was expressed using the HA tag, the expression was homogeneously distributed in differentiated neurons and we observed no GFP signal (Figure2C).

(9) Line 171: Isn't mNG 11 the eleventh beta strand of the neon green fluorescent protein, not alpha helix? Otherwise, can the authors confirm it acquires the shape of an alpha helix? Same at line 326.

We have corrected the mistake; thanks for pointing it out.

(10) Figure 2 is doubled. The legend of Fig 2 refers to Figure 3. There is no legend for Figure 2. Then, some figures are shifted in their numbering.

Fixed

(11) The fluorescence in the cell body must appear before the fluorescence in the axon due to higher volume. Please discuss.

The fluorescence progresses in the neurites extensions in a centripetal fashion. The volume of the neurite near the cell body is not significantly different from the end of the neurite. Thus the fluorescence data is consistent with translocation in soma and not with an effect due to higher volume in the soma.

(12) Figure 2D, right: the term intoxication is improper for this experiment. Rather, it is the presence of the BoNT/A-mNG11 that is detected. I believe the authors should be particularly careful about the use of terms: intoxication means blockade of neurosecretion, SNAPR cleavage means activity etc.

While the reviewer is correct that it is the presence of BoNT/A-mNG11 that is detected, it remains that it is an active toxin, so the neurons are effectively intoxicated; as they are when we use the wild type toxin. We do not imply that we are measuring intoxication, but simply that the neurons are put into contact with a toxin.

(13) Line 196: Should we read TXNRD1 is required for BoNT/A LC translocation? TXNRD1 in the current model of translocation is located in the cytoplasm and is supposed to play a role in the cleavage of the disulfide bond linking LC to HC. In the model proposed by this study, LC is translocated through the Sec61 translocon. In this case, I would assume that the protein disulfide isomerase (PDI) in the endoplasmic reticulum would reduce the LC-HC disulfide bond. In that case, TXNRD1 would not be required anymore. Please discuss.

Why should we assume that a PDI is involved in the reduction of the LC-HC disulfide bond? In our previous studies on A-B toxins (PE and Ricin), different reduction systems seemed to be at play. There is no conceptual imperative to assume reduction in the ER because the Sec61 translocon is implicated. Reduction might occur on the cytosolic side by TXNRD1 or the effect of this reductase could be indirect.

(14) The legend of Figure 4 (in principle Figure 5?) is not matching with the panels and panel entries are missing (Figure 4F in particular).

Fixed

(15) Figure 6 panels E and H, please match colors with legend (grey and another color).

Not clear

(16) Please indicate BoNT/A construct concentrations in all Figure legends.

Done

(17) Line 416: isn't SV2 also involved in epilepsy?

Yes it is.

(18) Line 433: as above, shouldn't the disulfide bond linking LC to HC be cleaved by PDI in the ER in this model (as for other translocating bacterial toxins) rather than by thioredoxin reductases in the cytoplasm? Please discuss.

See above

(19) Identification of vATPase in the screen could be consistent with the endocytic vesicle acidification model of translocation.

Yes

(20) Did the authors add KCl in screening controls without toxins? This should be detailed in the Materials and Methods. Could there be a KCl effect on the cells? KCl exposure for 48 hours may be highly stressful for cells. The KCl exposure should last only several minutes for toxin entry.

We did not observe significant cell detah with the cell culture conditions used. Cell viability was controlled at multiple stages using nuclei number for instance

Reviewer #3 (Recommendations For The Authors):

Main comments: (1) In Figure 1B: could you devise a means to prevent proteosomal degradation of the tGFP cleaved part to assess whether this is formed?

We have also used a FRET assay after tintoxication and obtained similar results

(2) Line 152: Where it reads "was not surprising", maybe I missed something, but to me, this is indeed surprising. If the toxin is rapidly internalized and translocated (therefore, it is able to cleave SNAP25), the fact that tGFP requires 48 hours to be degraded seems surprising to me. Or does it mean that the toxin also slows down the degradation of the tGFP fragment? So, how can you differentiate between the effect being on cleavage of the fragment or in tGFP degradation?

The reviewer is correct, the “not” was a typo due to re-writting; the long delay between adding the toxin and observing cleavage was suprising indeed. Our interpretation is that it is trafficking that takes time, indeed, the split-GFP data kinetics indicates that the toxin takes about 48h to fill up the entire cytosol (Fig. 2D).

(3) Regarding the effect of Sec61G knockdown, is it possible that the observed effects are indirect and not due to the translocon being directly responsible for translocating the protein?

As discussed in the last part of the results,Sec61 knock-down results in block of intoxication, but does not prevent BonT from reaching the lumen of the ER (Figure 6G,H). Thus, Sec61 is “is instrumental to the translocation of BoNT/A LC into the neuronal cytosol at the soma.”

Minor comments:

(1) Fig. 3E: in the legend I think one of the NT3+ should be NT3-.

Yes, thanks for spotting it

(2) Would you consider adding Figure S4 as a main figure?

Thanks for the suggestion

(3) Please, check that all microscopy image panels have scale bars.

Done

(4) Figure 6B (bottom panes): why does it seem that there is a lot of mNeonGreen positive signal in regions that are not positive for HA? Shouldn't complementation keep HA in the complemented protein.

Our assumption i sthat there is an excess of receptor protein (HA tag) over reconstituted protein (GFP protein) given the relatively low concentration of toxin being internalized and translocated Refs: (1) Pirazzini M, Azarnia Tehran D, Leka O, Zanetti G, Rossetto O, Montecucco C. On the translocation of botulinum and tetanus neurotoxins across the membrane of acidic intracellular compartments. Biochim Biophys Acta. 2016 Mar;1858(3):467–474. PMID: 26307528

(2) Pirazzini M, Rossetto O, Eleopra R, Montecucco C. Botulinum Neurotoxins: Biology, Pharmacology, and Toxicology. Pharmacol Rev. 2017 Apr;69(2):200–235. PMCID: PMC5394922

(3) Dong M, Masuyer G, Stenmark P. Botulinum and Tetanus Neurotoxins. Annu Rev Biochem. Annual Reviews; 2019 Jun 20;88(1):811–837.

(4) Rossetto O, Pirazzini M, Fabris F, Montecucco C. Botulinum Neurotoxins: Mechanism of Action. Handb Exp Pharmacol. 2021;263:35–47. PMCID: 6671090

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(7) Hoch DH, Romero-Mira M, Ehrlich BE, Finkelstein A, DasGupta BR, Simpson LL. Channels formed by botulinum, tetanus, and diphtheria toxins in planar lipid bilayers: relevance to translocation of proteins across membranes. Proc Natl Acad Sci U S A. 1985 Mar;82(6):1692–1696. PMCID: PMC397338

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Author response:

We would like to thank the reviewers for their helpful comments. We note that both reviews are strongly supportive with comments including, “a biophysical tour de force” (rev #1), “the study is exemplary” (rev #2), and “represents a roadmap for future work” (rev #2). Below we respond to each reviewer comment.

Reviewer #1

This study provides a detailed and quantitative description of the allosteric mechanisms resulting in the paradoxical activation of BRAF kinase dimers by certain kinase inhibitors. The findings provide a much needed quantiative basis for this phenomenon and may lay the foundation for future drug development efforts aimed at the important cancer target BRAF. The study builds on very evidence obtained by multiple independent biophysical methods.

Summary:

The authors quantitatively describe the complex binding equilibria of BRAF and its inhibitors resulting in some cases in the paradoxical activation of BRAF dimer when bound to ATP competitive inhibitors. The authors use a biophysical tour de force involving FRET binding assays, NMR, kinase activity assays and DEER spectroscopy.

We are gratified by the reviewer’s supportive summary.

Strengths:

The strengths of the study are the beautifully conducted assays that allow for a thorough characterization of the allostery in this complex system. Additionally, the use of F-NMR and DEER spectroscopy provide important insights into the details of the process. The resulting model for binding of inhibitors and dimerization (Fig.4) is very helpful.

Weaknesses:

This is a complex system and its communication is inherently challenging. It might be of interest to the broader readership to understand the implications of the model for drug development and therapy.

We agree with the reviewer that this is a complicated system. With regard to inhibitor development, a key insight is that designing aC-in state inhibitors that avoid paradoxical activation may be non-trivial because these molecules not only induce dimers but also tend to bind the second dimer subunit more weakly than the first, due to allosteric asymmetry and/or inherently different affinities for each RAF isoform. We feel the full implications for future therapeutic development are an extensive topic that is beyond the scope of our work, which is focused on the properties of current inhibitors.

Recommendations for the author:

The experimental work, analysis and resulting model are excellent. I had some difficulty following the complex model in some instances and it may be useful to review the description of the model and see whether it can be made more palatable to the broader readership. I think it would be useful to discuss the model presented in reference 40 (Kholodenko) and to compare it to the presented model here.

We regret any confusion with regards to the nature of the model. Our analysis was built upon the model developed by Boris Kholodenko as reported in his 2015 Cell Reports paper. This formed the theoretical framework that combined with our experimental data allowed us to parameterize this model to obtain experimental values for the equilibrium constants and allosteric coupling factors.

Reviewer #2

This manuscript combines elegant biophysical solution measurements to address paradoxical kinase activation by Type II BRAF inhibitors. The novel findings challenge prevailing models, through experiments that are rigorous and carefully controlled. The study is exemplary in the breadth of strategies it uses to address protein kinase dynamics and inhibitor allostery.

Summary:

This manuscript uses FRET, 19F-NMR and DEER/EPR solution measurements to examine the allosteric effects of a panel of BRAF inhibitors (BRAFi). These include first-generation aC-out BRAFi, and more recent Type I and Type II aC-in inhibitors. Intermolecular FRET measurements quantify Kd for BRAF dimerization and inhibitor binding to the first and second subunits. Distinct patterns are found between aC-in BRAFi, where Type I BRAFi bind equally well to the first and second subunits within dimeric BRAF. In contrast, Type II BRAFi show stronger affinity for the first subunit and weaker affinity for the second subunit, an effect named "allosteric asymmetry". Allosteric asymmetry has the potential for Type II inhibitors to promote dimerization while favoring occupancy of only one subunit (BBD form), leading to enrichment of an active dimer.

Measurements of in vitro BRAF kinase activity correlate amazingly well with the calculated amounts of the half site-inhibited BBD forms with Type II inhibitors. This suggests that the allosteric asymmetry mechanism explains paradoxical activation by this class of inhibitors. DEER/EPR measurements further examine the positioning of helix aC. They show systematic outward movement of aC with Type II inhibitors, relative to the aC-in state with Type I inhibitors, and further show that helix aC adopts multiple states and is therefore dynamic in apo BRAF. This makes a strong case that negative cooperativity between sites in the BRAF dimer can account for paradoxical kinase activation by Type II inhibitors by creating a half site-occupied homodimer, BBD. In contrast, Type I inhibitors and aC-out inhibitors do not fit this model, and are therefore proposed to be explained by previous proposed models involving negative allostery between subunits in BRAF-CRAF heterodimers, RAS priming, and transactivation.

Strengths:

This study integrates orthogonal spectroscopic and kinetic strategies to characterize BRAF dynamics and determine how it impacts inhibitor allostery. The unique combination of approaches presented in this study represents a road map for future work in the important area of protein kinase dynamics. The work represents a worthy contribution not only to the field of BRAF regulation but protein kinases in general.

Weaknesses:

Some questions remain regarding the proposed model for Type II inhibitors and its comparison to Type I and aC-out inhibitors that would be useful to clarify. Specifically, it would be helpful to address whether the activation of BRAF by Type II inhibitors, while strongly correlated with BBD model predictions in vitro, also depends on CRAF via BRAF-CRAF in cells and therefore overlaps with the mechanisms of paradoxical activation by Type I and aC-out inhibitors.

We agree with the reviewer that this is a worthy question to be pursued. However, given the substantial experimental effort required for such an endeavor, and the highly supportive nature of the reviewer comments, including that “This is a strong manuscript that I feel is well above the bar for publication”, we believe this effort is more appropriate for a future study.

This is a strong manuscript that I feel is well above the bar for publication. Nevertheless, it is recommended that the authors consider addressing the following points in order to support their major conclusions.

(1) Fig 3D shows similar effects of Type II and Type I inhibitors in the biphasic increase of cellular pMEK/pERK. From this, the authors argue that Type II inhibitors are explained by negative allostery in the BRAF homodimer (based on Fig 2E), while Type I inhibitors are not. But it seems possible that despite the terrific correlation between BBD and BRAF kinase activities measured in vitro, CRAF is still important to explain pathway activation in cells. It also seems conceivable that the calculated %BBD between different Type II inhibitors may not correlate as well with their effects on pathway activation in cells. These possibilities should be addressed.

We agree with the reviewer that it is likely that CRAF contributes to paradoxical activation by type II inhibitors in cells. It is also likely that other cellular factors such as RAS-priming and membrane recruitment play a role in activation. However, we note that for the type II inhibitors there is good agreement between the biophysical predictions and the concentration regimes in which activation is observed in cells, suggesting that these predictions are capturing a key part of the activation process that occurs in cells.

(2) In Fig 2A, is it possible to report the activity of dimeric BRAF-WT in the absence of inhibitor? This would help confirm that the maximal activity measured after titrating inhibitor is indeed consistent with the predicted %BBD population, which would be expected to have half of the specific activity of BB.

In principle, it is possible to determine the catalytic activity of apo dimers (BB) by combining our model predictions for the concentration of BB dimers and our activity measurements. However, because the activity assays are performed at nanomolar kinase concentrations, whereas the baseline dimerization affinity of BRAF is in the micromolar range, the observed activity of apo BRAF arises from a small subpopulation of dimers (on the order of 4 percent under the conditions of our experiments) and is therefore difficult to define accurately. As a result, we deemed it more suitable to compare our results to published activity measurements derived from 14-3-3-activated dimers which should represent fully dimerized BRAF. This analysis, as reported in Figure 2E, suggests that the BBD activity is approximately half of that of BB.

(3) The 19F-NMR experiments make a good case for broadening of the helix aC signal in the BRAF dimer. From this, the study proposes that after inhibitor binds one subunit, the second unoccupied subunit retains dynamics. It would be useful to address this experimentally, if possible. For example, can the 19F-NMR signal be measured in the presence of inhibitor, to support the prediction that the unoccupied subunit is indeed dynamic and samples multiple conformations as in apo BRAF?

We agree with the reviewer that it would be interesting to determine the dynamic response of BRAF to inhibitor binding. However, this is a challenging undertaking due to the biochemical heterogeneity that occurs at sub saturating inhibitor concentrations. For example, at any given inhibitor concentration, BRAF exists as a mixture of monomers, apo dimers, dimers with one inhibitor molecule, and dimers with two inhibitor molecules bound. This makes it challenging to relate the 19F NMR signal to a single biochemical state. Addressing this would require a substantial experimental effort that we feel is beyond the scope of this study.

Author response:

Reviewer 1:

The paper “Quantifying gliding forces of filamentous cyanobacteria by self-buckling” combines experiments on freely gliding cyanobacteria, buckling experiments using two-dimensional V-shaped corners, and micropipette force measurements with theoretical models to study gliding forces in these organisms. The aim is to quantify these forces and use the results to perhaps discriminate between competing mechanisms by which these cells move. A large data set of possible collision events are analyzed, bucking events evaluated, and critical buckling lengths estimated. A line elasticity model is used to analyze the onset of buckling and estimate the effective (viscous type) friction/drag that controls the dynamics of the rotation that ensues post-buckling. This value of the friction/drag is compared to a second estimate obtained by consideration of the active forces and speeds in freely gliding filaments. The authors find that these two independent estimates of friction/drag correlate with each other and are comparable in magnitude. The experiments are conducted carefully, the device fabrication is novel, the data set is interesting, and the analysis is solid. The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion. While consistent with the data, this conclusion is inferred.

We thank the reviewer for the positive evaluation of our work.

Summary:

The paper addresses important questions on the mechanisms driving the gliding motility of filamentous cyanobacteria. The authors aim to understand these by estimating the elastic properties of the filaments, and by comparing the resistance to gliding under a) freely gliding conditions, and b) in post-buckled rotational states. Experiments are used to estimate the propulsion force density on freely gliding filaments (assuming over-damped conditions). Experiments are combined with a theoretical model based on Euler beam theory to extract friction (viscous) coefficients for filaments that buckle and begin to rotate about the pinned end. The main results are estimates for the bending stiffness of the bacteria, the propulsive tangential force density, the buckling threshold in terms of the length, and estimates of the resistive friction (viscous drag) providing the dissipation in the system and balancing the active force. It is found that experiments on the two bacterial species yield nearly identical values of f (albeit with rather large variations). The authors conclude that the experiments are consistent with the propulsion being generated by adhesion forces rather than slime extrusion.

We appreciate this comprehensive summary of our work.

Strengths of the paper:

The strengths of the paper lie in the novel experimental setup and measurements that allow for the estimation of the propulsive force density, critical buckling length, and effective viscous drag forces for movement of the filament along its contour – the axial (parallel) drag coefficient, and the normal (perpendicular) drag coefficient (I assume this is the case, since the post-buckling analysis assumes the bent filament rotates at a constant frequency). These direct measurements are important for serious analysis and discrimination between motility mechanisms.

We thank the reviewer for this positive assessment of our work.

Weaknesses:

There are aspects of the analysis and discussion that may be improved. I suggest that the authors take the following comments into consideration while revising their manuscript.

The conclusion that adhesion via focal adhesions is the cause for propulsion rather than slime protrusion is consistent with the experimental results that the frictional drag correlates with propulsion force. At the same time, it is hard to rule out other factors that may result in this (friction) viscous drag - (active) force relationship while still being consistent with slime production. More detailed analysis aiming to discriminate between adhesion vs slime protrusion may be outside the scope of the study, but the authors may still want to elaborate on their inference. It would help if there was a detailed discussion on the differences in terms of the active force term for the focal adhesion-based motility vs the slime motility.

We appreciate this critical assessment of our conclusions. Of course we are aware that many different mechanisms may lead to similar force/friction characteristics, and that a definitive conclusion on the mechanism would require the combination of various techniques, which is beyond the scope of this work. Therefore, we were very careful in formulating the discussion of our findings, refraining, in particular, from a singular conclusion on the mechanism but instead indicating “support” for one hypothesis over another, and emphasizing “that many other possibilities exist”.

The most common concurrent hypotheses for bacterial gliding suggest that either slime extrusion at the junctional pore complex [A1], rhythmic contraction of fibrillar arrays at the cell wall [A2], focal adhesion sites connected to intracellular motor-microtubule complexes [A3], or modified type-IV pilus apparati [A4] provide the propulsion forces. For the slime extrusion hypothesis, which is still abundant today, one would rather expect an anticorrelation of force and friction: more slime extrusion would generate more force, but also enhance lubrication. The other hypotheses are more conformal to the trend we observed in our experiments, because both pili and focal adhesion require direct contact with a substrate. How contraction of fibrilar arrays would micromechanically couple to the environment is not clear to us, but direct contact might still facilitate force transduction. Please note that these hypotheses were all postulated without any mechanical measurements, solely based on ultra-structural electron microscopy and/or genetic or proteomic experiments. We see our work as complementary to that, providing a mechanical basis for evaluating these hypotheses.

We agree with the referee that narrowing down this discussion to focal adhesion should have been avoided. We rewrote the concluding paragraph (page 8):

“…it indicates that friction and propulsion forces, despite being quite vari able, correlate strongly. Thus, generating more force comes, inevitably, at the expense of added friction. For lubricated contacts, the friction coefficient is proportional to the thickness of the lubricating layer (Snoeijer et al., 2013 ), and we conjecture active force and drag both increase due to a more intimate contact with the substrate. This supports mechanisms like focal adhesion (Mignot et al., 2007 ) or a modified type-IV pilus (Khayatan et al., 2015 ), which generate forces through contact with extracellular surfaces, as the underlying mechanism of the gliding apparatus of filamentous cyanobacteria: more contacts generate more force, but also closer contact with the substrate, thereby increasing friction to the same extent. Force generation by slime extrusion (Hoiczyk and Baumeister, 1998 ), in contrast, would lead to the opposite behavior: More slime generates more propulsion, but also reduces friction. Besides fundamental fluid-mechanical considerations (Snoeijer et al., 2013 ), this is rationalized by two experimental observations: i. gliding velocity correlates positively with slime layer thickness (Dhahri et al., 2013 ) and ii. motility in slime-secretion deficient mutants is restored upon exogenous addition of polysaccharide slime. Still we emphasize that many other possibilities exist. One could, for instance, postulate a regulation of the generated forces to the experienced friction, to maintain some preferred or saturated velocity.”

Can the authors comment on possible mechanisms (perhaps from the literature) that indicate how isotropic friction may be generated in settings where focal adhesions drive motility? A key aspect here would probably be estimating the extent of this adhesion patch and comparing it to a characteristic contact area. Can lubrication theory be used to estimate characteristic areas of contact (knowing the radius of the filament, and assuming a height above the substrate)? If the focal adhesions typically cover areas smaller than this lubrication area, it may suggest the possibility that bacteria essentially present a flat surface insofar as adhesion is concerned, leading to a transversely isotropic response in terms of the drag. Of course, we will still require the effective propulsive force to act along the tangent.

We thank the referee for suggesting to estimate the dimensions of the contact region. Both pili and focal adhesion sites would be of sizes below one micron [A3, A4], much smaller than the typical contact region in the lubricated contact, which is on the order of the filament radius (few microns). So indeed, isotropic friction may be expected in this situation [A5] and is assumed frequently in theoretical work [A6–A8]. Anisotropy may then indeed be induced by active forces [A9], but we are not aware of measurements of the anisotropy of friction in bacterial gliding.

For a more precise estimate using lubrication theory, rheology and extrusion rate of the secreted polysaccharides would have to be known, but we are not aware of detailed experimental characterizations.

We extended the paragraph in the buckling theory on page 5 regarding the assumption of isotropic friction:

“We use classical Kirchhoff theory for a uniform beam of length L and bending modulus B, subject to a force density ⃗b = −f ⃗t− η ⃗v, with an effective active force density f along the tangent ⃗t, and an effective friction proportional to the local velocity ⃗v, analog to existing literature (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Presumably, this friction is dominated by the lubrication drag from the contact with the substrate, filled by a thin layer of secreted polysaccharide slime which is much more viscous than the surrounding bulk fluid. Speculatively, the motility mechanism might also comprise adhering elements like pili (Khayatan et al., 2015 ) or foci (Mignot et al., 2007 ) that increase the overall friction (Pompe et al., 2015 ). Thus, the drag due to the surrounding bulk fluid can be neglected (Man and Kanso, 2019 ), and friction is assumed to be isotropic, a common assumption in motility models (Fei et al., 2020; Tchoufag et al., 2019; Wada et al., 2013 ). We assume…”

We also extended the discussion regarding the outcome of isotropic friction (page 7):

“…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

I am not sure why the authors mention that the power of the gliding apparatus is not rate-limiting. The only way to verify this would be to put these in highly viscous fluids where the drag of the external fluid comes into the picture as well (if focal adhesions are on the substrate-facing side, and the upper side is subject to ambient fluid drag). Also, the friction referred to here has the form of a viscous drag (no memory effect, and thus not viscoelastic or gel-like), and it is not clear if forces generated by adhesion involve other forms of drag such as chemical friction via temporary bonds forming and breaking. In quasi-static settings and under certain conditions such as the separation of chemical and elastic time scales, bond friction may yield overall force proportional to local sliding velocities.

We agree with the referee that the origin of the friction is not easily resolved. Lubrication yields an isotropic force density that is proportional to the velocity, and the same could be generated by bond friction. Importantly, both types of friction would be assumed to be predominantly isotropic. We explicitly referred to lubrication drag because it has been shown that mutations deficient of slime extrusion do not glide [A4].

Assuming, in contrast, that in free gliding, friction with the environment is not rate limiting, but rather the internal friction of the gliding apparatus, i.e., the available power, we would expect a rather different behavior during early-buckling evolution. During early buckling, the tangential motion is stalled, and the dynamics is dominated by the growing buckling amplitude of filament regions near the front end, which move mainly transversely. For geometric reasons, in this stage the (transverse) buckling amplitude grows much faster than the rear part of the filament advances longitudinally. Thus that motion should not be impeded much by the internal friction of the gliding apparatus, but by external friction between the buckling parts of the filament and the ambient. The rate at which the buckling amplitude initially grows should be limited by the accumulated compressive stress in the filament and the transverse friction with the substrate. If the latter were much smaller than the (logitudinal) internal friction of the gliding apparatus, we would expect a snapping-like transition into the buckled state, which we did not observe.

In our paper, we do not intend to evaluate the exact origin of the friction, quantifying the gliding force is the main objective. A linear force-velocity relation agrees with our observations. A detailed analysis of friction in cyanobacterial gliding would be an interesting direction for future work.

To make these considerations more clear, we rephrased the corresponding paragraph on page 7 & 8:

“…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

For readers from a non-fluids background, some additional discussion of the drag forces, and the forms of friction would help. For a freely gliding filament if f is the force density (per unit length), then steady gliding with a viscous frictional drag would suggest (as mentioned in the paper) f ∼ v! L η||. The critical buckling length is then dependent on f and on B the bending modulus. Here the effective drag is defined per length. I can see from this that if the active force is fixed, and the viscous component resulting from the frictional mechanism is fixed, the critical buckling length will not depend on the velocity (unless I am missing something in their argument), since the velocity is not a primitive variable, and is itself an emergent quantity.

We are not sure what “f ∼ v! L η||” means, possibly the spelling was corrupted in the forwarding of the comments.

We assumed an overdamped motion in which the friction force density ff (per unit length of the filament) is proportional to the velocity v0, i.e. ff ∼ η v0, with a friction coefficient η. Overdamped means that the friction force density is equal and opposite to the propulsion force density, so the propulsion force density is f ∼ ff ∼ η v0. The total friction and propulsion forces can be obtained by multiplication with the filament length

L, which is not required here. In this picture, v0 is an emergent quantity and f and η are assumed as given and constant. Thus, by observing v0, f can be inferred up to the friction coefficient η. Therefore, by using two descriptive variables, L and v0, with known B, the primitive variable η can be inferred by logistic regression, and f then follows from the overdamped equation of motion.

To clarify this, we revised the corresponding section on page 5 of the paper:

“The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over- damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E,F show the buckling behavior…”

Reviewer 2:

In the presented manuscript, the authors first use structured microfluidic devices with gliding filamentous cyanobacteria inside in combination with micropipette force measurements to measure the bending rigidity of the filaments.

Next, they use triangular structures to trap the bacteria with the front against an obstacle. Depending on the length and rigidity, the filaments buckle under the propulsive force of the cells. The authors use theoretical expressions for the buckling threshold to infer propulsive force, given the measured length and stiffnesses. They find nearly identical values for both species, f ∼ (1.0 ± 0.6) nN/µm, nearly independent of the velocity.

Finally, they measure the shape of the filament dynamically to infer friction coefficients via Kirchhoff theory. This last part seems a bit inconsistent with the previous inference of propulsive force. Before, they assumed the same propulsive force for all bacteria and showed only a very weak correlation between buckling and propulsive velocity. In this section, they report a strong correlation with velocity, and report propulsive forces that vary over two orders of magnitude. I might be misunderstanding something, but I think this discrepancy should have been discussed or explained.

We regret the misunderstanding of the reviewer regarding the velocity dependence, which indicates that the manuscript should be improved to convey these relations correctly.

First, in the Buckling Measurements section, we did not assume the same propulsion force for all bacteria. The logistic regression yields an ensemble median for Lc (and thus an ensemble median for f ), along with the width ∆Lc of the distribution (and thus also the width of the distribution of f ). Our result f ∼ (1.0 ± 0.6) nN/µm indicates the median and the width of the distribution of the propulsion force densities across the ensemble of several hundred filaments used in the buckling measurements. The large variability of the forces found in the second part is consistently reflected by this very wide distribution of active forces detected in the logistic regression in the first part.

We did small modifications to the buckling theory paragraph to clarify that in the first part, a distribution of forces rather than a constant value is inferred (page 6)

“Inserting the population median and quartiles of the distributions of bending modulus and critical length, we can now quantify the distribution of the active force density for the filaments in the ensemble from the buckling measurements. We obtain nearly identical values for both species, f ∼ (1.0±0.6) nN/µm, where the uncertainty represents a wide distribution of f across the ensemble rather than a measurement error.”

The same holds, of course, when inferring the distribution of the friction coefficients (page 5):

“The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over- damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E,F show the buckling behavior…”

The (naturally) wide distribution of force (and friction) leads to a distribution of Lc as well. However, due to the small exponent of 1/3 in the buckling threshold Lc ∼ f 1/3, the distribution of Lc is not as wide as the distributions of the individually inferred f or η. This is visualized in panel G of Figure 3, plotting Lc as a function of v0 (v0 is equivalent to f , up to a proportionality coefficient η). The natural length distribution, in contrast, is very wide. Therefore, the buckling propensity of a filament is most strongly characterized by its length, while force variability, which alters Lc of the individual, plays a secondary role.

In order to clarify this, we edited the last paragraph of the Buckling Measurements section on page 5 of the manuscript:

“…Within the characteristic range of observed velocities (1 − 3 µm/s), the median Lc depends only mildly on v0, as compared to its rather broad distribution, indicated by the bands in Figure 3 G. Thus a possible correlation between f and v0 would only mildly alter Lc. The natural length distribution (cf. Appendix 1—figure 1 ), however, is very broad, and we conclude that growth rather than velocity or force distributions most strongly impacts the buckling propensity of cyanobacterial colonies. Also, we hardly observed short and fast filaments of K. animale, which might be caused by physiological limitations (Burkholder, 1934 ).”

Second, in the Profile analysis section, we did not report a correlation between force and velocity. As can be seen in Figure 4—figure Supplement 1, neither the active force nor the friction coefficient, as determined from the analysis of individual filaments, show any significant correlation with the velocity. This is also written in the discussion (page 7):

We see no significant correlation between L or v0 and f or η, but the observed values of f and η cover a wide range (Figure 4 B, C and Figure 4—figure Supplement 1 ).

Note that this is indeed consistent with the logistic regression: Using v0 as a second regressor did not significantly reduce the width of the distribution of Lc as compared to the simple logistic regression, indicating that force and velocity are not strongly correlated.

In order to clarify this in the manuscript, we modified that part (page 7):

“…We see no significant correlation between L or v0 and f or η, but the observed values of f and η cover a wide range (Figure 4 B,C and Figure 4— figure Supplement 1 ). This is consistent with the logistic regression, where using v0 as a second regressor did not significantly reduce the width of the distribution of critical lengths or active forces. The two estimates of the friction coefficient, from logistic regression and individual profile fits, are measured in (predominantly) orthogonal directions: tangentially for the logistic regression where the free gliding velocity was used, and transversely for the evolution of the buckling profiles. Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic…”

From a theoretical perspective, not many new results are presented. The authors repeat the well-known calculation for filaments buckling under propulsive load and arrive at the literature result of buckling when the dimensionless number (f L3/B) is larger than 30.6 as previously derived by Sekimoto et al in 1995 [1] (see [2] for a clamped boundary condition and simulations). Other theoretical predictions for pushed semi-flexible filaments [1–4] are not discussed or compared with the experiments. Finally, the Authors use molecular dynamics type simulations similar to [2–4] to reproduce the buckling dynamics from the experiments. Unfortunately, no systematic comparison is performed.

[1]        Ken Sekimoto, Naoki Mori, Katsuhisa Tawada, and Yoko Y Toyoshima. Symmetry breaking instabilities of an in vitro biological system. Physical review letters, 75(1):172, 1995.

[2]       Raghunath Chelakkot, Arvind Gopinath, Lakshminarayanan Mahadevan, and Michael F Hagan. Flagellar dynamics of a connected chain of active, polar, brownian particles. Journal of The Royal Society Interface, 11(92):20130884, 2014.

[3]       Rolf E Isele-Holder, Jens Elgeti, and Gerhard Gompper. Self-propelled worm-like filaments: spontaneous spiral formation, structure, and dynamics. Soft matter, 11(36):7181–7190, 2015.

[4]       Rolf E Isele-Holder, Julia J¨ager, Guglielmo Saggiorato, Jens Elgeti, and Gerhard Gompper. Dynamics of self-propelled filaments pushing a load. Soft Matter, 12(41):8495–8505, 2016.

We thank the reviewer for pointing us to these publications, in particular the work by Sekimoto we were not aware of. We agree with the referee that the calculation is straight forward (basically known since Euler, up to modified boundary conditions). Our paper focuses on experimental work, the molecular dynamics simulations were included mainly as a consistency check and not intended to generate the beautiful post-buckling patterns observed in references [2-4]. However, such shapes do emerge in filamentous cyanobacteria, and with the data provided in our manuscript, simulations can be quantitatively matched to our experiments, which will be covered by future work.

We included the references in the revision of our manuscript, and a statement that we do not claim priority on these classical theoretical results.

Introduction, page 2:

“…Self-Buckling is an important instability for self-propelling rod-like micro-organisms to change the orientation of their motion, enabling aggregation or the escape from traps (Fily et al., 2020; Man and Kanso, 2019; Isele-Holder et al., 2015; Isele-Holder et al., 2016 ). The notion of self-buckling goes back to work of Leonhard Euler in 1780, who described elastic columns subject to gravity (Elishakoff, 2000 ). Here, the principle is adapted to the self-propelling, flexible filaments (Fily et al., 2020; Man and Kanso, 2019; Sekimoto et al., 1995 ) that glide onto an obstacle. Filaments buckle if they exceed a certain critical length Lc ∼ (B/f)1/3, where B is the bending modulus and f the propulsion force density…”

Buckling theory, page 5:

“…The buckling of gliding filaments differs in two aspects: the propulsion forces are oriented tangentially instead of vertically, and the front end is supported instead of clamped. Therefore, with L < Lc all initial orientations are indifferently stable, while for L > Lc, buckling induces curvature and a resultant torque on the head, leading to rotation (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Buckling under concentrated tangential end-loads has also been investigated in literature (de Canio et al., 2017; Wolgemuth et al., 2005 ), but leads to substantially different shapes of buckled filaments. We use classical Kirchhoff theory for a uniform beam of length L and bending modulus B, subject to a force density ⃗b = −f ⃗t − η ⃗v, with an effective active force density f along the tangent ⃗t, and an effective friction proportional to the local velocity ⃗v, analog to existing literature (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 )…”

Further on page 6:

“To derive the critical self-buckling length, Equation 5 can be linearized for two scenarios that lead to the same Lc: early-time small amplitude buckling and late-time stationary rotation at small and constant curvature (Fily et al., 2020; Chelakkot et al., 2014 ; Sekimoto et al., 1995 ). […] Thus, in physical units, the critical length is given by Lc = (30.5722 B/f)1/3, which is reproduced in particle based simulations (Appendix Figure 2 ) analogous to those in Isele-Holder et al. (2015, 2016).”

Discussion, page 7 & 8:

“…This, in turn, has dramatic consequences on the exploration behavior and the emerging patterns (Isele-Holder et al., 2015, 2016; Abbaspour et al., 2021; Duman et al., 2018; Prathyusha et al., 2018; Jung et al., 2020 ): (L/Lc)3 is, up to a numerical prefactor, identical to the flexure number (Isele-Holder et al., 2015, 2016; Duman et al., 2018; Winkler et al., 2017 ), the ratio of the Peclet number and the persistence length of active polymer melts. Thus, the ample variety of non-equilibrium phases in such materials (Isele-Holder et al., 2015, 2016; Prathyusha et al., 2018; Abbaspour et al., 2021 ) may well have contributed to the evolutionary success of filamentous cyanobacteria.”

Reviewer 3:

Summary:

This paper presents novel and innovative force measurements of the biophysics of gliding cyanobacteria filaments. These measurements allow for estimates of the resistive force between the cell and substrate and provide potential insight into the motility mechanism of these cells, which remains unknown.

We thank the reviewer for the positive evaluation of our work. We have revised the manuscript according to their comments and detail our replies and modifications next to the individual points below.

Strengths:

The authors used well-designed microfabricated devices to measure the bending modulus of these cells and to determine the critical length at which the cells buckle. I especially appreciated the way the authors constructed an array of pillars and used it to do 3-point bending measurements and the arrangement the authors used to direct cells into a V-shaped corner in order to examine at what length the cells buckled at. By examining the gliding speed of the cells before buckling events, the authors were able to determine how strongly the buckling length depends on the gliding speed, which could be an indicator of how the force exerted by the cells depends on cell length; however, the authors did not comment on this directly.

We thank the referee for the positive assessment of our work. Importantly, we do not see a significant correlation between buckling length and gliding speeds, and we also do not see a correlation with filament length, consistent with the assumption of a propulsion force density that is more or less homogeneously distributed along the filament. Note that each filament consists of many metabolically independent cells, which renders cyanobacterial gliding a collective effort of many cells, in contrast to gliding of, e.g., myxobacteria.

In response also to the other referees’ comments, we modified the manuscript to reflect more on the absence of a strong correlation between velocity and force/critical length. We modified the Buckling measurements section on page 5 of the paper:

“The substrate contact requires lubrication from polysaccharide slime to enable bacteria to glide (Khayatan et al., 2015 ). Thus we assume an over-damped motion with co-linear friction, for which the propulsion force f and the free gliding velocity v0 of a filament are related by f = η v0, with a friction coefficient η. In this scenario, f can be inferred both from the observed Lc ∼ (f/B)−1/3 and, up to the proportionality coefficient η, from the observed free gliding velocity. Thus, by combining the two relations, one may expect also a strong correlation between Lc and v0. In order to test this relation for consistency with our data, we include v0 as a second regressor, by setting x = (L−Lc(v0))/∆Lc in Equation 1, with Lc(v0) = (η v0/(30.5722 B))−1/3, to reflect our expectation from theory (see below). Now, η rather than f is the only unknown, and its ensemble distribution will be determined in the regression. Figure 3 E, F show the buckling behavior…”

Further, we edited the last paragraph of the Buckling measurements section on page 5 of the manuscript:

“Within the characteristic range of observed velocities (1 − 3 µm/s), the median Lc depends only mildly on v0, as compared to its rather broad distribution, indicated by the bands in Figure 3 G. Thus a possible correlation between f and v0 would only mildly alter Lc. The natural length distribution (cf. Appendix 1—figure 1 ), however, is very broad, and we conclude that growth rather than velocity or force distributions most strongly impacts the buckling propensity of cyanobacterial colonies. Also, we hardly observed short and fast filaments of K. animale, which might be caused by physiological limitations (Burkholder, 1934 ).”

We also rephrased the corresponding discussion paragraph on page 7:

“…Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces…”

Weaknesses:

There were two minor weaknesses in the paper.

First, the authors investigate the buckling of these gliding cells using an Euler beam model. A similar mathematical analysis was used to estimate the bending modulus and gliding force for Myxobacteria (C.W. Wolgemuth, Biophys. J. 89: 945-950 (2005)). A similar mathematical model was also examined in G. De Canio, E. Lauga, and R.E Goldstein, J. Roy. Soc. Interface, 14: 20170491 (2017). The authors should have cited these previous works and pointed out any differences between what they did and what was done before.

We thank the reviewer for pointing us to these references. The paper by Wolgemuth is theoretical work, describing A-motility in myxobacteria by a concentrated propulsion force at the rear end of the bacterium, possibly stemming from slime extrusion. This model was a little later refuted by [A3], who demonstrated that focal adhesion along the bacterial body and thus a distributed force powers A-motility, a mechanism that has by now been investigated in great detail (see [A10]). The paper by Canio et al. contains a thorough theoretical analysis of a filament that is clamped at one end and subject to a concentrated tangential load on the other. Since both models comprise a concentrated end-load rather than a distributed propulsion force density, they describe a substantially different motility mechanism, leading also to substantially different buckling profiles. Consequentially, these models cannot be applied to cyanobacterial gliding.

We included both citations in the revision and pointed out the differences to our work in the introduction (page 2):

“…A few species appear to employ a type-IV-pilus related mechanism (Khayatan et al., 2015; Wilde and Mullineaux, 2015 ), similar to the better- studied myxobacteria (Godwin et al., 1989; Mignot et al., 2007; Nan et al., 2014; Copenhagen et al., 2021; Godwin et al., 1989 ), which are short, rod-shaped single cells that exhibit two types of motility: S (social) motility based on pilus extension and retraction, and A (adventurous) motility based on focal adhesion (Chen and Nan, 2022 ) for which also slime extrusion at the trailing cell pole was earlier postulated as mechanism (Wolgemuth et al., 2005 ). Yet, most gliding filamentous cyanobacteria do not exhibit pili and their gliding mechanism appears to be distinct from myxobacteria (Khayatan et al., 2015 ).”

And in Buckling theory, page 5:

“….The buckling of gliding filaments differs in two aspects: the propulsion forces are oriented tangentially instead of vertically, and the front end is supported instead of clamped. Therefore, with L < Lc all initial orientations are indifferently stable, while for L > Lc, buckling induces curvature and a resultant torque on the head, leading to rotation (Fily et al., 2020; Chelakkot et al., 2014; Sekimoto et al., 1995 ). Buckling under concentrated tangential end-loads has also been investigated in literature (de Canio et al., 2017; Wolgemuth et al., 2005 ), but leads to substantially different shapes of buckled filaments.”

The second weakness is that the authors claim that their results favor a focal adhesion-based mechanism for cyanobacterial gliding motility. This is based on their result that friction and adhesion forces correlate strongly. They then conjecture that this is due to more intimate contact with the surface, with more contacts producing more force and pulling the filaments closer to the substrate, which produces more friction. They then claim that a slime-extrusion mechanism would necessarily involve more force and lower friction. Is it necessarily true that this latter statement is correct? (I admit that it could be, but is it a requirement?)

We thank the referee for raising this interesting question. Our claim regarding slime extrusion is based on three facts: i. mutations deficient of slime extrusion do not glide, but start gliding as soon as slime is provided externally [A4]. ii. A positive correlation between speed and slime layer thickness was observed in Nostoc [A11]. iii. The fluid mechanics of lubricated sliding contacts is very well understood and predicts a decreasing resistance with increasing layer thickness.

We included these considerations in the revision of our manuscript (page 8):

“…it indicates that friction and propulsion forces, despite being quite variable, correlate strongly. Thus, generating more force comes, inevitably, at the expense of added friction. For lubricated contacts, the friction coefficient is proportional to the thickness of the lubricating layer (Snoeijer et al., 2013 ), and we conjecture active force and drag both increase due to a more intimate contact with the substrate. This supports mechanisms like focal adhesion (Mignot et al., 2007 ) or a modified type-IV pilus (Khayatan et al., 2015 ), which generate forces through contact with extracellular surfaces, as the underlying mechanism of the gliding apparatus of filamentous cyanobacteria: more contacts generate more force, but also closer contact with the substrate, thereby increasing friction to the same extent. Force generation by slime extrusion (Hoiczyk and Baumeister, 1998 ), in contrast, would lead to the opposite behavior: More slime generates more propulsion, but also reduces friction. Besides fundamental fluid-mechanical considerations (Snoeijer et al., 2013 ), this is rationalized by two experimental observations: i. gliding velocity correlates positively with slime layer thickness (Dhahri et al., 2013 ) and ii. motility in slime-secretion deficient mutants is restored upon exogenous addition of polysaccharide slime. Still we emphasize that many other possibilities exist. One could, for instance, postulate a regulation of the generated forces to the experienced friction, to maintain some preferred or saturated velocity.”

Related to this, the authors use a model with isotropic friction. They claim that this is justified because they are able to fit the cell shapes well with this assumption. How would assuming a non-isotropic drag coefficient affect the shapes? It may be that it does equally well, in which case, the quality of the fits would not be informative about whether or not the drag was isotropic or not.

The referee raises another very interesting point. Given the typical variability and uncertainty in experimental measurements (cf. error Figure 4 A), a model with a sightly anisotropic friction could be fitted to the observed buckling profiles as well, without significant increase of the mismatch. Yet, strongly anisotropic friction would not be consistent with our observations.

Importantly, however, we did not conclude on isotropic friction based on the fit quality, but based on a comparison between free gliding and early buckling (Figure 4 D). In early buckling, the dominant motion is in transverse direction, while longitudinal motion is insignificant, due to geometric reasons. Thus, independent of the underlying model, mostly the transverse friction coefficiont is inferred. In contrast, free gliding is a purely longitudinal motion, and thus only the friction coefficient for longitudinal motion can be inferred. These two friction coefficients are compared in Figure 4 D. Still, the scatter of that data would allow to fit a certain anisotropy within the error margins. What we can exclude based on out observation is the case of a strongly anisotropic friction. If there is no ab-initio reason for anisotropy, nor a measurement that indicates it, we prefer to stick with the simplest

assumption. We carefully chose our wording in the Discussion as “mainly isotropic” rather

than “isotropic” or “fully isotropic”.

We added a small statement to the Discussion on page 7 & 8:

“... Thus we plot f/v over η in Figure 4 D, finding nearly identical values over about two decades. Since f and η are not correlated with v0, this is due to a correlation between f and η. This relation is remarkable in two aspects: On the one hand, it indicates that friction is mainly isotropic. This suggests that friction is governed by an isotropic process like bond friction or lubrication from the slime layer in the contact with the substrate, the latter being consistent with the observation that mutations deficient of slime secretion do not glide but exogenous addition of slime restores motility (Khayatan et al., 2015 ). In contrast, hydrodynamic drag from the surrounding bulk fluid (Man and Kanso, 2019 ), or the internal friction of the gliding apparatus would be expected to generate strongly anisotropic friction. If the latter was dominant, a snapping-like transition into the buckling state would be expected, rather than the continuously growing amplitude that is observed in experiments. On the other hand, it indicates that friction and propulsion forces ...”

Recommendations for the authors

The discussion regarding how the findings of this paper imply that cyanobacteria filaments are propelled by adhesion forces rather than slime extrusion should be improved, as this conclusion seems questionable. There appears to be an inconsistency with a buckling force said to be only weakly dependent on the gliding velocity, while its ratio with the velocity correlates with a friction coefficient. Finally, data and source code should be made publicly available.

In the revised version, we have modified the discussion of the force generating mechanism according to the reviewer suggestions. The perception of inconsistency in the velocity dependence of the buckling force was based on a misunderstanding, as we detailed in our reply to the referee. We revised the corresponding section to make it more clear. Data and source code have been uploaded to a public data repository.

Reviewer #2 (recommendations for the authors)

Despite eLife policy, the authors do not provide a Data Availability Statement. For the presented manuscript, data and source code should be provided “via trusted institutional or third-party repositories that adhere to policies that make data discoverable, accessible and usable.” https://elifesciences.org/inside-elife/51839f0a/for-authors-updates- to-elife-s-data-sharing-policies

Most of the issues in this reviewer’s public review should be easy to correct, so I would strongly support the authors to provide an amended manuscript.

We added the Data Availability Statement in the amended manuscript.

References

[A1] E. Hoiczyk and W. Baumeister. “The junctional pore complex, a prokaryotic secretion organelle, is the molecular motor underlying gliding motility in cyanobacteria”. In: Curr. Biol. 8.21 (1998), pp. 1161–1168. doi: 10.1016/s0960-9822(07)00487-3.

[A2] N. Read, S. Connell, and D. G. Adams. “Nanoscale Visualization of a Fibrillar Array in the Cell Wall of Filamentous Cyanobacteria and Its Implications for Gliding Motility”. In: J. Bacteriol. 189.20 (2007), pp. 7361–7366. doi: 10.1128/jb.00706- 07.

[A3] T. Mignot, J. W. Shaevitz, P. L. Hartzell, and D. R. Zusman. “Evidence That Focal Adhesion Complexes Power Bacterial Gliding Motility”. In: Science 315.5813 (2007), pp. 853–856. doi: 10.1126/science.1137223.

[A4] Behzad Khayatan, John C. Meeks, and Douglas D. Risser. “Evidence that a modified type IV pilus-like system powers gliding motility and polysaccharide secretion in filamentous cyanobacteria”. In: Mol. Microbiol. 98.6 (2015), pp. 1021–1036. doi: 10.1111/mmi.13205.

[A5] Tilo Pompe, Martin Kaufmann, Maria Kasimir, Stephanie Johne, Stefan Glorius, Lars Renner, Manfred Bobeth, Wolfgang Pompe, and Carsten Werner. “Friction- controlled traction force in cell adhesion”. In: Biophysical journal 101.8 (2011), pp. 1863–1870.

[A6] Hirofumi Wada, Daisuke Nakane, and Hsuan-Yi Chen. “Bidirectional bacterial gliding motility powered by the collective transport of cell surface proteins”. In: Physical Review Letters 111.24 (2013), p. 248102.

[A7] Jo¨el Tchoufag, Pushpita Ghosh, Connor B Pogue, Beiyan Nan, and Kranthi K Mandadapu. “Mechanisms for bacterial gliding motility on soft substrates”. In: Proceedings of the National Academy of Sciences 116.50 (2019), pp. 25087–25096.

[A8] Chenyi Fei, Sheng Mao, Jing Yan, Ricard Alert, Howard A Stone, Bonnie L Bassler, Ned S Wingreen, and Andrej Kosmrlj. “Nonuniform growth and surface friction determine bacterial biofilm morphology on soft substrates”. In: Proceedings of the National Academy of Sciences 117.14 (2020), pp. 7622–7632.

[A9] Arja Ray, Oscar Lee, Zaw Win, Rachel M Edwards, Patrick W Alford, Deok-Ho Kim, and Paolo P Provenzano. “Anisotropic forces from spatially constrained focal adhesions mediate contact guidance directed cell migration”. In: Nature communications 8.1 (2017), p. 14923.

[A10] Jing Chen and Beiyan Nan. “Flagellar motor transformed: biophysical perspectives of the Myxococcus xanthus gliding mechanism”. In: Frontiers in Microbiology 13 (2022), p. 891694.

[A11] Samia Dhahri, Michel Ramonda, and Christian Marliere. “In-situ determination of the mechanical properties of gliding or non-motile bacteria by atomic force microscopy under physiological conditions without immobilization”. In: PLoS One 8.4 (2013), e61663.

Author response:

eLife assessment

This study provides valuable evidence indicating that Syngap1 regulates the synaptic drive and membrane excitability of parvalbumin- and somatostatin-positive interneurons in the auditory cortex. Since haplo-insufficiency of Syngap1 has been linked to intellectual disabilities without a well-defined underlying cause, the central question of this study is timely. However, the support for the authors' conclusions is incomplete in general and some parts of the experimental evidence are inadequate. Specifically, the manuscript requires further work to properly evaluate the impact on synaptic currents, intrinsic excitability parameters, and morphological features.

We are happy that the editors found that our study provides valuable evidence and that the central question is timely. We thank the reviewers for their detailed comments and suggestions. Below, we provide a point-by-point answer (in blue) to the specific comments and indicate the changes to the manuscript and the additional experiments we plan to perform to answer these comments.

Public Reviews:

Reviewer #1 (Public Review):

The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltage-gated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic transmission was not affected by Syngap1 haploinsufficiency.

Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

We agree with the reviewer on the proposed open questions, which we will certainly discuss in the revised manuscript we are preparing. We do have experimental evidence suggesting that Syngap1 mRNA is expressed by PV+ and SST+ neurons in different cortical areas, during early postnatal development and in adulthood; therefore, we agree that it will be important, in future experiments, to tackle the question of when the observed phenotypes arise.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

Major issues:

(1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

We understand the reviewer’s perspective; indeed, we asked ourselves the very same question regarding why the sEPSC and mEPSC frequency fall within a similar range when we analysed neuron means (bar graphs). We have already recorded sEPSCs followed by mEPSCs from several PV neurons (control and cHet) and are in the process of analyzing the data. We will add this data to the revised version of the manuscript. We will also rephrase the manuscript to present multiple potential interpretations of the data.

We hope that we have correctly interpreted the reviewer's concern. However, if the question is why sEPSC amplitude but not frequency is affected in cHet vs ctrl then the reviewer’s comment is perhaps based on the assumption that the amplitude and frequency of miniature events should be lower for all events compared to those observed for spontaneous events. However, it's essential to note that changes in the mean amplitude of sEPSCs are primarily driven by alterations in large sEPSCs (>9-10pA, as shown in cumulative probability in Fig. 1b right), with smaller ones being relatively unaffected. Consequently, a reduction in sEPSC amplitude may not necessarily result in a significant decrease in frequency since their values likely remain above the detection threshold of 3 pA. This could explain the lack of a significant decrease in average inter-interval event of sEPSCs (as depicted in Fig. 1b left).

If the question is whether we should see the same parameters affected by the genetic manipulation in both sEPSC and mEPSC, then another critical consideration is the involvement of the releasable pool in mEPSCs versus sEPSCs. Current knowledge suggests that activity-dependent and -independent release may not necessarily engage the same pool of vesicles or target the same postsynaptic sites. This concept has been extensively explored (reviewed in Kavalali, 2015). Consequently, while we may have traditionally interpreted activity-dependent and -independent data assuming they utilize the same pool, this is no longer accurate. The current discussion in the field revolves around understanding the mechanisms underlying such phenomena. Therefore, comparisons between sEPSCs and mEPSCs may not yield conclusive data but rather speculative interpretations. For a rigorous analysis, particularly in this context involving thousands of events, it is essential to assess these data sets (mEPSCs vs sEPSCs) separately and provide cumulative probability curves. This approach allows for a more comprehensive understanding of the underlying distributions and helps to elucidate any potential differences between the two types of events. We will rephrase the text, and as mentioned above, add additional data, to better reflect these considerations.

(2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells. Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here. Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

We apologize, as it appears that the images we provided have caused confusion. The selected images represent a single focal plane of a confocal stack, which was visually centered on the PV cell somata. We chose just one confocal plane because we thought it showed more clearly the apposition of presynaptic and postsynaptic immunolabeling around the somata. In the revised version of the manuscript, we will provide higher magnification images, which will clearly show how we identified and selected the region of interest for the quantification of colocalized synaptic markers. In our confocal stacks, we can also identify PV immunolabeled dendrites and colocalized vGlut1/PSD95 or vGlut2/PSD95 puncta on them; but these do not appear in the selected images because, as explained, only one focal plane, centered on the PV cell somata, was shown.

We acknowledge the reviewer's point that in PV+ cells the majority of excitatory inputs are formed onto dendrites; however, we focused on the somatic excitatory inputs to PV cells, because despite their lower number, they produce much stronger depolarization in PV neurons than dendritic excitatory inputs (Hu et al., 2010; Norenberg et al., 2010). Further, quantification of perisomatic putative excitatory synapses is more reliable since by using PV immunostaining, we can visualize the soma and larger primary dendrites, but smaller, higher order dendrites are not be always detectable. Of note, PV positive somata receive more excitatory synapses than SST positive and pyramidal neuron somata as found by electron microscopy studies in the visual cortex (Hwang et al., 2021; Elabbady et al., 2024).

Regarding the comment on the density of vGlut1 and vGlut2 puncta, the reason that the numbers appear high and similar between the two markers is because we present normalized data (cHet normalized to their control values for each set of immunolabelling) to clearly represent the differences between genotypes. This information is present in the legends but we apologize for not clearly explaining it the methods section. We will provide a more detailed explanation of our methods in the revised manuscript.

Briefly, immunostained sections were imaged using a Leica SP8-STED confocal microscope, with a 63x (NA 1.4) at 1024 X 1024, z-step =0.3 μm, stack size of ~15 μm. Images were acquired from the auditory cortex from at least 3 coronal sections per animal. All the confocal parameters were maintained constant throughout the acquisition of an experiment. All images shown in the figures are from a single confocal plane. To quantify the number of vGlut1/PSD95 or vGlut2/PSD95 putative synapses, images were exported as TIFF files and analyzed using Fiji (Image J) software. We first manually outlined the profile of each PV cell soma (identified by PV immunolabeling). At least 4 innervated somata were selected in each confocal stack. We then used a series of custom-made macros in Fiji as previously described (Chehrazi et al, 2023). After subtracting background (rolling value = 10) and Gaussian blur (σ value = 2) filters, the stacks were binarized and vGlut1/PSD95 or vGlut2/PSD95 puncta were independently identified around the perimeter of a targeted soma in the focal plane with the highest soma circumference. Puncta were quantified after filtering particles for size (included between 0-2μm2) and circularity (included between 0-1). Data quantification was done by investigators blind to the genotype, and presented as normalized data over control values for each experiment.

(3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

We agree with the reviewer, please see answer to point below.

(‎4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

We agree with the reviewer and we will perform additional paired-pulse ratio experiments at different intervals. We will rephrase the discussion and our interpretation and potential hypothesis according to the data obtained from this new experiment.

(5) The sampling rate of CC recordings is insufficient ‎to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.

We acknowledge that a higher sampling rate could offer a more detailed analysis of the action potential waveform. However, in the context of action potential analysis, it is acceptable to use sampling rates ranging from 10 kHz to 20 kHz (Golomb et al., 2007; Stevens et al., 2021; Zhang et al., 2023), which are considered adequate in the context of the present study. Indeed, our study aims to evaluate "relative" differences in the electrophysiological phenotype when comparing groups following a specific genetic manipulation. A sampling rate of 10 kHz is commonly employed in similar studies, including those conducted by our collaborator and co-author S. Kourrich (e.g., Kourrich and Thomas 2009, Kourrich et al., 2013), as well as others (Russo et al., 2013; Ünal et al., 2020; Chamberland et al., 2023).

Despite being acquired at a lower sampling rate than potentially preferred by the reviewer, our data clearly demonstrate significant differences between the experimental groups, especially for parameters that are negligibly or not affected by the sampling rate used here (e.g., #spikes/input, RMP, Rin, Cm, Tm, AP amplitude, AP latency, AP rheobase).

Regarding the phase-plots, we agree that a higher sampling rate would have resulted in smoother curves and more accurate absolute values. However, the differences were sufficiently pronounced to discern the relative variations in action potential waveforms between the experimental groups.

A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

We indeed performed bridge balance and neutralized the capacitance before starting every recording. We will add the information in the methods.

(6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

We are uncertain if we have correctly interpreted the reviewer's comment. While we observed distinct impacts on the rheobase (Fig. 7d and 7i), there seems to be a common effect on the AP threshold (Fig. 7c and 7h), as interpreted and indicated in the final sentence of the results section for Figure 7 (page 12). If our response does not address the reviewer's comment adequately, we would greatly appreciate it if the reviewer could rephrase their feedback.

(7) Design issue:

The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.

Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

While we acknowledge the reviewer's point that Kv1 currents and density weren't specifically tested, an important insight provided by Fig. 5 is the prolonged action potential latency. This delay is significantly influenced by slowly inactivating subthreshold potassium currents, namely the D-type K+ current. It's worth noting that D-type current is primarily mediated by members of the Kv1 family. The literature supports a role for Kv1.1-containing channels in modulating responses to near-threshold stimuli in PV cells (Wang et al., 1994; Goldberg et al., 2008; Zurita et al., 2018). However, we recognize that besides the Kv1 family, other families may also contribute to the observed changes.

To address this concern, we will revise our interpretation. We will opt for the more accurate term "D-type K+ current" and only speculate about the involved channel family in the discussion. It is not our intention to open unnecessary controversy, but present the data we obtained. We believe this approach and rephrasing the discussion as proposed will prevent unnecessary controversy and instead foster fruitful discussions.

(8) Writing issues:

Abstract:

The auditory system is not mentioned in the abstract.

One statement in the abstract is unclear‎. What is meant by "targeting Kv1 family of voltage-gated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.

We agree with the reviewer and we will rephrase the abstract accordingly.

Introduction:

There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST n‎eurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

We agree with the reviewer and we will rephrase more specifically.

(9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

In the initial experiments, we handled all PV cell data together because we wanted to be rigorous and not make assumptions/biases on the different PV cells, which in later experiments we were to distinguish based on the intrinsic properties alone. We will make this point clear in the revised manuscript.

(10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

We agree with the reviewer and apologize for the lack of clarity. Our unpublished work is in revision right now. We will provide more information and update references in the revised version of this manuscript.

(11) The introduction of Scholl analysis ‎experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

We apologize for the error, we will change SOM with SST (SOM and SST are two commonly used acronyms for Somatostatin expressing interneurons).

Reviewer #3 (Public Review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiency-related intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

(1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

The reviewer raises an interesting and pertinent open question which we will address in the discussion of the revised paper.

(2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences.

We respectfully disagree, we do not think the text and figures are inconsistent. In the cited example, large apparent difference in mean values does not show significance due to the large variability in the data; further, we did not exclude any data points, because we wanted to be rigorous. In particular, for Fig.1g, statistical analysis shows a significant increase in the inter-mEPSC interval (*p=0.027, LMM) when all events are considered (cumulative probability plots), while there is no significant difference in the inter-mEPSCs interval for inter-cell mean comparison (inset, p=0.354, LMM). Inter-cell mean comparison does not show difference with Mann-Whitney test either (p=0.101, the data are not normally distributed, hence the choice of the Mann-Whitney test). For Fig. 3f (eNMDA), the higher mean value for the cHet versus the control is driven by two data points which are particularly high, while the other data points overlap with the control values. The Mann-Whitney test show also no statistical difference (p=0.174).

In the manuscript, discussion of the data is based on the results of the LMM analysis, which takes in account both the number of cells and the numbers of mice from which these cells are recorded. We chose this statistical approach because it does not rely on the assumption that cells recorded from same mouse are independent variables. In the supplemental tables, we provided the results of the statistical analysis done with both LMM and the most commonly used Mann Whitney (for not normally distributed) or t-test (for normally distributed), for each data set.

Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

We apologize for our lack of clarity. In legend 9, we reported the statistical comparisons between 1) cHET mice in absence of a-DTX and control mice and 2) cHET mice in presence of a-DTX and control mice. We will rephrase result description and the legend of the figure to avoid confusion.

(3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

We agree with the reviewer that there appears to be a qualitative difference in eNMDA decay between conditions, although quantified eNMDA decay itself is similar between groups. We have used a cutoff of 15 % for the series resistance (Rs), which is significantly more stringent as compared to the cutoff typically used in electrophysiology, which are for the vast majority between 20 and 30%. To answer this concern, we re-examined the Rs, we compared Rs between groups and found no difference for Rs in eAMPA (13.2±0.5 in WT n=16 cells, 7 mice vs 13.7±0.3 in cHet n=14 cells, 7 mice, p=0.432 LMM) and eNMDA (12.7±0.7 in WT n=6 cells, 3 mice vs 13.8±0.7 in cHet n=6 cells, 5 mice, p=0.231, LMM). Thus, the apparent qualitative difference in eNMDA decay stems from inter-cell variability rather than inter-group differences. Notably, this discrepancy between the trace (Fig. 3a) and the data (Fig. 3f, right) is largely due to inter-cell variability, particularly in eNMDA, where a higher but non-significant decay rate is driven by a couple of very high values (Fig. 3f, right). In the revised manuscript, we will show traces that better represent our findings.

(4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

We acknowledge the variability in AP threshold data, with some APs appearing too depolarized to generate healthy spikes. However, we meticulously examined each AP that spiked at these depolarized thresholds and found that other intrinsic properties (such as Rin, Vrest, AP overshoot, etc.) all indicate that these cells are healthy. Therefore, to maintain objectivity and provide unbiased data to the community, we opted to include them in our analysis. It's worth noting that similar variability has been observed in other studies (Bengtsson Gonzales et al., 2020; Bertero et al., 2020).

Further, we conducted a significance test on AP threshold excluding these potentially unhealthy cells and found that the significant differences persist. After removing two outliers from the cHet group with values of -16.5 and 20.6 mV, we obtain: -42.6±1.01 mV in control, n=33, 15 mice vs -36.2±1.1 mV in cHet, n=38 cells, 17 mice, ***p<0.001, LMM. Thus, whether these cells are included or excluded, our interpretations and conclusions remain unchanged.

We would like to clarify that these data have not been corrected with the junction potential. We will add this info in the revised version.

(5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

We apologize for our lack of clarity. Although the analysis was done at high resolution, the figures were focused on showing multiple PV somata receiving excitatory inputs. We will add higher magnification figures and more detailed information in the methods of the revised version. Please also see our response to reviewer #2.

(6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

While we acknowledge the theoretical expectation that changes in intrinsic parameters should correlate with alterations in neuronal firing, the absence of differences in the parameters analyzed in this study should not overshadow the clear and significant decrease in firing rate observed in cHet SST+ cells. This decrease serves as a compelling indication of reduced intrinsic neuronal excitability. It's certainly possible that other intrinsic factors, not assessed in this study, may have contributed to this effect. However, exploring these mechanisms is beyond the scope of our current investigation. We will rephrase the discussion and add this limitation of our study in the revised version.

(7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

This study utilized a sampling rate of 10 kHz, which is a standard rate for action potential analysis in the present context. We will describe more extensively the technical details in the method section of the revised manuscript we are preparing. While we acknowledge that a higher sampling rate could have enhanced the clarity of the phase plot, our recording conditions, as detailed in our response to Rev#2/comment#5, were suitable for the objectives of this study.

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insightful observation

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Reply to the reviewers

General response of the authors to the editor and the reviewers:

We thank the reviewers for their feedback, input and questions as these have helped us to (hopefully) improve the manuscript. We have rewritten several sections of the manuscript, moved methodological descriptions from the Results to the Methods section, and added imaging data for two cytoskeletal proteins, Shot and Cofilin/Twinstar, which confirm the predicted differential DV expression. Because the changes to the text were extensive, we did not mark them by track changes (the manuscript would have been illegible), but would be happy to provide an additional version that includes the tracked changes.

We provide below the point-by-point response to each question and comment made by the reviewers. Our text is in blue.

__Reviewer #1 __

__Evidence, reproducibility and clarity __

__Summary __

This manuscript investigated changes in the proteome and phosphoproteome during dorsovental axis specification in the Drosophila embryo. To model the three regions in the embryo that are relevant for DV axis development, the authors used specific mutations to enrich for a single type of cells (ventral, lateral, or dorsal). The detected proteins and phosphopeptides were clustered according to the region of expression. There were differences between the protein and corresponding phosphopeptide abundance, suggesting that phosphorylation is a regulatory modification in DV axis establishment. Two different mutations that both result in a ventralized phenotype were found to change marker protein expression in different ways. Using inhibition of microtubule polymerization, this study also investigated the role of microtubules in epithelial folding.

__Major comments __

1. Generally, there is a lack of significance testing throughout the manuscript. Simply reporting fold changes can be misleading, if these changes are not significant. Examples:

2. Rigor of the proteomics evidence showing changes for the expected markers is insufficient because no statistical evaluation is provided. Specifically, in Fig. 1D and Suppl Fig 2: are the fold changes statistically significant?

3. Data in Fig. 4F, 5F need to be assessed for significance. There are other instances in the manuscript where significance should be tested.

We did ANOVA testing for all proteome and phosphoproteome data, and the outcome of these analyses is reported in Supplementary Tables 2 and 3. We have added references to significance throughout, wherever possible and relevant and have included a table that summarizes all p values for all comparisons in all of the figures (Supplementary Table 2). However, note that we do our clustering independent of statistical significance, i.e., we include all values, as we explain in the manuscript.

It is difficult to see the value of the obtained dataset for the community, in part because the data are analyzed by a linear model and cluster assignment developed by the authors, which is a somewhat arbitrary representation. Perhaps the authors could explain how their data could be used by other researchers, and maybe even develop an accessible portal for interacting with the data.

We do provide the entire set of data in a formatted Excel Table as Supplementary Tables 3 and 4, which contain common pairwise comparisons and ANOVA tests that allow a researcher without a strong proteomics background to explore the data, and we also provide the raw proteomics datasets deposited in PRIDE, so any interested colleague can re-analyse them in the manner that suits their purposes best.

We analysed the data in the way we did because it takes account of the knowledge from genetics that we have of all these cell populations. This also allowed us to include the important set of proteins and phosphosites that are completely absent from all but one mutant genotype, and would therefore have dropped out of the statistical analyses.

For example, what does it mean biologically that a protein is a member of a specific cluster shown in Fig. 3C? Is there a predictive value in such an assignment, and how does it relate to the main question of DV axis regulation? An example of a novel insight obtained for specific protein(s) would be useful to illustrate the utility of this analysis.

The clusters represent groups of proteins that are present at higher or lower abundance in subsets of cell populations. So, for example, being present in cluster 5 means (Fig. 3C) that this protein is predicted to be more abundant in the mesoderm than elsewhere (which includes being detected ONLY in the mesoderm, like Snail). This clustering therefore is the way for us to find new proteins that conform to these groups.

We provide here the immunostainings of two cytoskeleton-associated proteins that our proteomic analyses predicted to be more abundant in the ectoderm (Cluster 6: dorsal+lateral):

• The actin-microtubule crosslinker Short-stop (Shot), which is seen to be reduced in the mesoderm.
• The actin-severing protein Cofilin/Twinstar, which was also found downregulated in the mesoderm in the work cited in Ref.:10 Gong L. et al., Development (2004). The staining shows that cofilin-GFP is abundant in the entire subapical region of ectodermal cells, but strongly reduced in ventral furrow cells, where it is only retained in a few apical membrane blebs. These proteins are targets for functional analyses in follow-up work.

[Imaging Data for Reviewers]

Figure: Physical cross-sections of fixed embryos showing the enrichment of proteins in the ectoderm (cluster 6: DL). Dorsal is top, ventral is bottom. Scale bar: 50 um Top panel: Staining for short-stop (shot; cyan / grayscale) and snail (yellow) in embryos expressing gap43-mCherry. Bottom panel: staining for discs large (dlg, magenta) and GFP (green / grayscale) in embryos expressing cofilin-GFP (Kyoto protein trap for Cofilin/Twinstar).

Overall, at present the study appears to have limited novelty and mechanistic insight. The data generally align with prior expectations, but it is unclear how this work advances the field.

We were reassured that the data align with previous studies, but as we state in the text, they go well beyond these valuable and important studies in several dimensions. We had made the following assumptions:

1. DV patterning mutants recapitulate biological qualities of DV cell populations and the differential expression of DV fate determinants, as confirmed in Fig. 1 and Fig. 3D.
2. The differential regulation of the proteomes and phosphoproteomes across DV patterning mutants recapitulates the abundances of proteins and phosphosites within DV cell populations of a wildtype embryo. We confirmed this in Fig. 3A and Fig. 5C with the implementation of a linear model for the abundances of detected proteins and phosphosites. The resulting analysis revealed new avenues for future functional studies, as intended. Most of the work on cell shape regulation at the gastrulation stage has focused on actomyosin and a subset of cell adhesion molecules. We have identified networks of proteins and phosphoproteins that may also control gastrulation (Fig. 6 and Supplementary Fig. 5), including microtubules, which were significantly enriched in networks of phosphoproteins (Fig. 7 and Supplementary Fig. 6).

For example, the observed differences between marker proteins in Toll10B vs. spn27A data seem to confirm previous suggestions that spn27A has a stronger ventralizing effect.

This suggestion was made by colleagues who had unpublished observations on a limited number of gene expression patterns that supported their contention. A correlation analysis (see figure below) of our results now shows that proteins with a restricted dorso-ventral pattern change more in spn27Aex mutants than in Toll10B. If we look at the known mesodermal genes such as Snail, Twist, Mdr49 and CG4500 we find them at higher abundance in spn27Aex than Toll10B , while the ectodermal genes Egr, Zen, Dtg, Tsg, Bsk, and Ptr are reduced more strongly in spn27Aex than in Toll10B. This takes the prior observation of a stronger ventralization of spn27Aex from an anecdotal to a systematic analysis.

[Correlation analyses available for reviewers]

Cross-correlation between the fold changes (FCs) in Toll10B/WT vs. spn27Aex/WT for all proteins detected in wildtype, Toll10B and spn27Aex. Each dot is a protein. The green line is the 'identity' function (slope = 1) that would be expected if the FCs for each protein in both ventralized mutants were exactly the same. A set of proteins with restricted dorso-ventral distribution are highlighted in yellow: mesodermal (ventral) and blue: ectodermal (dorsal).

The role of microtubules in epithelial folding in the embryo has also been demonstrated before.

   The role of microtubules in epithelial folding in the *Drosophila *embryo has indeed been examined in three previous studies that studied dorsal fold formation (Ref.: 35, Takeda et al. NCB 2018), ventral furrow formation (VFF, Ref.: 36, Ko et al. JCB 2019), and salivary gland invagination (Booth et al. Dev Cell 2014). These data reveal diverse and non-conservative functional requirements, ranging from acto-myosin contractility during apical constriction (Booth et al. 2014), force transmission and repair of the supracellular contractile network (but not apical constriction per se, Ko et al 2019), to the generation of expansile forces during cell shape homeostasis (Takeda et al 2018). In light of this potentially broad functional spectrum, we sought to compare three epithelial folds that form within the context of gastrulation: ventral furrow, cephalic furrow and dorsal folds. We confirmed that the initiation of VFF was normal, but the final invagination failed, as per Ko et al. 2019, while dorsal fold initiation did not occur (extending conclusions from Takeda et al 2018). In contrast, cephalic furrow formation, though delayed, did not require microtubules. We also revealed a novel commonality of MT function. Specifically, prior to the initiation of all three epithelial folds, proper nuclear positioning requires MTs. We additionally discovered novel membrane abnormalities in two distinct types of blebs during ventral furrow and dorsal fold formation, respectively. Thus, our data provide insights into the roles of microtubules during epithelial folding that go beyond prior work.

The shown phosphorylation changes (if they are significant) for Toll and Cactus are difficult to explain. In Suppl Fig 2B, E: why is Toll more phosphorylated in the lateralized than in ventralized embryos? (the provided reference 20 does not seem to clarify this).

   These changes are indeed significant (Toll-S871: Vtl vs. WT p = 0.01 , Vsp vs. WT p = 0.002; Cactus-S463: Vsp vs WT p = 0.03); see Supplementary Figure 2B and Supplementary Table 2).

   We have corrected Ref. 20 (Shen B. and Manley J.L., Development 1998). Ref. 20 only shows that Tl is phosphorylated by Pelle (Ref 20: Fig. 6A), although neither the exact position of Tl phosphosite(s) nor the function of Tl phosphorylation were explored in this article. A hallmark of Toll Like Receptor (TLR) regulation is these receptors are subject to tyrosine phosphorylation, which has been widely connected to the regulation of the binding of adaptor proteins to the cytoplasmic tail of TLRs. Both our finding of Serine phosphorylation in Tl, and the differential phosphorylation across cell populations is new, but since we do not know what this particular Serine phosphorylation site does in TLRs in general, we cannot speculate on the meaning of it occurring more in lateral than in ventral cells. In Ref. 20, the authors speculate that Tl phosphorylation by Pelle regulates the association between Tl and Pelle, which then enables Dorsal translocation to the nucleus. It might also be part of a feedback regulation loop, but this is entirely speculative.

Also, certain Cactus phosphorylations appear higher in dorsalized and ventralized embryos, but not in lateralized embryos. Are such changes expected and do they make sense biologically? It is unclear why these phosphorylation data are used to validate the success of the approach.

   The three Cactus phosphosites S463, S467 and S468 were identified and characterised in the work cited in Ref. 19 (Liu Z.P. et al., Genes and Development, 1997), and we used these sites to validate that our approach was sensitive enough to detect known phosphosites in proteins that act on the dorso-ventral patterning pathway specifically at the point of gastrulation (Stage 6 of embryonic development). We also reported in this manuscript the detection of known phosphosites within the Rho-pathway (Fig. 5E,F, Myosin Light Chain: T21, S22; Cofilin: S3).

   Liu Z.P. et al. reported that these three sites map to the Cactus PEST domain, which is required for Cactus degradation in the mesoderm (Belvin M. et al, Genes and Development 1995).  Liu Z.P. et al. also showed that mutating these phosphosites impairs Cactus turnover without affecting the ability of Cactus to bind Dorsal. We can only speculate that the differential phosphorylation across dorso-ventral embryonic cell populations is associated with the regulation of Cactus turnover. Consistent with this, we find Cactus downregulated 1.5 log2 fold in ventralized embryos derived from *spn27Aex/def* mothers. Furthermore, there are a number of signalling pathways that act both in the dorsal and the ventral-lateral domain (e.g., rhomboid/EGF), so it is not surprising to find modifications that are shared by these regions.

The rationale to use a diffusion algorithm for data analysis is not clear. How would the analysis differ if diffusion was not used?

Phosphoproteomics data are often sparse and noisy for a number of reasons (technical; low abundance of phosphorylated peptides compared to other peptides in the cell; biological: not all phosphosites are functional). Network diffusion is a common way used for various data types to boost the signal-to-noise ratio. For example, if from a list of 10 phosphosites, 5 all fall in the same network region or process, and the rest are randomly distributed in the network, chances are that the first region is more representative of the regulated process in that dataset. Using network propagation, the signal coming from the first 5 phosphosites would give a higher score to that network region, marking it as the predominant signal. Our specific implementation, which uses the semantic similarity between nodes to model the edges in the network, further boosts the functional signal by preferentially including nodes that have a higher functional similarity to the initial phosphosites. Our approach therefore allows us to identify the processes that are predominantly ‘active’ in our dataset. We refer the reviewer to our recent preprint for more evidence that this strategy boosts the signal-to-noise ratio in phosphoproteomic datasets and further prioritises more functional phosphosites (https://www.biorxiv.org/content/10.1101/2023.08.07.552249v1). If this approach was not used and we based the identification of relevant processes only on the list of phosphosites, we would have acquired more spurious terms in our functional enrichment analysis. The above preprint also shows that different methods such as the Prize Collecting Steiner Forest algorithm perform worse for phosphoproteomics data.

Generally, the discussion of enriched GO categories presented in Fig. 6 is not rigorous, and it is unclear what biological insight is provided by this figure, probably because the categories are extremely diverse and not clustered in a meaningful way. Despite stating that the work on microtubules came out as a result of proteomic analysis, there is no connection between proteomic data (e.g., data shown in Fig. 6) and microtubule analysis in Fig. 7.

   The connection is between the __phosphoproteomic__ data and the microtubules. The reviewer is correct about the fact there is little connection at the proteomic level with microtubules. Only the diffused network analyses performed on the phosphoproteomic data pointed in this direction. We have improved the writing about this point.

The Discussion section touches on areas of differential protein degradation and mRNA regulation; however, these data are not presented in Results or Figures and so it is difficult to assess the relevance of this analysis.

     We present these data in Figure 6A,B. The network analyses of the clusters showed significant enrichment of cellular component terms that are connected with protein turnover and mRNA regulation. We have added a reference to figure 6 in the Discussion for clarity.

There is insufficient citation of prior literature throughout the manuscript: many statements are lacking proper references.

We have corrected the mistakes and added missing references.

Proteomics data should be deposited into a standard repository that is a member of ProtomeXchange Consortium, such as PRIDE, etc.

All proteomics and phosphoproteomics data have been uploaded to PRIDE:

The raw files for the proteomics and phosphoproteomics experiments were deposited in PRIDE under separate identifiers:

Proteome: Identifier PXD046050 (Reviewer account details: reviewer_pxd046050@ebi.ac.uk, pw: coJ9otiX).

Phosphoproteome: Identifier PXD046192 (Reviewer account details: reviewer_pxd046192@ebi.ac.uk, pw: nvkbwClp).

We have included a statement of raw data availability in the revised version of the manuscript with the PRIDE access information.

__Minor comments __

The text has several typos and should be proof-read, and references to figures and tables should be checked, as some of these are not correct.

We have corrected typos, references to figures and tables in the revised version of the manuscript.

The genotypes for the mutations used in this study should be accompanied by citation describing identification of these mutations and the resulting phenotypes. It would also be helpful to describe the nature of these alleles (molecular lesion, gain vs loss of function, etc.). Some of this information is included in the Discussion, but it would be useful for the reader to learn this early on, when the chosen genotypes are presented.

All this information is and was provided in the methods section and in Table 1, including stock numbers and sources of the stocks. Please see 'Methods, Drosophila genetics and embryo collections'.

2G,H - the X axis should be clearly labeled as logarithmic.

We introduced the log2 label in the X-axis of Fig. 2G,H and any other panel in which this was not expressly made clear.

In Fig. 2G the locations of lines showing fold changes for Twist and Snail seem incorrect. In Fig. 2H the dotted line does not appear to correspond to 50% of the number of phosphosites.

We apologise for these errors, both have been corrected in the revised version of the manuscript.

5D can be improved by adding letters for the coloured clusters.

We have labelled the clusters in Fig. 3B and Fig. 5D. to ease the identification of biologically relevant clusters.

It is unclear if any specific additional insight was obtained using SILAC, the authors may want to discuss this approach and outcomes more.

SILAC has been widely used to deal with the inherent variability of proteomic analyses by introducing a standard that is metabolically labelled, in our case, w1118 flies fed with SILAC yeast were used as the standard. Because the inherent variability is larger in phosphoproteomic experiments (because protein identification is based on phosphorylated peptides only, see Methods), we used SILAC labelling only in the phosphoproteomic experiment.

__Reviewer #2 __

Evidence, reproducibility and clarity

The present article by Gomez et al describes a deep proteomics analysis of the proteome and phosphoproteome of embryos mutated for key genes involved in the dorso-ventral axis in Drosophila melanogaster. Overall, this is a nice article showing new insight in this development process. The results are mainly descriptive, yet identifies potential new players in the definition of the dorso-ventral axis.

The generation of mutants for genes found up- or down-regulated in each mutant strain would be a significant addition to this manuscript. But I think in its current form the data brings enough new information on this particular developmental step and would be of interest for the fly community.

My main concern is that the manuscript can be difficult to read and overly convoluted at times even for experts in the field. I would suggest the author move some methodological explanations from the results to the methods section to further detail the goals of some results sections.

We have followed these suggestions and hope we have made the manuscript more easily readable.

As an example, the goal of part 3) « A linear model for quantitative interpretation of the proteomes » is not clear to me. Are the authors comparing the abundance of a protein in the WT versus a theoretical WT in order to determine which fractions of mesoderm, lateral ectoderm and dorsal region are actually present in the WT? (...)

Yes, in part, but the main purpose was to compare how well the theoretical WT, as ‘reconstituted’ from the mutants, corresponds to the observed actual WT (for which we have at least approximate values).

The question that we faced when we started these calculations was: what is the ‘correct’ fraction (or proportion) we should use to weight each protein (or phosphosite) measurement in the mutants. Theoretically, these values should be those that result in the best match between the theoretical WT and the measured WT abundance of each protein (or phosphosite). We knew from actual measurements only the mesodermal fraction, which was determined to be ~20% of the cross-sectional area (Ref. 21: Rahimi, N., et al Dev. Cell. 2016). The neuroectoderm and ectoderm fractions were estimated to be approx. 40% each (Ref.: 22, Jazwinska, A et al. Development 1999), but we lacked an exact number. The systematic exploration of these proportions led us to conclude that indeed both the neuroectoderm and ectoderm fractions should be around 40% each, provided the mesoderm is fixed at 20%. Thus, we used these fractions: D: 0.4 L: 0.4 V: 0.2 for our follow-up analyses.

(...) Or are they using it as a reference to obtain a fold change for the different proteins quantified (in this case why not use the WT?)?

yes, again, in part: as a reference for the EXPECTED fold changes, as would be predicted from the WT.

Since we have moved some of the details of this approach from the main text to the methods section, we have also revised the remaining text and hope it is now clearer.

The proteomics data must be deposited in a public repository. I did not see it stated in the methods section.

All proteomics and phosphoproteomics data have been uploaded to PRIDE; see further comments above in response 13.

The version of the uniprot database is quite old (2016) so is the version of MaxQuant used in this study. Any reasons for that (other than that the analysis was performed in 2016)?

That is indeed the reason.

The data were run on different MS platforms, how did the authors account for the variability in MS signals? What samples were run on which MS platform? Were the WT embryos ran on both?

We measured three replicates, and all five genotypes (four mutant genotypes plus wildtype) for each of the replicates were measured on the same instrument. Specifically, for the whole proteome analyses, replicate one and three of all genotypes were measured on the QExactive Plus instrument and replicate 2 of all genotypes were measured on a QExactive HF-x instrument, as were the phosphoproteomes. So, indeed, the wildtype was measured on both instruments. We thus did not observe instrument-specific bias in the PCA analysis for the proteome data.

We have added this in more detail to the method section:

“Samples of replicate one and three were measured on the QE-Plus system and replicate two was measured on the QE-HF-x system.

For phosphoproteome analysis, (…) Samples of all three replicates were measured on the QEx-HFx system. We added trial samples measured on the QEx-Plus system to increase the phosphosite coverage using the match between runs algorithm.”

In the methods section the authors mention that a high-pH reverse phase fractionation was performed? How many fractions of High-pH reverse phase separation were injected per sample? Was this separation performed for all the samples?

We have adjusted the Methods section regarding the high-pH fractionation by adding the following sentence: “Fractions were collected every 60s in a 96 well plate over 60 min gradient time collecting a total number of 8 fractions per sample.“

Why did the authors used label-free (proteome) and SILAC (phosphoproteome) quantification methods?

See our response to reviewer #1, point 19.

Why is the threshold based on the Q3 of the standard deviation (if I got it right) ? Couldn't they be calculated directly on the distribution of the ratio?

We could also have done it that way.

However, we had wanted also to take into account the variation between the replicates, i.e., the quality of the individual measurements, and we therefore devised the procedure we used, by which the standard deviation of the individual technical replicates enters the calculation with the ratio of the averages, the variability between replicates would have been ignored and we considered it more appropriate to take the more conservative approach. But as it turns out, the cut-off would have ended up being very similar had we calculated it the way the referee suggests,

Page 6: The supplementary figure 2E refers to the protein Cactus and the text to CKII, please modify one or the other to avoid any confusion. Page 7: A dot is missing at the end of the following sentence « if used with the assumed weightings for the populations »

We have corrected these sentences.

Page 19: Replace SppedVac by SpeedVac

We have corrected the error in the manuscript and thank the reviewer for the detailed inspection.

Page 8: why not using a z-score with thresholds directly instead of a -1/+1/0 system and then using the z-score?

Because we wanted to compare the relative changes over wt between mutants (i.e. the similarity between 1 0 0 and 0 -1 -1) rather than the relationship of their absolute values to the wt, and to assign proteins with similar relationships into the same dorso-ventral regulation categories.

The text states this (previously in main text, now in methods):

“The reason for this is that this method takes into account that value sets that represent similar relative differences between the mutants (for example, 0 -1 -1 vs. 1 -1 -1 or 1, 0, 0) are biologically more similar to each other than the raw values indicate. The z-scores for all of these cases would be 1.1547 -0.5774 -0.5774.”

In the abstract it is mentioned that 3,399 proteins are differentially regulated at the proteome level versus 1,699 significantly deregulated at a 10 % FDR in the main text (page 5). Is there a reason for this discrepancy? Same comment for the phosphopeptides.

But we now also see the need to better clarify this point, and we have edited the text accordingly.

The second number refers to those proteins that show statistically significant changes based on ANOVA (1699 proteins).

The first number (3398; note that the number 3399 in the abstract was a typo, now corrected) includes all proteins that were detected in at least 1 replicate in the wildtype (5883/6111) minus those that do not change between the genotypes (2156/6111) and minus all those that change in the same direction in all mutants (329).

This includes proteins that are automatically excluded from ANOVA, i.e., those that are detected only in the wildtype (35/6111 proteins) or in two or more genotypes but only in 1 technical replicate ANOVA negative ones.

As we stated, we did this because it “allows us to include the important group of proteins that show a ‘perfect’ behaviour, like dMyc and WntD, in that they are undetectable in the mutants that correspond to the regions in the normal embryo where these genes are not expressed.”. This 'regulated' set consists of those proteins that exceed the |0.5| fold threshold.

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

This review is a list of many individual critiques. It is unclear what the expertise of the reviewer is (they do not provide the answer to that question in the review form, unlike the other referees), but several of the criticisms are unfounded. Three of the PIs of this work are researchers with extensive experience in Drosophila genetics and early development but are nevertheless confounded by some of the comments made by this referee.

The mutants do not completely "flatten" the embryos.

We do not claim that they do. Nor are the ventral, lateral and dorsal regions in the normal embryo completely ‘flat’ or homogeneous. But the mutants are good representations of the major fates in these regions, as a wealth of published literature from the last 30 years indicates.

For instance, Tl10B broadly expresses snail but also expresses sog in the head. (i.e. Fig 1B - sog and sna expression in Figure 1B mutant backgrounds looks odd.) The sog expression likely relates to a deficiency specific effect.

This ‘sensitive’ area is well known also from other genetic conditions – e.g. partial loss of dorsal and indeed in Spn27A mutants. It is therefore not specific to the Tl10B deficiency but says something about gene interactions in this region. Thus, this cannot be a deficiency-specific effect.

Is sog seen in a Toll10B/+ mutant background?

Yes, it is, and more frequently than in Toll10B/Def.

The deficiency used for the Toll10B experiment is Df(3R)ro80b which is quite large and deletes 14+ genes.

True. However, this does not matter: the mothers are heterozygous, so the genes are not missing, they are present in one wildtype copy! And these mothers are then mated with wildtype fathers, so if expression of these genes were needed in the embryo, then there would be another full wt copy of each. We appreciate that maternal effect genetics can be difficult to follow, but this is all work that has been done a long time ago, and is not the point of this paper at all.

The deficiency used for the spn27A experiment is Df(2L)BSC7 and removes 4+ genes.

Again, this would only matter if these were maternal effect genes that were needed for the establishment of the dorso-ventral axis, and they are not.

Furthermore, the gd9 allele may not be a complete loss of function.

It may not be – but what matters is the well characterized phenotype which has been shown to represent dorsal cell types.

It is possible that the Toll10B allele picked up an accessory dominant mutation.

This again would only matter if it was a dominant AND maternal effect mutation that affects the DV axis in the embryo – and there are very few of these known. And nothing in our analysis of these embryos, with which we have been working on and off over 3 decades and therefore know very well, indicates that our current stock is any different from those we have seen in the past.

Unfortunately, these mutant phenotypes that affect DV and AP patterning mean that conclusions cannot be made that changes in protein relate to DV patterning.

We simply do not understand this statement.

Why do the mutant phenotypes (gene expression patterns and cell morphologies representative of the ventral, lateral and dorsal cell populations) not mean that the proteins downstream of the fate changes correspond to the cell fates?

To get a better view of the ventralized phenotype, the authors should repeat the analysis by ectopically expressing Toll10B using the Gal4-UAS system; UAS-activate Toll transgenes are available.

All Gal4-UAS maternal drivers, even the best and the strongest, result in mosaic expression. Our lab has extensive experience with this system and we know that, for example, the homogeneous, high levels of twist or snail expression that we see in spn or Tl10B embryos cannot be achieved with GAL4.

Fig 1C-F - due to combined AP and DV effects seen with ventralizing mutants, it is important that the authors confirm that cross-section views relate to the middle to posterior of the embryo.

We confirm this.

Costaining with anti-Kr or -Caudal would help to ensure they are assaying the correct AP domain for pure DV effects.

In our view, this is an unnecessary experiment. I know where the middle of the embryo is. If the reviewer does not believe when we say we are showing a section from the middle, they can see that the sections are not from the end region by, for example, the cell number, and the section angles.

The authors refer to reference [60] for stages but there is no information regarding morphological criteria used under the microscope to stage the embryos.

We have now added more detail in the methods section:

Briefly. using a Zeiss binocular, the embryos were individually hand-selected on wet agar which made the embryos semi-transparent, allowing the assessment of a range of morphological features, of which at least some are visible in each of the mutants:

• Yolk distance to embryonic surface: distinguishes between early (stage 5a) and late cellularisation (stage 5b).
• Yolk distribution within the embryo: identification of large embryonic movements of the germ band (e.g.: Initiation of germ band extension, marking the initiation of stage 7). In DV patterning mutants this is seen as twisting of the embryo.
• Change in the outline of the dorsal-posterior region: polar cell movement from the posterior most region of the embryo (stage 5a/b) to stage 6a/b.
• Formation of the cephalic and dorsal folds: identification of stage 6 (initiation of cephalic fold) and stage 7 (dorsal folds). The combined use of these morphological criteria, together with the synchronised egg collections allows accurate staging of wild type and mutant embryos.

Furthermore, what is stage 6a,b? Stage 6 is not typically divided in two stages nor is it clear what a,b relate to.

We used a generally accepted standard for staging embryos: Campos-Ortega J.A. and Hartenstein V. ‘The embryonic development of Drosophila melanogaster’ book (ref. Nº 60). In this book, they describe the morphological criteria that can be followed in living embryos for proper staging. These stages, with these exact names, are shown on pages 11 and 12 of the 1997 edition (2nd edition).

According to the published timetable of Drosophila development by Foe et al. 1993 (not cited), gastrulating embryos are 200 min or 3 hr 20'. It's unclear if this is the stage that was assayed.

Foe is a beautiful paper, but we did not cite it because the commonly used nomenclature predates it (Campos-Ortega and Hartenstein 1985).

In addition, timing depends on temperature whereas morphological criteria do not.

The mutant embryos likely develop at different rates relative to wildtype. It seems important to provide details about the staging of embryos. If the mutant embryos take longer to gastrulate, for instance, might that also be a factor that impacts the proteome.

As described above, we used a combination of criteria to accurately judge staging. DV patterning embryos could in principle develop faster or slower than wildtype. We performed synchronised egg collections (Methods: Embryo collections) for 15’. Therefore, any developmental timing defect would have become evident based on a difference in the number of embryos entering stage 6 and 7 at the point of visual inspection of the collections. This was not the case.

How many replicates for each genotype? In the text it states, "replicates from the same genotype clustered together (Fig. 2E)....." Similar vague reference for phosphoproteome follows (Fig 2F). It is then stated that it was impossible to determine the experimental source for this variation. Could it relate to differences in timing of samples?

We had given the numbers of replicates in the figure legend but have now also included them in the methods section for more clarity. We did 3 replicates for each genotype in each experiment, with the exception of gd9 and spn27aex mutants, for which we did 2 biological replicates each with 3 replicates, making a total of 6 replicates for these genotypes in the proteomic experiment. We have included an additional clarification in figure legend 2. The number of replicates per genotype per experiment can also be seen from the correlation matrices shown Fig. 2E and 2F, in which the replicates are shown individually. The measurements for each replicate for each genotype within each experiment were reported in Supplementary Tables 2 and 3, 'description' tabs of the worksheets.

The lengthy discussion of ratio estimation on page 7 should be streamlined and made more clear. Are the authors throwing out data and only keeping samples that support their model? This seems like overfitting - if I am understanding correctly, you are selecting the samples that support the "majority of proteins fit the linear model" but this isn't necessarily the case.

No, this is a misunderstanding. We do not select data.

We have rephrased this section, but to explain here briefly: We do not select any samples, we state that the majority of proteins fit the theoretical model (and that is not even surprising, because any protein that does not change across the populations will automatically fit the model). We then discuss why some might NOT fit the model. The model doesn’t need to be supported, it simply is a calculation that allows us to stratify the data.

They call this the 'correct' manner (see section 4 page 7) but it seems like a working model and presumptuous to imply that it is the correct way.

We explained in the text why we refer to this as ‘correct’. It is a matter or definition, not presumption, and we even used quotes to be clear about this. ’Correct’ indicates a combination of values that is consistent with the biological model that the DV mutants are good representations of the corresponding embryonic cell populations in a wild type embryo. We do not in any way ‘throw out’ other data, we just note they don’t fit that model. Clarifications on the concept for the model have been added in various places in the text

Figure 3C - it is confusing to use a circular diagram to show DV inferred position of the 14 clusters as their position on the circle does not correspond to where they are expressed on the embryos. Perhaps a stacked bar graph for 6 different domains would be better.

This figure does not show positions of clusters. It is simply a pie chart, as is stated in the figure legend and as can be seen by the numbers and the corresponding sizes of the sectors. We have tried a stacked representation (shown below), but find it no clearer and have therefore stuck with this very common way of representing quantities, and in particular, proportions. We use the same representation with the same colour schemes in all subsequent figures, so proportions can be compared across figures.

It is very hard to follow the text on page 9.

We have rephrased this section

It is very hard to see the gene expression patterns shown in Fig 4A with the color scheme/scale used.

We appreciate this colour scheme does not correspond to the commonly used dark colour on a light background which would mimic histochemistry to show gene expression. The ‘inferno’ colour scheme was used because it allows better quantitative comparisons between subtly different patterns. However, to make these figures more similar to the types of in situ hybridisations that embryologists are used to seeing, we now use a different representation.

In general, Figure 4 is uninterpretable - in particular, what do the numbers mean on the greyscale circle plots in panel D?

We apologize for having failed to explicitly include the explanation for this in the figure legend. The reader will notice that these numbers add up to the number in the circle to the left, and the numbers indicate the number of proteins showing perfect matches (white), partial overlaps (grey) and mismatches (black). We have improved the graphic representation and added an explanation in the figure legend.

Figure 5A. Why wasn't protein abundance and phosphosites identified from an individual, identical sample?

This was because of the way the project developed over the course of the research, and the protein part was originally intended only as a proof of concept, with the intended focus being the phosphoproteome. We later decided to include a full analysis of the proteome, but did not consider it worthwhile and necessary to repeat the entire laborious and expensive experiment with both analyses being done from the same samples.

How can one be sure that the phosphosites were correctly assigned if the proteins were not detected in the proteome but they were only identified in the phosphosite analysis?

We are not sure we understand this question. The phosphoproteomic analysis identifies phosphopeptides of proteins that in turn allow one to identify the protein itself and the amino acid in that peptide that is phosphorylated. So the identification is done only WITHIN the phosphoproteomic analysis and does not relate directly to the proteomic analysis. This explains why we found some phosphopeptides for which we did not detect the full host protein in the proteomic analysis.

Thus, if a protein was detected only in either of the experiments, this fact doesn’t modify the validity of the result, because the identification was done individually for each experiment.

Page 16 - much discussion about the difference between Spn27A and Toll10b/def mutant background. One has half as much Toll receptor. The phenotype of Toll10b/+ should be examined.

Both genotypes have been extensively examined in the past. Tl10B/def has only one copy of the gene from the mother, and the mutant protein is constitutively active. By putting it over a deficiency, we (and others in the past) made sure that the exclusive source for Tl signalling is from this gain of function Tl allele, and that the wildtype receptor, which would still be activated by the natural ligand in a graded pattern along the DV axis, does not confound the result.

The Tl10B/+ combination creates a less ventralized phenotype which is not more similar to that of spn27Aex/def but in fact less similar.

Page 12 - hard to follow the discussion of modeling (?) presented in Figure 6. The results (bottom of page 12 - #1 "most networks are enriched for cellular components associated with regulation of gene expression" and page 13 #2 - "cytoskleeton emerges as a major target of regulation") seem vague and unsubstantiated. Rhabdomere, P granule, micropyle, autophagosome?

We agree with the reviewer that there are many cellular components that are enriched in the diffused network analyses, many of them unrelated to morphogenesis. We had highlighted this finding on page 12, paragraph 3. Nevertheless, we have rephrased the statements as ‘the heat maps illustrate that most of the enriched cellular components in both experiments were highly enriched with cellular components associated with DNA and RNA metabolism or the regulation of gene expression.’ and have now included numbers.

We think ‘a major target’ for phosphorylation does in fact apply to the cytoskeleton, and we had already supplied the number to substantiate this in the manuscript (14/62).

Readers will be able to evaluate these network analyses based on their own fields of interest or particular questions they may wish to address. We haven’t excluded any cellular component terms.

Figure 7 seems like a separate study.

Why were the phosphopeptides investigated to determine if they relate to phosphorylated proteins? Phosphoantibodies could have been generated for a subset. Instead the manuscript pivots to analysis of microtubules.

We are reporting here one example of a proof-of-concept study that we carried out, chosen based on our own research interests and on available tools and reagents. There are clearly many other avenues that could have been explored and that others may want to explore, but that go well beyond this report. We have made this more explicit in the text.

Page 14 - discussion first paragraph. Please cite ref[10] when discussing the "previous study" otherwise the reader will not understand which study you are referring to until the next paragraph.

We have moved the reference from its current position to the one suggested by the reviewer.

• In general, the study would benefit from more attention to references and citations of prior work. A comparison of this work to the Gong et al. Development 2004 study should be made earlier. This work is cited very early on, namely in the introduction.

• The authors start off saying that no other study has looked at proteins from a spatial perspective. We are unsure what the reviewer refers to. We say precisely the opposite: we indicate that studies have been performed to look at differences in cell populations, including that by the lab of Jon Minden (Gong et al), a highly respected former co-author of one of the current authors (ML). We do state that the technologies at the time did not allow the same depth and temporal resolution as the methods that are available nowadays. For instance, Gong et al. used an excellent and original approach at the time, which however did not detect Snail and Twist in the ventralized mutants.

The only time we say ‘no other study’ is about ‘region-specific post-translational regulation of proteins’ - though we do state in the discussion that Gong et al would have detected some of these cases because they used 2D gels.

• Along these lines, there is another more recent proteomic study from Beati et al. Fly 2020 using similarly staged embryos. How do these other experiments compare to the current ones? As they apparently analyzed proteome and phosphopeptides from an identical sample, are the authors' new data using separate samples consistent? This study is actually about a later stage (stage 8 embryos, post-gastrulation). Again, an excellent study, but not directly relevant to our current analysis. It validates the use of SILAC in Drosophila, although it is not the first study to do this. Furthermore, it looks at a different question and biological process using a mutant, htl, to understand the effect of FGF signalling.

• Furthermore, Adam Martin's lab has been studying microtubule action along the dorsoventral axis (Denk-Lobnig et al 2021) and this work is not cited. Denk-Lobnig et al 2021 is about spatial patterns of myosin and actin and how that is governed genetically on the ventral side of the embryo, pertaining primarily to ventral furrow formation. It does not analyse microtubules nor dorsal-ventral cell populations.

It is possible there may be some confusion with another excellent study from Adam Martin’s lab, in which the role of microtubules is analysed. But this is exclusively in the ventral furrow, and the study did not look at the effect of microtubule depolymerisation on nuclear positioning nor membrane behaviour. We cite this work extensively (Ref.: 36, Ko et al. JCB 2019) and we compare our results to that paper. However, our work here goes beyond this study in that it looks at all cells along the DV axis.

General comments:

Typos throughout. For example, page .4 section heading "dorso-ventral cell..."

We have scanned the entire document for typos.

Font size extremely small - for example see Figure 1A gene names, and 1F magnified view.

We have adjusted the fonts in the main figures.

Scale bars not shown when showing magnified views. For example, see Fig 1E,

We have added these.

Reviewer #3 (Significance (Required)): This study by Gomez et al. uses a proteomic-centered approach to study proteomes associated with cell populations in the embryo that they argue relate to different positions along the dorso-ventral axis. They generate a proteomic resource, though it was unclear how anyone could use the data they produce. There is no searchable database and we have to trust that the authors will ultimately provide such a resource to the community.

All proteomics and phosphoproteomics data have been uploaded to PRIDE. Also see responses to the other referees’ queries about this point.

There is the potential for interesting insights but the work is not presented in a way that is accessible or useful. The presentation needs significant improvement.

We have improved the presentation and way the results are presented as per the suggestion of all reviewers.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Strengths:

This work (almost didactically) demonstrates how to develop, calibrate, validate and analyze a comprehensive, spatially resolved, dynamical, multicellular model. Testable model predictions of (also non-monotonic) emergent behaviors are derived and discussed. The computational model is based on a widely-used simulation platform and shared openly such that it can be further analyzed and refined by the community.

Weaknesses:

While the parameter estimation approach is sophisticated, this work does not address issues of structural and practical non-identifiability (Wieland et al., 2021, DOI:10.1016/j.coisb.2021.03.005) of parameter values, given just tissue-scale summary statistics, and does not address how model predictions might change if alternative parameter combinations were used. Here, the calibrated model represents one point estimate (column "Value" in Suppl. Table 1) but there is specific uncertainty of each individual parameter value and such uncertainties need to be propagated (which is computationally expensive) to the model predictions for treatment scenarios.

We thank the reviewer for the excellent suggestions and observations. The CaliPro parameterization technique applied puts an emphasis on finding a robust parameter space instead of a global optimum. To address structural non-identifiability, we utilized partial rank correlation coefficient with each iteration of the calibration process to ensure that the sensitivity of each parameter was relevant to model outputs. We also found that there were ranges of parameter values that would achieve passing criteria but when testing the ranges in replicate resulted in inconsistent outcomes. This led us to further narrow the parameters into a single parameter set that still had stochastic variability but did not have such large variability between replicate runs that it would be unreliable. Additional discussion on this point has been added to lines 623-628. We acknowledge that there are likely other parameter sets or model rules that would produce similar outcomes but the main purpose of the model was to utilize it to better understand the system and make new predictions, which our calibration scheme allowed us to accomplish.

Regarding practical non-identifiability, we acknowledge that there are some behaviors that are not captured in the model because those behaviors were not specifically captured in the calibration data. To ensure that the behaviors necessary to answer the aims of our paper were included, we used multiple different datasets and calibrated with multiple different output metrics. We believe we have identified the appropriate parameters to recapitulate the dominating mechanisms underlying muscle regeneration. We have added additional discussion on practical non-identifiability to lines 621-623.

Suggested treatments (e.g. lines 484-486) are modeled as parameter changes of the endogenous cytokines (corresponding to genetic mutations!) whereas the administration of modified cytokines with changed parameter values would require a duplication of model components and interactions in the model such that cells interact with the superposition of endogenous and administered cytokine fields. Specifically, as the authors also aim at 'injections of exogenously delivered cytokines' (lines 578, 579) and propose altering decay rates or diffusion coefficients (Fig. 7), there needs to be a duplication of variables in the model to account for the coexistence of cytokine subtypes. One set of equations would have unaltered (endogenous) and another one have altered (exogenous or drugged) parameter values. Cells would interact with both of them.

Our perturbations did not include delivery of exogenously delivered cytokines and instead were focused on microenvironmental changes in cytokine diffusion and decay rates or specific cytokine concentration levels. For example, the purpose of the VEGF delivery perturbation was to test how an increase in VEGF concentrations would alter regeneration outcome metrics with the assumption that the delivered VEGF would act in the same manner as the endogenous VEGF. We have clarified the purpose of the simulations on line 410. We agree that exploring if model predictions would be altered if endogenous and exogenous were represented separately; however, we did not explore this type of scenario.

This work shows interesting emergent behavior from nonlinear cytokine interactions but the analysis does not provide insights into the underlying causes, e.g. which of the feedback loops dominates early versus late during a time course.

Indeed, analyzing the model to fully understand the time-varying interactions between the multiple feedback loops is a challenge in and of itself, and we appreciate the opportunity to elaborate on our approach to addressing this challenge. First: the crosstalk/feedback between cytokines and the temporal nature was analyzed in the heatmap (Fig. 6) and lines 474-482. Second: the sensitivity of cytokine parameters to specific outputs was included in Table 9 and full-time course sensitivity is included in Supplemental Figure 2. Further correlation analysis was also included to demonstrate how cytokine concentrations influenced specific output metrics at various timepoints (Supplemental Fig. 3). We agree that further elaboration of these findings is required; therefore, we added lines 504-509 to discuss the specific mechanisms at play with the combined cytokine interactions. We also added more discussion (lines 637-638) regarding future work that could develop more analysis methods to further investigate the complex behaviors in the model.

Reviewer #2 (Public Review):

Strengths:

The manuscript identified relevant model parameters from a long list of biological studies. This collation of a large amount of literature into one framework has the potential to be very useful to other authors. The mathematical methods used for parameterization and validation are transparent.

Weaknesses:>

I have a few concerns which I believe need to be addressed fully.

My main concerns are the following:

(1) The model is compared to experimental data in multiple results figures. However, the actual experiments used in these figures are not described. To me as a reviewer, that makes it impossible to judge whether appropriate data was chosen, or whether the model is a suitable descriptor of the chosen experiments. Enough detail needs to be provided so that these judgements can be made.

Thank you for raising this point. We created a new table (Supplemental table 6) that describes the techniques used for each experimental measurement.

(2) Do I understand it correctly that all simulations are done using the same initial simulation geometry? Would it be possible to test the sensitivity of the paper results to this geometry? Perhaps another histological image could be chosen as the initial condition, or alternative initial conditions could be generated in silico? If changing initial conditions is an unreasonably large request, could the authors discuss this issue in the manuscript?

We appreciate your insightful question regarding the initial simulation geometry in our model. The initial configuration of the fibers/ECM/microvascular structures was kept consistent but the location of the necrosis was randomly placed for each simulation. Future work will include an in-depth analysis of altered histology configuration on model predictions which has been added to lines 618-621. We did a preliminary example analysis by inputting a different initial simulation geometry, which predicted similar regeneration outcomes. We have added Supplemental Figure 5 that provides the results of that example analysis.

(3) Cytokine knockdowns are simulated by 'adjusting the diffusion and decay parameters' (line 372). Is that the correct simulation of a knockdown? How are these knockdowns achieved experimentally? Wouldn't the correct implementation of a knockdown be that the production or secretion of the cytokine is reduced? I am not sure whether it's possible to design an experimental perturbation which affects both parameters.

We appreciate that this important question has been posed. Yes, in order to simulate the knockout conditions, the cytokine secretion was reduced/eliminated. The diffusion and decay parameters were also adjusted to ensure that the concentration within the system was reduced. Lines 391-394 were added to clarify this assumption.

(4) The premise of the model is to identify optimal treatment strategies for muscle injury (as per the first sentence of the abstract). I am a bit surprised that the implemented experimental perturbations don't seem to address this aim. In Figure 7 of the manuscript, cytokine alterations are explored which affect muscle recovery after injury. This is great, but I don't believe the chosen alterations can be done in experimental or clinical settings. Are there drugs that affect cytokine diffusion? If not, wouldn't it be better to select perturbations that are clinically or experimentally feasible for this analysis? A strength of the model is its versatility, so it seems counterintuitive to me to not use that versatility in a way that has practical relevance. - I may well misunderstand this though, maybe the investigated parameters are indeed possible drug targets.

Thank you for your thoughtful feedback. The first sentence (lines 32-34) of the abstract was revised to focus on beneficial microenvironmental conditions to best reflect the purpose of the model. The clinical relevance of the cytokine modifications is included in the discussion (lines 547-558) with additional information added to lines 524-526. For example, two methods to alter diffusion experimentally are: antibodies that bind directly to the cytokine to prevent it from binding to its receptor on the cell surface and plasmins that induce the release of bound cytokines.

(5) A similar comment applies to Figure 5 and 6: Should I think of these results as experimentally testable predictions? Are any of the results surprising or new, for example in the sense that one would not have expected other cytokines to be affected as described in Figure 6?

We appreciate the opportunity to clarify the basis for these perturbations. The perturbations included in Figure 5 were designed to mimic the conditions of a published experiment that delivered VEGF in vivo (Arsic et al. 2004, DOI:10.1016/J.YMTHE.2004.08.007). The perturbation input conditions and experimental results are included in Table 8 and Supplemental Table 6 has been added to include experimental data and method description of the perturbation. The results of this analysis provide both validation and new predictions, because some the outputs were measured in the experiments while others were not measured. The additional output metrics and timepoints that were not collected in the experiment allow for a deeper understanding of the dynamics and mechanisms leading to the changes in muscle recovery (lines 437-454). These model outputs can provide the basis for future experiments; for example, they highlight which time points would be more important to measure and even provide predicted effect sizes that could be the basis for a power analysis (lines 639-640).

Regarding Figure 6, the published experimental outcomes of cytokine KOs are included in Table 8. The model allowed comparison of different cytokine concentrations at various timepoints when other cytokines were removed from the system due to the KO condition. The experimental results did not provide data on the impact on other cytokine concentrations but by using the model we were able to predict temporally based feedback between cytokines (lines 474-482). These cytokine values could be collected experimentally but would be time consuming and expensive. The results of these perturbations revealed the complex nature of the relationship between cytokines and how removal of one cytokine from the system has a cascading temporal impact. Lines 533-534 have been added to incorporate this into the discussion.

(6) In figure 4, there were differences between the experiments and the model in two of the rows. Are these differences discussed anywhere in the manuscript?

We appreciate your keen observation and the opportunity to address these differences. The model did not match experimental results for CSA output in the TNF KO and antiinflammatory nanoparticle perturbation or TGF levels with the macrophage depletion. While it did align with the other experimental metrics from those studies, it is likely that there are other mechanisms at play in the experimental conditions that were not captured by simulating the downstream effects of the experimental perturbations. We have added discussion of the differences to lines 445-454.

(7) The variation between experimental results is much higher than the variation of results in the model. For example, in Figure 3 the error bars around experimental results are an order of magnitude larger than the simulated confidence interval. Do the authors have any insights into why the model is less variable than the experimental data? Does this have to do with the chosen initial condition, i.e. do you think that the experimental variability is due to variation in the geometries of the measured samples?

Thank you for your insightful observations and questions. The lower model variability is attributed to the larger sample size of model simulations compared to experimental subjects. By running 100 simulations it narrows in the confidence interval (average 2.4 and max 3.3) compared to the experiments that typically had a sample size of less than 15. If the number of simulations had been reduced to 15 the stochasticity within the model results in a larger confidence interval (average 7.1 and max 10). There are also several possible confounding variables in the experimental protocols (i.e. variations in injury, different animal subjects for each timepoint, etc.) that are kept constant in the model simulation. We have added discussion of this point to the manuscript (lines 517519). Future work with the model will examine how variations in conditions, such as initial muscle geometry, injury, etc, alter regeneration outcomes and overall variability. This discussion has been incorporated into lines 640-643.

(8) Is figure 2B described anywhere in the text? I could not find its description.

Thank you for pointing that out. We have added a reference for Fig. 2B on line 190.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The model code seems to be available from https://simtk.org/projects/muscle_regen but that website requests member status ("This is a private project. You must be a member to view its contents.") and applying for membership could violate eLife's blind review process. So, this reviewer liked to but couldn't run the model her/himself. To eLife: Can the authors upload their model to a neutral server that reviewers and editors can access anonymously?

The code has been made publicly available on the following sites:

SimTK: https://simtk.org/docman/?group_id=2635

Zendo: https://zenodo.org/records/10403014

GitHub: https://github.com/mh2uk/ABM-of-Muscle-Regeneration-with-MicrovascularRemodeling

Line 121 has been updated with the new link and the additional resources were added to lines 654-657.

(2) The muscle regeneration field typically studies 2D cross-sections and the present model can be well compared to these other 2D models but cells as stochastic and localized sources of diffusible cytokines may yield different cytokine fields in 3D vs. 2D. I would expect more broadened and smoothened cytokine fields (from sources in neighboring cross-sections) than what the 2D model predicts based on sources just within the focus cross-section. Such relations of 2D to 3D should be discussed.

We thank the reviewer for the excellent suggestions and observations. It has been reported in other Compucell3D models (Sego et al. 2017, DOI:10.1088/17585090/aa6ed4) that the convergence of diffusion solutions between 2D and 3D model configurations had similar outcomes, with the 3D simulations presenting excessive computational cost without contributing any noticeable additional accuracy. Similarly, other cell-based ABMs that incorporate diffusion mechanisms (Marino et al. 2018, DOI:10.3390/computation6040058) have found that 2D and 3D versions of the model both predict the same mechanisms and that the 2D resolution was sufficient for determining outcomes. Lines 615-618 were added to elaborate on this topic.

(3) Since the model (and title) focuses on "nonlinear" cytokine interactions, what would change if cytokine decay would not be linear (as modeled here) but saturated (with nonlinear Michaelis-Menten kinetics as ligand binding and endocytosis mechanisms would call for)?

Thank you for raising an intriguing point. The model includes a combination of cytokine decay as well as ligand binding and endocytosis mechanisms that can be saturated. For a cytokine-dependent model behavior to occur the cytokines necessary to induce that action had to reach a minimum threshold. Once that threshold was reached, that amount of the cytokine would be removed at that location to simulate ligand-receptor binding and endocytosis. These ligand binding and endocytosis mechanisms behave in a saturated way, removing a set amount when above a certain threshold or a defined ratio when under the threshold. Lines 313-315 was revised to clarify this point. There were certain concentrations of cytokines where we saw a plateau in outputs likely as a result of reaching a saturation threshold (Supplemental Fig. 3). In future work, more robust mathematical simulation of binding kinetics of cytokines (e.g., using ODEs) could be included.

(4) Limitations of the model should be discussed together with an outlook for model refinement. For example, fiber alignment and ECM ultrastructure may require anisotropic diffusion. Many of the rate equations could be considered with saturation parameters etc. There are so many model assumptions. Please discuss which would be the most urgent model refinements and, to achieve these, which would be the most informative next experiments to perform.

We appreciate your thoughtful consideration of the model's limitations and the need for a comprehensive discussion on model refinements and potential future experiments. The future direction section was expanded to discuss additional possible model refinements (lines 635-643) and additional possible experiments for model validation (lines 630-634).

(5) It is not clear how the single spatial arrangement that is used affects the model predictions. E.g. now the damaged area surrounds the lymphatic vessel but what if the opposite corner was damaged and the lymphatic vessel is deep inside the healthy area?

Thank you for highlighting the importance of considering different spatial arrangements in the model and its potential impact on predictions. We previously tested model perturbations that included specifying the injury surrounding the lymphatic vessel versus on the side opposite the vessel. Since this paper focuses more on cytokine dynamics, we plan to include this perturbation, along with other injury alterations, in a follow-on paper. We added more context about this in the future efforts section lines 640-643.

(6) It seems that not only parameter values but also the initial values of most of the model components are unknown. The parameter estimation strategy does not seem to include the initial (spatial) distributions of collagen and cytokines and other model components. Please discuss how other (reasonable) initial values or spatial arrangements will affect model predictions.

We appreciate your thoughtful consideration of unknown initial values/spatial arrangements and their potential influence on predictions. Initial cytokine levels prior to injury had a low relative concentration compared to levels post injury and were assumed to be negligible. Initial spatial distribution of cytokines was not defined as initial spatial inputs (except in knockout simulations) but are secreted from cells (with baseline resident cell counts defined from the literature). The distribution of cytokines is an emergent behavior that results from the cell behaviors within the model. The collagen distribution is altered in response to clearance of necrosis by the immune cells (decreased collagen with necrosis removal) and subsequent secretion of collagen by fibroblasts. The secretion of collagen from fibroblast was included in the parameter estimation sweep (Supplemental Table 1).

We are working on further exploring the model sensitivity to altered spatial arrangements and have added this to the future directions section (lines 618-621), as well as provided Supplemental Figure 5 to demonstrate that model outcomes are similar with altered initial spatial arrangements.

(7) Many details of the CC3D implementation are missing: overall lattice size, interaction neighborhood order, and "temperature" of the Metropolis algorithm. Are the typical adhesion energy terms used in the CPM Hamiltonian and if so, then how are these parameter values estimated?

Thank you for bringing attention to the missing details regarding the CC3D implementation in our manuscript. We have included supplemental information providing greater detail for CPM implementation (Lines 808-854). We also added two additional supplemental tables for describing the requested CC3D implementation details (Supplemental Table 4) and adhesion energy terms (Supplemental Table 5).

(8) Extending the model analysis of combinations of altered cytokine properties, which temporal schedules of administration would be of interest, and how could the timing of multiple interventions improve outcomes? Such a discussion or even analysis would further underscore the usefulness of the model.

In response to your valuable suggestion, lines 558-562 were added to discuss the potential of using the model as a tool to perturb different cytokine combinations at varying timepoints throughout regeneration. In addition, this is also included in future work in lines 636-637.

(9) The CPM is only weakly motivated, just one sentence on lines 142-145 which mentions diffusion in a misleading way as the CPM just provides cells with a shape and mechanical interactions. The diffusion part is a feature of the hybrid CompuCell3D framework, not the CPM.

Thank you for bringing up this distinction. We removed the statement regarding diffusion and updated lines 143-146 to focus on CPM representation of cellular behavior and interactions. We also added a reference to supplemental text that includes additional details on CPM.

(10) On lines 258-261 it does not become clear how the described springs can direct fibroblasts towards areas of low-density collagen ECM. Are the lambdas dependent on collagen density?

Thank you for highlighting this area for clarification. The fibroblasts form links with low collagen density ECM and then are pulled towards those areas based on a constant lambda value. The links between the fibroblast and the ECM will only be made if the collagen is below a certain threshold. We added additional clarification to lines 260-264.

(11) On line 281, what does the last part in "Fibers...were regenerating but not fully apoptotic cells" mean? Maybe rephrase this.

The last of part of that line indicates that there were some fibers surrounding the main injury site that were damaged but still had healthy portions, indicating that they were impacted by the injury and are regenerating but did not become fully apoptotic like the fiber cells at the main site of injury. We rephrased this line to indicate that the nearby fibers were damaged but not fully apoptotic.

(12) Lines 290-293 describe interactions of cells and fields with localized structures (capillaries and lymphatic vessel). Please explain in more detail how "capillary agents...transport neutrophiles and monocytes" in the CPM model formalism. Are new cells added following rules? How is spatial crowding of the lattice around capillaries affecting these rules? Moreover, how can "lymphatic vessel...drain the nearby cytokines and cells"? How is this implemented in the CPM and how is "nearby" calculated? We appreciate your detailed inquiry into the interactions of cells and fields with localized structures. The neutrophils and monocytes are added to the simulation at the lattice sites above capillaries (within the cell layer Fig. 2B) and undergo chemotaxis up their respective gradients. The recruitment of the neutrophils and monocytes are randomly distributed among the healthy capillaries that do not have an immune cell at the capillary location (a modeling artifact that is a byproduct of only having one cell per lattice site). This approach helped to prevent an abundance of crowding at certain capillaries. Because immune cells in the simulation are sufficiently small, chemotactic gradients are sufficiently large, and the simulation space is sufficiently large, we do not see aggregation of recruited immune cells in the CPM.

The lymphatic vessel uptakes cytokines at lattice locations corresponding to the lymphatic vessel and will remove cells located in lattice sites neighboring the lymphatic vessel. In addition, we have included a rule in our ABM to encourage cells to migrate towards the lymphatic vessel utilizing CompuCell3D External Potential Plugin. The influence of this rule is inversely proportional to the distance of the cells to the lymphatic vessel.

We have updated lines 294-298 and 305-309 to include the above explanation.

(13) Tables 1-4 define migration speeds as agent rules but in the typical CPM, migration speed emerges from random displacements biased by chemotaxis and other effects (like the slope of the cytokine field). How was the speed implemented as a rule while it is typically observable in the model?

We appreciate your inquiry regarding the implementation of migration speeds. To determine the lambda parameters (Table 7) for each cell type, we tested each in a simplified control simulation with a concentration gradient for the cell to move towards. We tuned the lambda parameters within this simulation until the model outputted cell velocity aligned with the literature reported cell velocity for each cell type (Tables 1-4). We have incorporated clarification on this to lines 177-180.

(14) Line 312 shows the first equation with number (5), either add eqn. (1-4) or renumber.

We have revised the equation number.

(15) Typos: Line 456, "expect M1 cell" should read "except M1 cell".

Line 452, "thresholds above that diminish fibroblast response (Supplemental Fig 3)." remains unclear, please rephrase.

Line 473, "at 28." should read "at 28 days.".

Line 474, is "additive" correct? Was the sum of the individual effects calculated and did that match?

Line 534, "complexity our model" should read "complexity in our model".

We have corrected the typos and clarified line 452 (updated line 594) to indicate that the TNF-α concentration threshold results in diminished fibroblast response. We updated terminology line 474 (updated line 512) to indicate that there was a synergistic effect with the combined perturbation.

(16) Table 7 defines cell target volumes with the same value as their diameter. This enforces a strange cell shape. Should there be brackets to square the value of the cell diameter, e.g. Value=(12µm)^2 ?

The target volume parameter values were selected to reflect the relative differences in average cell diameter as reported in the literature; however, there are no parameters that directly enforce a diameter for the cells in the CPM formalism separate from the volume. We have observed that these relative cell sizes allow the ABM to effectively reproduce cell behaviors described in the literature. Single cells that are too large in the ABM would be unable to migrate far enough per time step to carry out cell behaviors, and cells that are too small in the CPM would be unstable in the simulation environment and not persist in the simulation when they should. We removed the units for the cell shape values in Table 7 since the target volume is a relative parameter and does not directly represent µm.

(17) Table 7 gives estimated diffusion constants but they appear to be too high. Please compare them to measured values in the literature, especially for MCP-1, TNF-alpha and IL-10, or relate these to their molecular mass and compare to other molecules like FGF8 (Yu et al. 2009, DOI:10.1038/nature08391).

We utilized a previously published estimation method (Filion et al. 2004, DOI:10.1152/ajpheart.00205.2004) for estimating cytokine diffusivity within the ECM. This method incorporates the molecular masses and accounts for the combined effects of the collagen fibers and glycosaminoglycans. The paper acknowledged that the estimated value is faster than experimentally determined values, but that this was a result of the less-dense matrix composition which is more reflective of the tissue environment we are simulating in contrast to other reported measurements which were done in different environments. Using this estimation method also allowed us to more consistently define diffusion constants versus using values from the literature (which were often not recorded) that had varied experimental conditions and techniques (such as being in zebrafish embryo Yu et al. 2009, DOI:10.1038/nature08391 as opposed to muscle tissue). This also allowed for recalculation of the diffusivity throughout the simulation as the collagen density changed within the model. Lines 318-326 were updated to help clarify the estimation method.

(18) Many DOIs in the bibliography (Refs. 7,17,20,31,40,47...153) are wrong and do not resolve because the appended directory names are not allowed in the DOI, just with a journal's URL after resolution.

Thank you for bringing this to our attention. The incorrect DOIs have been corrected.

Reviewer #2 (Recommendations For The Authors):

Minor comments:

(9) On line 174, the authors say "We used the CC3D feature Flip2DimRatio to control the number of times the Cellular-Potts algorithm runs per mcs." What does this mean? Isn't one monte carlo timestep one iteration of the Cellular Potts model? How does this relate to physical timescales?

We appreciate your attention to detail and thoughtful question regarding the statement about the use of the CC3D feature Flip2DimRatio. Lines 175-177 were revised to simplify the meaning of Flip2DimRatio. That parameter alters the number of times the Cellular-Potts algorithm is run, which is the limiting factor for cell movement. The physical timescale is kept to a 15-minute timestep but a high Flip2DimRatio allows more flexibility and stability to allow the cells to move faster in one timestep.

(10) Has the costum matlab script to process histology images into initial conditions been made available?

The Matlab script along with CC3D code for histology initialization with documentation has been made available with the source code on the following sites:

SimTK: https://simtk.org/docman/?group_id=2635

Zendo: https://zenodo.org/records/10403014

GitHub: https://github.com/mh2uk/ABM-of-Muscle-Regeneration-with-MicrovascularRemodeling

(11) Equation 5 is provided without a reference or derivation. Where does it come from and what does it mean?

Thank you for highlighting the diffusion equation and seeking clarification on its origin and significance. Lines 318-326 were revised to clarify where the equation comes from. This is a previously published estimation method that we applied to calculate the diffusivity of the cytokines considering both collagen and glycosaminoglycans.

(12) Line 326: "For CSA, experimental fold-change from pre-injury was compared with fold-change in model-simulated CSA". Does this step rely on the assumption that the fold change will not depend on the CSA? If so, is this something that is experimentally known, or otherwise, can it be confirmed by simulations?

We appreciate the opportunity to clarify our rationale. The fold change was used as a method to normalize the model and experiment so that they could be compared on the same scale. Yes, this step relies on the assumption that fold change does not depend on pre-injury CSA. Experimentally it is difficult to determine the impact of initial fiber morphology on altered regeneration time course. This fold-change allows us to compare percent recovery which is a common metric utilized to assess muscle regeneration outcomes experimentally. Line 340-343 was revised to clarify.

(13) Line 355: "The final passing criteria were set to be within 1 SD for CSA recovery and 2.5 SD for SSC and fibroblast count" Does this refer to the experimental or the simulated SD?

The model had to fit within those experimental SD. Lines 371-372 was edited to specify that we are referring the experimental SD.

(14) "Following 8 iterations of narrowing the parameter space with CaliPro, we reached a set that had fewer passing runs than the previous iteration". Wouldn't one expect fewer passing runs with any narrowing of the parameter space? Why was this chosen as the stopping criterion for further narrowing?

We appreciate your observation regarding the statement about narrowing the parameter space with CaliPro. We started with a wide parameter space, expecting that certain parameters would give outputs that fall outside of the comparable data. So, when the parameter space was narrowed to enrich parts that give passing output, initially the number of passing simulations increased.

Once we have narrowed the set of possible parameters into an ideal parameter space, further narrowing will cut out viable parameters resulting in fewer passing runs. Therefore, we stopped narrowing once any fewer simulations passed the criteria that they had previously passed with the wider parameter set. Lines 375-379 have been updated to clarify this point.

(15) Line 516: 'Our model could test and optimize combinations of cytokines, guiding future experiments and treatments." It is my understanding that this is communicated as a main strength of the model. Would it be possible to demonstrate that the sentence is true by using the model to make actual predictions for experiments or treatments?

This is demonstrated by the combined cytokine alterations in Figure 7 and discussed in lines 509-513. We have also added in a suggested experiment to test the model prediction in lines 691-695.

(16) Line 456, typo: I think 'expect' should be 'except'.

Thank you for pointing that out. The typo has been corrected.

for - key insight - all roads lead to progress - progress trap - Prometheus complex - impuslive urge to invent

Comment - This is fleshed out in the final three paragraphs of this article - I disagree with the closing sentence, however

• “It’s not possible [to avoid invention],
  • because all knowledge is interconnected like a web,” Carlin told Big Think.
• “If you walled off a certain part of it because you saw the potential downside,
  • you would get to the same outcome sort of in a roundabout way, right?

• The connections might not be direct, like saying, ‘Oh, I see nuclear weapons in the distance; let’s go there,’

  • but we would go through the back door, and eventually we would discover everything around that thing.”

• To bring Carlin’s analogy home,

  • we can think about the idea of artificial general intelligence, or AGI.
• AGI is the point at which AI can perform a wide variety of tasks so competently
  • that it matches or exceeds human intelligence and performance.
• Some people might see AGI as dangerous.
• Others may see AGI as the savior of humanity.
• But while we have debates and conversations,
  • we’re still marching toward AGI.
• Scientists and programmers behind their computers are
  • solving “everything around that thing.”
• Our hands and our brains will,
  • perhaps unconsciously,

• drift toward the very thing we’re debating if we should do.

• The Prometheus complex can be seen over and over again

  • in the history of science.
• It is not simply that Edenic urge to eat the fruit or push the red button.
• It’s the fact that
  • as the rational, intellectual part of ourselves wrestles with the decision,
  • a deeper, Promethean part of ourselves has pressed it already.
• Thankfully, it usually turns out okay.

comment - I disagree with the last line - If the meta-poly-perma-crisis is what is meant by "OK", then it is a very distorted use of that word. - Rather, this Promethian way of thinking and act - compounded over the lifetime of human civilization - is EXACTLY what has brought us to the brink of civilizational disaster - and it may not turn out to be "ok"!

Software development as a conversation

Author response:

Reviewer #1 (Public Review):

Summary:

The authors collected genomic information from public sources covering 423 eukaryote genomes and around 650 prokaryote genomes. Based on pre-computed CDS annotation, they estimated the frequency of alternative splicing (AS) as a single average measure for each genome and computed correlations with this measure and other genomic properties such as genome size, percentage of coding DNA, gene and intergenic span, etc. They conclude that AS frequency increases with genome complexity in a somewhat directional trend from "lower" organisms to "higher" organisms.

Strengths:

The study covers a wide range of taxonomic groups, both in prokaryotes and eukaryotes.

Weaknesses:

The study is weak both methodologically and conceptually. Current high throughput sequencing technologies, coupled with highly heterogeneous annotation methods, can observe cases of AS with great sensitivity, and one should be extremely cautious of the biases and rates of false positives associated with these methods. These issues are not addressed in the manuscript. Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

We are aware of the bias that may exist in annotation files. Since the source of noise can be highly variable, we have assumed that most of the data has a similar bias. However, we agree with the reviewer that we could perform some analysis to test for these biases and their association to different methodologies. Thus, we will measure the uncertainty present in the data. From one side, we will be more explicit about the data limitations and the biases it can generate in the results. On the other side, while analyzing the false positives in the data is out of our scope, we will perform a statistical test to detect possible biases regarding different methods of sequencing and annotation, and types of organisms (model or non-model organisms). If positive, we will proceed, as far as possible, to normalize the data or to estimate a confidence interval.

Here, AS measures seem to be derived directly from CDS annotations downloaded from public databases, and do not account for differing annotation methods or RNA sequencing depth and tissue sample diversity.

Beyond taking into account the differential bias that may exist in the data, we do not consider that our AS measure is problematic. The NCBI database is one of the most reliable databases that we have to date and is continuously updated from all scientific community. So, the use of this data and the corresponding procedures for deriving the AS measure are perfectly acceptable for a comparative analysis on such a huge global scale. Furthermore, the proposal of a new genome-level measure of AS that allows to compare species spanning the whole tree of life is part of the novelty of the study. We understand that small-scale studies require a high specificity about the molecular processes involved in the study. However, this is not the case, where we are dealing with a large-scale problem. On the other side, as we have previously mention, we agree with the reviewer to analyze the degree of uncertainty in the data to better interpret the results.

There is no mention of the possibility that AS could be largely caused by random splicing errors, a possibility that could very well fit with the manuscript's data. Instead, the authors adopt early on the view that AS is regulated and functional, generally citing outdated literature.

There is no question that some AS events are functional, as evidenced by strongly supported studies. However, whether all AS events are functional is questionable, and the relative fractions of functional and non-functional AS are unknown. With this in mind, the authors should be more cautious in interpreting their data.

Many studies suggest that most of the AS events observed are the result of splicing errors and are therefore neither functional nor conserved. However, we still have limited knowledge about the functionality of AS. Just because we don’t have a complete understanding of its functionality, doesn’t mean there isn’t a fundamental cause behind these events. AS is a highly dynamic process that can be associated with processes of a stochastic nature that are fundamental for phenotypic diversity and innovation. This is one of the reasons why we do not get into a discussion about the functionality of AS and consider it as a potential measure of biological innovation. Nevertheless, we agree with the reviewer’s comments, so we will add a discussion about this issue with updated literature and look at any possible misinterpretation of the results.

The "complexity" of organisms also correlates well (negatively) with effective population size. The power of selection to eliminate (slightly) deleterious mutations or errors decreases with effective population size. The correlation observed by the authors could thus easily be explained by a non-adaptive interpretation based on simple population genetics principles.

We appreciate the observation of the reviewer. We know well the M. Lynch’s theory on the role of the effective population size and its eventual correlation with genomic parameters, but we want to emphasize that our objective is not to find an adaptive or non-adaptive explanation of the evolution of AS, but rather to reveal it. Nevertheless, as the reviewer suggests, we will look at the correlation between the AS and the effective population size and discuss about a possible non-adaptive interpretation.

The manuscript contains evidence that the authors might benefit from adopting a more modern view of how evolution proceeds. Sentences such as "... suggests that only sophisticated organisms optimize alternative splicing by increasing..." (L113), or "especially in highly evolved groups such as mammals" (L130), or the repeated use of "higher" and "lower" organisms need revising.

As the reviewer suggests, we will proceed with the corresponding linguistic corrections.

Because of the lack of controls mentioned above, and because of the absence of discussion regarding an alternative non-adaptive interpretation, the analyses presented in the manuscript are of very limited use to other researchers in the field. In conclusion, the study does not present solid conclusions.

Reviewer #2 (Public Review):

Summary:

In this contribution, the authors investigate the degree of alternative splicing across the evolutionary tree and identify a trend of increasing alternative splicing as you move from the base of the tree (here, only prokaryotes are considered) towards the tips of the tree. In particular, the authors investigate how the degree of alternative splicing (roughly speaking, the number of different proteins made from a single ORF (open reading frame) via alternative splicing) relates to three genomic variables: the genome size, the gene content (meaning the fraction of the genome composed of ORFs), and finally, the coding percentage of ORFs, meaning the ratio between exons and total DNA in the ORF. When correlating the degree of alternative splicing with these three variables, they find that the different taxonomic groups have a different correlation coefficient, and identify a "progressive pattern" among metazoan groups, namely that the correlation coefficient mostly increases when moving from flowering plants to arthropods, fish, birds, and finally mammals. They conclude that therefore the amount of splicing that is performed by an organismal group could be used as a measure of its complexity.

Weaknesses:

While I find the analysis of alternative splicing interesting, I also find that it is a very imperfect measure of organismal complexity and that the manuscript as a whole is filled with unsupported statements. First, I think it is clear to anyone studying evolution over the tree of life that it is the complexity of gene regulation that is at the origin of much of organismal structural and behavioral complexity. Arguably, creating different isoforms out of a single ORF is just one example of complex gene regulation. However, the complexity of gene regulation is barely mentioned by the authors.

We disagree with the reviewer with that our measure of AS is imperfect. Just as we responded to the first reviewer, we will quantify the uncertainty in the data and correct for differential biases caused by annotation and sequencing methods. Thus, beyond correcting relevant biases in the data, we consider that our measure is adequate for a comparative analysis at a global scale. A novelty of our study is the proposal of a genome-level measure of AS that takes into account data from the entire scientific community. 

We want also to emphasize that we assume from the beginning that AS may reflect some kind of biological complexity, it is not a conclusion from the results. An argument in favor of such an assumption is that AS is associated with stochastic processes that are fundamental for phenotypic diversity and innovation. Of course, we agree with the reviewer that it is not the only mechanism behind biological complexity, so we will emphasize it in the manuscript. On the other side, we will be more explicit about the assumptions and objectives, and will correct any unsupported statement.

Further, it is clear that none of their correlation coefficients actually show a simple trend (see Table 3). According to these coefficients, birds are more complex than mammals for 3 out of 4 measures.

An evolutionary trend is broadly defined as the gradual change in some characteristic of organisms as they evolve or adapt to a specific environment. Under our context, we define an evolutionary trend as the gradual change in genome composition and its association with AS across the main taxonomic groups. If we look at Figure 4 and Table 3 we can conclude that there is a progressive trend. We will be more precise about how we define an evolutionary trend and correct any possible misinterpretation of the results. On the other side, we do not assume that mammals should be more complex than birds. First, we will emphasize that our results show that birds have the highest values of such a trend. Second, after reading the reviewer’s comments, we have decided that we will perform an additional analysis to correct for differences in the taxonomic group sizes, which will allow us to have more confidence in the results.

It is also not clear why the correlation coefficient between alternative splicing ratio and genome length, gene content, and coding percentage should display such a trend, rather than the absolute value. There are only vague mechanistic arguments.

The study analyzes the relationship of AS with genomic composition for the large taxonomic groups. We assume that significant differences in these relationships are indicators of the presence of different mechanisms of genome evolution. However, we agree with the reviewer that a correlation does not imply a causal relation, so we will be more cautious when interpreting the results.

To quantify the relationships we use correlation coefficients, the slopes of such correlations, and the relation of variability. Although the absolute values of AS are also illustrated in Table 4, we consider that they are less informative than if we include how it relates to the genomic composition. For example, we observe that plants have a different genome composition and relation with AS if compared to animals, which suggest that they follow different mechanisms of genome evolution. On the other hand, we observe a trend in animals, where high values of AS are associated to a large percentage of introns and a percentage of intergenic DNA of about the 50% of genomes.

Much more troubling, however, is the statement that the data supports "lineage-specific trends" (lines 299-300). Either this is just an ambiguous formulation, or the authors claim that you can see trends *within* lineages.

We agree with the reviewer that this statement is not correct, so we will proceed to correct it.

The latter is clearly not the case. In fact, within each lineage, there is a tremendous amount of variation, to such an extent that many of the coefficients given in Table 3 are close to meaningless. Note that no error bars or p-values are presented for the values shown in Table 3. Figure 2 shows the actual correlation, and the coefficient for flowering plants there is given as 0.151, with a p-value of 0.193. Table 3 seems to quote r=0.174 instead. It should be clear that a correlation within a lineage or species is not a sign of a trend.

The reviewer is not understanding correctly the results in Table 3. It is precisely the variation of the genome variables what we are measuring. Given the standardization of these values by the mean values, we have proceeded to compare the variability between groups, which is the result shown in Table 3. In this case there are no error bars or p-values associated. On the other hand, we agree that a correlation is not a sign of a trend. But the relations of variability, together with the results obtained in Figure 3, are indicators of a trend. As we mentioned before, we will proceed to analyze whether the variation in the group sizes is causing a bias in the results.

There are several wrong or unsupported statements in the manuscript. Early on, the authors state that the alternative splicing ratio (a number greater or equal to one that can be roughly understood as the number of different isoforms per ORF) "quantifies the number of different isoforms that can be transcribed using the same amount of information" (lines 51-52). But in many cases, this is incorrect, because the same sequence can represent different amounts of information depending on the context. So, if a changed context gives rise to a different alternative splice, it is because the genetic sequence has a different meaning in the changed context: the information has changed.

We agree that there are not well supported statements, so we will proceed to revise them.

In line 149, the authors state that "the energetic cost of having large genomes is high". No citation is given, and while such a statement seems logical, it does not have very solid support.

We will also revise the bibliography and support our statements with updated references.

If there was indeed a strong selective force to reduce genome size, we would not see the stunning diversity of genome sizes even within lineages. This statement is repeated (without support) several times in the manuscript, apparently in support of the idea that mammals had "no choice" to increase complexity via alternative splicing because they can't increase it by having longer genomes. I don't think this reasoning can be supported.

We agree with the reviewer in this issue, so we will carefully revise the statements that indirectly (or directly) assume the action of selective forces on the genome composition.

Even more problematic is the statement that "the amount of protein-coding DNA seems to be limited to a size of about 10MB" (line 219). There is no evidence whatsoever for this statement.

In Figure 1A we observe a one-to-one relationship between the genome size and the amount of coding. However, in multicellular organisms, although the genome size increases we observe that the amount of coding does not increase by more than 10Mb, which suggest the presence of some genomic limitation. Of course, this is not an absolute or general statement, but rather a suggestion. We are only describing our results.

The reference that is cited (Choi et al 2020) suggests that there is a maximum of 150GB in total genome size due to physiological constraints. In lines 257-258, the authors write that "plants are less restricted in terms of storing DNA sequences compared to animals" (without providing evidence or a citation).

We will revise the bibliography and add updated references.

I believe this statement is made due to the observation that plants tend to have large intergenic regions. But without examining the functionality of these interagency regions (they might host long non-coding RNA stretches that are used to regulate the expression of other genes, for example) it is quite adventurous to use such a simple measure as being evidence that plants "are less restricted in terms of storing DNA sequences", whatever that even means. I do not think the authors mean that plants have better access to -80 freezers. The authors conclude that "plant's primary mechanism of genome evolution is by expanding their genome". This statement itself is empty: we know that plants are prone to whole genome duplication, but this duplication is not, as far as we understand, contributing to complexity. It is not a "primary mechanism of genome evolution".

We will revise these statements.

In lines 293-294, the authors claim that "alternative splicing is maximized in mammalian genomes". There is no evidence that this ratio cannot be increased. So, to conclude (on lines 302-303) that alternative splicing ratios are "a potential candidate to quantify organismal complexity" seems, based on this evidence, both far-fetched and weak at the same time.

Our results show the highest values of AS in mammals, but we understand that the results are limited to the availability and accuracy of data, which we will emphasize in the manuscript. As we previously mention, we will also proceed to analyze the uncertainty in data and carry out the appropriate corrections.

I am also not very comfortable with the data analysis. The authors, for example, say that they have eliminated from their analysis a number of "outlier species". They mention one: Emmer wheat because it has a genome size of 900 Mb (line 367). Since 900MB does not appear to be extreme, perhaps the authors meant to write 900 Gb. When I consulted the paper that sequenced Triticum dicoccoides, they noted that 14 chromosomes are about 10GB. Even a tetraploid species would then not be near 900Gb. But more importantly, such a study needs to state precisely which species were left out, and what the criteria are for leaving out data, lest they be accused of selecting data to fit their hypothesis.

The reviewer is right, we wanted to say 900Mb, which is approximately 7.2Gb. We had a mistake of nomenclature. This value is extreme compared to the typical values, so it generates large deviations when applying measures of central tendency and dispersion. We want to obtain mean values that are representative of the most species composing the taxonomic groups, so we find appropriate to exclude all outlier values in the study. Nevertheless, we will specify the criteria that we have used to select the data in a rigorous way.

I understand that Methods are often put at the end of a manuscript, but the measures discussed here are so fundamental to the analysis that a brief description of what the different measures are (in particular, the "alternative splicing ratio") should be in the main text, even when the mathematical definition can remain in the Methods.

We agree with the reviewer, so we will add a brief description of the genomic variables at the beginning of the Results section.

Finally, a few words on presentation. I understand that the following comments might read differently after the authors change their presentation. This manuscript was at the border of being comprehensible. In many cases, I could discern the meaning of words and sentences in contexts but sometimes even that failed (as an example above, about "species-specific trends", illustrates). The authors introduced jargon that does not have any meaning in the English language, and they do this over and over again.

Note that I completely agree with all the comments by the other reviewer, who alerted me to problems I did not catch, including the possible correlation with effective population size: a possible non-adaptive explanation for the results.

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

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Reply to the reviewers

Manuscript number: RC-2023-02154

Corresponding author(s): Marco, Galardini

1. General Statements

We have carefully read the comments put forward by the two reviewers and we have produced a revised version of the manuscript that we believe addresses all the concerns expressed by the reviewers. In short, we have validated our approach against experimentally derived epistatic coefficients, compared our mutual information (MI) method against one that uses direct coupling analysis (DCA), and experimentally tested three interactions in the spike RBD that we have predicted and which emerged only in summer 2023, thus demonstrating the potential predictive power of this approach. We have also carefully reworded the manuscript to acknowledge the inherent limitation of a method based on MI to identify epistatic interactions. We believe that the revised manuscript is now more robust with these new in-silico and in-vitro validations, and more direct in exposing the advantages (speed) and caveats (higher false-positives) of this approach.

Note: the line numbers referenced in the responses to reviewers below refer to the document in which the changes are highlighted.

Point-by-point description of the revisions

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

Summary: The authors inferred the pairwise epistasis through the Mutual Information provided by the spydrpick algorithm. They claim that the MIs could serve as a real-time identification of the epistatic interactions with the SARS-CoV-2 genomes due to the fast inference and high sensitivities.

Major comments:

1.The authors take a data-driven approach to infer the Mutation Information as the epistatic interactions between the mutations over different sites over SARS-CoV-2 genomes. However, it would be better to specify why this metric is reliable to be used as the representation of the pairwise epistatic interactions, and any theoretical explanations to support this.

We agree that readers should be better informed on why MI can be used to estimate epistatic interactions from genomic data. We have therefore expanded the introduction (lines 93-98), methods (lines 540-543) and discussion (lines 453-457) sections to provide a proper theoretical and practical foundation on the use of a MI-based method. Furthermore, we have expanded the results section to add one additional in-silico validation (lines 244-249, Supplementary Figure 5, and updated Supplementary Figure 8) and an in-vitro one (Figure 5, see also reply to comment 2 from reviewer #2), which we believe give strong support to the MI-based method.

2.The authors claimed that the DCA method requires more computational resources and more time to complete. However, with a proper filtering procedure, the computational time could be reduced heavily. An example is Physical Review E 106 (4), 044409, 2002, in which the DCA was used to investigate the real-time pair-wise interactions (month-to-month). There the DCA results were compared with the correlation analysis. It would be nice to have comparisons of the inferred interactions between MIs and other methods.

We agree that our MI-based approach should be compared against DCA-based methods. The original manuscript had in fact one such comparison (for the 2023-03 dataset, Figure 3C), which indicated a strong correlation between the two methods. To make this result more robust we have computed the DCA values for the complete time-series dataset and measured the correlation with the MI values (Supplementary Figure 4)

We observed a relatively high correlation in estimated values between the two methods, with the exception of three time points, i.e., 2020-11, 2023-02 and 2023-03. We can explain these lower correlations with the low overall sequence diversity observed in the early phase of the pandemic (2020-11) and with the different weighting scheme of our approach, which would significantly alter the dataset when compared to the one used by the DCA method, especially towards the later timepoints (see also the reply to reviewer #2, comment 3, section iv). When those three timepoints are excluded, the two methods show a high degree of correlation, implying that they are comparably suitable in detecting coevolutionary signals.

We have also used the 2nd order coefficients derived from experimental data in Moulana et al., 2022 (10.1038/s41467-022-34506-z) to validate both approaches (see methods, lines 624-631).

The panels which we have combined to create the new Supplementary Figure 5, indicate how both approaches (MI for panel A and C, and DCA for panels B and D) correctly recover the interaction with 2nd order epistatic coefficient > 0.15, based on the odds-ratio metric. Our MI-based approach has, however, a higher recall across multiple time points, which is especially visible comparing panels A and B. The DCA-based method did correctly identify known epistatic interactions, but did so only in sporadic timepoints, even though the distribution of the underlying variants did not change significantly month to month. We believe that the higher recall of the MI-based method has a higher value for genomic epidemiology, at least for SARS-CoV-2.

3.In Figure 1C, the authors show that their spydrpick algorithm provides more pairwise MIs for longer distances, where the outliers are denser than those with short distances. How do we explain this phenomenon?

We thank the reviewer for bringing this point up; we actually think that our data shows the opposite, meaning that we observe a higher proportion of close interactions when normalizing by the number of possible interactions. If we take an arbitrary distance threshold of 1'000 bases to define "close" Vs. "distant" interactions, we observe 194 and 280 interactions, respectively. It is true that distant interactions would be more, but the space of possible interactions is orders of magnitude larger for "distant" interactions, simply by the fact that there are more sites from which interactions can originate. As a crude estimate we can use the combinations between 1,000 sites (499,500 possible interactions) Vs those between 28,903 sites (the full SARS-CoV-2 genome length 29,903 bp minus 1,000, 417,677,253). Based on these estimates we have indeed observed less "close" than "distant" interactions.

Minor comments:

4.The explanations of Fig. 1E could be in more detail. Say, the grey dots in Fig. 1E, which is marked as "other" and such "other"s are dominated here. Why?

We thank the reviewer for pointing out a section where more clarity was needed. We have added the following sentence to the figure legend: "The category "other" indicates positions which are not known to have an impact on affinity to ACE2, immune escape or otherwise flagged as MOI/MOC.". This indicates that predicted interactions involving a site classified as "other" are either false positives or previously undiscovered interactions.

5.On line 210, the authors mentioned that the weights of the old sequences are lower "at around six months (120 days)". It would be better to specify why six months is 120 days instead of 180 days,

We have corrected this mistake and indicated 4 months. We thank the reviewer for spotting this error.

Referees cross-commenting

I agree with what Reviewer #2 presented in the Consults Comments. The authors should present the reasons why MIs can be explained as the epistatic interations between sites as both of us mentioned this point. I checked the other revision points that raised by the Reviewer #2. They would be definetely helpful for enhancing the quality of the manuscript.

Reviewer #1 (Significance (Required)):

The work in the current manuscript is interesting and presented nicely. However, the theoretical foundations that the MIs could be explained as epistatic interactions should be illustrated. Otherwise, the tools would be useful for SARS-CoV-2 and other potential pandemics by different virus.

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

The manuscript proposes an approach to identify epistatic interactions in the SRAR-CoV-2 genome using the large amount of genomic data which accumulated during the COVID pandemics. They argue that due to a relatively low computational cost, this can be done online in any ongoing pandemics nowadays (i.e. in the situation where the viral spreading and evolution are closely monitored by massive sequencing). In principle, this is interesting, but in my opinion the manuscript has some strong problems and will require major rewrighting:

1) In difference to the claims of the manuscript, detected correlation does not necessarily imply epistatic couplings:

• Even in a totally neutral setting, mutations may occur by chance together, and expand due to genetic drift or when ecountering a susceptible population. Equally, to independent muations may spread in different geographic regions, without the double mutant ever arising. Both cases lead to non-zero mutual information.

• In evolution, frequently driver and passenger mutations are observed, in particular in settings of relatively high mutation rate. The passenger will rise in frequency with the driver, without any epistatic coupling.

• The very unequal sequencing across geographic areas will enhance certain variants and leave others undetected. Even if the authors avoid double counting of identical sequences, more small variation is detected when sequencing deeper. The Omicron variant illustrates an extreme case here: it combined a large number of mutations, never detected before, but epistasis is not the most likely explanation, but rather lack of monitoring of the evolutionary path from the ancestral variants to Omicron.

• MI has been criticised because it overestimates the effect of indirecrt correlations in particular in dense epistatic networks. The situation in the spike protein in Fig. 1B seems very dense.

Currently the manuscript does not make any effort to disentangle any of these effects.

Following this (and reviewer 1) comments, we have made a number of changes to the manuscript in order to provide more context into how MI can be used to estimate epistatic interactions and the inherent limitations of this approach. In particular, we have expanded the introduction (lines 93-98), methods (lines 540-543) and discussion (lines 453-457) sections in a way that we believe exposes the limitations of the approach. Despite these limitations, we still believe that a MI-based approach strikes a good balance between speed, ease of implementation, and sensitivity. To further demonstrate this point we have added two additional validations to our results: the first one (in-silico) uses estimated 2nd order epistatic coefficients derived from experimental data (Moulana et al., 2022, 10.1038/s41467-022-34506-z), and the second (in-vitro) our own experimental data on three predicted interactions. The results of the new in-vitro validation have been described in the reply to comment #2 from reviewer 1; in short they show how the MI-based method has comparable sensitivity and specificity as the DCA-based method, and most importantly they allow the recovery of known epistatic interactions across the time period in which they have appeared. The results of the in-vitro validation are discussed in the reply to the next comment from this reviewer, as they directly address the predictive power of our approach: in short, we show how we could also validate these predictions. We think that these new results clearly show how, despite its limitations, the MI-based approach is able to identify bona-fide epistatic interactions, with the advantage of being a simple method to be implemented and with the possibility to be run in real time. For a more detailed discussion of the merits of the MI-based approach over DCA, see the reply to comment #3 from this reviewer.

2) What are the predictive capacities of the approach? Mutual information is bounded from above by the individual site entropies. So high MI can be detected only in highly mutated sites - i.e. in sides for sure already under monitoring. In fact, the sites in Fig. 1B with many links reflect the overall profile of variant frequencies in single sites (i.e. a totally non-epistatic measure) available on Nextstrain, and extracted from the same data sources.

The discussion of the results is very anecdotal and it is not clear to me in how far there is any real prediction in the paper, which might surprise and trigger observation or further analyses.

There is an entire line of related research in estimating and exploiting epistatic couplings in HIV evolution (A Chakraborty, M. Kardar, J. Barton, M MacKay and others) - not cited in the manuscript but relevant for the question how to detect epistatic couplings and what they are good for.

We thank the reviewer for pointing out relevant literature we had not covered in the original manuscript, and which can be used to indicate how epistatic interaction signals can be leveraged when studying viruses. We have added citations to these studies in the introduction (lines 76-78) to provide a better background for our own study. Regarding the broader concern of showing the predictive power of our approach, we had a similar concern after the manuscript was submitted, and we had already planned a "blind" in-vitro validation to put our approach to the test. In order to make this validation as "blind" as possible, we expanded the dataset to include sequences until August 2023. We then selected interactions within the spike RBD with confidence level O4 in at least the last 4 time points and with one position already flagged as either "affinity", "escape" or "other MOI/MOC"

We then selected the top three interactions (446-460, 446-486 and 452-490) for our validation, as they have an outlier confidence O4 in at least the 4 time points, and lower or no prediction before. We also added the known 498-501 interaction as a control (Figure 5, panel B)

We then focused on selecting a set of non-synonymous substitutions to test for their potential epistatic interactions. We decided to select 6 substitutions affecting the 3 predicted interactions based on their frequency in the time points after the cutoff of the original manuscript, shown in Figure 5, panel C.

Of those, L452R/F490S and G446S/F486V are anti-correlated in their frequency and virtually never observed together in our dataset, G446S/F486S is observed at low frequency (87 samples after 2023-05), and G446S/N460H is virtually never observed (5 samples). We chose the anti-correlated pairs to test the potential of the MI method to explain these "avoidance" phenomenon, and the low frequency pairs as a way to test an early warning system for mutation signatures that might rise in the future. We then planned to test the impact of the individual variants, the double variants, both in the wild-type background and in the Q498R/N501Y background as a crude model for the Omicron variant.

We then used a pseudovirus assay to test mutated RBDs across two phenotypes: infectivity (i.e. the ability to infect Vero B4 cells) and immune escape (i.e. antibody neutralization curves). We then tested for the presence of epistatic interactions for the double mutants in both backgrounds using a simple linear model (see Methods, lines 711-727). The results of these in-vitro assays are summarized below (Figure 5, panel E for infectivity, F for immune escape).

Double mutants with a significant (p-value -10) interaction have been highlighted with an asterisk. We confirmed the epistatic interaction for the Q498R/N501H, both for its effect on infectivity and immune escape. For both anti-correlated pairs we found a significant interaction for either the infectivity assay (both) and immune escape (G446S/F486V). In particular, we found that the one hand the G446S/F486V pair induced a large drop in infectivity in the Q498R/N501H background while the double mutant was fairly similar to the immune escape profile of the single G446S variant, thus compensating for the loss of escape shown by the F486V variant alone. We observed the opposite for the L452R/F490S pair in terms of infectivity, with the pair showing a large increase in infectivity in the Q498R/N501H background, an effect we found to be significant. The double mutant had a slightly better immune escape profile than the single mutants, although not significant. From these observations we can hypothesize that the G446S/F486V is anticorrelated for their strong defect in infectivity; we cannot apply the same reasoning for the L452R/F490S pair, whose absence from circulating variants could be ascribed to stochasticity in population dynamics or interactions with other variants. We observed a similar impact of the G446S/F486S and G446S/N460H pairs on infectivity as G446S/F486V; based on these results we could estimate that variants carrying these pairs might have a fitness disadvantage. The inability of unsupervised methods (MI or DCA based) to predict the direction of the effect of course makes it difficult to inform which of the two pairs should be added to a "watchlist", but it would potentially reduce the number of interactions to be tested. We believe that the results of this admittedly small scale in-vitro validation demonstrates the potential of the MI-based approach to flag emerging interactions worthy of further studying. Recent advances in scalability of molecular assays (e.g. 10.1101/2024.03.08.584176) could then be coupled with a real-time system as the one we describe in our manuscript to filter out the more relevant interactions. We have added this forward-looking observation in the discussion as well (lines 465-474).

3) The authors say that more involved methods like the Direct Coupling Analysis with Pseudolikelihood maximisation would be too slow for the analysis, but several papers show the contrary. The paper by Zeng et al. (Ref. [39]) does so very early in the pandemics in 2020, and another uncited paper of the same authors (Physical Review 2022) uses a nearly identical approach to study the time evolution of epistatic couplings (extractions from Gisaid at several times). As one of theit results, they show that their approach is not only feasible, but delivers more stable results than simpler correlation measures like MI.

We thank the reviewer for pointing out a relevant reference we had missed in the initial manuscript. At a general level Zeng et al. take a similar approach to what we have described, namely to divide the data according to the isolation date to look for temporal trends. We however see a few differences that we think are in favor of the approach we describe:

1- Our manuscript covers the time period after the emergence of the Omicron variant, in which epistatic interactions are known and have been characterized and validated experimentally, a crucial requirement for validation. We have also conducted an in-vitro validation on a selected set of predicted interactions (see the reply to the previous comment), which indicates that the method is sound and predictive.

2- We have prepared a cumulative time-series dataset, meaning that each month introduces new sequences on top of the ones already selected from the previous time points. To the best of our knowledge the Zheng et al. dataset has "insulated" sequences at each month. We believe our approach has the advantage of allowing for a higher recall, as it includes a representation of extinct lineages, which may increase diversity at key loci and thus boost the signal. As described in the original manuscript and in the reply to this reviewer's comments "iv" and "v", we have added a weighting scheme in order to reduce the influence of older sequences and increase the relevance of smaller lineages.

3- While we have not tested the DCA implementation used by Zeng et al., and we cannot therefore directly comment on its scalability, we have encountered serious limitations when scaling up the popular plmc C implementation developed by the lab of Deborah Marks. In particular we were unable to successfully run it for datasets with more than ~300k sequences, encountering segmentation faults.

Regarding the third point, while this meant that we could not test the DCA approach on the full dataset, we could still manage to apply it on the time series data, focusing exclusively on the spike (S) gene. As shown above in the reply to reviewer's 1 comment #2, the two methods have a high correlation and are both able to recover known interactions, although with the DCA method having a lower recall. Taken together we believe that the MI-based approach we describe is robust enough to be considered when a tradeoff between speed, ease of implementation and sensitivity has to be struck, which we believe may be the case for a rapid response during a potential future pandemic. We have added more details to the part of the discussion in which the comparison with the DCA-based methods was made to point out how those are still feasible with very large collections of sequences (lines 444-448).

It would therefore be essential that the authors strongly revise their manuscript to show the relaibility of the results, the predictive value of the predicted couplings, and the originality and robustness of the approach.

We believe that our response to both reviewers have addressed these concerns, and as a result we have provided a more nuanced view on the use of MI-based methods in the prediction of epistatic interactions in pandemic viruses. Our wording has been modified to make sure that readers interested in replicating our approach are aware of its strengths (speed, ease of implementation) and limitations.

Furthermore, there are some minor issues in the formulations, which should be corrected

i) "the virus has differentiated into a number of lineages, almost all of which have taken over the whole population..." This is wrong. SARS-CoV-2 has always been very heterogeneous, with diverse variants circulating (the authors use millions of non-redundant sequences), and only very few have become VOIs or VOCs at some point. This image of competition between multiple coexisting strains is much closer to clonal interference than what the authors describe (even if clonal interference does not rely on population structure, which has always been an important element in COVID).

We thank the reviewer for pointing out this error in our observation. We have changed "almost all" to "some", which we agree is more accurate.

ii) The authors say that pseudolikelihood methods would require "aggressive subsampling". This is not true, in machine learning massive training data are frequently used in the context of batch learning, i.e. in each learning epoch a "batch" is sampled from the full data. This leads to stochasticity in learning, but all data are eventually used.

We have reformulated this sentence (lines 85-90) to indicate how batch learning could also be used to make certain methods scalable, with the caveat that they would be more complicated to implement.

iii) The authors say that the download also a phylogenetic tree, but I do not see where it is used.

As indicated in the methods section, we have used the phylogenetic tree for two purposes:

1- To single out high quality sequences from the raw MSA (line 515)

2- To compute the weight of each sequence in the final MSA, as described in line 540-549

iv)The authors use sequence weights as implemented in Ref. [31]. There a weighting at sequence similarity threshold of 90% is used. I would expect that there are no SARS-CoV-2 genomes having accumulated more than 10% of nucleotide mutations, i.e. the weighting procedure would be without any effect.

We realized that the sequence weighting scheme we have used is not described in Pensar et al. (10.1093/nar/gkz656), but rather in the implementation of the spydrpick algorithm used by the panaroo software (Tonkin-Hill et al., 10.1186/s13059-020-02090-4). This weighting scheme is based on the more granular metric that is the patristic distance of each sequence from the root of the tree, divided at each branching point by the number of its terminal leaves. In practical terms this means that sequences belonging to smaller lineages (i.e. with fewer observed samples) will have a larger weight, regardless of a discrete sequence similarity threshold, as was done in the original implementation. We have updated the methods section to clearly indicate that the weighting scheme is that first shown in the panaroo software package (line 543).

v)The authors estimate that they need 10,000-100,000 sequences to estimate MI, but find the epistatic coupling in spike residues 498-501 as soon as 6 double mutants are present, which is a frequency of about 1e-4. The corresponding entropies should be low and in consequence the MI, too.

We thank the reviewer for raising this point, which prompted us to devise a way to better illustrate the sequence weighting scheme we have used. As a side note we also discovered that the number of Omicron sequences at the 2021-11 was actually 7, and not 6 as stated throughout the original manuscript, an error we have now fixed. As described in the methods section we have combined two weights in the time-series analysis: the first one, described in the response to the previous comment, is based on the "density" of the phylogenetic tree, which deflates the contribution of "denser" regions of the tree, and the second reduces the relevance of older sequences. The two weights are then combined multiplicatively. As a result the "real" (i.e. effective) number of sequences harboring a particular double mutation will be different than by just counting their occurrences.

As shown in Supplementary Figure 3, the combination of both weights (first column) leads to an increased effective number of sequences for "younger" samples and those that come from "sparser" regions of the overall phylogenetic tree. This is particularly evident for the middle row (2021-11); the light orange dot, which indicates sequences belonging to the first Omicron lineage to appear in the dataset (BA.1), has an actual N of 7, but an effective N of ~100 (exact value 86), thanks to its "novelty" both in the tree (middle panel) and in terms of time (right panel). We again thank the reviewer for raising this point, which led us to generate this visualization, which will hopefully clarify the rationale for the weighting strategy we have used for moist readers.

vi)The authors say that the public health toll of COVID has been "balanced" by scientific discovery - I would urge the authors to avoid such formulations, which sound cynical.

We agree with the reviewer that this comment might sound cynical and tone-deaf, and have reformulated to indicate that the impact of the pandemic has coincided with an accelerated pace of applied scientific discovery.

Referees cross-commenting

Both reports bring up very similar points (points 1 of both reports, point 2 of Reviewer #1 vs. my point 3) but add partially complementary questions (point 3 of Reviewer #1, my point 2), both related to the interpretation of the data. My report is more severe, but reading the ms I am convinced that the paper requires serious revision. So reports seem coherent but with different degrees of recommendations. However, none of the comments of one reviewer is contradiction to the other reviewer.

Reviewer #2 (Significance (Required)):

While the paper asks interesting questions and wants to make use of the quite unique data which have accumulated during the COVID pandemics, the above mentioned problems raise important questions about the manuscript. It would be essential that the authors strongly revise their manuscript to show the relaibility of the results, the predictive value of the predicted couplings, and the originality and robustness of the approach.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is a follow-up study to the authors' previous eLife report about the roles of an alpha-arrestin called protein thioredoxin interacting protein (Txnip) in cone photoreceptors and in the retinal pigment epithelium. The findings are important because they provide new information about the mechanism of glucose and lactate transport to cone photoreceptors and because they may become the basis for therapies for retinal degenerative diseases.

Strengths:

Overall, the study is carefully done and, although the analysis is fairly comprehensive with many different versions of the protein analyzed, it is clearly enough described to follow. Figure 4 greatly facilitated my ability to follow, understand and interpret the study. The authors have appropriately addressed a few concerns about statistical significance and the relationship between their findings and previous studies of the possible roles of Txnip on GLUT1 expression and localization on the surfaces of RPE cells.

We are delighted that Reviewer #1 is satisfied with this revised version.

Reviewer #2 (Public Review):

The hard work of the authors is much appreciated. With overexpression of a-arrestin Txnip in RPE, cones and the combined respectively, the authors show a potential gene agnostic treatment that can be applied to retinitis pigmentosa. Furthermore, since Txnip is related to multiple intracellular signaling pathway, this study is of value for research in the mechanism of secondary cone dystrophy as well.

There are a few areas in which the article may be improved through further analysis and application of the data, as well as some adjustments that should be made in to clarify specific points in the article.

Strengths

• The follow-up study builds on innovative ground by exploring the impact of TxnipC247S and its combination with HSP90AB1 knockdown on cone survival, offering novel therapeutic pathways.

• Testing of different Txnip deletion mutants provides a nuanced understanding of its functional domains, contributing valuable insights into the mechanism of action in RP treatment.

• The findings regarding GLUT1 clearance and the differential effects of Txnip mutants on cone and RPE cells lay the groundwork for targeted gene therapy in RP.

Weaknesses

• The focus on specific mutants and overexpression systems might overlook broader implications of Txnip interactions and its variants in the wider context of retinal degeneration.

Txnip is not expressed in WT or RP cones, as described in our previous study (Xue et al., 2021, eLife), so we could not perform loss of function assays. We thus chose overexpression, and assayed various alleles, based upon the literature, as we describe in our manuscript.

• The study's reliance on cell count and GLUT1 expression as primary outcomes misses an opportunity to include functional assessments of vision or retinal health, which would strengthen the clinical relevance.

In our previous study, we demonstrated that the optomotor response of Txnip-treated RP mice improved (Xue et al., 2021, eLife). Also, as described in our previous Txnip study, as well as an independent study (Xue et al., 2021, eLife; Xue et al., 2023, PNAS), ERG assays of Txnip-treated RP cones were no different than the controls. Other therapies that prolong RP cone survival and the optomotor response in our lab also failed to save the ERG, suggesting that there are other pathways that need to be addressed, e.g. the visual cycle. A combination therapy addressing multiple problems is one of our goals.

• The paper could benefit from a deeper exploration of why certain treatments (like Best1-146 Txnip.C247S) do not lead to cone rescue and the potential for these approaches to exacerbate disease phenotypes through glucose shortages.

This system is more complicated than we currently understand, and more work needs to be done.

• Minor inconsistencies, such as the missing space in text references and the need for clarification on data representation (retinas vs. mice), should be addressed for clarity and accuracy.

The missing spaces are added.

We described the strategy of injecting the same mouse in each eye, one eye with control and one with the experimental vector. However, the following sentence has been added to the Materials and Methods to better assist the reader:

“In almost all experiments, other than as noted, one eye of the mouse was treated with control (AAV8-RedO-H2BGFP, 2.5 × 108 vg/eye), and the other eye was treated with the experimental vector plus AAV8-RedO-H2BGFP, 2.5 × 108 vg/eye.”

• The observation of promoter leakage and potential vector tropism issues raise questions about the specificity and efficiency of the gene delivery system, necessitating further discussion and validation.

The following sentences have been added to the Results. We do not think this phenomenon affects the practice of the experiments or the interpretation of the results in this study.

“To enable automated cone counting and trace the infection, we co-injected an AAV (AAV8-RedO-H2BGFP-WPRE-bGHpA) encoding an allele of GFP fused to histone 2B (H2BGFP), which localized to the nucleus. As the red opsin promoter was used to express this gene, H2BGFP was seen in cone nuclei, but not in the RPE, if AAV8-RedO-H2BGFP-WPRE-bGHpA was injected alone. However, when an AAV that expressed in the RPE, i.e. AAV8-Best1-Sv40intron-(Gene)-WPRE-bGHpA, was co-injected with AAV8-RedO-H2BGFP-WPRE-bGHpA, H2BGFP was expressed in the RPE, along with expression in cones (Figure 2A). We speculate that this is due to concatenation or recombination of the two genomes, such that the H2BGFP comes under the control of the RPE promoter. This may be due to the high copy number of AAV in the RPE, as it did not happen in the reverse combination, i.e. AAV with an RPE promoter driving GFP and a cone promoter driving another gene, perhaps due to the observation that the AAV genome copy number is »10 fold lower in cones than in the RPE (Wang et al., 2020).”

Author response:

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

Reviewer #1 (Public Review):

Summary:

This paper provides a straightforward mechanism of how mycobacterial cAMP level is increased under stressful conditions and shows that the increase is important for the survival of the bacterium in animal hosts. The cAMP level is increased by decreasing the expression of an enzyme that degrades cAMP.

We thank the reviewer for these extremely encouraging comments.

Strengths:

The paper shows that under different stresses the response regulator PhoP represses a phosphodiesterase (PDE) that degrades cAMP specifically. Identification of PhoP as a regulator of cAMP is significant progress in understanding Mtb pathogenesis, as increase in cAMP apparently increases bacterial survival upon infection. On the practical side, reduction of cAMP by increasing PDE can be a means to attenuate the growth of the bacilli. The results have wider implications since PhoP is implicated in controlling diverse mycobacterial stress responses and many bacterial pathogens modulate host cell cAMP level. The results here are straightforward, internally consistent, and of both theoretical and applied interests. The results also open considerable future work, especially how increases in cAMP level help to increase survival of the pathogen.

Weaknesses:

It is not clear whether PhoP-PDE Rv0805 is the only pathway to regulate cAMP level under stress.

Reviewer 1 (Recommendations for the authors):

(1) L.1: "maintenance of" or 'regulating'- I thought change in cAMP level upon stress is the whole point of the paper. Also, can replace "intracellular survival" with 'survival in host macrophages' if you want to be more specific.

We agree with the reviewer, and therefore, we have now replaced “maintenance of” with “regulating cAMP level” in the title. However, we feel more comfortable with “intracellular survival” rather than being more specific with ‘survival in host macrophages’ as we have also shown animal experiments to demonstrate ‘in vivo’ effect in mice lung and spleen.

(2) L.26: ---requires the bacterial virulence regulator –

The suggested change has been made to the text.

(3) L.30: Replace "phoP locus since the" with 'PhoP since this'. (The product, not the locus, is the regulator). The same comment for l.113.

We agree with the reviewer. The suggested changes have been made to the text.

(4) L.31: Change represtsor to repressor.

We are sorry for the embarrassing spelling mistake. We have rectified the mistake in the revised version.

(5) L.32: "hydrolytically degrades" or hydrolyses? (lytic and degrade sound like tautology). Same comment for l.117.

We agree. The suggested change has been made to the text in both places of the revised manuscript.

(6) L.35: I would also suggest changing "intra-mycobacterial" to 'intra bacterial' because you are talking about one bacterium here. The same change is recommended in l.29.

Following reviewer’s recommendation, we have made the changes in the revised manuscript.

(7) L.37: bacillus unless use of the plural form is the norm in the field.

We agree. The suggested change has been made to the text.

(8) L.43: Delete "intracellular" and change "intracellular" to host in l.44.

The suggested changes have been made to the text.

(9) L.66: --that a burst--

We have corrected the mistake in the revised manuscript.

(10) L.76: Receptor or receptor?

We have corrected the mistake in the revised manuscript.

(11) L.86: -- mechanisms of regulation of mycobacterial cAMP level. (homeostasis needs to be introduced first, and not used in the concluding statement for the first time).

The suggested changes have been made to the text.

(12) L.96: "essential" or 'a requirement'. (reduction is not the same as elimination)

We understand the reviewer’s concern. However, several studies have independently established that phoPR remains an essential requirement for mycobacterial virulence.

(13) L.97: Moreover, a mutant

The suggested change has been made to the text.

(14) L.113: --locus since PhoP has been –

The suggested change has been made to the text.

(15) L.119: mechanism or manner? (you are stating a fact, not a mechanism)

We agree. We have now replaced ‘mechanism’ with ‘manner’ in the revised manuscript.

(16) L.130: --lacking copies of both phoP and phoR (I am assuming you don't have two copies of each gene)

We understand the reviewer’s concern. For better clarity, we have now clearly mentioned that the phoPR-KO mutant lacks both the single copies of phoP and phoR genes.

(17) L.156: Indicate why GroEL2? - cells as another cytoplasmic protein, GroEL2 was also undetectable

We have now mentioned it in the secretion experiments that mycobacterial cells did not undergo autolysis. To prove this point, we have used cytoplasmic GroEL2 as a marker protein. The absence of detectable GroEL2 in the culture filtrates (CFs) suggests absence of autolysis. To this end, we have modified the sentence in the revised manuscript (duplicated below):

“Fig. 1C confirms absence of autolysis of mycobacterial cells as GroEL2, a cytoplasmic protein, was undetectable in the culture filtrates (CF).”

(18) L.266: May delete "Together". Start with These data--, which would draw more attention to integrated view. In l.268-270, a reminder that intracellular pH is acidic in the normal course would enhance the physiological significance of the present results.

We agree. We have made the suggested changes to the text. In view of the second comment of the reviewer, we have modified the text (duplicated below):

“These data represent an integrated view of our results suggesting that PhoP-dependant repression of rv0805 regulates intra-mycobacterial cAMP level. In keeping with these results, activated PhoP under acidic pH conditions significantly represses rv0805, and intracellular mycobacteria most likely utilizes a higher level of cAMP to effectively mitigate stress for survival under hostile environment including acidic pH of the phagosome.”

(19) L.272: Delete "and intracellular survival" (?) (I am assuming the survival is due to stress tolerance; also the section talks about stress only). No period in l.273.

Following reviewer’s recommendations, the suggested changes have been made to the text.

(20) L.295: Start the sentence thus: It appears that at least one of ---. (This would put more emphasis on the inference)

We agree. We have now incorporated the recommended changes in the revised version.

(21) L.301: No parenthesis.

The parenthesis has been removed in the revised manuscript.

(22) L.306: Together already implies these. Either delete Together (which I would prefer) or say 'Together, the results suggest that strains expressing wild type and mutant----properties, and the results are

We agree. We have now deleted ‘Together’ in the revised manuscript.

(23) L.311: These results support our view that higher---- (to avoid repetition of l.266)

We agree. We have now incorporated the suggested change in the revised manuscript.

(24) L.316: Using or with?

We think “with” goes well with the statement.

(25) L.329: Rephrase thus: Effect of intra-bacterial cAMP level on in vivo--

The recommended change has been made to the text.

(26) L.333: I would use ~, if you want to indicate about.

We agree. We have now used ‘~’ in the revised version. Changes were incorporated in lines 328, 330 and 333 of the revised manuscript.

(27) L.350: Change "somewhat functionally" to phenotypically?

We thank the reviewer for this suggestion. We have changed “somewhat functionally” to “phenotypically” in the revised manuscript.

(28) L.361: Change "is connected to" to 'regulates'.

The suggested change has been made to the text.

(29) L.365: ACs (to be parallel with PDEs)

We agree. The suggested change has been made to the text.

(30) L.366: delete "very" (let the readers decide how recent from the reference date).

The suggested change has been made to the text.

(31) L.382: level remained unknown before the present study.

The recommended change has been made to the text.

(32) L.399: add at the end of the sentence 'under stress'. Also, represent, not represents.

The recommended changes have been made to the text.

(33) L.560 and 571: Section headings formatted differently from the rest. Similar problem in l.900.

We have rectified the issue and all of the section headings are now formatted in the same style.

Reviewer #2 (Public Review):

Summary:

In the manuscript, the authors have presented new mechanistic details to show how intracellular cAMP levels are maintained linked to the phosphodiesterase enzyme which in turn is controlled by PhoP. Later, they showed the physiological relevance linked to altered cAMP concentrations.

Strengths:

Well thought out experiments. The authors carefully planned the experiments well to uncover the molecular aspects of it diligently.

We thank the reviewer for these extremely encouraging comments.

Weaknesses:

Some fresh queries were made based on the author's previous responses and hope to get satisfactory answers this time.

We provide below a point-by-point response to the fresh queries.

(2) Line 134: please describe the complementation strain features as it is mentioned for the first time (plasmid, copy number, promoter etc.) in the manuscript. Especially under NO stress what could be the authors' justification regarding the high cAMP concentration in the complementation strain?

As recommended by the reviewer, the details of construction of the complemented strain have been incorporated in the 'Materials and Methods' section of the revised manuscript (duplicated below): "To complement phoPR expression, pSM607 containing a 3.6-kb DNA fragment of M. tuberculosis phoPR including 200-bp phoP promoter region, a hygromycin resistance cassette, attP site and the gene encoding phage L5 integrase, as detailed earlier (Walters et al., 2006) was used to transform phoPR mutant to integrate at the L5 attB site.

" To address the reviewer's other concern, we have now included the following sentence in the 'Results' section of the revised manuscript (duplicated below): "A higher cAMP level in the complemented strain under NO stress is possibly attributable to reproducibly higher phoP expression in the complemented mutant under specific stress condition (Khan et al., 2022)."

Reference: Khan et al. (2022) Convergence of two global regulators to coordinate expression of essential virulence determinants of Mycobacterium tuberculosis. eLife 2022, 11:e80965.

New query: The complemented gene (in pSM607 plasmid) becomes a single copy after chromosomal integration, so it should ideally behave like a WT strain. How could authors still justify the high cAMP concentration under NO stress?

We agree with the reviewer. We are unable to provide a cogent justification regarding this result. We speculate that PhoP is strikingly activated under NO stress by a non-canonical mechanism and strongly represses rv0805 expression. As a result, there is a significantly higher cAMP concentration in case of the complemented mutant under NO stress.

(13) Line 292: There is a difference between red and green bars. Authors should do statistical analysis and then comment on whether overexpression of WT and mutant pde are different or similar, to me they are different; also, explain why the WT-Rv0805 strain is different than the phoPR-KO strain in the context of cell wall metabolism.

As recommended by the reviewer, we have now included statistical significance of the data in the revised version, and modified the text accordingly in the manuscript.

New query: Authors are asked to put a statistical significance test between WT-Rv0805 and WT-Rv0805M.

We have included it in the modified figure. Also, to explain it we incorporated new text in the legend to Fig. 4C of the revised manuscript (duplicated below):

“Note that similar to phoPR-KO, WT-Rv0805 shows a comparably higher sensitivity to CHP relative to WT bacilli. However, WT-Rv0805M expressing a mutant Rv0805, shows a significantly lower sensitivity to CHP relative to WT-Rv0805, as measured by the corresponding CFU values.”

(14) Line 299-303: Authors should explain how the colocalization % are calculated. Also, in the figure 4D merge panel please highlight the difference.

As suggested by the reviewer, we have now explained the methodology used to calculate percent colocalization in greater details. Also, we have modified Figure 4D to highlight the difference between samples shown in merge panel. Please see our response to comment # 33 from the Reviewer 1.

New query: In the figure legend it should be mentioned that the white arrow indicates non-co-localization which is visibly higher in WT and WT Rvo805M.

We thank the reviewer for this very important suggestion. We have now included the following text in the legend to Fig. 4D of the revised manuscript.

“White arrowheads in the merge panels indicate non-colocalization, which remains higher in WT-H37Rv and WT-Rv0805M relative to phoPR-KO or WT-Rv0805.”

I did not previously think about how age impacts the way you experience poverty, but I can see how this may be true. When we are little kids, we are still unaware of a lot of the different facets of identity that set us apart, and are more likely to be open minded to more things-- we are still very easily impressionable. As we become older though, and cliques form, there is a way clearer understanding of what may be deemed as cool or desirable for teenagers and what is embarrassing or something to be ashamed of.

Author response:

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

Reviewer #1 (Recommendation for the authors):

(1) On a few occasions, I found that the authors would introduce a concept, but provide evidence much later on. For example, in line 57, they introduced the idea that feedback timing modulates engagement of the hippocampus and striatum, but they provided the details much later on around line 99. There are a few instances like these, and the authors may want to go through the manuscript critically to bridge such gaps to improve the flow of reading.

First, we thank the reviewer for acknowledging the contribution of our study and the methodological choices. We acknowledge the concern raised about the flow of information in the introduction. We have critically reviewed the manuscript, especially on writing style and overall structure, to ensure a smoother transition between the introduction of concepts and the provision of supporting evidence. In the case of the concept of feedback timing and memory systems, lines 46-58 first introduce the concept enhanced with evidence regarding adults, and we then pick up the concept around line 103 again to relate it to children and their brain development to motivate our research question. To further improve readability, we have included an outline of what to expect in the introduction. Specifically, we added a sentence in line 66-68 that provides an overview of the different paragraphs: “We will introduce the key parameters in reinforcement learning and then we review the existing literature on developmental trajectories in reinforcement learning as well as on hippocampus and striatum, our two brain regions of interest.”

This should prepare the reader better when to expect more evidence regarding the concepts introduced. We included similar “road-marker” outline sentences in other occasions the reviewer commented on, to enhance consistency and readability.

(2) I am curious as to how they think the 5-second delay condition maps onto real-life examples, for example in a classroom setting feedback after 5 seconds could easily be framed as immediate feedback.

The authors may want to highlight a few illustrative examples.

Thank you for asking about the practical implications of a 5-second delay condition, which may be very relevant to the reader. We have modified the introduction example in line 39-41 towards the role of feedback timing in the classroom to point out its practical relevance early on: “For example, children must learn to raise their hand before speaking during class. The teacher may reinforce this behavior immediately or with a delay, which raises the question whether feedback timing modulates their learning”.

We have also expanded a respective discussion point in lines 720-728 to pick up the classroom example and to illustrate how we think timescale differences may apply: “In scenarios such as in the classroom, a teacher may comment on a child’s behavior immediately after the action or some moments later, in par with our experimental manipulation of 1 second versus 5 seconds. Within such short range of delay in teachers’ feedback, children’s learning ability during the first years of schooling may function equally well and depend on the striatal-dependent memory system. However, we anticipate that the reliance on the hippocampus will become even more pronounced when feedback is further delayed for longer time. Children’s capacity for learning over longer timescales relies on the hippocampal-dependent memory system, which is still under development. This knowledge could help to better structure learning according to their development.”

(3) In the methods section, there are a few instances of task description discrepancies which make things a little bit confusing, for example, line 173 reward versus punishment, or reward versus null elsewhere e.g. line 229. In the same section, line 175, there are a few instances of typos.

We appreciate your attention to detail in pointing out discrepancies in task descriptions and typos in the method section. We have revised the section, corrected typos, and now phrased the learning outcomes consistently as “reward” and “punishment”.

(4). I wasn't very clear as to why the authors did not compute choice switch probability directly from raw data but implemented this as a model that makes use of a weight parameter. Former would-be much easier and straightforward for data plotting especially for uninformed readers, i.e., people who do not have backgrounds in computational modelling.

Thank you for asking for clarification on the calculation of switching behavior. Indeed, in the behavioral results, switching behavior was directly calculated from the raw data. We now stressed this in the methods in lines 230-235, also by naming win-stay and lose-shift as “proportions” instead of as “probabilities”:“As a first step, we calculated learning outcomes diretly from the raw data, which where learning accuracy, win-stay and lose-shift behavior as well as reaction time.

Learning accuracy was defined as the proportion to choose the more rewarding option, while win-stay and lose-shift refer to the proportion of staying with the previously chosen option after a reward and switching to the alternative choice after receiving a punishment, respectively.”

In contrast to the raw data switching behavior, the computational heuristic strategy model indeed uses a weight for a relative tendency of switching behavior. We have also stressed the advantage of the computational measure and its difference to the raw data switching behavior in lines 248-252 and believe that the reader can now clearly distinguish between the raw data and the computational results: “Note that these model-based outcomes are not identical to the win-stay and lose-shift behavior that were calculated from the raw data. The use of such model-based measure offers the advantage in discerning the underlying hidden cognitive process with greather nuance, in contrast to classical approaches that directly use raw behavioral data.”

(5) I agree with the authors' assertion that both inverse temperature and outcome sensitivity parameters may lead to non-identifiability issues, but I was not 100% convinced about their modelling approach exclusively assessing a different family of models (inv temperature versus outcome sensitivity). Here, I would like to make one mid-way recommendation. They may want to redefine the inverse temperature term in terms of reaction time, i.e., B=exp^(s+g(RT-mean (RT)) where s and g are free parameters (see Webb, 2019), and keep the outcome sensitivity parameter in the model with bounds [0,2] so that the interpretation could be % increase or decrease in actual outcome. Personally, in tasks with binary outcomes i.e. [0,1: null vs reward] I do not think outcome sensitivity parameters higher than 2 are interpretable as these assign an inflated coefficient to outcomes.

We appreciate the mid-way recommendation regarding the modeling approach for inverse temperature and outcome sensitivity parameters. We have carefully revised our analysis approach by considering alternative modeling choices. Regarding the suggestion to redefine the inverse temperature in terms of reaction time by B=exp^(s+g(RT-mean (RT)), we unfortunately were not able to identify the reference Webb (2019), nor did we find references to the suggested modeling approach. Any further information that the reviewer could provide will be greatly appreciated. Regardless, we agree that including reaction times through the implementation of drift-diffusion modeling may be beneficial. However, changing the inverse temperature model in such a way would necessitate major changes in our modeling approach, which unfortunately would result in non-convergence issues in our MCMC pipeline using Rstan. Hence, this approach goes beyond the scope of the manuscript. Nonetheless, we have decided to mention the use of a drift-diffusion model, along with other methodological considerations, as future recommendation for disentangling outcome sensitivity from inverse temperature in lines 711-712: “Future studies might shed new light by examining neural activations at both task phases, by additionally modeling reaction times using a drift-diffusion approach, or by choosing a task design that allows independent manipulations of these phases and associated model parameters, e.g., by using different reward magnitudes during reinforcement learning, or by studying outcome sensitivity without decisionmaking.“

Regarding the upper bound of outcome sensitivity, we agree that traditionally, limiting the parameter values at 2 is the choice for the parameter to be best interpretable. During model fitting, we had experienced non-convergence issues and ceiling effects in the outcome sensitivity parameter when fixing the inverse temperature at 1. The non-convergence issue was not resolved when we fixed the inverse temperature at 15.47, which was the group mean of the winning inverse temperature family. Model convergence was only achieved after increasing the outcome sensitivity upper bound to 20, with inverse temperature again fixed at 1. Since this model also performed well during parameter and model recovery, we argue that the parameter is nevertheless meaningful, despite the more extreme trial-to-trial value fluctuations under higher outcome sensitivity. We described our choice for this model in the methods section in lines 282-288: “Even though outcome sensitivity is usually restricted to an upper bound of 2 to not inflate outcomes at value update, this configuration led to ceiling effects in outcome sensitivity and non-converging model results. Further, this issue was not resolved when we fixed the inverse temperature at the group mean of 15.47 of the winning inverse temperature family model. It may be that in children, individual differences in outcome sensitivity are more pronounced, leading to more extreme values. Therefore, we decided to extend the upper bound to 20, parallel to the inverse temperature, and all our models converged with Rhat < 1.1.”.

(6) I think the authors reporting optimal parameters for the model is very important (line 464), but the learning rate they report under stable contingencies is much higher than LRs reported by for example Behrens et al 2007, LRs around 0.08 for the optimal learning behaviour. The authors may want to discuss why their task design calls for higher learning rates.

Thank you for appreciating our optimal parameter analysis, and for the recommendation to discuss why optimal learning rates in our task design may call for higher learning rates compared to those reported in some other studies. As largely articulated in Zhang et al (2020; primer piece by one of our co-authors), the optimal parameter combination is determined by several factors, such as the reward schedule (e.g., 75:25, vs 80:20) and task design (e.g., no reversal, one reversal, vs multiple reversal) and number of trials (e.g., 80, vs 100, vs, 120). Notably, in these taskrelated regards, our task is different from Behrens et al. (2007), which hinders a quantitative comparison among the optimal parameters in the two tasks. We have now included more details in our discussion in lines 643-656: “However, the differences in learning rate across studies have to be interpreted with caution. The differences in the task and the analysis approach may limit their comparability. Task proporties such as the trial number per condition differed across studies. Our study included 32 trials per cue in each condition, while in adult studies, the trials per condition ranged from 28 to 100. Optimal learning rates in a stable learning environment were at around 0.25 for 10 to 30 trials, another study reported a lower optimal learning rate of around 0.08 for 120 trials. This may partly explain why in our case of 32 trials per condition and cue, optimal learning rates called for a relatively high optimal learning rate of 0.29, while in other studies, optimal learning rates may be lower. Regarding differences in the analysis approach, the hierarchical bayesian estimation approach used in our study produces more reliable results in comparison to maximum likelihood estimation, which had been used in some of the previous adult studies and may have led to biased results towards extreme values. Taken together, our study underscores the importance of using longitudinal data to examine developmental change as well as the importance of simulation-based optimal parameters to interpret the direction of developmental change.”

(7) The authors may want to report degrees of freedom in t-tests so that it would be possible to infer the final sample size for a specific analysis, for example, line 546.

We appreciate the recommendation to include degrees of freedom, which are now added in all t-test results, for example in line 579: “Episodic memory, as measured by individual corrected object recognition memory (hits - false alarms) of confident (“sure”) ratings, showed at trend better memory for items shown in the delayed feedback condition (𝛽!""#$%&’(#")%*"# = .009, SE =.005, t(df = 137) = 1.80, p = .074, see Figure 5A).”

(8) I'm not sure why reductions in lose shift behaviour are framed as an improvement between 2 assessment points, e.g. line 578. It all depends on the strength of the contingency so a discussion around this point should be expanded.

We acknowledge that a reduction in lose-shift behavior only reflect improvements under certain conditions where uncertainty is low and the learning contingencies are stable, which is the case in our task. We have added Supplementary Material 4 to illustrate the optimality of win-stay and lose-shift proportions from model simulation and to confirm that children’s longitudinal development was indeed towards more optimal switching behavior. In the manuscript, we refer to these results in lines 488-490: “We further found that the average longitudinal change in win-stay and lose-shift proportion also developed towards more optimal value-based learning (Supplementary Material 4).”

(9) If I'm not mistaken, the authors reframe a trend-level association as weak evidence. I do not think this is an accurate framing considering the association is strictly non-significant, therefore should be omitted line 585.

We thank for the point regarding the interpretation of a trend-level association as weak evidence. We changed our interpretation, corrected in lines 581-585: “The inclusion of poor learners in the complete dataset may have weakend this effect because their hippocampal function was worse and was not involved in learning (nor encoding), regardless of feedback timing. To summarize, there was inconclusive support for enhanced episodic memory during delayed compared to immediate feedback, calling for future study to test the postulation of a selective association between hippocampal volume and delayed feedback learning.” as well as lines 622-623: “Contrary to our expectations, episodic memory performance was not enhanced under delayed feedback compared to immediate feedback.”

Reviewer # 2 (Public Review):

We thank the reviewer for acknowledging the strength of our study and pointing out its weaknesses.

Weaknesses:

There were a few things that I thought would be helpful to clarify. First, what exactly are the anatomical regions included in the striatum here?

We appreciate the clarification question regarding the anatomical regions included in the striatum. The striatum included ventral and dorsal regions, i.e., accumbens, caudate and putamen. We have now specified the anatomical regions that were included in the striatum in lines 211-212: “We extracted the bilateral brain volumes for our regions of interest, which were striatum and hippocampus. The striatum regions included nucleus accumbens, caudate and putamen.”

Second, it was mentioned that for the reduced dataset, object recognition memory focused on "sure" ratings. This seems like the appropriate way to do it, but it was not clear whether this was also the case for the full analyses in the main text.

Thank you for pointing out that in the full dataset analysis, the use of “sure” ratings for object recognition memory was previously not mentioned. Including only “sure” ratings was used consistently across analyses. This detail is now described under methods in lines 332-333: “Only confident (“sure”) ratings were included in the analysis, which were 98.1 % of all given responses.”

Third, the children's fitted parameters were far from optimal; is it known whether adults would be closer to optimal on the task?

We thank for your question on whether adult learning rates in the task have been reported to be more optimal than those of the children in our study. This indeed seems to be the case, and we added this point in our discussion in line 639-643: “Adult studies that examined feedback timing during reinforcement learning reported average learning rates range from 0.12 to 0.34, which are much closer to the simulated optimal learning rates of 0.29 than children’s average learning rates of 0.02 and 0.05 at wave 1 and 2 in our study. Therefore, it is likely that individuals approach adult-like optimal learning rates later during adolescence.”

The main thing I would find helpful is to better integrate the differences between the main results reported and the many additional results reported in the supplement, for example from the reduced dataset when excluding non-learners. I found it a bit challenging to keep track of all the differences with all the analyses and parameters. It might be helpful to report some results in tables side-by-side in the two different samples. And if relevant, discuss the differences or their implication in the Discussion. For example, if the patterns change when excluding the poor learners, in particular for the associations between delayed feedback and hippocampal volume, and those participants were also those less well fit by the value-based model, is that something to be concerned about and does that affect any interpretations? What was not clear to me is whether excluding the poor learners at one extreme simply weakens the general pattern, or whether there is a more qualitative difference between learners and non-learners. The discussion points to the relevance of deficits in hippocampaldependent learning for psychopathology and understanding such a distinction may be relevant.

We appreciate the feedback that it might seem challenging to keep track of differences between the analyses of the full and the reduced dataset. We have now gathered all the analyses for the reduced dataset in Supplementary Material 6, with side-by-side tables for comparison to the full dataset results. Whenever there were differences between the results, they were pointed out in the results section, see lines 557-560: “In the results of the reduced dataset, the hippocampal association to the delayed learning score was no longer significant, suggesting a weakened pattern when excluding poor learners (Supplementary Material 6). It is likely that the exclusion reduced the group variance for hippocampal volume and delayed learning score in the model.” and lines 579-581: “Note that in the reduced dataset, delayed feedback predicted enhanced item memory significantly (Supplementary Material 6).”

The found differences were further included in our discussion in lines 737-740 in the context of deficits in hippocampal-dependent learning and psychopathology: “Interestingly, poor learners showed relatively less value-based learning in favor of stronger simple heuristic strategies, and excluding them modulated the hippocampal-dependent associations to learning and memory in our results. More studies are needed to further clarify the relationship between hippocampus and psychopathology during cognitive and brain development.”

Author response:

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

Reviewer #1 (Public Review):

(1) There appears to be a flaw in the exploration of cortical inputs. the authors never show that HFS of cortical inputs has no effect in the absence of thalamic stimulation. It appears that there is a citation showing this, but I think it would be important to show this in this study as well.

We understand that the reviewer would like us to induce an HFS protocol on cortical input and then test if there is any change in synaptic strength in thalamic input. We have done this experiment which shows that without a footshock, high-frequency stimulation (HFS) of the cortical inputs did not induce synaptic potentiation on the thalamic pathway (Extended Data Fig. 4d).

(2) t is somewhat confusing that the authors refer to the cortical input as driving heterosynaptic LTP, but this is not shown until Figure 4J, that after non-associative conditioning (unpaired shock and tone) HFS of the cortex can drive freezing and heterosynaptic LTP of thalamic inputs.

We agree with the reviewer that it is in figure 4j and figure 5,b,c which we show electrophysiological evidence for cortical input driving heterosynaptic LTP. It is only to be consistent with our terminology that initially we used behavioral evidence as the proxy for heteroLTP (figure 3c).

…, the authors are 'surprised' by this outcome, which appears to be what they predict.

We removed the phrase “To our surprise”.

(3) 'Cortex' as a stimulation site is vague. The authors have coordinates they used, it is unclear why they are not using standard anatomical nomenclature.

We replaced “cortex” with “auditory/associative cortex”.

(4) The authors' repeated use of homoLTP and heteroLTP to define the input that is being stimulated makes it challenging to understand the experimental detail. While I appreciate this is part of the goal, more descriptive words such as 'thalamic' and 'cortical' would make this much easier to understand.

We agree with the reviewer that a phrase such as “an LTP protocol on thalamic and cortical inputs” would be more descriptive. We chose the words “homoLTP” and “heteroLTP” only to clarify (for the readers) the physiological relevance of these protocols. We thought by using “thalamic” and “cortical” readers may miss this point. However, when for the first time we introduce the words “homoLTP” and “heteroLTP”, we describe which stimulated pathway each refers to.

Reviewer #2 (Public Review):

(1) …The experimental schemes in Figs. 1 and 3 (and Fig. 4e and extended data 4a,b) show that one group of animals was subjected to retrieval in the test context at 24 h, then received HFS, which was then followed by a second retrieval session. With this design, it remains unclear what the HFS impacts when it is delivered between these two 24 h memory retrieval sessions.

We understand that the reviewer has raised the concern that the increase in freezing we observed after the HFS protocol (ex. Fig. 1b, the bar labeled as Wth+24hHFSth) could be caused or modulated by the recall prior to the HFS (Fig. 1a, top branch). To address this concern, in a new group of mice, 24 hours after weak conditioning, we induced the HFS protocol, followed by testing (that is, no testing prior to the HFS protocol). We observed that homoLTP was as effective in mice that were tested prior to the induction protocol as those that were not (Fig. 1b, Extended Data Fig. 1d,e).

It would be nice to see these data parsed out in a clean experimental design for all experiments (in Figs 1, 3, and 4), that means 4 groups with different treatments that are all tested only once at 24 h, and the appropriate statistical tests (ANOVA). This would also avoid repeating data in different panels for different pairwise comparisons (Fig 1, Fig 3, Fig 4, and extended Fig 4).

While we understand the benefit of the reviewer’s suggestion, the current presentation of the data was done to match the flow of the text and the delivery of the information throughout the manuscript. We think it is unlikely that the retrieval test prior to the HFS impacts its effectiveness, as confirmed by homosynaptic HFS data (Extended Data Fig. 1d,e). It is beyond the scope of current manuscript to investigate the mechanisms and manipulations related to reconsolidation and retrieval effects.

(2) … It would be critical to know if LFPs change over 24 h in animals in which memory is not altered by HFS, and to see correlations between memory performance and LFP changes, as two animals displayed low freezing levels. … They would suggest that thalamo-LA potentiation occurs directly after learning+HFS (which could be tested) and is maintained over 24 h.

We have performed the experiment where we recorded the evoked LFP 2hrs and 24hrs following the weak conditioning protocol. We observed that a weak conditioning protocol that was not followed by an optical LTP protocol on the cortical inputs failed to produce synaptic potentiation of the thalamic inputs (tested 2hrs and 24hrs after the LTP protocol; Extended Data Fig. 5d,e).

(3) The statistical analyses need to be clarified. All statements should be supported with statistical testing (e.g. extended data 5c, pg 7 stats are missing). The specific tests should be clearly stated throughout. For ANOVAs, the post-hoc tests and their outcomes should be stated. In some cases, 2-way ANOVAs were performed, but it seems there is only one independent variable, calling for one-way ANOVA.

All the statistical analyses have been revised and the post-hoc tests performed after the ANOVAs are mentioned in the relevant figure legends.

Reviewer #2 (Recommendations For The Authors):

The wording "transient" and "persistent" used here in the context of memory seems a bit misleading, as only one timepoint was assessed for memory recall (24 h), at which the memory strength (freezing levels) seem to change.

As the reviewer mentioned, we have tested memory recall only at one time point. For this reason, throughout the text we used “transient” exclusively to refer to the experience (receiving footshock) and not to the memory. We replaced “persistence” with “stabilization” where it refers to a memory (“the induction of plasticity influences the stabilization of the memory”).

For the procedures in which the CS and US were not paired, the term "unpairing" is used (which is probably the more adequate one), but the term "non-associative conditioning" appears in the text, which seems a bit misleading, as this term may have another connotation. There is also literature that an unpairing of CS and US could lead to the formation of a safety memory to the CS, that may be disrupted by HFS stimulation.

We replaced "non-associative" with “unpaired”.

Validation of viral injection sites for all experiments: Only representative examples are shown, it would be nice to see all viral expression sites.

For this manuscript, we have used 155 mice. For this reason, including the injection sites for all the animals in the manuscript is not feasible. Except for the mice that have been excluded, (please see exclusion criteria added in the methods), the expression pattern we observed was consistent across animals and therefore the images shown are true representatives.

Extended Data 1b: Please explain what N, U, W, and S behavioral groups mean. To what groups mentioned in the text (pg 2,3) do these correspond?

The requested clarifications are implemented in the figure legend.

Please elaborate on the following aspects of your methods and approaches:

• Please explain if the protocol for HFS to manipulate behavior was the same as the one used for the LTP experiments (Fig 1d, Fig 4j) and was identical for homo/hetero inputs from thal and ctx?

We used the same HFS protocol for all the HFS inductions. We included this information in the methods section.

• Please state when the HFS was given in respect to the conditioning (what means immediately before and after?) and in which context it was given. Were animals subjected to HFS exposed to the context longer (either before or after the conditioning while receiving HFS) than the other groups? When the HFS was given in another context (for the 24 h group)- how was this controlled for?

Requested information has been added to the methods section. The control and intervention groups were treated in the same way.

• When were the footshocks given in the anesthesized recordings (Fig. 4j) and how was the temporal relationship to the HFS? Was the timing the same as for the HFS in the behavioral experiments?

Requested information has been added to the methods section.

• Please add information on how the LFP was stimulated and how the LFP- EPSP slope was determined in in vivo recordings, likewise for the whole cell recordings of EPSPs in Fig. 5d-f.

Requested information has been added to the methods section.

Here, the y-Axis in Fig. 5e should be corrected to EPSP slope rather than fEPSP slope if these are whole-cell recordings.

This has been corrected.

• Please include information if the viral injections and opto-manipulations were done bilateral or unilateral and if so in which hemisphere. Likewise, indicate where the LFP recordings were done.

Requested information has been added to the methods section.

• Were there any exclusion criteria for animals (e.g. insufficient viral targeting or placement of fibers and electrodes), other than the testing of the optical CS for adverse effects?

Requested information has been added to the methods section.

Statistics: In addition to clarifying analytical statistics, please clarify n-numbers for slice recordings (number of animals, number of slices, and number of cells if applicable).

Requested information has been added to the methods section.

It would be nice to scrutinize the results in extended data 4b. The freezing levels with U+24h HFS show a strong trend towards an increase, the effect size may be similar to immediate HFS Fig 4f and extended data 4a) if n was increased.

We agree with the reviewer. To address this point, we added “HomoLTP protocol when delivered 24hrs later, produced an increase in freezing; however, the value was not statistically significant.” To show this point, we used the same scale for freezing in Extended Data Fig. 4a and b.

In the final experiment (Fig. 5a-c), Fig. 5b seems to show results from only one animal, but behavioral results are from 4 animals (Fig 5c). It would be helpful to see the quantification of potentiation in each animal.

The results (now with error bar) include all mice.

Please spell out the abbreviation "STC".

Now, it is spelled out.

Page 8 last sentence of the discussion does not seem to fit there.

The sentence has been removed.

Reviewer #3 (Recommendations For The Authors):

(1) The authors did not determine how WTh affects Th-LA synapses, as field EPSPs were recorded only after HFS. WTh was required for the effects of HFS, as HFS alone did not produce CR in naïve and/or unpaired controls. As such the effects of the WTh protocol on synaptic strength must be investigated.

We have performed the experiment where we recorded the evoked LFP 2hrs and 24hrs following the weak conditioning protocol. We observed that a weak conditioning protocol that was not followed by an optical LTP protocol on the cortical inputs failed to produce synaptic potentiation of the thalamic inputs (tested 2hrs and 24hrs after the LTP protocol; Extended Data Fig. 5d,e).

(2) The authors provide some evidence that their dual opsin approach is feasible, particularly the use of sustained yellow light to block the effects of blue light on ChrimsonR. However, this validation was done using single pulses making it difficult to assess the effect of this protocol on Th input when HFS was used. Without strong evidence that the optogenetic methods used here are fault-proof, the main conclusions of this study are compromised. Why did the authors not use a protocol in which fibers were placed directly in the Ctx and Th while using soma-restricted opsins to avoid cross-contamination?

We understand that the reviewer raises the possibility that our dual-opsin approach, although effective with single pulses, may fail in higher frequency stimulation protocols (10Hz and 85Hz). To address this concern, in a new group of mice we applied our approach to 10Hz and 85Hz stimulation protocols. We show that our approach is effective in single-pulse as well as in 10Hz and 85Hz stimulation protocols (Fig. 2d-h).

Author response:

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

We sincerely appreciate the reviewer’s dedication to evaluating our manuscript and raising essential considerations regarding the classification of the migration behavior we described. While the reviewer suggests that this behavior aligns with the concept of itinerancy, we contend that it represents a distinct phenomenon, albeit with similarities, as both involve the non-breeding movements of birds. We acknowledge that our manuscript did not adequately address this distinction and have considered the reviewer’s feedback. In our response, we clarify the difference between the described phenomenon and itinerancy. Our revised manuscript will include a new section in the Discussion to address this issue comprehensively.

In the first part of the review, the reviewer emphasizes that the pattern we are describing is consistent with itinerancy. Regardless of the terminology used, we want to highlight the existence of two different types of migratory behavior, both of which involve movement in non-breeding areas.

The first type, called itinerancy, was first described by Moreau in 1972 in “The Palaearctic-African Bird Migration Systems.” As noted by the reviewer, this behavior involves an alternation of stopovers and movements between different short-term non-breeding residency areas. They usually occur in response to food scarcity in one part of the non-breeding range, causing birds to move to another part of the same range. These movements typically cover distances of 10 to 100 kilometers but are neither continuous nor directional. Moreau (1972) defined itinerancy as prolonged stopovers, normally lasting several months, primarily in tropical regions. He noted observations of certain species disappearing from his study areas in sub-Saharan Africa in December and others appearing, suggesting they may have multiple home ranges during the non-breeding season. Subsequent research, as mentioned by the reviewer, has confirmed itinerancy in many species, particularly among Palaearctic-African migrants in sub-Saharan Africa. In particular, the Montagu’s Harrier has been extensively studied in this regard. The reviewer rightly points out that our study does not include recent findings on this species. In our revised version, we will include references to recent studies, such as those by Trierweiler et al. (2013, Journal of Animal Ecology, 82:107-120) and Schlaich et al. (2023, Ardea, 111:321-342), which show that Montagu’s Harrier has an average of 3-4 home ranges separated by approximately 200 kilometers. These studies suggest that the species spends approximately 1.5 months at each site, with the most extended period typically observed at the last site before migrating to the breeding grounds.

In the second type, birds undertake a post-breeding migration, arrive in their non-breeding range, and then gradually move in a particular direction throughout the season. This continuous directional movement covers considerable distances and continues throughout the non-breeding period. In our study, this movement covered about 1000 km, comparable to the total migration distance of Rough-legged Buzzards of about 1500 km. As observed in our research, these movements are influenced by external factors such as snow cover. In such cases, the progression of snow cover in a south-westerly direction during winter can prevent birds from finding food, forcing them to continue migrating in the same direction. In essence, this movement represents a prolonged phase of the migration process but at a slower pace. Similar behavior has been documented in buzzards, as reported by Strandberg et al. (2009, Ibis 151:200-206). Although several transmitters in their study stopped working in mid-winter, the authors observed a phenomenon they termed ‘prolonged autumn migration.’

In the second part of the review, the reviewer questions the need to distinguish between the two behaviors we have discussed. However, we believe these behaviors differ in their structure (with the first being intermittent and often non-directional, whereas the second is continuous and directional) and in their causes (with the first being driven by seasonal food resource cycles and the second by advancing snow cover). We therefore argue that it is worth distinguishing between them. To differentiate these forms of non-breeding movement, we propose to use ‘itinerancy’ for the first type, as described initially by Moreau in 1972, and introduce a separate term for the second behavior. Although ‘slow directional itinerancy’ could be considered, we find it too cumbersome.

Moreover, ‘itinerancy’ in the literature refers not only to non-breeding movements but also to the use of different nesting sites, e.g., Lislevand et al. (2020, Journal of Avian Biology: e02595), reinforcing its association with movements between multiple sites within habitats. We, therefore, propose that the second behavior be given a distinct name. We acknowledge the reviewer’s point that we did not adequately address this distinction in the Discussion and plan to include a separate section in our paper’s revised version. In the third part of his review, the reviewer suggests an alternative title. Another reviewer, Dr Theunis Piersma, suggested the current title during the first round of reviewing, and we have chosen his version.

In the fourth part of the review, the reviewer questions whether it is appropriate to discuss the conservation aspect of this study. This type of non-breeding movement raises concerns about accurately determining non-breeding ranges and population dynamics for species that exhibit this behavior. We believe that accurate determination of range and population dynamics is critical to conservation efforts. While this may be less important for species breeding in Europe and migrating to Africa, for which monitoring breeding territories is more feasible, it’s essential for Arctic and sub-Arctic breeding species. Large-scale surveys in these regions have historically been challenging and have become even more so with the end of Arctic cooperation following Russia’s war with Ukraine (Koivurova, Shibata, 2023). For North America and Europe, non-breeding abundance is typically estimated once per season in mid-winter. In North America, these are the so-called Christmas counts (which take place once at the end of December), and in Europe, they are the IWC counts mentioned by the reviewer (as follows from their official website - “The IWC requires a single count at each site, which should be repeated each year. The exact dates vary slightly from region to region, but take place in January or February”). Because of such a single count in mid-winter, non-breeding habitats occupied in autumn and spring will be listed as ‘uncommon’ at best, while south-western habitats where birds are only present in mid-winter will be listed as ‘common.’ However, the situation will be reversed if we consider the time birds spend in these habitats.

The reviewer also highlights the introduction’s unconventional structure and information redundancy at the beginning. We have chosen this structure and provided basic explanations to improve readability for a wider audience, given eLife’s readership. At the same time, we will certainly take the reviewers’ feedback into account in the revised version. We plan to include the references to modern itinerancy research mentioned above and to add a section on itinerancy to the Discussion.

We appreciate the reviewer’s input and sincerely thank them for their time and effort in reviewing our paper. While we may not fully agree on the classification of the behavior we describe, we value the opportunity to engage in discussion and believe that presenting arguments and counterarguments to the reader is beneficial to scientific progress.

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

Reviewer #1 (Recommendations For The Authors):

I much enjoyed reading this manuscript, that is, once I understood what it is about. Titles like "Conserving bird populations in the Anthropocene: the significance of non-breeding movements" are a claim to so-called relevance, they have NOTHING to do with the content of the paper, so once I understood that this paper was about the "Quick quick slow: the foxtrot migration of rough-legged buzzards is a response to habitat and snow" (an alternative title), it was becoming very interesting. So the start of the abstract as well as the introduction is very tedious, as clearly much trouble is taken here to establish reputability. In my eyes this is unnecessary: eLife should be interested in publishing such a wonderful description of such a wonderful migrant in a study that comes to grips with limiting factors on a continental scale!

We sincerely appreciate your time and effort in reviewing our manuscript. Thank you for your appreciation of our study.

We agree that the focus of the article should be changed from conservation to migration patterns. We have rewritten the Introduction and Discussion as suggested. We have added the application of this pattern including conservation at the end of the Discussion by completely changing Figure 5. We have also changed the title to the suggested one.

Not sure that the first paragraph statements that seek to downplay what we know about wintering vs breeding areas are valid (although I see what purpose they serve). Migratory shorebirds have extensively been studied in the nonbreeding areas, for example, including movement aspects (see, as just one example, Verhoeven, M.A., Loonstra, A.H.J., McBride, A.D., Both, C., Senner, N.R. & Piersma, T. (2020) Migration route, stopping sites, and non breeding destinations of adult Black tailed Godwits breeding in southwest Fryslân, The Netherlands. Journal of Ornithology 162, 61-76) and there are very impressive studies on the winter biology of migrants across large scale (for example in Zwarts' Living on the Edge book on the Sahel wetlands). Think also about geese and swans and about seabirds!

We have rewritten the first paragraph and it now talks about patterns of migratory behavior. We have also rewritten the second paragraph, now it is devoted to studies of movements in the non-breeding period. We explain how our pattern differs from those already studied and give references to the papers you mentioned.

Directional movements in nonbreeding areas as a function of food (in this case locusts) have really beautifully been described by Almut Schlaich et al in JAnimEcol for Montagu's harriers.

We have added Montagu's harrier example in the second paragraph of the Introduction and the Discussion. We have added a reference to Schlaich and to Garcia and Arroyo, who suggested that Montagu's harriers have long directional migrations during the non-breeding period.

Once the paper starts talking buzzards, and the analyses of the wonderful data, all is fine. It is a very competent analysis with a description of a cool pattern.

Thank you for your appreciation of our study. We hope the revised version is better and clearer.

However, i would say that it is all a question of spatial scale. The buzzards here respond to changes in food availability, but there is not an animal that doesn't. The question is how far they have to move for an adequate response: in some birds movements of 100s of meters may be enough, and then anything to the scale of rough-legged buzzards.

In the new version of the manuscript, we emphasize that this is a large distance (about 1000 km), comparable to the distance of the fall and spring migrations (about 1400 km) in lines 70-72 of the Introduction and 379-383 of the Discussion.

And actually, several of the shorebirds I know best also do a foxtrot, such as red knots and bar-tailed godwits moulting in the Wadden Sea, then spending a few months in the UK estuaries, before returning to the Wadden Sea before the long migrations to Arctic breeding grounds. The publication of the rough-legged buzzard story may help researchers to summarize patterns such as this too. Mu problem with this paper is the framing. A story on the how and why of these continental movements in response to snow and other habitat features would be a grand contribution. Drop Anthropocene, and rethink whether foxtrot should be introduced as a hypothesis or a summary of cool descriptions. I prefer the latter, and recommend eLife to go with that too, rather than encourage "disconnected frames that seek 'respectability'" Good luck, theunis piersma

We thank the reviewer again for his valuable comments and suggestions. We have changed the framing to the suggested one and removed the Anthropocene from the article.

Reviewer #2 (Recommendations For The Authors):

We sincerely appreciate the time and effort you have taken to review our manuscript. We have carefully considered all of your comments, including both public and author comments, and provided detailed responses to each of them below. In addition, we would like to address the most important public comments.

We agree with the suggestion to shift the focus of the article from conservation to migration patterns. Accordingly, we have rewritten both the Introduction and Discussion sections to focus on migration behavior rather than conservation.

However, we respectfully disagree with the suggestion that the migration patterns we describe are synonymous with itinerancy. We acknowledge that our original presentation may have been unclear and may have hindered full understanding. In the revised version, we provide a detailed analysis of migratory behavior in the Introduction that describes how our pattern differs from itinerancy. We also revisit this distinction in the Discussion section. We have also carefully revised Figure 1 to improve clarity and avoid potential misunderstandings.

Regarding the applicability of the described migration pattern, we acknowledge that the Rough-legged Buzzard is not listed as an endangered species. However, we believe that our findings have practical implications. We have moved our discussion of this issue to the end of the Discussion section and have completely revised Figure 5. While the overall population of Rough-legged Buzzards is not declining, certain regions within its range are experiencing declines. We show that this decline does not warrant listing the species as endangered. Instead, it may represent a redistribution within the non-breeding range - a shift in range dynamics. We use the example of the Rough-legged Buzzard to illustrate this concept and emphasize the importance of considering such dynamics when assessing the conservation status of species in the future.

We also acknowledge that the hypothesis of this form of behavior has been proposed previously for Montagu's Harrier, and we have included this information in the revised manuscript. In addition, we agree that the focus on the Anthropocene is unnecessary in this context and have therefore removed it.

We believe that these revisions significantly improve the clarity and robustness of the manuscript, and we are grateful for your insightful comments and suggestions.

As a general comment, please note that including line numbers (as it is the standard in any manuscript submission) would facilitate reviewers providing more detailed comments on the text.

We apologize for this oversight and have added line numbers to our revised manuscript.

Dataset: unclear what is the frequency of GPS transmissions. Furthermore, information on relative tag mass for the tracked individuals should be reported.

We have included this information in our manuscript (L 157-163). We also refer to the study in which this dataset was first used and described in detail (L 164).

Data pre-processing: more details are needed here. What data have been removed if the bird died? The entire track of the individual? Only the data classified in the last section of the track? The section also reports on an 'iterative procedure' for annotating tracks, which is only vaguely described. A piecewise regression is mentioned, but no details are provided, not even on what is the dependent variable (I assume it should be latitude?).

Regarding the deaths. We only removed the data when the bird was already dead. We have corrected the text to make this clear (L 170).

Regarding the iterative procedure. We have added a detailed description on lines 175-188.

Data analysis: several potential issues here:

(1) Unclear why sex was not included in all mixed models. I think it should be included.

Our dataset contains 35 females and eight males. This ratio does not allow us to include sex in all models and adequately assess the influence of this factor. At the same time, because adult females disperse farther than males in some raptor species, we conducted a separate analysis of the dependence of migration distance on sex (Table S8) and found no evidence for this in our species. We have written a separate paragraph about this. This paragraph can be found on lines 356-360 of the new manuscript.

(2) Unclear what is the rationale of describing habitat use during migration; is it only to show that it is a largely unsuitable habitat for the species? But is a formal analysis required then? Wouldn't be enough to simply describe this?

Habitat use and snow cover determine the two main phases (quick and slow) of the pattern we describe. We believe that habitat analysis is appropriate in this case and that a simple description would be uninformative and would not support our conclusions.

(3) Analysis of snow cover: such a 'what if' analysis is fine but it seems to be a rather indirect assessment of the effect of snow cover on movement patterns. Can a more direct test be envisaged relating e.g. daily movement patterns to concomitant snow cover? This should be rather straightforward. The effectiveness of this method rests on among-year differences in snow cover and timing of snowfall. A further possibility would be to demonstrate habitat selection within the entire non-breeding home range of an individual in relation snow cover. Such an analysis would imply associating presence-absence of snow to every location within the non-breeding range and testing whether the proportion of locations with snow is lower than the proportion of snow of random locations within the entire non-breeding home range (95% KDE) for every individual (e.g. by setting a 1/10 ratio presence to random locations).

The proposed analysis will provide an opportunity to assess whether the Rough-legged Buzzard selects areas with the lowest snow cover, but will not provide an opportunity to follow the dynamics and will therefore give a misleading overall picture. This is especially true in the spring months. In March-April, Rough-legged Buzzards move northeast and are in an area that is not the most open to snow. At this time, areas to the southwest are more open to snow (this can be seen in Figure 4b). If we perform the proposed analysis, the control points for this period would be both to the north (where there is more snow) and to the south (where there is less snow) from the real locations, and the result would be that there is no difference in snow cover.

A step-selection analysis could be used, as we did in our previous work (Curk et al 2020 Sci Rep) with the same Rough-legged Buzzard (but during migration, not winter). But this would only give us a qualitative idea, not a quantitative one - that Rough-legged Buzzards move from snow (in the fall) and follow snowmelt progression (in the spring).

At the same time, our analysis gives a complete picture of snow cover dynamics in different parts of the non-breeding range. This allows us to see that if Rough-legged Buzzards remained at their fall migration endpoint without moving southwest, they would encounter 14.4% more snow cover (99.5% vs. 85.1%). Although this difference may seem small (14.4%), it holds significance for rodent-hunting birds, distinguishing between complete and patchy snow cover. Simultaneously, if Rough-legged Buzzards immediately flew to the southwest and stayed there throughout winter, they would experience 25.7% less snow cover (57.3% vs. 31.6%). Despite a greater difference than in the first case, it doesn't compel them to adopt this strategy, as it represents the difference between various degrees of landscape openness from snow cover.

We write about this in the new manuscript on lines 385-394.

Results: it is unclear whether the reported dispersion measures are SDs or SEs. Please provide details.

For the date and coordinates of the start and end of the different phases of migration, we specified the mean, sd, and sample size. We wrote this in line 277. For the values of the parameters of the different phases of the migration (duration, distance, speed, and direction), we used the mean, the standard error of the mean, and the confidence interval (obtained using the ‘emmeans’ package). We have indicated this in lines 302-303 and the caption of Table 1 (L 315) and Figure 2 (L 293-294). For the values of habitat and snow cover experienced by the Rough-legged Buzzards, we used the mean and the error of the mean. We reported this on lines 322 and 337 and in Figures 3 (L 332-333) and 4 (L 355-356).

Discussion: in general, it should be reshaped taking into account the comments. It is overlong, speculative and quite naive in several passages. Entire sections can be safely removed (I think it can be reduced by half without any loss of information). I provide some examples of the issues I have spotted below. For instance, the entire paragraph starting with 'Understanding....' is not clear to me. What do you mean by 'prohibited management' options? Without examples, this seems a rather general text, based on unclear premises when related to the specific of this study. Some statements are vague, derive from unsubstantiated claims, and unclear. E.g. "Despite their scarcity in these habitats, forests appear to hold significant importance for Rough-legged buzzards for nocturnal safety". I could not find any day-night analysis showing that they actually roost in forests during nighttime. Being a tundra species, it may well be possible that rough-legged buzzards perceive forests as very dangerous habitats and that they prefer instead to roost in open habitats. Analysing habitat use during day and night during the non-breeding period may be of help to clarify this. Furthermore, considering the fast migration periods, what is the flight speed during day and night above forests? Do these birds also migrate at night or do they roost during the night? Perhaps a figure visualizing day and night track segments could be of help (or an analysis of day vs. night flight speed) (there are several R packages to annotate tracks in relation to day and night). This is an example of another problematic statement: "The progression of snow cover in the wintering range of Rough-legged buzzards plays a significant role in their winter migration pattern." The manuscript does not contain any clear demonstration of this, as I wrote in my previous comments. Without such evidence, you must considerably tone down such assertions. But since providing a direct link is certainly possible, I think that additional analyses would clearly strengthen your take-home message.

The paragraph starting with "The quantification of environmental changes that could prove fatal to bird species presents yet another challenge for conservation efforts in an era of rapid global change." is quite odd. Take the following statement "For instance, the presence of small patches of woodland in the winter range might appear crucial to the survival of the Rough-legged buzzard. Elimination of these seemingly minor elements of vegetation cover through management actions could have dire consequences for the species.". It is based on the assumption that minor vegetation elements play a key role in the ecology of the species, without any evidence supporting this. Does it have any sense? I could safely say exactly the opposite and I would believe it might even be more substantiated.

We agree with these comments.

We have completely rewritten this section. As suggested, we have shortened it by removing statements that were not supported by the research. We have completely removed the statements about "prohibited management". We have also removed the statement that "forests appear to be of significant importance to Rough-legged buzzards for nocturnal safety" and everything associated with that statement, e.g. the statement about "small elements of vegetation cover", etc. We do believe that this statement is true in substance, but we also agree that it is not supported by the results and requires separate analysis. At the same time, we believe that this is a topic for a separate study and would be redundant here. Therefore, we leave it for a separate publication.

Conclusion paragraph: I believe this severely overstates the conservation importance of this study. That the results have "crucial implications for conservation efforts in the Anthropocene, where rapidly changing environmental factors can severely impact bird migration" seems completely untenable to me. What is the evidence for such crucial implications? For instance, these results may suggest that climate change, because global warming is predicted to reduce snow cover in the non-breeding areas, might well be beneficial for populations of this species, by reducing non-breeding energy expenditure and improving non-breeding survival. I think statements like these are simply not necessary, and that the study should be more focused on the actual results and evidence provided.

We have completely rewritten this section. We removed the reference to the Anthropocene and focused on migratory behavior and migration patterns.

Author response:

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

eLife assessment

Connelly and colleagues provide convincing genetic evidence that importation from mainland Tanzania is a major source of Plasmodium falciparum lineages currently circulating in Zanzibar. This study also reveals ongoing local malaria transmission and occasional near-clonal outbreaks in Zanzibar. Overall, this research highlights the role of human movements in maintaining residual malaria transmission in an area targeted for intensive control interventions over the past decades and provides valuable information for epidemiologists and public health professionals.

Reviewer #1 (Public Review):

Zanzibar archipelago is close to achieving malaria elimination, but despite the implementation of effective control measures, there is still a low-level seasonal malaria transmission. This could be due to the frequent importation of malaria from mainland Tanzania and Kenya, reservoirs of asymptomatic infections, and competent vectors. To investigate population structure and gene flow of P. falciparum in Zanzibar and mainland Tanzania, they used 178 samples from mainland Tanzania and 213 from Zanzibar that were previously sequenced using molecular inversion probes (MIPs) panels targeting single nucleotide polymorphisms (SNPs). They performed Principal Component Analysis (PCA) and identity by descent (IBD) analysis to assess genetic relatedness between isolates. Parasites from coastal mainland Tanzania contribute to the genetic diversity in the parasite population in Zanzibar. Despite this, there is a pattern of isolation by distance and microstructure within the archipelago, and evidence of local sharing of highly related strains sustaining malaria transmission in Zanzibar that are important targets for interventions such as mass drug administration and vector control, in addition to measures against imported malaria.

Strengths:

This study presents important samples to understand population structure and gene flow between mainland Tanzania and Zanzibar, especially from the rural Bagamoyo District, where malaria transmission persists and there is a major port of entry to Zanzibar. In addition, this study includes a larger set of SNPs, providing more robustness for analyses such as PCA and IBD. Therefore, the conclusions of this paper are well supported by data.

Weaknesses:

Some points need to be clarified:

(1) SNPs in linkage disequilibrium (LD) can introduce bias in PCA and IBD analysis. Were SNPs in LD filtered out prior to these analyses?

Thank you for this point. We did not filter SNPs in LD prior to this analysis. In the PCA analysis in Figure 1, we did restrict to a single isolate among those that were clonal (high IBD values) to prevent bias in the PCA. In general, disequilibrium is minimal only over small distances <5-10kb without selective forces at play. This is much less than the average spacing of the markers in the panel. If there is minimal LD, the conclusions drawn on relative levels and connections at high IBD are unlikely to be confounded by any effects of disequilibrium.

( 2) Many IBD algorithms do not handle polyclonal infections well, despite an increasing number of algorithms that are able to handle polyclonal infections and multiallelic SNPs. How polyclonal samples were handled for IBD analysis?

Thank you for this point. We added lines 157-161 to clarify. This section now reads:

“To investigate genetic relatedness of parasites across regions, identity by descent (IBD) estimates were assessed using the within sample major alleles (coercing samples to monoclonal by calling the dominant allele at each locus) and estimated utilizing a maximum likelihood approach using the inbreeding_mle function from the MIPanalyzer package (Verity et al., 2020). This approach has previously been validated as a conservative estimate of IBD (Verity et al., 2020).”

Please see the supplement in (Verity et al., 2020) for an extensive simulation study that validates this approach.

Reviewer #1 (Recommendations For The Authors):

(3) I think Supplementary Figures 8 and 9 are more visually informative than Figure 2.

Thank you for your response. We performed the analysis in Figure 2 to show how IBD varies between different regions and is higher within a region than between.

Reviewer #2 (Public Review):

This manuscript describes P. falciparum population structure in Zanzibar and mainland Tanzania. 282 samples were typed using molecular inversion probes. The manuscript is overall well-written and shows a clear population structure. It follows a similar manuscript published earlier this year, which typed a similar number of samples collected mostly in the same sites around the same time. The current manuscript extends this work by including a large number of samples from coastal Tanzania, and by including clinical samples, allowing for a comparison with asymptomatic samples.

The two studies made overall very similar findings, including strong small-scale population structure, related infections on Zanzibar and the mainland, near-clonal expansion on Pemba, and frequency of markers of drug resistance. Despite these similarities, the previous study is mentioned a single time in the discussion (in contrast, the previous research from the authors of the current study is more thoroughly discussed). The authors missed an opportunity here to highlight the similar findings of the two studies.

Thank you for your insights. We appreciated the level of detail of your review and it strengthened our work. We have input additional sentences on lines 292-295, which now reads:

“A recent study investigating population structure in Zanzibar also found local population microstructure in Pemba (Holzschuh et al., 2023). Further, both studies found near-clonal parasites within the same district, Micheweni, and found population microstructure over Zanzibar.”

Strengths:

The overall results show a clear pattern of population structure. The finding of highly related infections detected in close proximity shows local transmission and can possibly be leveraged for targeted control.

Weaknesses:

A number of points need clarification:

(1) It is overall quite challenging to keep track of the number of samples analyzed. I believe the number of samples used to study population structure was 282 (line 141), thus this number should be included in the abstract rather than 391. It is unclear where the number 232 on line 205 comes from, I failed to deduct this number from supplementary table 1.

Thank you for this point. We have included 282 instead of 391 in the abstract. We added a statement in the results at lines 203-205 to clarify this point, which now reads:

“PCA analysis of 232 coastal Tanzanian and Zanzibari isolates, after pruning 51 samples with an IBD of greater than 0.9 to one representative sample, demonstrates little population differentiation (Figure 1A).”

(2) Also, Table 1 and Supplementary Table 1 should be swapped. It is more important for the reader to know the number of samples included in the analysis (as given in Supplementary Table 1) than the number collected. Possibly, the two tables could be combined in a clever way.

Thank you for this advice. Rather than switch to another table altogether, we appended two columns to the original table to better portray the information (see Table 1).

Methods

(3) The authors took the somewhat unusual decision to apply K-means clustering to GPS coordinates to determine how to combine their data into a cluster. There is an obvious cluster on Pemba islands and three clusters on Unguja. Based on the map, I assume that one of these three clusters is mostly urban, while the other two are more rural. It would be helpful to have a bit more information about that in the methods. See also comments on maps in Figures 1 and 2 below.

Cluster 3 is a mix of rural/urban while the clusters 2, 4 and 5 are mostly rural. This analysis was performed to see how IBD changes in relation to local context within different regions in Zanzibar, showing that there is higher IBD within locale than between locale.

(4) Following this point, in Supplemental Figure 5 I fail to see an inflection point at K=4. If there is one, it will be so weak that it is hardly informative. I think selecting 4 clusters in Zanzibar is fine, but the justification based on this figure is unclear.

The K-means clustering experiment was used to cluster a continuous space of geographic coordinates in order to compare genetic relatedness in different regions. We selected this inflection point based on the elbow plot and based the number to obtain sufficient subsections of Zanzibar to compare genetic relatedness. This point is added to the methods at lines 174-178, which now reads:

“The K-means clustering experiment was used to cluster a continuous space of geographic coordinates in order to compare genetic relatedness in different regions. We selected K = 4 as the inflection point based on the elbow plot (Supplemental Figure 5) and based the number to obtain sufficient subsections of Zanzibar to compare genetic relatedness.”

(5) For the drug resistance loci, it is stated that "we further removed SNPs with less than 0.005 population frequency." Was the denominator for this analysis the entire population, or were Zanzibar and mainland samples assessed separately? If the latter, as for all markers <200 samples were typed per site, there could not be a meaningful way of applying this threshold. Given data were available for 200-300 samples for each marker, does this simply mean that each SNP needed to be present twice?

Population frequency is calculated based on the average within sample allele frequency of each individual in the population, which is an unbiased estimator. Within sample allele frequency can range from 0 to 1. Thus, if only one sample has an allele and it is at 0.1 within sample frequency, the population allele frequency would be 0.1/100 = 0.001. This allele is removed even though this would have resulted in a prevalence of 0.01. This filtering is prior to any final summary frequency or prevalence calculations (see MIP variant Calling and Filtering section in the methods). This protects against errors occurring only at low frequency.

Discussion:

(6) I was a bit surprised to read the following statement, given Zanzibar is one of the few places that has an effective reactive case detection program in place: "Thus, directly targeting local malaria transmission, including the asymptomatic reservoir which contributes to sustained transmission (Barry et al., 2021; Sumner et al., 2021), may be an important focus for ultimately achieving malaria control in the archipelago (Björkman & Morris, 2020)." I think the current RACD program should be mentioned and referenced. A number of studies have investigated this program.

Thank you for this point. We have added additional context and clarification on lines 275-280, which now reads:

“Thus, directly targeting local malaria transmission, including the asymptomatic reservoir which contributes to sustained transmission (Barry et al., 2021; Sumner et al., 2021), may be an important focus for ultimately achieving malaria control in the archipelago (Björkman & Morris, 2020). Currently, a reactive case detection program within index case households is being implemented, but local transmission continues and further investigation into how best to control this is warranted (Mkali et al. 2023).”

(7) The discussion states that "In Zanzibar, we see this both within and between shehias, suggesting that parasite gene flow occurs over both short and long distances." I think the term 'long distances' should be better defined. Figure 4 shows that highly related infections rarely span beyond 20-30 km. In many epidemiological studies, this would still be considered short distances.

Thank you for this point. We have edited the text at lines 287-288 to indicate that highly related parasites mainly occur at the range of 20-30km, which now reads:

“In Zanzibar, highly related parasites mainly occur at the range of 20-30km.”

(8) Lines 330-331: "Polymorphisms associated with artemisinin resistance did not appear in this population." Do you refer to background mutations here? Otherwise, the sentence seems to repeat lines 324. Please clarify.

We are referring to the list of Pfk13 polymorphisms stated in the Methods from lines 146-148. We added clarifying text on lines 326-329:

“Although polymorphisms associated with artemisinin resistance did not appear in this population, continued surveillance is warranted given emergence of these mutations in East Africa and reports of rare resistance mutations on the coast consistent with spread of emerging Pfk13 mutations (Moser et al., 2021). “

(9) Line 344: The opinion paper by Bousema et al. in 2012 was followed by a field trial in Kenya (Bousema et al, 2016) that found that targeting hotspots did NOT have an impact beyond the actual hotspot. This (and other) more recent finding needs to be considered when arguing for hotspot-targeted interventions in Zanzibar.

We added a clarification on this point on lines 335-345, which now reads:

“A recent study identified “hotspot” shehias, defined as areas with comparatively higher malaria transmission than other shehias, near the port of Zanzibar town and in northern Pemba (Bisanzio et al., 2023). These regions overlapped with shehias in this study with high levels of IBD, especially in northern Pemba (Figure 4). These areas of substructure represent parasites that differentiated in relative isolation and are thus important locales to target intervention to interrupt local transmission (Bousema et al., 2012). While a field cluster-randomized control trial in Kenya targeting these hotspots did not confer much reduction of malaria outside of the hotspot (Bousema et al. 2016), if areas are isolated pockets, which genetic differentiation can help determine, targeted interventions in these areas are likely needed, potentially through both mass drug administration and vector control (Morris et al., 2018; Okell et al., 2011). Such strategies and measures preventing imported malaria could accelerate progress towards zero malaria in Zanzibar.”

Figures and Tables:

(10) Table 2: Why not enter '0' if a mutation was not detected? 'ND' is somewhat confusing, as the prevalence is indeed 0%.

Thank you for this point. We have put zero and also given CI to provide better detail.

(11) Figure 1: Panel A is very hard to read. I don't think there is a meaningful way to display a 3D-panel in 2D. Two panels showing PC1 vs. PC2 and PC1 vs. PC3 would be better. I also believe the legend 'PC2' is placed in the wrong position (along the Y-axis of panel 2).

Supplementary Figure 2B suffers from the same issue.

Thank you for your comment. A revised Figure 1 and Supplemental Figure 2 are included, where there are separate plots for PC1 vs. PC2 and PC1 vs. PC3.

(12) The maps for Figures 1 and 2 don't correspond. Assuming Kati represents cluster 4 in Figure 2, the name is put in the wrong position. If the grouping of shehias is different between the Figures, please add an explanation of why this is.

Thank you for this point. The districts with at least 5 samples present are plotted in the map in Figure 1B. In Figure 2, a totally separate analysis was performed, where all shehias were clustered into separate groups with k-means and the IBD values were compared between these clusters. These maps are not supposed to match, as they are separate analyses. Figure 1B is at the district level and Figure 2 is clustering shehias throughout Zanzibar.

The figure legend of Figure 1B on lines 410-414 now reads:

“B) A Discriminant Analysis of Principal Components (DAPC) was performed utilizing isolates with unique pseudohaplotypes, pruning highly related isolates to a single representative infection. Districts were included with at least 5 isolates remaining to have sufficient samples for the DAPC. For plotting the inset map, the district coordinates (e.g. Mainland, Kati, etc.) are calculated from the averages of the shehia centroids within each district.”

The figure legend of Figure 2 on lines 417-425 now reads:

“Figure 2. Coastal Tanzania and Zanzibari parasites have more highly related pairs within their given region than between regions. K-means clustering of shehia coordinates was performed using geographic coordinates all shehias present from the sample population to generate 5 clusters (colored boxes). All shehias were included to assay pairwise IBD between differences throughout Zanzibar. Pairwise comparisons of within cluster IBD (column 1 of IBD distribution plots) and between cluster IBD (column 2-5 of IBD distribution plots) was done for all clusters. In general, within cluster IBD had more pairwise comparisons containing high IBD identity.”

(13) Figure 2: In the main panel, please clarify what the lines indicate (median and quartiles?). It is very difficult to see anything except the outliers. I wonder whether another way of displaying these data would be clearer. Maybe a table with medians and confidence intervals would be better (or that data could be added to the plots). The current plots might be misleading as they are dominated by outliers.

Thank you for this point and it greatly improved this figure. We changed the plotting mechanisms through using a beeswarm plot, which plots all pairwise IBD values within each comparison group.

(14) In the insert, the cluster number should not only be given as a color code but also added to the map. The current version will be impossible to read for people with color vision impairment, and it is confusing for any reader as the numbers don't appear to follow any logic (e.g. north to south).

Thank you very much for these considerations. We changed the color coding to a color blind friendly palette and renamed the clusters to more informative names; Pemba, Unguja North (Unguja_N), Unguja Central (Unguja_C), Unguja South (Unguja_S) and mainland Tanzania (Mainland).

(15) The legend for Figure 3 is difficult to follow. I do not understand what the difference in binning was in panels A and B compared to C.

Thank you for this point. We have edited the legend to reflect these changes. The legend for Figure 3 on lines 427-433 now reads:

“Figure 3. Isolation by distance is shown between all Zanzibari parasites (A), only Unguja parasites (B) and only Pemba parasites (C). Samples were analyzed based on geographic location, Zanzibar (N=136) (A), Unguja (N=105) (B) or Pemba (N=31) (C) and greater circle (GC) distances between pairs of parasite isolates were calculated based on shehia centroid coordinates. These distances were binned at 4km increments out to 12 km. IBD beyond 12km is shown in Supplemental Figure 8. The maximum GC distance for all of Zanzibar was 135km, 58km on Unguja and 12km on Pemba. The mean IBD and 95% CI is plotted for each bin.”

(16) Font sizes for panel C differ, and it is not aligned with the other panels.

Thank you for pointing this out. Figure 3 and Supplemental Figure 10 are adjusted with matching formatting for each plot.

(17) Why is Kusini included in Supplemental Figure 4, but not in Figure 1?

In Supplemental Figure 4, all isolates were used in this analysis and isolates with unique pseudohaplotypes were not pruned to a single representative infection. That is why there are additional isolates in Kusini. The legend for Supplemental Figure 4 now reads:

“Supplemental Figure 4. PCA with highly related samples shows population stratification radiating from coastal Mainland to Zanzibar. PCA of 282 total samples was performed using whole sample allele frequency (A) and DAPC was performed after retaining samples with unique pseudohaplotypes in districts that had 5 or more samples present (B). As opposed to Figure 1, all isolates were used in this analysis and isolates with unique pseudohaplotypes were not pruned to a single representative infection.”

(18) Supplemental Figures 6 and 7: What does the width of the line indicate?

The sentence below was added to the figure legends of Supplemental Figures 6 and 7 and the legends of each network plot were increased in size:

“The width of each line represents higher magnitudes of IBD between pairs.”

(19) What was the motivation not to put these lines on the map, as in Figure 4A? This might make it easier to interpret the data.

Thank you for this comment. For Supplemental Figure 8 and 9, we did not put these lines that represent lower pairwise IBD to draw the reader's attention to the highly related pairs between and within shehias.

Reviewer #2 (Recommendations For The Authors):

(1) There is a rather long paragraph (lines 300-323) on COI of asymptomatic infections and their genetic structure. Given that the current study did not investigate most of the hypotheses raised there (e.g. immunity, expression of variant genes), and the overall limited number of asymptomatic samples typed, this part of the discussion feels long and often speculative.

Thank you for your perspective. The key sections highlighted in this comment, regarding immunity and expression of variant genes, were shortened. This section on lines 300-303 now reads:

“Asymptomatic parasitemia has been shown to be common in falciparum malaria around the globe and has been shown to have increasing importance in Zanzibar (Lindblade et al., 2013; Morris et al., 2015). What underlies the biology and prevalence of asymptomatic parasitemia in very low transmission settings where anti-parasite immunity is not expected to be prevalent remains unclear (Björkman & Morris, 2020).”

(2) As a detail, line 304 mentions "few previous studies" but only one is cited. Are there studies that investigated this and found opposite results?

Thank you for this comment. We added additional studies that did not find an association between clinical disease and COI. These changes are on lines 303-308, which now reads:

“Similar to a few previous studies, we found that asymptomatic infections had a higher COI than symptomatic infections across both the coastal mainland and Zanzibar parasite populations (Collins et al., 2022; Kimenyi et al., 2022; Sarah-Matio et al., 2022). Other studies have found lower COI in severe vs. mild malaria cases (Robert et al., 1996) or no significant difference between COI based on clinical status (Earland et al. 2019; Lagnika et al. 2022; Conway et al. 1991; Kun et al. 1998; Tanabe et al. 2015)”

(3) Table 2: Percentages need to be checked. To take one of several examples, for Pfk13-K189N a frequency of 0.019 for the mutant allele is given among 137 samples. 2/137 equals to 0.015, and 3/137 to 0.022. 0.019 cannot be achieved. The same is true for several other markers. Possibly, it can be explained by the presence of polyclonal infections. If so, it should be clarified what the total of clones sequenced was, and whether the prevalence is calculated with the number of samples or number of clones as the denominator.

Thank you for this point. We mistakenly reported allele frequency instead of prevalence. An updated Table 2 is now in the manuscript. The method for calculating the prevalence is now at lines 148-151:

“Prevalence was calculated separately in Zanzibar or mainland Tanzania for each polymorphism by the number of samples with alternative genotype calls for this polymorphism over the total number of samples genotyped and an exact 95% confidence interval was calculated using the Pearson-Klopper method for each prevalence.”

Author response:

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

eLife assessment

This study presents useful findings regarding the role of formin-like 2 in mouse oocyte meiosis. The submitted data are supported by incomplete analyses, and in some cases, the conclusions are overstated. If these concerns are addressed, this paper would be of interest to reproductive biologists.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The presented study focuses on the role of formin-like 2 (FMNL2) in oocyte meiosis. The authors assessed FMNL2 expression and localization in different meiotic stages and subsequently, by using siRNA, investigated the role of FMNL2 in spindle migration, polar body extrusion, and distribution of mitochondria and endoplasmic reticulum (ER) in mouse oocytes.

Strengths:

Novelty in assessing the role of formin-like 2 in oocyte meiosis.

Weaknesses:

Methods are not properly described.

Overstating presented data.

It is not clear what statistical tests were used.

My main concern is that there are missing important details of how particular experiments and analyses were done. The material and methods section are not written in the way that presented experiments could be repeated - it is missing basic information (e.g., used mouse strain, timepoints of oocytes harvest for particular experiments, used culture media, image acquisition parameters, etc.). Some of the presented data are overstated and incorrectly interpreted. It is not clear to me how the analysis of ER and mitochondria distribution was done, which is an important part of the presented data interpretation. I'm also missing important information about the timing of particular stages of assessed oocytes because the localization of both ER and mitochondria differs at different stages of oocyte meiosis. The data interpretation needs to be justified by proper analysis based on valid parameters, as there is considerable variability in the ER and mitochondria structure and localization across oocytes based on their overall quality and stage.

Thank you for your comment. We regret the oversight of omitting critical information in the manuscript. In the revised manuscript, we have included essential details such as mouse strains, culture media, stages of oocyte and statistical methods in the materials and methods section. Please find our details responses in the “Recommendations for the authors” part.

Reviewer #2 (Public Review):

Summary:

This research involves conducting experiments to determine the role of Fmnl2 during oocyte meiosis I.

Strengths:

Identifying the role of Fmnl2 during oocyte meiosis I is significant.

Weaknesses:

The quantitative analysis and the used approach to perturb FMNL2 function are currently incomplete and would benefit from more confirmatory approaches and rigorous analysis.

(1) Most of the results are expected. The new finding here is that FMNL2 regulates cytoplasmic F-actin in mouse oocytes, which is also expected given the role of FMNL2 in other cell types. Given that FMNL2 regulates cytoplasmic F-actin, it is very expected to see all the observed phenotypes. It is already established that F-actin is required for spindle migration to the oocyte cortex, extruding a small polar body and normal organelle distribution and functions.

Thank you for your comment. In the recent decade, Arp2/3 complex (Nat Cell Biol 2011), Formin2 (Nat Cell Biol 2002, Nat Commun 2020), and Spire (Curr Biol 2011) were reported to be 3 key factors to involve into this process. These factors regulate actin filaments in different ways. However, how they cross with each other for the subcellular events were still fully clear. Our current study identified that FMNL2 played a critical role in coordinating these molecules for actin assembly in oocytes. Our findings demonstrate that FMNL2 interacts with both the Arp2/3 complex and Formin2 to facilitate actin-based meiotic spindle migration. Additionally, we discovered a novel role for FMNL2 in determining the distribution and function of the endoplasmic reticulum and mitochondria, which may in turn influence meiotic spindle migration in oocytes. Our results not only uncover the novel functions of FMNL2-mediated actin for organelle distribution, but also extend our understanding of the molecular basis for the unique meiotic spindle migration in oocyte meiosis.

(2) The authors used Fmnl2 cRNA to rescue the effect of siRNA-mediated knockdown of Fmnl2. It is not clear how this works. It is expected that the siRNA will also target the exogenous cRNA construct (which should have the same sequence as endogenous Fmnl2) especially when both of them were injected at the same time. Is this construct mutated to be resistant to the siRNA?

Thank you for your question. We regret any misunderstanding that may have been caused by the inappropriate description in our manuscript. In the rescue experiments, we initially injected FMNL2 siRNA into oocytes, followed by the microinjection of FMNL2 mRNA 18-20 hours later. After conducting our previous experiments, we have verified through Western blotting that endogenous FMNL2 is effectively suppressed 18-20 hours following the microinjection of FMNL2 siRNA. Additionally, we observed a significant increase in exogenous FMNL2 protein expression 2 hours after the injection of FMNL2 mRNA. We believe that the exogenous FMNL2 could compensate the decrease by FMNL2 knockdown, and this approach was adopted in many oocyte studies.

(3) The authors used only one approach to knockdown FMNL2 which is by siRNA. Using an additional approach to inhibit FMNL2 would be beneficial to confirm that the effect of siRNA-mediated knockdown of FMNL2 is specific.

Thank you for your question. Yes, the specificity is always the concern for siRNA or morpholino microinjection due to the off-target issue. Due to the limitation we could not generate the knock out model, and there are no known inhibitors with specific targeting capabilities for FMNL2. To solve this, we performed the rescue study with exogenous mRNA to confirm the effective knock down of FMNL2. These measures provide reassurance regarding the credibility of the experimental outcomes, and this is also the general way to avoid the off-target of siRNA or morpholino.

Reviewer #3 (Public Review):

Summary:

The authors focus on the role of formin-like protein 2 in the mouse oocyte, which could play an important role in actin filament dynamics. The cytoskeleton is known to influence a number of cellular processes from transcription to cytokinesis. The results show that downregulation of FMNL2 affects spindle migration with resulting abnormalities in cytokinesis in oocyte meiosis I.

Weaknesses:

The overall description of methods and figures is overall dismissively poor. The description of the sample types and number of replicate experiments is impossible to interpret throughout, and the quantitative analysis methods are not adequately described. The number of data points presented is unconvincing and unlikely to support the conclusions. On the basis of the data presented, the conclusions appear to be preliminary, overstated, and therefore unconvincing.

Thank you for your comment. We regret the oversight of omitting critical information in the manuscript. In the revised manuscript, we have incorporated your suggestions for modification, particularly regarding the Materials and Methods section. Please see the detailed revision and responses in the “Recommendations for the authors” part.

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

My main concern is that there are missing important details of how particular experiments and analyses were done. The material and methods section is not written in the way that presented experiments could be repeated - it is missing basic information (e.g., used mouse strain, timepoints of oocytes harvest for particular experiments, used culture media, image acquisition parameters, etc.). Some of the presented data are overstated and incorrectly interpreted. It is not clear to me how the analysis of ER and mitochondria distribution was done, which is an important part of the presented data interpretation. I'm also missing important information about the timing of particular stages of assessed oocytes because the localization of both ER and mitochondria differs at different stages of oocyte meiosis. The data interpretation needs to be justified by proper analysis based on valid parameters, as there is considerable variability in the ER and mitochondria structure and localization across oocytes based on their overall quality and stage. My specific comments are listed below.

(1) Information about statistical tests that were used needs to be provided for all quantification experiments.

Thank you for your suggestion. Based on your suggestions, we revised the statistical analysis description in the Materials and Methods section. Additionally, we also included a description of the statistical methods in the legends of the relevant result figures.

(2) I recommend replacing the plunger plots, used in most quantification data, with alternatives allowing evaluation of the distribution of the data (dot plots, box plots, whisker plots).

Thank you for your suggestion. Following your suggestion, we replaced the plunger plots in Fig 2C, D, H, I and Fig3 B, C with dot plots.

(3) Can the authors provide information about particular time points when were individual oocyte stages (GVBD, meiosis I, and meiosis II) harvested/used for immunofluorescence protein detection, western blotting, microinjection, and ER and mitochondria staining? Were the time points always the same in all presented experiments and experimental vs control group? If not, this needs to be clarified.

Thank you for your suggestion. We used oocytes in the metaphase I (MI) stage for the statistical analysis of spindle migration, actin filament aggregation, endoplasmic reticulum localization, and mitochondrial localization. In the Western blot analysis, GV stage oocytes were utilized to evaluate the efficiency of knockdown and rescue experiments. The protein expression levels of Arp2, Formin2, INF2, Cofilin, Grp78, and Chop in different treatment groups were detected using MI-stage oocytes. In the revised version, we provided all the detailed information about the stages.

(4) Figure 1B: Can the authors comment on why there is a missing representative image of MII oocyte FMBL2-Ab? I recommend including this in the figure to have a complete view of comparing overexpressed and endogenous FMNL2 localization in oocyte meiosis.

Thank you for your suggestion. In the revised manuscript, we added immunostaining images of FMNL2 antibody in MII stage oocytes.

(5) Figure 1C: The figure legend says, "FMNL2 and actin overlapped in cortex and spindle surrounding". In MI oocytes, there is usually no accumulated actin signal around the spindle, which is also true in the presented images, so there cannot be overlapping with the FMNL2 signal. The interpretation should be changed.

We apologize for this inappropriate description that was used, and we deleted this sentence.

(6) Figure 2B: What were the parameters of the "large" and "normal" polar bodies for performing the analysis?

Thank you for your question. In order to assess the size of the polar body, we conducted a comparison between the diameter of the polar body and that of the oocyte. If the diameter of the polar body was found to be less than 1/3 of the oocyte's diameter, we categorized it as normal-sized polar body. Conversely, if the polar body's diameter exceeded 1/3 of the oocyte's diameter, we categorized it as a large polar body. We have included these details in the Results section of the manuscript.

(7) Figure 2F: Can the authors comment on what can be the second band in the rescue group?

Thank you for your question. In the rescue experiment, we microinjected exogenous FMNL2-EGFP mRNA into the oocytes. As a result, compared to endogenous FMNL2, the protein size increased due to the addition of the EGFP tag, approximately 27 kDa. Hence, in the Western blot bands of the rescue group, the upper band represents the expression of exogenous FMNL2-EGFP, while the lower band corresponds to the expression of endogenous FMNL2. We have provided annotations in the revised Figure 2F to clarify this.

(8) Can the authors comment on the variability of PBE between 2C and 2H in the FMNL2-KD groups? In panel C, the PBE in the KD group was 59.5 {plus minus} 2.82%; in panel H, the PBE in the KD group was 48.34 {plus minus} 4.2%, and in the rescue group, the PBE was 62.62 {plus minus} 3.6%. The rescue group has a similar PBE rate as the KD group in panel C. How consistent was the FMNL2 knockdown across individual replicates? Can the authors provide more details on how the rescue experiment was performed?

Thank you for your question. We believe that the difference in PBE observed in Figure 2C and 2H of the FMNL2-KD group was due to the microinjection times and the duration of in vitro arrest. The results shown in Figure 2C depict the outcome of a single injection of FMNL2 siRNA into GV stage oocytes, followed by 18 hours of in vitro arrest; the results shown in Figure 2H contain a subsequent additional injection of FMNL2-EGFP mRNA with another 2 hours of arrest. The two rounds of microinjection and the extended period of in vitro arrest both affect oocyte maturation rates.

(9). Figure 2J and K: What groups were compared together? The used statistic needs to be properly described.

Thank you for your question. The FMNL2-KD, FMNL3-KD, and FMNL2+3-KD groups were all compared to the Control group, therefore, t-test was used for analysis. We have provided explanations in the revised manuscript.

(10) Figure 4B and C: Can the authors provide representative images without oversaturated actine signal?

Thank you for your question. For the analysis of oocyte F-actin, the F-actin are divided into cortex actin and cytoplasmic actin. Due to the contrast during imaging, the strong cortex actin signals affected the detection of cytoplasmic actin, therefore, it is necessary to increase the scanning index, which will cause the overexpose the cortex actin signal. This is for the better observation of the cytoplasmic signals.

(11) Figure 4G + 5H: Can the authors comment on why they used as a housekeeping gene actin instead of tubulin, which was used in the rest of the WB experiments?

Thank you for your question. In most of the western blot experiments conducted in this study, we used tubulin as a housekeeping gene. However, due to the supply of antibodies by delivery period, we had GAPDH and actin as well for some experiments. These housekeeping genes were all valid for the study.

(12) Based on what parameters was ER considered normally or abnormally distributed, and what stages of oocytes were assessed?

Thank you for your question. In this study, we employed oocytes at the MI stage for the analysis of ER localization. In the MI stage, the ER localized around the spindle, which is regarded as the typical localization pattern. The ER displayed a dispersed distribution throughout the cytoplasm or clustered were categorized as aberrant positioning. We included relevant descriptions in the revised version of the manuscript.

(13) Figure 5H: As a housekeeping gene was used actin - the quantification is labeled as a Grp78 to tubulin ratio.

Thank you for pointing out the error. This is a label mistake and we corrected it.

(14) Information about how JC-1 staining was done needs to be provided.

Thank you for your carefully reading. We included a description of JC1 staining in the Materials and Methods section.

(15). Line 231-232: "As shown in Figure 4A" - the text doesn't correspond to the figure.

Thank you for pointing out the error. We revised this mistake in the revised manuscript by correcting "Fig3A" to "Fig4A."

(16) Line 265: there is probably a missing word "Formin2".

Thank you and we corrected the error and made the necessary changes in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

(1) Quantification and analysis:

• Fig. 3B: The rate of spindle migration should be quantified based on the distance from the spindle to the cortex. Also, the orientation of the spindle (Z-position) needs to be taken into consideration.

• Fig. 5C, D: It is unclear how the rate of ER distribution was calculated.

• Western blot: In many experiments (such as Fig. 5H), the bands are saturated which will prevent accurate intensity measurements and quantifications.

For spindle migration, we specifically focused on spindles exhibiting a distinctive spindle-like shape with clear bipolarity to eliminate any statistical discrepancies potentially caused by variations in Z-axis alignment. Our criterion for determining successful migration was based on the contact between the spindle pole and the cortical region of the oocyte. Therefore, we think that the rate is better to reflect the phenotype than the distance.

For the examination of ER localization, Reviewer 1 also raised this issue. We utilized oocytes at the MI stage in this study. The ER localized around the spindle in MI stage. The ER displayed a dispersed distribution throughout the cytoplasm or clustered were categorized as aberrant positioning. We included relevant descriptions in the revised version of the manuscript.

For the bands of the western blot results, during the experimental procedure we typically capture multiple images at different exposure levels (3-5 images). In the revised manuscript, we have replaced the inappropriate images with more suitable ones.

(2) Given that all Immunoprecipitation experiments in this manuscript were performed on the whole ovary which contains more somatic cells than oocytes, the results do not necessarily reflect meiotic oocytes. Please consider this possibility during the interpretation.

Thank you for your suggestion. Yes, we agree with you. In the revised manuscript, we made appropriate modifications to the relevant descriptions.

(3) 351-365: The conclusion that Arp2/3 compensates for the decreased formin 2 in FMNL2 knockdown oocytes is a bit unconvincing. 1- In mouse oocytes, it is already known that Arp2/3 and formin 2 regulate different pools of F-actin nucleation. 2- The authors found an increase in Arp2/3 in FMNL2 knockdown oocytes compared to control oocytes without any change in cortical F-actin. Given that Arp2/3 is primarily promoting cortical F-actin, it is expected to see an increase in cortical F-actin in FMNL2 knockdown oocytes, which was not the case.

Thank you for your question. Yes, previous studies showed that formin2 localizes to the cytoplasm of oocytes and accumulates around the spindle, which facilitate cytoplasmic actin assembly. While Arp2/3 is primarily responsible for actin assembly at the cortex region of oocytes. In invasive cells, FMNL2 is mainly localized in the leading edge of the cell, lamellipodia and filopodia tips, to improve cell migration ability by actin-based manner (Curr Biol 2012). We showed that FMNL2 localized both at spindle periphery and cortex, but depletion of FMNL2 did not affect cortex actin intensity. We think that FMNL2 and Arp2/3 both contribute to the cortex actin dynamics, when FMNL2 decreased, ARP2 increased to compensate for this, which maintained the cortex actin level. In the revised manuscript, we have made modifications to avoid excessive extrapolation from our results, ensuring that our conclusions are presented in a more objective manner.

(4) Lines 195-197: The spindle is initially formed soon after the GVBD, so there is no spindle during GVBD. Also, I can't see oocytes at anaphase I or telophase I in this figure. Please revise.

Thank you for your suggestion. We apologize for the inappropriate descriptions that were used. In the revised manuscript, we have made modifications to the respective descriptions in the Results part.

(5) Fig. 2E: It seems that the control oocyte is abnormal with mild cytokinesis defects. Please replace or delete it since this information is already included in Fig. 3A.

Thank you for your suggestion. Based on our observations, during the extrusion of the first polar body in oocytes, there is a temporary occurrence of cellular morphological fragmentation due to cortical reorganization (11h in control oocyte from Fig 2E). However, after the extrusion of the first polar body, the oocyte morphology returns to normal. Figure 2E illustrates the meiotic division process of oocytes, while Figure 3A primarily focuses on the process of oocyte spindle migration. We think that it is better to retain both to present our results.

Reviewer #3 (Recommendations for The Authors):

In the case of the observed phenotype, the stage of GV is important. The phenotypes presented also occur in meiotic or developmentally incompetent oocytes. In addition, the images of GV oocytes appear as NSN, which also show the KD phenotype in Figs. 2 and 3.

Thank you for your concern. As the oocyte grows, the proportion of SN-type oocytes gradually increases. When the oocyte diameter reaches 70-80 μm, the proportion of SN oocytes is approximately 52.7% (Mol Reprod Dev. 1995). In our study, both the control and knockdown groups collected oocytes with a diameter of around 80 μm, which is considered as fully-grown oocytes, predominantly in the SN phase. Since the collection period and size of the oocytes were consistent, we can sure that the observed differences between the control and knockdown groups in phenotype analysis could be solid and reliable.

MII is absent in Fig. 1B.

In the revised manuscript, we added immunostaining images of FMNL2 in MII stage oocytes.

The result of KD is not convincing. Also, discuss whether the heterozygous effect of Fmnl2 deletion affects reproductive fitness.

Thank you for your concern. In our investigation, limited to the setup of knock out model, we employed siRNA to knockdown FMNL2 expression, to avoid the risk of off-target, we performed rescue experiment with exogenous mRNA, which we believe that it could solve this issue. When designing siRNA sequences, we ensured their specificity for binding to FMNL2 mRNA only, and we assessed the levels of FMNL2 and FMNL3 mRNA in oocytes after injection of FMNL2 siRNA. The results showed that, compared to the control group, the expression of FMNL2 mRNA decreased by approximately 70% after 18 hours of FMNL2 siRNA injection, while the level of FMNL3 mRNA was not decreased.

Fig. 2F rescue experiment with double bands. What bands are seen here? Did the authors inject tagged or untagged FMNL2? Or does endogenous FMNL2 appear higher in the sample after KD?

Thank you for your question. In the rescue experiment, we microinjected exogenous FMNL2-EGFP mRNA into the oocytes. As a result, compared to endogenous FMNL2, the protein size increased due to the addition of the EGFP tag, approximately 27 kDa. Hence, in the Western blot bands of the rescue group, the upper band represents the expression of exogenous FMNL2-EGFP, while the lower band corresponds to the expression of endogenous FMNL2. We provided annotations in the revised Figure 2F to clarify this.

Variability in mitochondria and ER distribution patterns is also known in healthy and developing oocytes, although the authors described only a single phenotype.

Thank you for your concern. Yes, mitochondria and ER show dynamic localization in different stage of oocyte maturation. However, in this study we employed oocyte MI stage for the analysis of ER and mitochondria localization, and in MI stage, both the ER and mitochondria localize around the spindle. This pattern is considered as the normal localization. Several studies showed that dispersed or clustered localization contributed to maturation defects. We included relevant descriptions in the revised manuscript.

What exactly is meant by input in the IP experiments? Why is the target missing in the input sample?

Thank you for your question. We subjected the input samples to electrophoresis on a single channel, all the analyzed proteins demonstrated normal expression, thereby confirming the viability of the input sample. However, upon simultaneous exposure with the IP samples, we observed a lack of clear signal for certain proteins in the input group. This phenomenon is due to the excessive signal intensity resulting from protein enrichment in the IP group, which caused the low exposure of proteins in input group.

Explain the rationale for using, actin or tubulin as loading or normalization controls in the study focusing on the cytoskeleton.

Thank you for your question. Actin and tubulin are both widely used as the control due to their stable expression. For actin, there are α-actin and β-actin isoforms. Formins and Arp2/3 complex regulate the polymerization of α-actin and β-actin to form F-actin, not isoform expression. In our study F-actin (the functional type) was examined. While α-tubulin and β-tubulin are two subtypes of tubulin, and they interact with each other to form stable α/β-tubulin heterodimers. The changes of cytoskeleton dynamics could not change the expression of α/β-tubulin. Therefore, β-actin and α-tubulin could be used as normalization controls.

Fig. 6E shows only , but the legend says *.

Thank you for pointing out the error. We correct the mistake in the revised manuscript.

Spindle positioning appears to differ between control and KD. Does this affect the quantification of Fig. 6F? Adequate nomenclature should be used here.

Thank you for your question. Yes, spindle positioning was affected by FMNL2 depletion. However, central spindle or cortex spindle all belong to MI stage, and JC1 is not related with the stage difference. To avoid misunderstanding we replaced the representative images and corresponding description in Figure 6F.

The description of the methods and legends should be significantly improved.

Thank you for your suggestion. Reviewer 1 and 2 also raised the similar concern. We enriched the description of methods and legends in the revised manuscript.

Author response:

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

We thank the reviewers for their thoughtful comments. We were pleased that they thought our study was "well crafted and written", "important", and that it provides a "valuable resource for researchers studying color vision". They also expressed several constructive criticisms, concerning – among other things – the lack of details regarding experimental procedures and analysis, the challenge in relating retinal data to cortical recordings, and consistency of results across animals. In response to the reviewers’ comments and following their suggestions, we performed additional analyses, and substantially revised the paper:

We added a section in the Discussion about "Limitations of the stimulus paradigm". In addition, we added a new Suppl. Figure that illustrates the effect of deconvolution of calcium traces on our results and clarified in the text why we use deconvolved signals for all analyses. The new Suppl. Figure also shows an additional analysis with a more conservative threshold of neuron exclusion.

We now clarify how retinal signaling relates to our cortical results and rewrote the text to be more conservative regarding our conclusions.

In addition, we added a new Suppl. Figure showing the key analyses from Figures 2 and 4 separately for each animal. We now mention consistency across animals in the Results section and clearly state which analyses were performed an data pooled across animals.

We are positive that these additions address the issues raised by the reviewers. Please find our point-by-point replies to all comments below.

eLife assessment

Franke et al. explore and characterize the color response properties in the mouse primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The data is solid; however, the evidence supporting some conclusions and details about some procedures are incomplete. In its current form, the paper makes a useful contribution to how color is coded in mouse V1. Significance would be enhanced with some additional analyses and resolution of some technical issues.

We thank the reviewers for appreciating our manuscript and their thoughtful comments.

Referee 1 (Remarks to the Author):

Summary:

In this study, Franke et al. explore and characterize the color response properties across the primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The authors use awake-behaving 2P imaging to define the spectral response properties of visual interneurons in Layer 2/3. They find that opponent responses are more prominent at photopic light levels, and diversity in color opponent responses exists across the visual science, with green ON/ UV OFF responses being stronger represented in the upper visual field. This is argued to be relevant for detecting certain features that are more salient when the chromatic space is used, possibly due to noise reductions.

Strengths:

The work is well crafted and written and provides a thorough characterization that reveals an uncharacterized diversity of visual properties in V1. I find this characterization important because it reveals how strongly chromatic information can modulate the response properties in V1. In the upper visual field, 25% of the cells differentially relay chromatic information, and one may wonder how this information will be integrated and subsequently used to aid vision beyond the detection of color per see. I personally like the last paragraph of the discussion that highlights this fact.

We thank the reviewer for appreciating our manuscript.

Weaknesses: One major point highlighted in this paper is the fact that Green ON/UV OFF responses are not generated in the retina. But glancing through the literature, I saw this is not necessarily true. Fig 1. of Joesch and Meister, a paper cited, shows this can be the case. Thus, I would not emphasize that this wasn’t present in the retina. This is a minor point, but even if the retina could not generate these signals, I would be surprised if the diversity of responses would only arise through feed-forward excitation, given the intricacies of cortical connectivity. Thus, I would argue that the argument holds for most of the responses seen in V1; they need to be further processed by cortical circuitries.

We thank the reviewer for this comment. When analyzing available data from the retina using a similar center-surround color flicker stimulus (Szatko et al. 2020), we found that Green On/UV Off color opponency is very rare in the RF center of retinal ganglion cells (Suppl. Fig. 5). This suggests that center Green On/UV Off color opponency in V1 neurons is not inherited by the RF center of retinal neurons. However, we agree with the reviewer that retinal neurons might still contribute to V1 color opponency, for example by being center-surround color opponent (e.g. Joesch et al. 2016 and Szatko et al. 2020). We rephrased the text to acknowledge this fact.

This takes me to my second point, defining center and surround. The center spot is 37.5 deg of visual angle, more than 1 mm of the retinal surface. That means that all retinal cells, at least half and most likely all of their surrounds will also be activated. Although 37.5 deg is roughly the receptive field size previously determined for V1 neurons, the one-to-one comparison with retinal recording, particularly with their center/surround properties, is difficult. This should be discussed. I assume that the authors tried a similar approach with sparse or dense checker white noise stimuli. If so, it would be interesting if there were better ways of defining the properties of V1 neurons on their complex/simple receptive field properties to define how much of their responses are due to an activation of the true "center" or a coactivation of the surround. Interestingly, at least some of the cells (Fig. 1d, cells 2 and 5) don’t have a surround. Could it be that in these cases, the "center" and "surround" are being excited together? How different would the overall statistics change if one used a full-filed flicker stimulus instead of a center/surround stimulus? How stable are the results if the center/surround flicker stimulus is shifted? These results won’t change the fact that chromatic coding is present in the VC and that there are clear differences depending on their position, but it might change the interpretation. Thus, I would encourage you to test these differences and discuss them.

Thanks for this comment. We agree with the reviewer that a one-to-one comparison of retina and V1 data is challenging, due to differences in both RF and stimulus size. We rephrased the Results text to clarify this point and now also mention it in the Discussion.

To be able to record from many V1 neurons simultaneously, we used a stimulus size of 37.5 degree visual angle in diameter, which is slightly larger than center RFs of single V1 neurons. As the reviewer mentions, the disadvantage of this approach is that the stimulus is only roughly centered on the neurons’ center RFs. To reduce the impact of potential stimulus misalignment on our results, we used the following steps:

For each recording, we positioned the monitor such that the mean RF across all neurons lies within the center of the stimulus field of view.

We confirmed that this procedure results in good stimulus alignment for the large majority of recorded neurons within individual recording fields by using a sparse noise stimulus (Suppl. Fig. 1a-c). Specifically, we found that for 83% of tested neurons, more than two thirds of their center RF, determined by the sparse noise stimulus, overlapped with the center spot of the color noise stimulus.

For analysis, we excluded neurons without a significant center STA, which may be caused by misalignment of the stimulus.

Together, we believe these points strongly suggest that the center spot and the surround annulus of the noise stimulus predominantly drive center (i.e. classical RF) and surround (i.e. extraclassical RF), respectively, of the recorded V1 neurons. This is further supported by the fact that color response types identified using an automated clustering method were robust across mice (Suppl. Fig. 6c), indicating consistent stimulus centering.

Nevertheless, we cannot exclude that the stimulus was misaligned for a subset of the recorded neurons used for analysis. We agree with the reviewer that such misalignment might have contributed to cells not having surround STAs, due to simultaneous activation of antagonistic center and surround RF components by the surround stimulus. While a full-field stimulus would get rid of the misalignment problem, it would not allow to study color tuning in center and surround RF components separately. Instead, one could compare the results of our approach with an approach that centers the stimulus on individual neurons. However, we believe that performing these additional experiments is out of the scope of the current study.

To acknowledge the experimental limitations of our study and the concerns brought up by the reviewer, we now explicitly mention the steps we perform to reduce the effects of stimulus misalignment in the Results section and discuss the problem of stimulus alignment in the Discussion. We believe these changes will help the reader to interpret our results.

Referee 2 (Remarks to the Author):

Summary:

Franke et al. characterize the representation of color in the primary visual cortex of mice and how it changes across the visual field, with a particular focus on how this may influence the ability to detect aerial predators. Using calcium imaging in awake, head-fixed mice, they characterize the properties of V1 neurons (layer 2/3) using a large center-surround stimulation where green and ultra-violet were presented in random combinations. Using a clustering approach, a set of functional cell-types were identified based on their preference to different combinations of green and UV in their center and surround. These functional types were demonstrated to have varying spatial distributions in V1, including one neuronal type (Green-ON/UV-OFF) that was much more prominent in the posterior V1 (i.e. upper visual field). Modelling work suggests that these neurons likely support the detection of predator-like objects in the sky.

Strengths:

The large-scale single-cell resolution imaging used in this work allows the authors to map the responses of individual neurons across large regions of the visual cortex. Combining this large dataset with clustering analysis enabled the authors to group V1 neurons into distinct functional cell types and demonstrate their relative distribution in the upper and lower visual fields. Modelling work demonstrated the different capacity of each functional type to detect objects in the sky, providing insight into the ethological relevance of color opponent neurons in V1.

We thank the reviewer for appreciating our manuscript.

Weaknesses:

While the study presents solid evidence a few weaknesses exist, including the size of the dataset, clarity regarding details of data included in each step of the analysis and discussion of caveats of the work. The results presented here are based on recordings of 3 mice. While the number of neurons recorded is reasonably large (n > 3000) an analysis that tests for consistency across animals is missing. Related to this, it is unclear how many neurons at each stage of the analysis come from the 3 different mice (except for Suppl. Fig 4).

Thank you for this comment. We apologize that the original manuscript did not clearly indicate the consistency of our results across animals. We have revised the manuscript in the following ways:

We have added an additional Suppl. Figure, which shows the variability of the data within and across animals (Suppl. Fig. 4). Specifically, we show the distribution of color and luminance selectivity for (i) center and surround components of V1 RFs and (ii) for upper and lower visual field. This data is used for all analyses shown in Figures 2-4. The figure legend of this figure also states the number of neurons per animal.

We now clearly state in the Results section that all analyses in the main figures were performed by pooling data across animals, and refer to the Suppl. Figures for consistency across animals.

We believe these changes help the reader to interpret our results.

Finally, the paper would greatly benefit from a more in depth discussion of the caveats related to the conclusion drawn at each stage of the analysis. This is particularly relevant regarding the caveats related to using spike triggered averages to assess the response preferences of ON-OFF neurons, and the conclusions drawn about the contribution of retinal color opponency.

Thanks. We substantially revised the text to discuss caveats and limitations of the approach. For example, we added a section into the Discussion called "Limitations of the stimulus paradigm". In addition, we clarified how retinal signals relate to cortical ones and phrased our conclusions more conservatively.

The authors provide solid evidence to support an asymmetric distribution of color opponent cells in V1 and a reduced color contrast representation in lower light levels. Some statements would benefit from more direct evidence such as the integration of upstream visual signals for color opponency in V1.

Based on the comments from Reviewer 1, we have rephrased the statements regarding the integration of upstream visual signals for color opponency in V1. We think these revisions increase the clarity of the results and help the reader with interpretation.

Overall, this study will be a valuable resource for researchers studying color vision, cortical processing, and the processing of ethologically relevant information. It provides a useful basis for future work on the origin of color opponency in V1 and its ethological relevance.

Thanks! We thank the reviewer again for the helpful comments.

Referee 3 (Remarks to the Author):

This paper studies chromatic coding in mouse primary visual cortex. Calcium responses of a large collection of cells are measured in response to a simple spot stimulus. These responses are used to estimate chromatic tuning properties - specifically sensitivity to UV and green stimuli presented in a large central spot or a larger still surrounding region. Cells are divided based on their responses to these stimuli into luminance or chromatic sensitive groups. Several technical concerns limit how clearly the data support the conclusions. If these issues can be fixed, the paper would make a valuable contribution to how color is coded in mouse V1.

We thank the reviewer for the helpful comments.

Analysis: The central tool used to analyze the data is a "spike triggered average" of the responses to randomly varying stimuli. There are several steps in this analysis that are not documented, and hence evaluating how well it works is difficult. Central to this is that the paper does not measure spikes. Instead, measured calcium traces are converted to estimated spike rates, which are then used to estimate STAs. There are no raw calcium traces shown, and the approach to estimate spike rates is not described in any detail. Confirming the accuracy of these steps is essential for a reader to be able to evaluate the paper. Further, it is not clear why the linear filters connecting the recorded calcium traces and the stimulus cannot be estimated directly, without the intermediate step of estimating spike rates.

Thank you for this comment. We have used the genetically encoded calcium sensor GCaMP6s in our recordings. This sensor is a very sensitive GCaMP6 variant, but also one with slow kinetics. To remove the effect of the slow sensor kinetics from recorded calcium responses, the recorded traces are commonly deconvolved with the impulse function of the sensor to obtain the deconvolved calcium traces. We now include this reasoning in the Results section. To illustrate the effect of the deconvolution, we added a new Suppl. Figure (Suppl. Fig. 2) showing raw calcium and deconvolved traces, and the STAs estimated from both types of traces. This illustrates that the results regarding neuronal color preferences are consistent across raw and deconvolved calcium traces.

We agree with the reviewer that the term STA might be confusing. We have replaced it with the term "even-triggered-average" (ETA). In addition, we have replaced the phrase "estimated spike rate" with "deconvolved calcium trace" throughout the manuscript because the unit of the deconvolved traces is not interpretable, like spike rate would be (spikes per second). In the revised version, we now clarify in the Methods section that we estimate the ETAs based on deconvolved calcium traces, which is correlated with and an approximation for spike rate.

A further issue about the STAs is that the inclusion criterion (correlation of predicted vs measured responses of 0.25) is pretty forgiving. It would be helpful to see a distribution of those correlation values, and some control analyses to check whether the STA is providing a sufficiently accurate measure to support the results (e.g. do the central results hold for the cells with the highest correlations).

We thank the reviewer for this comment. To exclude noisy neurons from analysis, we used the following procedure:

For each of the four stimulus conditions (center and surround for green and UV stimuli), kernel quality was measured by comparing the variance of the STA with the variance of the baseline, defined as the first 500 ms of the STA. Only cells with at least 10-times more variance of the kernel compared to baseline for UV or green center STA were considered for further analysis.

We have added the distribution of quality values to a new Suppl. Figure (Suppl. Fig. 2d,e). We now also show the percentage of neurons above threshold, given different quality thresholds. Finally, we have repeated the analysis shown in Figure 2 for a much more conservative threshold, including only the top 25% of neurons (Suppl. Fig. 2e,f). We now mention this new analysis in the Methods and Results section.

Limitations of stimulus choice: The paper relies on responses to a large (37.5 degree diameter) modulated spot and surrounding region. This spot is considerably larger than the receptive fields of both V1 cells and retinal ganglion cells. As a result, the spot itself is very likely to strongly activate both center and surround mechanisms, and responses of cells are likely to depend on where the receptive fields are located within the spot (and, e.g., how much of the true neural surround samples the center spot vs the surround region). The impact of these issues on the conclusions is considered briefly at the start of the results but needs to be evaluated in considerably more detail. This is particularly true for retinal ganglion cells given the size of their receptive fields (see also next point).

We agree with the reviewer that the centering of the stimulus is critical and apologize if this point was not discussed sufficiently. To be able to record from many V1 neurons simultaneously, we used a stimulus size of 37.5 degree visual angle in diameter, which is slightly larger than center RFs of single V1 neurons. As the reviewer mentions, the disadvantage of this approach is that the stimulus is only roughly centered on the neurons’ center RFs. To reduce the impact of potential stimulus misalignment on our results, we have used different experimental and analysis steps and controls (see also second comment of Reviewer 1):

For each recording, we positioned the monitor such that the mean RF across all neurons lies within the center of the stimulus field of view.

We confirmed that this procedure results in good stimulus alignment for the large majority of recorded neurons within individual recording fields by using a sparse noise stimulus (Suppl. Fig. 1a-c). Specifically, we found that for 83% of tested neurons, more than two thirds of their center RF, determined by the sparse noise stimulus, overlapped with the center spot of the color noise stimulus.

For analysis, we excluded neurons without a significant center STA, which may be caused by misalignment of the stimulus.

We now mention those clearly in the Results section and added the limitations of our approach to the Discussion section.

Comparison with retina: A key conclusion of the paper is that the chromatic tuning in V1 is not inherited from retinal ganglion cells. This conclusion comes from comparing chromatic tuning in a previously-collected data set from retina with the present results. But the retina recordings were made using a considerably smaller spot, and hence it is not clear that the comparison made in the paper is accurate. This issue may be handled by the analysis presented in the paper, but if so it needs to be described more clearly. The paper from which the retina data is taken argues that rod-cone chromatic opponency originates largely in the outer retina. This mechanism would be expected to be shared across retinal outputs. Thus it is not clear how the Green-On/UV-Off vs Green-Off/UV-On asymmetry could originate. This should be discussed.

We agree with the reviewer that a one-to-one comparison of retina and V1 data is challenging, due to differences in both RF and stimulus size. We rephrased the Results text to clarify this point and now also mention it in the Discussion.

When analyzing available data from the retina using a similar center-surround color flicker stimulus (Szatko et al. 2020), we found that Green On/UV Off color opponency is very rare in the RF center of retinal ganglion cells (Suppl. Fig. 5). This suggests that center Green On/UV Off color opponency in V1 neurons is not inherited by the RF center of retinal neurons. However, we agree with the reviewer that retinal neurons might still contribute to V1 color opponency, for example by being center-surround color opponent (e.g. Joesch et al. 2016 and Szatko et al. 2020). We rephrased the text to acknowledge this fact.

Residual chromatic cells at low mesopic light levels The presence of chromatically tuned cells at the lowest light level probed is surprising. The authors describe these conditions as rod-dominated, in which case chromatic tuning should not be possible. This again is discussed only briefly. It either reflects the presence of an unexpected pathway that amplifies weak cone signals under low mesopic conditions such that they can create spectral opponency or something amiss in the calibrations or analysis. Data collected at still lower light levels would help resolve this.

Thank you for this comment. We call the lowest light level "low mesopic" and "rod-dominated" because the spectral contrast of V1 center responses in posterior recording fields is green-shifted for this light level (Fig. 3a). This is only expected if responses in the UV-cone dominant ventral retina are predominantly driven by rod photoreceptors. We now explain this rationale in the Results section. In addition, we mention in the Discussion that future studies are required to test whether cone signals need to be amplified for low light levels. While we agree with the reviewer that it would be exciting to use even lower light levels during recordings, we believe this is out of the scope of the current study due to the technical challenges involved in achieving scotopic stimulation.

Author response:

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

We have revised the manuscript mainly in the following aspects: (1) the data of electrophysiological and behavioral responses of larvae and adults to trehalose have been added, and the related figures and texts have been modified accordingly; (2) the photos of taste organs of larvae and adults indicating the position of recorded sensilla have been added; (3) the potential off-target effects of GR knock-out on other GR expressions has been carefully explained and revised in the relevant text; (4) the abstract has been revised to present the findings more technically in a limited number of words; (5) some details of experiments in Materials and Methods and some new literatures have been added; (6) a new figure (Figure 8) summarizing the main findings of the study has been added.

In the following, we respond to the reviewers’ comments and suggestions one by one. We hope that our answers will satisfy you and the three reviewers. We are also very happy to get further valuable advices from you.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The process of taste perception is significantly more intricate and complex in Lepidopteran insects. This investigation provides valuable insights into the role of Gustatory receptors and their dynamics in the sensation of sucrose, which serves as a crucial feeding cue for insects. The article highlights the differential sensitivity of Grs to sucrose and their involvement in feeding and insect behavior.

Strengths:

To support the notion of the differential specificity of Gr to sucrose, this study employed electrophysiology, ectopic expression of Grs in Xenopus, genome editing, and behavioral studies on insects. This investigation offers a fundamental understanding of the gustation process in lepidopteran insects and its regulation of feeding and other gustation-related physiological responses. This study holds significant importance in advancing our comprehension of lepidopteran insect biology, gustation, and feeding behavior.

Thank you for your recognition of our research.

Weaknesses:

While this manuscript demonstrates technical proficiency, there exists an opportunity for additional refinement to optimize comprehensibility for the intended audience. Several crucial sugars have been overlooked in the context of electrophysiology studies and should be incorporated. Furthermore, it is imperative to consider the potential off-target effects of Gr knock-out on other Gr expressions. This investigation focuses exclusively on Gr6 and Gr10, while neglecting a comprehensive narrative regarding other Grs involved in sucrose sensation.

We accept the reviewer's suggestion. Because trehalose is a main sugar in insect blood, and it is converted by insects after feeding on plant sugars, we have added the new data on electrophysiological and behavioral responses of larvae and adults of Helicoverpa armigera to trehalose (see Figure 1-2, Figure 1-figure supplement 1, Figure 2-figure supplement 1). Now, the total eight sugars include 2 pentoses (arabinose and xylose), 4 hexoses (fructose, fucose, galactose and glucose), and 2 disaccharides (sucrose and trehalose), which were chosen because they are mainly present in host-plants of H. armigera and/or representative in the structure and source of sugars.

We fully agree to the reviewer’s opinion and have already taken the potential off-target effects of CRISPR/Cas9 knockout of Gr on other GR expressions into consideration. To predict the potential off-target sites of sgRNA of Gr6 and Gr10 establishing homozygous mutants using CRISPR/Cas9 technology, we first use online software CasOFFinder (http://www.rgenome.net/cas-offinder/) to blast the genome of the wild type cotton bollworm and set the mismatch number less than or equal to 3. We found that Gr10 sgRNA had no potential potential off-target site, and the sgRNA of Gr6 had only one potential off-target site. Therefore, we designed primers according to the sequence of potential off-target sites of Gr6 sgRNA, and conducted PCR using genomic DNA of homozygous mutant as a template， performed Sanger sequencing on the PCR products obtained, and found that the potential off-target sites of Gr6 sgRNA were no different from those of the wild type. Particularly, concerning the sgRNA of Gr6 and Gr10 may produce off-target effects on other sugar receptor genes of H. armigera, we conducted the same off-target site analysis with the designed sgRNA on each of the other eight sugar receptor genes, and found that there were no off-target sites on these receptor genes (see Line254-256).

Reviewer #2 (Public Review):

Summary:

To identify sugar receptors and assess the capacity of these genes the authors first set out to identify behavioral responses in larvae and adults as well as physiological response. They used phylogenetics and gene expression (RNAseq) to identify candidates for sugar reception. Using first an in vitro oocyte system they assess the responses to distinct sugars. A subsequent genetic analysis shows that the Gr10 and Gr6 genes provide stage specific functions in sugar perception.

Strengths:

A clear strength of the manuscript is the breadth of techniques employed allowing a comprehensive study in a non-canonical model species.

Thank you for your recognition of our research.

Weaknesses:

There are no major weaknesses in the study for the current state of knowledge in this species. Since it is much basic work to establish a broader knowledge, context with other modalities remains unknown. It might have been possible to probe certain contexts known from the fruit fly, which would have strengthened the manuscript.

Thank you so much for your suggestion. According to this suggestion, we further added some sentences probing sugar sensing and behaviors of fruit fly larvae in the Introduction and discussion sections (Line 68-71 in Introduction section, Line 395-399 in Discussion section).

Reviewer #3 (Public Review):

In this study, the authors combine electrophysiology, behavioural analyses, and genetic editing techniques on the cotton bollworm to identify the molecular basis of sugar sensing in this species.

The larval and adult forms of this species feed on different plant parts. Larvae primarily consume leaves, which have relatively lower sugar concentrations, while adults feed on nectar, rich in sugar. Through a series of experiments-spanning electrophysiological recordings from both larval and adult sensillae, qPCR expression analysis of identified GRs from these sensillae, response profiles of these GRs to various sugars via heterologous expression in Xenopus oocytes, and evaluations of CRISPR mutants based on these parameters-the authors discovered that larvae and adults employ distinct GRs for sugar sensing. While the larva uses the highly sensitive GR10, the adult uses the less sensitive and broadly tuned GR6. This differential use of GRs are in keeping with their behavioral ecology.

The data are cohesive and consistently align across the methodologies employed. They are also well presented and the manuscript is clearly written.

Recommendations for the authors:

While appreciating the quality of the work and its presentation, we have a few comments for the authors, should they wish to consider them, that would significantly improve the presentation of the work.

Title: Could the authors please revisit their title to better reflect the main finding of their work?

The title has been changed into “The larva and adult of Helicoverpa armigera use differential gustatory receptors to sense sugars”.

Text: There are a few comments related to the text, and these are listed below:

(1) Could the authors place their work in the context of what's known about sugar sensing in Drosophila larva and adult?

In the Introduction section, we added the status of research on sugar perception in Drosophila larvae, pointing out "No external sugar-sensing mechanism in Drosophila larvae has yet been characterized." (Line 70-71); in the Discussion section, the research progress of sugar sensing in Drosophila adults and larvae was also summarized (Line 397-399).

(2) For each results section, could the authors please include a sentence or two that interprets the data in the context of previously presented data?

We accept the reviewer's suggestion. In order to make it easy for readers to follow up, we included a sentence interprets the above data at the beginning of each part of the Results on the premise of avoiding duplication.

(3) Could the authors please provide details of the generation and screening of the CRISPR mutants?

We have added more details on mutant establishment and screening in the Materials and Methods section (Line 722-726, 729-732).

Figures: Could the authors please include images and schematics wherever possible? For example, a schematic depicting the position of the sense organs and one summarising the main findings of the studies.

In Figure 1 we added the photo of each taste organ, on which the recorded sensilla were indicated. We also added a new figure, Figure 8, summarizing the main findings of the study.

Choice of Sugars: Could the authors please justify their choice of sugars they have used in the analyses?

In the first paragraph of the Results section of the article, we further explain the reasons for using the sugars in the study. “We first investigated the electrophysiological responses of the lateral and medial sensilla styloconica in the larval maxillary galea to eight sugars. These sugars were chosen because they are mostly found in host-plants of H. armigera or are representative in the structure and source of sugars.”

In addition to this, there are several specific comments in the detailed reviewers comments below, which the authors could consider responding to.

Reviewer #1 (Recommendations For The Authors):

The article titled "Sucrose taste receptors exhibit dissimilarities between larval and adult stages of a moth" by Shuai-Shuai Zhang and colleagues provides an intriguing analysis. The authors have conducted a meticulously planned and executed study. However, I do have some inquiries.

(1) What precisely does the term "differ" signify in the title? It can be expounded upon in terms of differing in expression or sensitivity. The title could benefit from being more informative. The authors should appropriately specify the insect species in the title of the paper. This would make it more comprehensible to readers. Merely mentioning the term "moth" does not provide any information about the model organism. Hence, it would be preferable to mention Helicoverpa armigera instead of using the generic term "moth" in the title.

Thank you for your suggestions. We considered it better to emphasize that the receptors for sucrose are different, and we have accepted the suggestion of adding the name of the animal. The title has been changed into “The larva and adult of Helicoverpa armigera use differential gustatory receptors to sense sugars”.

(2) The abstract is written in a simple and easily understandable manner, but it overlooks important findings from a technical standpoint.

We add some key experimental techniques to illustrate some important findings in the Abstract.

(3). Almost all herbivorous insects are said to consume plants and utilize sucrose as a stimulus for feeding, as stated by the authors. Sucrose, glucose, and fructose sugar are among the commonly observed stimulants for feeding in numerous insects. It would be appropriate to incorporate not only sucrose but also glucose and fructose as feeding stimulants for almost all herbivorous insects.

Thank you for your suggestion. Sucrose is the major sugar in plants, and its concentration varies greatly from tissue to tissue, while the concentration of the hexose sugars is much lower and the concentration does not change much. In Line 48, we state that sucrose, glucose, and fructose are feeding stimuli for herbivorous insects. From the previous studies, it seems that sucrose is the strongest, followed by fructose, and finally glucose. The cotton bollworm larvae showed no electrophysiological and behavioral response to glucose.

(4) The reason why trehalose is not considered in the electrophysiology analysis is unclear. Given that trehalose is a major sugar in insects and plants, it would be intriguing to include it in the analysis.

We have accepted the reviewer's suggestion, and supplemented the electrophysiological responses of taste organs in larvae and adults of Helicoverpa armigera to trehalose (Figure 1, Figure 1-Figure Supplement 1), and also tested the behavioral responses of the larvae and adults to trehalose (Figure 2, Figure 2-Figure Supplement 1). Therefore, all the related figures have been changed.

(5) The author's intention regarding the co-receptor relationship between Gr5 and Gr6 (line 211) is unclear. If this is indeed the case, then the reason for considering Gr5 in further studies remains uncertain.

We have changed the sentence as follows: “Since Gr5 was highly expressed with Gr6 in the proboscis and tarsi (Figure 3D-3E, Figure 3—figure supplement 1), we suspected that Gr5 and Gr6 might be expressed in the same cells, and then tested the response profile of their co-expression in oocytes.”

(6) The homologous nature of Grs is emphasized by the authors. It is not specified how the author ensured that the guide RNA targeting Gr6 or Gr10 did not result in off-target effects on other Grs.

Thank you so much for your suggestion. We have rewritten the relevant paragraph (Line 238-251), detailing our tests and the results on the potential off-target effects of knocking out GRs by CRISPR/Cas9: “In order to predict the potential off-target sites of sgRNA of Gr6 and Gr10, we used online software Cas-OFFinder (http://www.rgenome.net/cas-offinder/) to blast the genome of H. armigera, and the mismatch number was set to less than or equal to 3. According to the predicted results, the Gr10 sgRNA had no potential off-target region but Gr6 sgRNA had one. Therefore, we amplified and sequenced the potential off-target region of Gr6-/- and found there was no frameshift or premature stop codon in the region compared to WT (Figure 5—figure supplement 2). It is worth mentioning that there was no potential off-target region of Gr6 and Gr10 sgRNA in other sugar receptor genes of H. armigera, Gr4, Gr5, Gr7, Gr8, Gr9, Gr11 and Gr12. We further found there was no difference in the response to xylose of the medial sensilla styloconica among WT, Gr10-/- and Gr6-/- (Figure 5—figure supplement 2). Furthermore, WT, Gr10-/- and Gr6-/- did not show differences in the larval body weight, adult lifespan, and number of eggs laid per female (Figure 5—figure supplement 2). All these results suggest that no off-target effects occurred in the study.”

(7) Is it possible that knocking out Gr10 is not compensated for by the overexpression of Gr6 or other sucrose sensing Grs? Similarly, would the vice versa scenario hold true?

In the Discussion section, we have added some sentences to discuss this issue: “From our results, knocking out Gr10 or Gr6 is unlikely to be compensated by overexpression of other sugar GRs. One of our recent studies showed that Orco knockout had no significant effect on the expression of most OR, IR and GR genes in adult antennae of H. armigera, but some genes were up- or down-regulated (Fan et al., 2022).”

(8) What was the rationale for selecting nine candidate GR genes for expression analysis?

Based on the reviewer's suggestion, we expanded the relevant paragraphs to illustrate the rationale for selecting nine candidate GR genes for expression analysis: “To reveal the molecular basis of sugar reception in the taste sensilla of H. armigera, we first analyzed the putative sugar gustatory receptor genes based on the reported gene sequences of GRs in H. armigera and their phylogenetic relationship of D. melanogaster sugar gustatory receptors (Jiang et al., 2015; Pearce et al., 2017; Xu et al., 2017). Nine putative sugar GR genes, Gr4–12 were identified, and their full-length cDNA sequences were cloned (The GenBank accession number is provided in Appendix—Table S1).” (Line 155-161)

(9) What is the potential reason for the difference between the major larval sugar receptors of Drosophila and Lepidopterans?

The difference between the major larval sugar receptors of Drosophila and Lepidopterans is probably due to differences in the food their larvae feed on. Fruit fly larvae feed on rotten fruit, the main sugar of which is fructose. The larvae of Lepidoptera mainly feed on plants, and the main sugar is sucrose. In the Discussion section, we have added a sentence “This is most likely due to fruit fly larvae feeding on rotten fruits, which contain fructose as the main sugar.” (Line 399-401)

(10) There is a disparity in GRs, specifically GR5 and GR6, between the female antenna, proboscis, and tarsi. What could be the possible justification and significance of this?

Thank you so much for this question. We have added a sentence in the Discussion section, “In this study, the expression patterns of 9 sugar GRs in three taste organs of adult H. armigera show that there is a disparity in GRs, specifically GR5 and GR6, between the female antenna, tarsi and proboscis, which may be an evolutionary adaptation reflecting subtle differentiation in the function of these taste organs in adult foraging. Antennae and tarsi play a role in the exploration of potential sugar sources, while the proboscis plays a more precise role in the final decision to feed.” (Line 433-438)

(11) I suggest that a visual representation illustrating the positioning of GSNs, particularly the lateral and medial sensilla, in both larva and adult stages would enhance the correlation with the results.

In Figure 1 we added the photo of each taste organ and the position of the recorded sensilla, and also added a new figure, Figure 8 summarizing the main findings of the studies.

(12) Further experiments can be conducted to elucidate the precise molecular mechanisms, particularly the downstream effects of GRs, in order to establish the specificity of GRs more convincingly.

Thank you so much for your suggestion. We have discussed the further experiments in the Discussion section, “To elucidate the precise molecular mechanisms of sugar reception in H. armigera is necessary to compare a series of single, double and even multiple Gr knock-out lines and investigate the downstream effects of the GRs.” (Line 363-369)

(13) Figure 6 caption: In Figure 6 (D to I), the percentage of PER is depicted. There is redundancy in the Y-axis title (Percentage of PER) and the legend. This appears to be repetitive. I suggest that it would be better to include the Y-axis title only in Figure D or in Figures D and G.

We accept the suggestion. Figure 7 (not Figure 6) has been revised accordingly.

(14) In Figures 6A and 6C, there is inconsistency in the colors used for WT, Gr6, and Gr10. This could potentially confuse the reader. I recommend using the same colors in both figures instead of using a blue color. Please specify how the authors calculated the feeding area in Figure 6.

We accept the reviewer's suggestion and have changed the color of Figure 7A, B. We have also added the detail method for calculating feeding area (Line 541-545).

(15) In Two-choice tests, why did the authors use 0.01% Tween 80? Please provide comments on this.

Use of 0.01% Tween 80 is to reduce the surface tension and increase the malleability of the solution. We have given detailed explanation in the Method section and cite the reference. (Line538-540)

(16) It would be valuable if the authors could comment on the prospects of this study, considering that GRs play a vital role in controlling behavior and developmental pathways. What are the potential consequences of blocking or disrupting these receptors in terms of behavioral and developmental phenotypic deformities? Could this potentially lead to increased insect mortality?

Thank you so much for your suggestions. In the last paragraph of the Discussion section, we have added the following perspectives, “Knockout of Gr10 or Gr6 led to a significant decrease in sugar sensitivity and food preference of the larvae and adults of H. armigera, respectively, which is bound to bring adverse consequences to survival and reproduction of the insects. Therefore, studying the molecular mechanisms underlying sugar perception in phytophagous insects may provide new insights into the behavioral ecology of this important and highly diverse group of insects, and measures blocking or disrupting sugar receptors could also have applications to control agricultural pests and improve crop yields worldwide” (Line 449-456).

Reviewer #2 (Recommendations for The Authors):

There are a few comments, that I feel would be beneficial to be addressed.

• The authors used 7 different sugars for their experimental approach. While I agree that this is a sufficiently large collection for a study, I was wondering why they specifically chose these sugars; an explanatory section might be helpful for a reader to follow the reasoning.

According to reviewer 1's suggestion, we increased trehalose to 8 sugars in experiments. Trehalose is a main sugar in insect blood. It is converted by insects after feeding on plant sugars. The 8 sugars were chosen because they are present in host-plants of H. armigera or are representative in the structure and source of sugars. They contain 2 pentoses (arabinose and xylose), 4 hexoses (fructose, fucose, galactose and glucose), and 2 disaccharides (sucrose and trehalose).

• It might be beneficial to provide some broader overview on the gustatory system in the cotton bollworm, particularly at the larval stage since this may not be common knowledge. Along these lines eg. the complexity of sensilla types, organs and overall number (or estimation) of neurons might be good to know, a graphical representation of the sense organs might be informative.

In the Introduction section, we give a more specific description on sugar sensitive GSNs in the taste system of the larva and adult of H. armigera, and cite the corresponding references.

• Concerning phylogeny of GRs, it might be relevant to know how complete the genome information is and some more general background on GR diversity in the cotton bollworm.

We agree to your opinion. According to this idea, we got the putative sugar GRs from the previously published genome (Pearce et al. 2017) and the related annotation of GRs (Jiang et al. 2015, Xu et al. 2012). We have made a more detailed explanation about this in the new version of the manuscript, “We first analyzed the putative sugar gustatory receptor genes based on the genome data of H. armigera (Pearce et al. 2017), the reported gene sequences of sugar GRs in H. armigera and their phylogenetic relationship of D. melanogaster sugar gustatory receptors (Jiang et al. 2015, Xu et al. 2012). All nine putative sugar GR genes in H. armigera, Gr4–12 were validated, and their full-length cDNA sequences were cloned (The GenBank accession number is provided in Appendix—Table S1).” (Line 155-161).

• Generation of mutants based on CRISPR is intriguing and a powerful step. While the techniques are well described in the method section, there is no information concerning efficiency or broader feasibility of the approach. I feel it would be quite interesting to learn about how feasible or laborious the approach is to generate mutants (e.g. number of initial injected eggs, the resulting F0 offspring, number of back-crosses, number of screened F1s ....).

In the Materials and Methods section, we have added specific success rates for each step in the process of building the two mutants (Line 722-726, 729-732).

Reviewer #3 (Recommendations For The Authors):

I want to congratulate the authors on this very nice study and have only minor comments for them.

(1) It would be very nice to include pictures of the larva and adult of H. armigera. It would also help to have schematics of where the sensilla they are recording from are.

We have added photos of four taste organs on which the recoded sensilla were indicated (Figure 1), and picture of the larva and adult on which the stimulating site was indicated (Figure 2).

(2) A schematic summarising their findings, including the relevance to the animal's behavioural ecology, will greatly improve interpretations for the broader audience.

A schematic summarizing the findings has been added.

(3) The manner in which PIs are represented in figure 2A, B (among others) is confusing. Can the authors please plot the PI and not the feeding area? From the PI values listed beside the plot, it actually suggests that the larvae don't really show a preference. Could the authors please comment on this?

Yes, sucrose has a significant stimulating effect on larva feeding, but the effect is not as large as the predicted based on the sensitivity of the sensillum, the main reasons are as follows: (1) there are many factors affecting larva feeding, sucrose is only one of them; (2) due to the substrate leaf discs also contain sugar, the effect of newly added sucrose may be reduced. After careful consideration, we think it is better to display the feeding area and PI together so that readers have a complete understanding of the data.

(4) The heterologous expression experiments suggest that co-expression of GR6 with either GR10 or GR5 somehow suppress the response of the GR6 alone to fucose. Am I reading the data correctly? Why would this be? Perhaps the authors could discuss this. In this context, it would help to reproduce all the GR6 data together.

Your interpretation is reasonable to a certain extent. The result of co-injection might be that Gr10 or Gr5 inhibited the response of Gr6. However, there is another possibility that the amount of Gr6 sRNA was diluted by co-injection of two GRs, resulting in a reduced response of Gr6 to fucose.

(5) In general, for each results section, it would help to have a sentence or two that interprets the data in the context of previously presented data. This would help the reader digest the data and interpret it as they read along. Currently, the authors summarise the observations and leave all the interpretation to the discussion section.

We accept the suggestion. In each part of the results, we have added a sentence to explain the above data, which will help readers to clarify the context of the research more easily.

(6) Is the GR6 data in 4C not lined up correctly?

Yes, it is right.

(7) Line 228 suggests that the mutants were validating with qPCRs - I don't see that data.

The mutants were not validating with qPCR. We used the ordinary PCR technology at the mRNA level to verify whether the related sequences were really deleted in the mutants.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors present a detailed study of a nearly complete Entomophthora muscae genome assembly and annotation, along with comparative analyses among related and non-related entomopathogenic fungi. The genome is one of the largest fungal genomes sequenced, and the authors document the proliferation and evolution of transposons and the presence/absence of related genetic machinery to explore how this may have occurred. There has also been an expansion in gene number, which appears to contain many "novel" genes unique to E. muscae. Functionally, the authors were interested in CAZymes, proteases, circadian clock related genes (due to entomopathogenicity/ host manipulation), other insect pathogenspecific genes, and secondary metabolites. There are many interesting findings including expansions in trahalases, unique insulinase, and another peptidase, and some evidence for RIP in Entomophthoralean fungi. The authors performed a separate study examining E. muscae species complex and related strains. Specifically, morphological traits were measured for strains and then compared to the 28S+ITSbased phylogeny, showing little informativeness of these morpho characters with high levels of overlap.

This work represents a big leap forward in the genomics of non-Dikarya fungi and large fungal genomes. Most of the gene homologs have been studied in species that diverged hundreds of millions of years ago, and therefore using standard comparative genomic approaches is not trivial and still relatively little is known. This paper provides many new hypotheses and potential avenues of research about fungal genome size expansion, entomopathogenesis in zygomycetes, and cellular functions like RIP and circadian mechanisms.

Strengths:

There are many strengths to this study. It represents a massive amount of work and a very thorough functional analysis of the gene content in these fungi (which are largely unsequenced and definitely understudied). Too often comparative genomic work will focus on one aspect and leave the reader wondering about all the other ways genome(s) are unique or different from others. This study really dove in and explored the relevant aspects of the E. muscae genome.

The authors used both a priori and emergent properties to shape their analyses (by searching for specific genes of interest and by analyzing genes underrepresented, expanded, or unique to their chosen taxa), enabling a detailed review of the genomic architecture and content. Specifically, I'm impressed by the analysis of missing genes (pFAMs) in E. muscae, none of which are enriched in relatives, suggesting this fungus is really different not by gene loss, but by its gene expansions.

Analyzing species-level boundaries and the data underlying those (genetic or morphological) is not something frequently presented in comparative genomic studies, however, here it is a welcome addition as the target species of the study is part of a species complex where morphology can be misleading and genetic data is infrequently collected in conjunction with the morphological data.

Thank you for your careful reading of our work. We’re glad that you identified these areas as strengths.

Weaknesses:

The conclusions of this paper are mostly well supported by data, but a few points should be clarified.

In the analysis of Orthogroups (OGs), the claim in the text is that E. muscae "has genes in multi-species OGs no more frequently than Enotomophaga maimaiga. (Fig. 3F)" I don't see that in 3F. But maybe I'm really missing something.

Thank you for catching this. You were, in fact, not missing anything at all. There was a mismatch between the data plotted in F and G and how the caption described these data. We very much apologize for the confusion that this must have caused. We have corrected these plots and also made changes to improve interpretability (see below).

Also related, based on what is written in the text of the OG section, I think portions of Figure 3G are incorrect/ duplicated. First, a general question, related to the first two portions of the graph. How do "Genes assigned to an OG" and "Genes not assigned to an OG" not equal 100% for each species? The graph as currently visualized does not show that. Then I think the bars in portion 3 "Genes in speciesspecific OG" are wrong (because in the text it says "N. thromboides had just 16.3%" species-specific OGs, but the graph clearly shows that bar at around 50%. I think portion 3 is just a duplicate of the bars in portion 4 - they look exactly the same - and in addition, as stated in the text portion 4 "Potentially speciesspecific genes" should be the simple addition of the bars in portion 2 and portion 3 for each species.

As mentioned above, we sincerely regret the error made in the plot and for the confusion that this caused. F now reflects the percentage of orthogroups (OGs) that possess at least one representative from the indicated species (left) and the percentage of OGs that are species-specific (only possess genes from one species; right). The latter is a subset of the former. G now reflects the percentage of annotated genes that were assigned an OG, per species, as well as the inverse of this - genes that were not assigned to any OG. These should, and now do, sum to 100%. The “Within species-specific OG” data summed with the “Not assigned OG” data yields the “Potentially species-specific data” in the rightmost column.

In the introduction, there is a name for the phenomenon of "clinging to or biting the tops of plants," it's called summit disease. And just for some context for the readers, summit disease is well-documented in many of these taxa in the older literature, but it is often ignored in modern studies - even though it is a fascinating effect seen in many insect hosts, caused by many, many fungi, nematodes (!), etc. This phenomenon has evolved many times. Nice discussions of this in Evans 1989 and Roy et al. 2006 (both of whom cite much of the older literature).

You’re right. We have now clarified that this behavior is called “summit disease” and referenced the suggested articles, along with a more recent review.

Reviewer #2 (Public Review):

In their study, Stajich and co-authors present a new 1.03 Gb genome assembly for an isolate of the fungal insect parasite Entomophthora muscae (Entomophthoromycota phylum, isolated from Drosophila hydei). Many species of the Entomophthoromycota phylum are specialised insect pathogens with relatively large genomes for fungi, with interesting yet largely unexplored biology. The authors compare their new E. muscae assembly to those of other species in the Entomophthorales order and also more generally to other fungi. For that, they first focus on repetitive DNA (transposons) and show that Ty3 LTRs are highly abundant in the E. muscae genome and contribute to ~40% of the species' genome, a feature that is shared by closely related species in the Entomophthorales. Next, the authors describe the major differences in protein content between species in the genus, focusing on functional domains, namely protein families (pfam), carbohydrate-active enzymes, and peptidases. They highlight several protein families that are overrepresented/underrepresented in the E. muscae genome and other

Entomophthorales genomes. The authors also highlight differences in components of the circadian rhythm, which might be relevant to the biology of these insect-infecting fungi. To gain further insights into E. muscae specificities, the authors identify orthologous proteins among four Entomophthorales species. Consistently with a larger genome and protein set in E. muscae, they find that 21% of the 17,111 orthogroups are specific to the species. To finish, the authors examine the consistency between methods for species delineation in the genus using molecular (ITS + 28S) or morphological data (# of nuclei per conidia + conidia size) and highlight major incongruences between the two.

Although most of the methods applied in the frame of this study are appropriate with the scripts made available, I believe there are some major discrepancies in the datasets that are compared which could undermine most of the results/conclusions. More precisely, most of the results are based on the comparison of protein family content between four Entomophthorales species. As the authors mention on page 5, genome (transcriptome) assembly and further annotation procedures can strongly influence gene discovery. Here, the authors re-annotated two assemblies using their own methods and recovered between 30 and 60% more genes than in the original dataset, but if I understand it correctly, they perform all downstream comparative analyses using the original annotations. Given the focus on E. muscae and the small sample size (four genomes compared), I believe performing the comparisons on the newly annotated assemblies would be more rigorous for making any claim on gene family variation.

Thank you for this comment. While we did compare gene model predictions for two of these assemblies to assess if this difference could account for discrepancies in gene counts, completely reannotating all non-E. muscae datasets was outside of the scope of this study. In our opinion, the total number of predicted genes in a genome is not a best representation of differences since splitting or fusing gene models can inflate seeming differences; the orthology and domain counts are a more accurate assessment of the content. It’s possible that annotation differences may have inflated some gene family counts, however we will note that similar domain trends were observed between the closest species to E. muscae, Entomophaga maimaiga, suggesting that these differences were not sufficient to prevent us from detecting real biological signals. We look forward to continued improvement of our genome through additional sequencing and more clarity on total gene content of E. muscae.

The authors also investigate the putative impact of repeat-induced point mutation on the architecture of the large Entomophthorales genomes (for three of the eight species in Figure 1) and report low RIP-like dinucleotide signatures despite the presence of RID1 (a gene involved in the RIP process in Neurospora crassa) and RNAi machinery. They base their analysis on the presence of specific PFAM domains across the proteome of the three Entomophthorales species. In the case of RID1, the authors searched for a DNA methyltransferase domain (PF00145), however other proteins than RID1 bear such functional domain (DNMT family) so that in the current analysis it is impossible to say if the authors are actually looking at RID1 homologs (probably not, RID1 is monophyletic to the Ascomycota I believe). Similar comments apply to the analysis of components of the RNAi machinery. A more reliable alternative to the PFAM analysis would be to work with full protein sequences in addition to the functional domains.

While we understand this concern regarding domain vs. full length protein, the advantage of the domain search is that HMM-based searches are sensitive to detecting more distantly related homologs. Entomophthoralean fungi are distantly related from the ascomycetes in which these mechanisms have been characterized, so we chose a broader search approach that may identify proteins with similar domain structure, but are not necessarily homologs. These searches are presented in the manuscript as preliminary, but worth further investigation. However, our RID-based analysis did not identify convincing homologs for RID1 in entomophthoralean fungi included in our investigation, and we reported low homology (i.e., 12-14%) among our orthogroup of interest and RID1. We have further edited this section to clarify our understanding that these candidates are not RID1 homologs. We had hoped to avoid this implication, but we felt this investigation and null result were worth reporting.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Specific points:

Results:

"1.03 Gb genome consisting of 7,810 contigs (N50 = 301.1 kb). Additional... resulted in a final contig count of 7,810 (N50 = 329.6 kb)" So you started and ended with the same contig count but a different N50? Is this a typo?

Yes, this was a typo. Thank you for bringing this to our attention.

Figure 1D.

The colors of Complete1x and Complete2x are too similar to tell them apart.

The colors have been made more distinct.

Figure 4B.

I know C. rosea has been found from insects before, but it's mostly a mycoparasite and occasionally an endophyte, and has bioactivity against a lot of things. I just saw that it's listed as an entomopathogen, and I was surprised. Anyway, leave it as is if you want to, but it's definitely better studied and better known (Google Scholar) as a mycoparasite.

Thanks for this comment. For the sake of including a more diverse representation of entomopathogenic fungi, we have opted to leave this as is.

Full references (from the public comment)

Evans, H.C., 1989. Mycopathogens of insects of epigeal and aerial habitats. Insect-fungus interactions, pp.205-238.

Roy, H.E., Steinkraus, D.C., Eilenberg, J., Hajek, A.E. and Pell, J.K., 2006. Bizarre interactions and endgames: entomopathogenic fungi and their arthropod hosts. Annu. Rev. Entomol., 51, pp.331-357.

Reviewer #2 (Recommendations For The Authors):

I believe the manuscript could largely benefit from restructuring the results section to enhance clarity. The results section reads like a lot of descriptive back and forth, so that the reader lacks a clear rationale. The absence of a consistent dataset used for the different comparisons made all along the manuscript makes it hard to follow.

Minor comments:

(No line numbers were available so I refer to page numbers).

p1

• not sure about the use of "allied" to describe other fungal species in the title and after (sister species?).

We didn’t want to use the word sister because not all of these species could be considered sister.

• Genomic defence against transposable elements rather than "anti"?

We have rephrased to genomic defense.

p3

• Extra parenthesis at Bronski et al.

This is now corrected.

• What does newly-available mean here?

We mean recent. A lot of the datasets we used were very new, and we wanted to emphasize that point.

• The back and forth between genomes and transcriptomes makes it hard to follow, would clarify from the beginning (in addition to the sequencing method - short vs long-read assemblies as in Figure 1B) or perhaps use a consistent dataset for all subsequent comparative analysis in the Entomophthorales.

We have denoted our transcriptomic datasets in Fig 1C using parentheses.

p5

• Perhaps clarify that class II DNA transposons can also "copy" (single-strand excisions can be repaired by the host machinery).

We have now included mention of “copy” as well as “jump” mechanisms of Class II transposons per your suggestion.

p6

• "beginning roughly concurrently", not clear what "began".

This is now corrected.

• "control" rather than "protect against"?

We’ve changed “protect against” to “counter”.

• I believe RIP has only been observed (experimentally) in a handful of fungal species, all from the Ascomycota phylum.

Hood et al, 2005 found signatures of RIP in anther-smut fungus and Horns et al, 2012, found evidence of hypermutability across repeat elements within several Pucciniales species.

• "RID1 contains two DNA_methylase domains", RID1 has one methyltransferase domain according to the reference Freitag et al, 2002.

Thank you for drawing this to our attention. It is true RID1 has one methyltransferase region; however, the sequence deposited by Freitag et al, 2002 (AAM27408) is predicted by HMMer to have two adjacent Pfam DNA_methylase domains (i.e., PF00145). In this exploratory analysis, we tried to leverage this characteristic to identify candidate proteins of interest. We have reworded this section to clarify this.

p8

• Here and after I would use more informative titles for each paragraph.

With the exception of the headings for Pfam, CAZy and MEROPs analyses, we believe the other headings are informative. We appreciate this comment, but opt to leave the heading titles as is.

• I believe presenting the orthology analysis before the more in-depth protein family domain search.

We leveraged the OG analysis mostly as a way to identify potentially unique genes in E. muscae, so we think the current order makes the most sense.

p10

• Figures 3F and G are confusing. The legend for Figure 3F mentions "OGs with >= 2 species" while the figure shows "multi-species OGs", and reads as redundant with the "species-specific" OGs. For the "OGs within species" do I understand it correctly that it represents the number of genes assigned to OGs for each species? If yes, the numbers are in contradiction with Figure 3G. And in Figure 3G shouldn't the sum of "genes assigned in OGs" and "genes nor assigned in OGs" add up to 100? I'm probably missing something here, but I would clarify what the different sets of orthogroups are in the figure and in the text (perhaps adopting a pangenome-like nomenclature).

Thanks for this comment. This legend, unfortunately, reflected an earlier version of the figure and was overlooked prior to submission. We have since amended this and sincerely apologize for the error on our part.

p12

• The whole first paragraph reads more like it should be part of an introduction/discussion.

We’ve moved some of this paragraph to the discussion but left the background information necessary for the reader to understand why we were looking for homologs of wc and frq.

p13

• The last paragraph reads like discussion.

We have revised this paragraph so it now reads: “Because E. muscae is an obligate insect-pathogen only living inside live flies, we investigate the presence of canonical entomopathogenic enzymes in the genome. We find that E. muscae appear to have an expanded group of acid-trehalases compared to other entomopathogenic and non-entomopathogenic Entomophthorales (Fig. 4A), which correlates with the primary sugar in insect blood (hemolymph) being trehalose (Thompson, 2003). The obligate insectpathogenic lifestyle is also evident when comparing the repertoire of lipases, subtilisin-like serine proteases, trypsins, and chitinases in our focal species versus Zoopagomycota and Ascomycota fungi that are not obligate insect pathogens (Fig. 4B). Sordariomycetes within Ascomycota contains the other major transition to insect-pathogenicity within the kingdom Fungi (Araújo and Hughes, 2016). Based on our comparison of gene numbers, Entomophthorales possess more enzymes suitable for cuticle penetration than Sordariomycetes (Fig. 4B). In contrast, insect-pathogenic fungi within Hypocreales possess a more diverse secondary metabolite biosynthesis machinery as evidenced by the absence of polyketide synthase (PKS) and indole pathways in Entomophthorales (Fig. 4C).”

p15 and 16

• This all reads as redundant with the previous protein family domain analysis. I would try to merge them.

Thank you for this comment, however we have opted to maintain the current structure.

p18

• In the first sentence, I'm not sure about what was performed here.

This has been reworded to clarify.

p20

• Regarding the assembly, do I understand it correctly that a nuclear genome can be partially haploid / diploid?

Thanks for your comment. The genome itself is, of course, some integer multiple of n, but based on BUSCO scores our assembly doesn’t appear to have completely collapsed into a haploid genome. We think it makes more sense here to say “partially haploid” than “partially diploid” so have altered this.

p21

• RIP has only been observed in a couple of Ascomycetes. RIP-like genomic signatures (GC bias) have been observed elsewhere.

Hood et al, 2005 found signatures of RIP in anther-smut fungus and Horns et al, 2012, found evidence of hypermutability across repeat elements within several Pucciniales species.

p23

• Interesting that the peptidase A2B domain is found uniquely in E. muscae genome and is associated with Ty3 activity. Does the domain often overlap with annotated Ty3 in E. muscae genome? Or how come the domain is not present in other sister species with large genomes full of Ty3 transposons? Could it relate to a new active transposon in E. muscae specifically?

Thanks for this comment. The domain-based analysis was only performed on the predicted transcriptome of the genome assembly, which does not include the repeat elements (e.g., Ty3). It could be that this peptidase reflects a new active transposon that’s specific to E. muscae, which would certainly be very interesting. We’ve now included this idea in the discussion.

p26

• In the case of fungal genomes, I would not advise masking the assembly for repeated sequences prior to gene annotation (in particular given the current focus on protein family variation).

Thank you for this comment, however we disagree with this assertion as a typical approach for genome annotation in fungi and eukaryotic genomes is to use soft masking of transposable elements before performing gene prediction to avoid over-prediction. While there could be alternative approaches that compare masked or unmasked. This is a recommended protocol for underlying tools like Augustus (10.1002/cpbi.57) and in general descriptions of genome annotation (10.1002/0471250953.bi0401s52). The false positive rate of genes predicted through TE regions is likely to be more a problem than false negatives of missed genes in our experience. Further it seems appropriate to use consistent approach to annotation throughout when including genomes from other sources (e.g., Joint Genome Institute annotated genomes) which also use a repeat masking approach first before annotation. It seems most appropriate to use consistent methods when generating datasets to be used for comparative analyses. It is outside the scope of this project to reannotate all genomes with and without repeat masking.

p27

• Interrupted sentence at "Classification of DNA and LTR .. by similarity The".

This was an unnecessary partial phrase as the information on classification of elements via RepBase was made a few sentences above this.

p28

• Enriched/depleted rather than "significantly different"?

Thank you for this comment, however we have opted to maintain the current phrasing.

Author response:

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

We thank the reviewers for a careful review of the manuscript and for their comments, which we address below.

Reviewer #1:

(1) …the authors could examine division in a population of cells with only one centrosome. Seeing some restoration of mitotic progression in the absence of SAC-dependent delays would suggest that even one centrosome with uninhibited Eg5 is sufficient to negate SAC-dependent delays, and would limit models for what exactly centrosomes contribute.

We agree that the one-centrosome question (i.e. whether cells with a single centriole, and therefore a single centrosome, have the same SAC dependence) would be interesting to address. It is known that cells with a single centriole generated through centrinone treatment also have elongated mitoses, like cells lacking centrioles (see Chinen, et. al. 2021, compare Fig 2C to Fig 2D), We have tried this experiment in RPE-1 cells with preliminary results confirming that there is a mitotic delay. It is not known whether this delay requires SAC activity, and we hope to address that in future work. In addition, we note that we show in Fig. 4b-c that cells with the normal centrosome number but with a single focus of microtubules due to Eg5 inhibition, were also sensitive to MPS1 inhibition. This suggests that centrosome presence alone cannot overcome the requirement for SAC activity, rather, the centrosomes need to be able to separate in a timely fashion.

Reviewer #2:

(1) An example is how to interpret the effect of Aurora B inhibition, which does not block acentrosomal cell division. If Aurora B is required for SAC activity, it suggests this effect of MPS1 may be a function other than SAC. Given the complexity of the SAC, it would be informative to test other SAC components. Instead, the authors conclude that the mitotic delay caused by MPS is required for acentrosomal cell division. I don't think they have ruled out, or even addressed other functions of MPS1.

We agree that it is possible that functions of the MPS1 kinase other than those involved in the SAC could be important. Although we have not directly tested other SAC components, we did “mimic” SAC activity by delaying anaphase onset using APC/C inhibition while also inhibiting MPS1 (Fig. 2b-b’’). The fact that this restored division suggests that it is the SAC function of MPS1 kinase activity that is relevant to this delay. 

(2) The authors find that when both the APC and MPS1 are inhibited, the cells eventually divide. These results are intriguing, but hard to interpret. The authors suggest that the failure to divide in MPS1-inhibited cells is because they enter anaphase, and then must back out. This is hard to understand and there is not data supporting some kind of aborted anaphase. Is the division observed with double inhibition some sort of bypass of the block caused by MPS1 inhibition alone? It is not clear why inhibition of APC causes increased cell division when MPS1 is inhibited.

As described in the response to 1), we believe that reinstating the delay to anaphase onset by APC/C inhibition provided the time needed to establish a functional bipolar spindle even in the absence of the SAC, and that cells eventually overcome the proTAME block and proceed through mitosis, as observed in control cells in our experiments. We note that we chose concentrations of proTAME specifically for each cell line (RPE-1 and U2OS) that would result only in a temporary block, following on the work of Lara-Gonzalez and Taylor (2012), who reported similar findings for HeLa cells.

(3) The authors characterize MTOC formation in these cells, which is also interesting. MTOCs are established after NEB in acentrosomal cells. Indeed, forming these MTOCs is probably a key mechanism for how these cells complete a division, like mouse oocytes.

We agree that the observed intermediates of MTOCs are interesting and likely crucial to the mechanism of cell division in acentrosomal somatic cells. We are investigating further the differences and similarities between somatic cell MTOC formation in the absence of centrosomes and the naturally-occurring form of that process in oocytes.

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

Seleit and colleagues set out to explore the genetics of developmental timing and tissue size by mapping natural genetic variation associated with segmentation clock period and presomitic mesoderm (PSM) size in different species of Medaka fish. They first establish the extent of variation between five different Medaka species of in terms of organismal size, segmentation rate, segment size and presomitic mesoderm size, among other traits. They find that these traits are species-specific but strongly correlated. In a massive undertaking, they then perform developmental QTL mapping for segmentation clock period and PSM size in a set of ~600 F2 fish resulting from the cross of Orizyas sakaizumii (Kaga) and Orizyas latipes (Cab). Correlation between segmentation period and segment size was lost among the F2s, indicating that distinct genetic modules control these traits. Although the researchers fail to identify causal variants driving these traits, they perform proof of concept perturbations by analyzing F0 Crispants in which candidate genes were knocked out. Overall, the study introduces a completely new methodology (QTL mapping) to the field of segmentation and developmental tempo, and therefore provides multiple valuable insights into the forces driving evolution of these traits.

Major comments: - The first sentence in the abstract reads "How the timing of development is linked to organismal size is a longstanding question". It is therefore disappointing that organismal size is not reported for the F2 hybrids. Was larval length measured in the F2s? If so, it should be reported. It is critical to understand whether the correlation between larval size and segmentation clock period is preserved in F2s or not, therefore determining if they represent a single or separate developmental modules. If larval length data were not collected, the authors need to be more careful with their wording.

The question the reviewer raises here is indeed a very relevant one, and a question that we also were curious about ourselves. While it was not possible (logistically) to grow the 600 F2 fish to adulthood, we did measure larval length in a subset of F2 hatchling (n=72) to ask precisely the question the reviewer raises here. Our results (new Supplementary Figure 5) show that the correlation between larval length and segmentation timing (which we report across the Oryzias species) is absent in the F2s. This indeed argues that the traits represent separate developmental modules.

In the current version of the paper, organismal size is often incorrectly equated to tissue size (e.g. PSM size, segment size). For example, in page 3 lines 33-34, the authors state that faster segmentation occurred in embryos of smaller size (Fig. 1D). However, Fig. 1D shows correlation between segmentation rate and unsegmented PSM area. The appropriate data to show would be segmentation rate vs. larval or adult length.

The reviewer is correct. We have now linked the data more clearly to data we show in Supplementary Figure 1, which shows that adult length and adult mass are strongly correlated (S1A) and that adult mass is in turn strongly correlated with segmentation rate in the different Oryzias species (S1B). Additionally main Figure 1B shows that larval length is correlated with PSM length. We have corrected the main text to reflect these relationships more clearly.

• Is my understanding correct in that the her7-venus reporter is carried by the Cab F0 but not the Kaga F0? Presumably only F2s which carried the reporter were selected for phenotyping. I would expect the location of the reporter in the genome to be obvious in Figure 3J as a region that is only Cab or het but never Kaga. Can the authors please point to the location of the reporter?

The reviewer is correct. Indeed the location of our her7-venus KI is on chromosome 16 and the recombination patterns on this chromosome overwhelmingly show either Hom Cab (green) or Het Cab/Kaga (Black). This is expected as we selected fish carrying the her7-venus KI for phenotyping.

• devQTL mapping in this study seems like a wasted opportunity. The authors perform mapping only to then hand pick their targets based on GO annotations. This biases the study towards genes known to be involved in PSM development, when part of the appeal of QTL mapping is precisely its unbiased nature and the potential to discover new functionally relevant genes. The authors need to better justify their rationale for candidate prioritization from devQTL peaks. The GO analysis should be shown as supplemental data. What criteria were used to select genes based on GO annotations?

We have now commented on these valid points and outlined our rationale in more detail in the text (page 4, lines 20-30). Our rationale now also includes selection of differentially expressed genes (n=5 genes) that fall within segmentation timing devQTL hits (for more details see below). Essentially, while we indeed finally focused on the proof of principle using known genes, these genes were previously not known to play a role in either setting the timing of segmentation or controlling the size of the PSM. Hence, we do think our strategy demonstrates the "the potential to discover new functionally relevant genes", even though the genes themselves had been involved overall in somitogenesis. We added the GO analysis as supplemental data as requested (new Supplementary Figure 7E).

• Analysis of the predicted functional consequence of divergent SNPs (Fig. S6B, F) is superficial. Among missense variants, which genes harbor the most deleterious mutations? Which missense variants are located in highly conserved residues? Which genes carry variants in splice donors/acceptors? Carefully assessing the predicted effect of SNPs in coding regions would provide an alternative, less biased approach to prioritize candidate genes.

We now included our analysis of SNPs based on the Variant effect predictor (VEP) tool from ensembl. This analysis does rank the predicted severity of the SNP on protein structure and function (Impact: low, moderate, high) and does annotate which variants can affect splice donors/acceptors. The VEP analysis for both phenotypes is now added to the manuscript as supplemental data (new Supplementary Data S2, S5).

• Another potential way to prioritize candidate genes within devQTL peaks would be to use the RNA seq data. The authors should perform differential expression analysis between Kaga and Cab RNA-seq datasets. Do any of the differentially expressed genes fall within the devQTL peaks?

As suggested we have performed this additional experiment and report the RNAseq differential analysis in new Supplement Figure 7C-D. The analysis revealed 2606 differentially expressed genes in the PSM between Kaga and Cab, five of which were candidate genes from the devQTL analysis. We now tested all of these (5 in total, 4 new and 1 previously targeted adgrg1) for segmentation timing by CRISPR/Cas9 KO in the her7-venus background, none of which showed a timing phenotype (new Supplementary Figure 7F-F'). We provide the complete set of results in new Supplementary Figure 7 , Supplementary Data file 3 (DE-genes), all data were deposited on publicly available repository Biostudies under accession number: E-MTAB-13927.

• The use of crispants to functionally test candidate genes is inappropriate. Crispants do not mimic the effect of divergent SNPs and therefore completely fail to prove causality. While it is completely understandable that Medaka fish are not amenable to the creation of multiple knock-in lines where divergent SNPs are interconverted between species, better justification is needed. For instance, is there enough data to suggest that the divergent alleles for the candidate genes tested are loss of function? Why was a knockout approach chosen as opposed to overexpression?

We agree with the reviewer that we do not address the causality of SNPs with the CRISPR/Cas9 KO approach we followed. And medaka does offer the genome editing capabilities to create tailored sequence modifications. So in principle, this can be done. In practice, however, we reasoned that any given SNP will contribute only partially to the observed phenotypes and combinatorial sequence edits are simply very laborious given the current state of the art in genome editing technologies. We therefore opted for an alternative proof of principle approach that aims to "to discover new functionally relevant genes", not SNPs.

-Along the same line, now that two candidate genes have been shown to modulate the clock period in crispants (mespb and pcdh10b), the authors should at least attempt to knock in the respective divergent SNPs for one of the genes. This is of course optional because it would imply several months of work, but it would significantly increase the impact of the study.

As above, this is in principle the correct rationale to follow though very time, cost and labour intensive. It is for the later practical consideration that we decided not to follow this option.

Minor Comments - It would be highly beneficial to describe the ecological differences between the two Medaka species. For example, do the northern O. sakaizumii inhabit a colder climate than the southern O. latipes? Is food more abundant or easily accessible for one species compared to the other? What, if anything, has been described about each species' ecology?

There are indeed differences in the ecology of both species, with the northern O.sakaizumii inhabiting a colder climate than the southern O. latipes. In addition, it is known that the breeding season is shorter in the north than the south, and also there is the fact that northern species have been shown to have a faster juvenile growth rate than southern species. While it would be premature to link those ecological factors to the timing differences we observe, we can certainly speculate. A line to this effect has been added to the main text (Page 5, line 28-30).

• The authors describe two different methods for quantifying segmentation clock period (mean vs. intercept). It is still unclear what is the difference between Figs. 3A (clock period), S4A (mean period) and S4B (intercept period). Is clock period just mean period? Are the data then shown twice? How do Fig. 3A and S4A differ?

The clock period shown in all the main figures is the intercept period, which was also used for the devQTL analysis. Both measurements (mean and intercept) are indeed highly correlated and we include both in supplement for completeness.

• devQTL as shorthand for developmental QTL should be defined in page 4 line 1 (where the term first appears), not later in line 12 of the same page.

Noted and corrected, we thank the reviewer for spotting this error.

• Python code for period quantification should be uploaded to Github and shared with reviewers.

All period quantification code that was used in this study was obtained from the publicly available tool Pyboat (https://www.biorxiv.org/content/10.1101/2020.04.29.067744v3). All code that is used in PyBoat is available from the Github page of the creator of the tool (https://github.com/tensionhead/pyBOAT). Both are linked in the references and materials and methods sections.

• RNA-seq data should be uploaded to a publicly accessible repository and the reviewer token shared with reviewers.

We have uploaded all RNA-sequencing Data to public repository BioStudies under accession numbers : E-MTAB-13927, E-MTAB-13928. This information is now also added to material and methods in the manuscript text.

Why are the maintenance (27-28C) vs. imaging (30C) temperatures different?

Medaka fish have a wide range of temperatures they can physiologically tolerate, i.e. 17-33. The temperature 30C was chosen for practical reasons, i.e. a slightly faster developmental rate enables higher sample throughput in overnight real-time imaging experiments.

• For Crispants, control injections should have included a non-targeting sgRNA control instead of simply omitting the sgRNA.

We agree a non-targeting sgRNA control can be included, though we choose a different approach. For clarity, we now also include a control targeting Oca2, a gene involved in the pigmentation of the eye to probe for any injection related effect on timing and PSM size. As expected, 3 sgRNAs + Cas9 against Oca2 had no impact on timing or PSM size. This data is now shown in new Supplementary Figure 9 F-G'.

It is difficult to keep track of the species and strains. It would be most helpful if Fig. S1 appeared instead in main figure 1.

We agree and included an overview of the phylogenetic relationship of all species and their geographical locales in new Figure 1 A-B.

Significance

• The study introduces a new way of thinking about segmentation timing and size scaling by considering natural variation in the context of selection. This new framing will have an important impact on the field.
• Perhaps the most significant finding is that the correlation between segment timing and size in wild populations is driven not by developmental constraints but rather selection pressure, whereas segment size scaling does form a single developmental module. This finding should be of interest to a broad audience and will influence how researchers in the field approach future studies.
• It would be helpful to add to the conclusion the author's opinion on whether segmentation timing is a quantitative trait based on the number of QTL peaks identified.
• The authors should be careful not to assign any causality to the candidate genes that they test in crispants.
• The data and results are generally well-presented, and the research is highly rigorous.
• Please note I do have the expertise to evaluate the statistical/bioinformatic methods used for devQTL mapping.

Reviewer #2

Evidence, reproducibility and clarity

Seleit et al. investigate the correlation between segment size, presomitic mesoderm and the rhythm of periodic oscilations in the segmentation clock of developing medaka fish. Specifically, they aim to identify the genetic determinants for said traits. To do so, they employ a common garden approach and measure such traits in separate strains (F0) and in interbreedings across two generations (F1 and F2). They find that whereas presomitic mesoderm and segment size are genetically coupled, the tempo of her7 oscilations it is not. Genetic mapping of the F0 and F2 progeny allows them to identify regions associated to said traits. They go on an perturb 7 loci associated to the segmentation clock and X related to segment size. They show that 2/7 have a tempo defect, and 2/ affect size.

Major comments: The conclusions are convincing and well supported by the data. I think the work could be published as is in its current state, and no additional experiments that I can think of are needed to support the claims in the paper.

Minor comments: - The authors could provide a more detailed characterization of the identified SNPs associated to the clock and to PSM size. For the segmentation clock, the authors identify 46872 SNPs, most of which correspond to non-coding regions and are associated to 57 genes. They narrow down their approach to those expressed in the PSM of Cab Kaga. Was the RNA selected from F1 hybrids? I wonder if this would impact the analysis for tempo and or size in any way, as F2 are derived from these, and they show broader variability in the clock period than the F0 and F1 fishes.

The RNA was obtained from the pure F0 strains and we have now extended this analysis by deep bulk-RNA sequencing and differential gene expression analysis. As indicated also to reviewer 1, this revealed 2606 differentially expressed genes in the unsegmented tails of Kaga and Cab embryos, some of which occurred in devQTL peaks. Based on this information we expanded our list of CRISPR/Cas9 KOs by targeting all differentially expressed genes (5 in total, 4 new and 1 previously targeted) for segmentation timing, none of which showed a timing phenotype (new Supplementary figure 7C-D). We provide the complete set of results in new Supplementary Figure 7, Supplementary Data file 3 (DE-genes). All data were deposited on publicly available repository Biostudies under accession number: E-MTAB-13927.

It would be good if the authors could discuss if there were any associated categories or overall functional relationships between the SNPs/genes associated to size. And what about in the case of timing?

In the case of PSM size there were no clear GO terms or functional relationships between the genes that passed the significance threshold on chromosome 3.

For the 35 genes related to segmentation timing, there were a number of GO enrichment terms directly related to somitogenesis. We have included the GO analysis in the new Supplementary Figure 7E.

• Have any of the candidate genes or regulatory loci been associated to clock defects (57) or segment size (204) previously in the literature?

To the best of our knowledge none of the genes have been associated with clock or PSM size defects so far. It might be worthwhile using our results to probe their function in other systems enabling higher throughput functional analysis, such as newly developed organoid models.

• When the authors narrow down the candidate list, it is not clear if the genes selected as expressed in the PSM are tissue specific. If they are, I wonder if genes with ubiquitous expression would be more informative to investigate tempo of development more broadly. It would be good if the authors could specifically discuss this point in the manuscript.

We have not addressed the spatial expression pattern of the 35 identified PSM genes in this study, so we cannot speculate further. But the reviewer raises an important point, how timing of individual processes (body axis segmentation) are linked at organismal scale is indeed a fundamental, additional, question that will be addressed in future studies, indeed the in-vivo context we follow here would be ideal for such investigations.

Can the authors speculate mechanistically why mespb or pchd10b accelerates the period of her7 oscillations?

While we do not have a mechanistic explanation yet, an additional experiment we performed, i.e. bulk-RNAsequencing on WT and mespb mutant tails, provided additional insight, we now added this data to the manuscript . This analysis revealed 808 differentially expressed genes between wt and mespb mutants. Interestingly, many of these affected genes are known to be expressed outside of the mespb domain, i.e. in the most posterior PSM (i.e. tbxt, foxb1,msgn1, axin2, fgf8, amongst others). This indicates that the effect of mespb downregulation is widespread and possibly occurs at an earlier developmental stage. This requires more follow up studies. This data is now shown in new Supplementary figure 9A, Supplementary Data file S4. We now comment on this point in the revised manuscript.

• Are there any size difference associated to the functionally validated clock mutants?

We addressed this point directly and added this analysis as supplementary Figure 9H-H'. While pcdh10b mutants do not show any detectable difference in PSM size, we find a small, statistically significant reduction in PSM size (area but not length) in mespb mutants. All this data is now included in the revised manuscript.

-Ref 27 shows a lack of correlation between body size and the segmentation period in various species of mammals. The work supports their findings, and it would be good to see this discussed in the text.

We are not certain how best to compare our in-vivo results in externally developing fish embryos to in-vitro mammalian 2-D cell cultures. In our view, the correlation of embryo size, larval and adult size that we find in Oryzias might not necessarily hold in mammalian species, which would make a comparison more difficult. We do cite the work mentioned so the reader is pointed towards this interesting, complementary literature.

Significance

The work is quite remarkable in terms of the multigenerational genetic analysis performed. The authors have analysed >600 embryos from three separate generations to obtain quantitative data to answer their question (herculean task!). Moreover, they have associated this characterization to specific SNPs. Then, to go beyond the association, they have generated mutant lines and identified specific genes associated to the traits they set out to decipher.

To my knowledge, this is the first project that aims to identify the genetic determinants for developmental timing. Recent work on developmental timing in mammals has focused on interspecies comparisons and does not provide genetic evidence or insight into how tempo is regulated in the genome. As for vertebrates, recent work from zebrafish has profiled temperature effects on cell proportions and developmental timing. However, the genetic approach of this work is quite elegant and neat.

Conceptually, it is quite important and unexpected that overall size and tempo are not related. Body size, lifespan, basal metabolic rates and gestational period correlate positively and we tend to think that mechanistically they would all be connected to one another. This paper and Lazaro et al. 2023 (ref 27) are one of the first in which this preconception is challenged in a very methodical and conclusive manner. I believe the work is a breakthrough for the field and this work would be interesting for the field of biological timing, for the segmentation clock community and more broadly for all developmental biologists.

My field is quantitative stem cell biology and I work on developmental timing myself, so I acknowledge that I am biased in the enthusiasm for the work. It should be noted that as an expert on the field, I have identified instances where other work hasn't been as insightful or well developed in comparison to this piece. It is also worth noting that I am not an expert in fish development, phylogenetic studies or GWAS analyses, so I am not capable to asses any pitfalls in that respect.

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

__Summary: __

This manuscript explores the temporal and spatial regulation of vertebrate body axis development and patterning. In the early stages of vertebrate embryo development, the axial mesoderm (presomitic mesoderm - PSM) undergoes segmentation, forming structures known as somites. The exact genetic regulation governing somite and PSM size, and their relationship to the periodicity of somite formation remains unclear.

To address this, the authors used two evolutionarily closely related Medaka species, Oryzias sakaizumii and Oryzias latipes, which, although having distinct characteristics, can produce viable offspring. Through analysis spanning parental (generation F0) and offspring (generations F1 and F2) generations, the authors observed a correlation between PSM and somite size. However, they found that size scaling does not correlate with the timing of somitogenesis.

Furthermore, employing developmental quantitative trait loci (devQTL) mapping, the authors identified several new candidate loci that may play a role during somitogenesis, influencing timing of segment formation or segment size. The significance of these loci was confirmed through an innovative CRISPR-Cas9 gene editing approach.

This study highlights that the spatial and temporal aspects of vertebrate segmentation are independently controlled by distinct genetic modular mechanisms.

__Major comments: __

1) In the main text page 3, lines 11 and 12, the authors state that the periodicity of the embryo clock of the F1 generation is the intermediate between the parental F0 lineages. However, the authors look only at the periodicity of the Cab strain (Oryzias latipes) segmentation clock. The authors should have a reporter fish line for the Kaga strain (Oryzias sakaizumii) to compare the segmentation clock of both parental strains and their offspring. Since it could be time consuming and laborious, I advise to alternatively rephrase the text of the manuscript.

We agree a careful distinction between segment forming rate (measured based on morphology) and clock period (measured using the novel reporter we generated) is essential. We show that both measures correlate very well in Cab, in both F0 and F1 and F2 carrying the Cab allele. For Kaga F0, we indeed can only provide the rate of somite formation, which nevertheless allows comparison due to the strong correlation to the clock period we have found. We have rephrased the text accordingly.

2) It is evident that only a few F0 and F1 animals were analyzed in comparison with the F2 generation. Could the authors kindly explain whether and how this could bias or skew the observed results?

We provide statistical evidence through the F-test of equality that the variances between the F0, F1 and F2 samples are equal. Additionally if we sub-sample and separate the F2 data into groups of 100 embryos (instead of all 638) we get the same distribution of the F2s. We therefore believe that this is sufficient evidence against a bias or skew in the results.

3) It would be interesting to create fish lines with the validated CRISPR-Cas9 gene manipulations in different genetic contexts (Cab or Kaga) to analyze the true impact on the segmentation clock and/or PSM & somite sizes.

We agree with the reviewer this would in principle be of interest indeed, please see our response to reviewer 1 earlier.

4) Please add the results of the Go Analysis as supplementary material.

We have added the GO analysis in new Supplementary Figure 7E.

__Minor comments: __

1) In the main text, page 2, line 29, Supplementary Figure 1D should be referenced.

We have added a clearer phylogeny and geographical location of the different species in new Figure 1 A-B. And reference it at the requested location.

2) In the main text, page 2, line 32, the authors refer to Figure 1B, but it should be 1C.

We have corrected the information.

3) Regarding the topic "Correlation of segmentation timing and size in the Oryzias genus" the authors should also give information on the total time of development of the different Oryzias species, as well as the total number of formed somites.

We follow this recommendation and have added this information in new Supplementary Figure 5. We also now include segment number measured in F2 embryos. We indeed view segmentation rate as a proxy for developmental rate, which however needs to be distinguished from total developmental time. The latter can be measured for instance by quantifying hatching time, which we did. These measurements show that Kaga, Cab and O.hubbsi embryos kept at constant 28 degrees started hatching on the same day while O.minutillus and O.mekongensis embryos started hatching one day earlier. We have not included this data in the manuscript because we think a distinction should be made between rate of development and total development time.

4) In Figures 3A and B, please add info on the F1 lines for comparison.

The information on F1 lines is provided in Supplementary Figure 3

5) Supplementary Figures 2F shows that the generation F1 PSM is similar to Cab F0, and not an intermediate between Kaga F0 and Cab F0. This is interesting and should be discussed.

We show that the F1 PSM is indeed closer to the PSM of Cab than it is to the Kaga PSM. This is indeed intriguing and we have now commented on this point directly in the text.

6) Supplementary Figures 6C to H are not mentioned either in the main text or in the extended information. Please add/mention accordingly.

We have added references to both in the text

7) The order of Supplementary Figure 8 E to H and A to D appears to be not correct and not following the flow of the text. Please update/correct accordingly.

We have updated the text accordingly.

8) The authors should choose between "Fig.", "Fig", "fig.", "fig" or "Figure". All 'variants' can be found in the text.

Noted, and updated. Fig. is used for main figures and fig. is used for supplementary figures.

9) The color scheme of several figures (graphs with colored dots) should be revised. Several appear to be difficult to discern and analyze.

We have enhanced the colours and increased the font on the figure panels. The colour panel was chosen to be colour-blind friendly.

10) Please address/discuss following questions: What are the known somitogenesis regulating genes in Medaka? How do they correlate with the new candidates?

The candidates we found and tested had not been implicated in regulating the tempo of segmentation or PSM size, while for some a role in somite formation had been previously established, hence the enrichment in GO analysis Somitogenesis.

Reviewer #3 (Significance (Required)):

General assessment:

This interesting manuscript describes a novel approach to study and find new players relevant to the regulation of vertebrate segmentation. By employing this innovative methodology, the authors could elegantly demonstrate that the segmentation clock periodicity is independent from the sizes of the PSM and forming somites. The authors were further able to find new genes that may be involved in the regulation of the segmentation clock periodicity and/or the size of the PSM & somites. A limitation of this study is the fact that the results mainly rely on differences between the two species. The integration of additional Medaka species would be beneficial and may help uncover relevant genes and genetic contexts.

Advance:

To my best knowledge this is the first time that such a methodology was employed to study the segmentation clock and axial development. Although the topic has been extensively studied in several model organisms, such as mice, chicken, and zebrafish, none of them correlated the size of the embryonic tissues and the periodicity of the embryo clock. This study brings novel technological and functional advances to the study of vertebrate axial development.

Audience:

This work is particularly interesting to basic researchers, especially in the field of developmental biology and represents a fresh new approach to study a core developmental process. This study further opens the exciting possibility of using a similar methodology to investigate other aspects of vertebrate development. It is a timely and important manuscript which could be of interest to a wider scientific audience and readership.

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

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Reply to the reviewers

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

Summary:* In this paper the authors explore the function of Syndecan in Drosophila stem cells focussing primarily on the intestinal stem cells. They use RNAi knockdown to conclude that Syndecan is required for long term stem cell maintenance as its knockdown results in apoptosis. They suggest that this effect is independent of LINC complex proteins but is associated with changes to nuclear morphology and DNA damage. They go on to show that a similar impact on nuclear shape can be seen in larval neuroblasts but not in stem cells of the female germline. *

Major Comments: *The key conclusion that underpins the paper is that reduced Syndecan causes loss of stem cells. This is based entirely on evidence from cell-type specific RNAi using 3 independent RNAi lines. Overexpression has no phenotype and there is no analysis of loss of function mutants. SdcRNAi3 gives strong phenotypes that are statistically significant and is used throughout the paper. SdcRNAi2 gives comparatively moderate phenotypes which trend in the same direction but it is not clear if these are statistically significant (Fig S1). SdcRNAi line 1 appears to have very little effect (and if anything trends in the opposite direction in S1A). In addition, the knockdown efficiency of the three lines has not been assessed. Another possible concern given the dependence on RNAi3 is that the RNAi control line used is not an ideal match for the VDRC GD RNAi lines as it is in a different genetic background. In order to robustly draw conclusions: the phenotypes with RNAi lines 1 and 2 should be tested for significance; the extent of knockdown in each should be quantified either by qPCR in whole tissue knockdown, or by staining for protein levels if possible, to assess whether the variation in phenotypes is due to different knockdown levels. The use of a loss of function mutant in clones or tissue specific CRISPR-Cas9 KO or KD would also significantly increase confidence in the findings. *

• Our qPCR data indicate that SdcRNAi3 produces the most efficient knockdown, whilst SdcRNAi1 generates the weakest knockdown. The new manuscript version will incorporate this data in figure S1. Knockdown efficacy of SdcRNAi 3 has also been previously reported (Eveland et al., 2016).

• We apologise for omitting to add the statistical tests on phenotypic categories in figure S1A, this will be revised. We confirm that all Sdc RNAi phenotypic distributions are significantly different to that seen for age-matched controls (p- It should also be noted that despite weaker knockdowns with SdcRNAi1 and 2, we still observed statistically significant ISC depletion after 28 days of RNAi expression - we will add this data in figure S1. Overall, we are confident about Sdc’s role in maintaining intestinal stem cells.

*Similarly, the evidence for a lack of LINC protein role in the phenotype relies on single RNAi lines without validation of knockdowns. The authors should ideally validate these lines in this system or reference other studies that have validated the lines in this or other contexts. *

• The klarsicht RNAi line (BDSC 36721) and klaroid RNAi line (BDSC 40924) used in this study have been validated and used in other studies. (Falo-Sanjuan & Bray, 2022; Collins et al., 2017)

• For Msp300 RNAi knockdown we have used two independent RNAi lines which gave similar results. We will amend the text to clarify these points. In addition, the line reported in the manuscript was previously validated (Dondi et al., 2021; Frost et al., 2016).

Minor Comments: *The figures are generally very clear but some of the IF image panels are very small and require significant on-screen enlargement to be legible. In particular in Figure 1B the cross section views make it difficult to assess expression in the different cell types (and don't show very many cells), could this be shown in wholemount or as separated channels in a supplementary figure? In addition, it would strengthen the argument to include counterstains for markers of the different cell types (particularly to distinguish ISC/EB from EE). This could include esg-lacZ to mark ISC/EBs or prospero for EEs. However, if a broader view of these panels makes it clearer that all epithelial cells are expressing Syndecan this may not be essential. *

• We are happy to incorporate larger fields of view, and co-immunostaining with different cell type markers.

*Syndecan is referred to throughout as a stem cell regulator. This implies that in certain contexts or in response to certain stimuli its expression may be altered to elicit a stem cell response but no examples of this are shown. Moreover, only knockdown and not overexpression gives phenotypes suggesting its role may be as a required protein than a regulator. Either examples of its expression being modulated in homeostasis or in response to a challenge could be included or the wording could be amended. *

• We agree with the reviewer and will amend the wording.

*Expression of Syndecan in neuroblasts is described as data not shown, it would be better to include this for completeness. *

• We will add this data in figure 4.

*In addition to the intestinal validation of the Syndecan RNAi lines, validation of knockdown in the germline would be valuable to support the conclusions of Fig S4 given differences of knockdown in the germline with some RNAi lines (although inclusion of Dicer in the driver line should have overcome this). *

• Sdc expression is very low in the germline, compared to the surrounding somatic cells, therefore we are not confident that we can detect differences in expression level after knockdown. We suggest adding a panel in figure S4 to show the low expression and adding a comment in the text. Reviewer #1 (Significance (Required)): *The study describes a potentially very interesting, novel link between Syndecan, nuclear shape and apoptosis in cycling cells that could have broad relevance. If fully validated this could have implications for other stem cell populations, including those in mammals and disease relevance in the context of cancer. The paper is fundamentally descriptive in nature and so the level of significance hinges on the strength of evidence and how interesting the phenotype itself is. At this stage the audience will be primarily in the areas of fundamental research in biology of the nucleus and cytoskeleton. Defining the mechanistic link between Syndecan and nuclear morphology will be a critical next step and while not essential for this study would significantly increase the likely interest in the paper. *

• We thank the reviewer for these constructive comments. We agree that discovering the mechanistic links between Syndecan and nuclear morphology in future studies, in this and other model systems, will be relevant to many areas of biological research.

*In terms of significance in stem cell biology the distinction between a regulator and a requirement to prevent stem cell apoptosis is important and the lack of evidence for a context in which Syndecan plays a regulatory role somewhat detracts from the breadth of impact. My field of expertise is in epithelial stem cell biology. *

• We agree and will amend our wording.

Reviewer #2 *(Evidence, reproducibility and clarity (Required)): ** Summary: Stem cell (SC) maintenance and proliferation are necessary for tissue morphogenesis and homeostasis. The basement membrane (BM) has been shown to play a key role in regulating stem cell behavior. In this work, the authors unravel a new connection between the receptor for BM components Syndecan (Sdc) and SC behavior, using Drosophila as model system. They show that Sdc is required for intestine stem cell (ISC) maintenance, as Sdc depletion results in their progressive loss. At a cellular level, they also find that Sdc depletion in ISCs affects cell survival, cell and nuclear shape, nuclear lamina and DNA damage. In addition, they show that the defects in shape are not related to cell death. They also find that Sdc depletion in neural stem cells also results in nuclear envelope remodeling during cell division. This is in contrast to what happens in female germline stem cells where Sdc does not seem to be required for their survival or maintenance. In general, I believe that this work unravels a connection between Sdc and stem cell behavior. However, I think the study is still at a preliminary stage, as how Sdc regulates different facets of stem cell behavior remains unclear.

Major comments: 1. To clearly show that the cellular changes produced by loss of Sdc are not due to cell death, one should quantify the ISC area and shape of Sdc-depleted ISCs expressing DIAP1 and compare it to that of Sdc-depleted ISCs. As DIAP1 overexpression only partially rescues ISC loss due to Sdc depletion, one should show that the Sdc-depleted ISCs expressing DIAP1 that still show cellular changes are not dying, as overexpression of Diap1 might not be sufficient to completely rescue cell death in all Sdc-depleted ISCs. In fact, apoptosis in Sdc depleted guts and the ability of Diap1 overexpression to rescue cell death should be analyzed using markers of caspase activity, this will provide a better idea of the contribution of apoptosis to the phenotypes associated to Sdc depletion. *

• We can, as suggested by the reviewer, quantify the area and shape of Sdc-depleted ISCs expressing DIAP1 and compare it to that of Sdc-depleted ISCs. However, our immunostainings with anti-Caspase 3 or Drice do not pick up apoptotic cells in the fly gut. This is not entirely unexpected, as apoptosis is unfortunately not easily detected in this tissue. In the absence of a positive readout of apoptosis, we will not be able to discriminate between apoptotic and non-apoptotic stem cells when quantifying area and shape and will only have global quantifications.

• The authors show that ISC loss is associated with reduced cell density, suggesting that this is most likely due to failure in new cell production. What do they mean with cell production? Is this related to a problem in regulating cell division or to the fact that as some ISCs are lost by apoptosis there is progressively less ISCs or to a combination of both? I think that cell division should be monitored throughout time as well as cell death in ISCs.*

• Based on esgF/O experiments (fig. 1D-F and S1C) where we can trace the production of new cells with GFP, we know that Sdc RNAi expression (i) impairs the appearance of newly differentiated cells in the tissue and (ii) results in the disappearance of progenitor cells (fig. S1C). Supporting these points, (i) we have observed PH3+ mitotic stem cells upon Sdc RNAi, so we are confident the cells are able to initiate cell division (see also fig. 2G), and (ii) we have occasionally noted in fixed samples stem cells looking like they were in the process of delaminating. Overall, the failure of cell production is likely related to problems with both completion of cell division and progressive stem cell loss. High resolution live imaging will in future give us a better insight into stem cell division dynamics/behaviour, however, the technical improvements required are beyond the scope of this project. In the meantime, we propose to clarify our statement in the text.

• The authors report that in contrast to what happens when Sdc is eliminated from ISCs, its elimination from EEs results in an increase in the number of these cells. An explanation for this result is missing.*

• Based on known roles of Syndecan in other Drosophila tissues (Johnson et al., 2004; Steigemann et al., 2004; Chanana et al., 2009; Schulz et al., 2011), we speculate that Syndecan may contribute to robo/slit signalling, which is an important regulator of EE activity in the Drosophila gut (Biteau & Jasper 2014; Zeng et al., 2015). We propose to amend the text to express this hypothesis.

• The authors suggest that "Sdc function is unlikely to be fully accounted for by individual LINC complex proteins, although these proteins might act redundantly". Checking redundancy seems a straight forward experiment, which only requires the simultaneous expression of RNAis against several of these proteins. This would help to settle the implication of LINC complex proteins on Sdc function.*

• To check redundancy, we propose to combine Klaroid RNAi with Msp300 or Klarsicht RNAis, and express two RNAis at a time in ISCs. We will then measure stem cell proportions and the proportion of ISCs with DNA damage.

• Although quantification of DNA damage, by immunolabelling with gH2Av, reveals that knockdown of individual LINC complex components did not recapitulate the damage observed upon Sdc depletion (Fig.3G), the image shown in Fig.3F reflects much higher levels of gH2Av in Msp300 RNAi cells compared to Sdc RNAi cells. Authors should clarify this. *

• Like the reviewer, we are intrigued by the higher levels of H2Av staining in the tissue, despite Msp300 knockdown in stem cells only (fig. 3F). It is worth noting that we observed this with two independent RNAi lines (we showed only one RNAi in the manuscript, but we will amend the text to indicate this). In fig. 3F, we will indicate with an arrow the only ISC that is H2Av positive, and mention in the text that the majority of DNA damage signal observed in the Msp300 RNAi condition is in enterocytes, not ISCs. We currently do not have an explanation for why loss of Msp300 in ISCs should cause DNA damage in neighboring cells.

*In addition, the consequences of the simultaneous elimination of more than one component of the LINC complex on DNA damage should be analyzed. *

• We agree, and as we check for redundancy (as in point 4), we will also immunostain the tissues for H2Av.

• The authors claim that the fact that "DNA damage was found more frequently in Sdc-depleted ISCs with lamina invaginations compared to those without (Figure 3H), supports a model whereby the development of nuclear lamina invaginations precedes the acquisition of DNA damage". However, to me, these results show that there is a relation between these two phenotypes, but not that one precedes the other. In order to show which one is the possible cause and which the consequence, the authors should perform a time course of the appearance of each of these phenotypes.*

• We agree with the reviewer that we should rephrase our statement to indicate a relationship between lamina invaginations and DNA damage, rather than a causality (as stated in fig. 3H).

(In terms of performing a time course analysis, the difficulty is that after 3 days of Sdc RNAi expression, the apparent DNA damage (fig. 3G) corresponds to a very small proportion of stem cells, meaning that an exceptionally large sample size would be required to achieve robust statistical analysis.)

• When studying the role of Sdc in neural stem cells, the authors show that elimination of Sdc in neuroblasts also affect nuclear envelope and shape. Furthermore, in this case, they also show that Sdc elimination affects cell division. To look for a more conserved role of Sdc in stem cell behavior, I believe the authors should also analyze whether Sdc elimination in neural stem cells results in an increase in DNA damage, as it is the case in ISCs.*

• We will stain larval brains for H2Av to see if DNA damage is also observed following Sdc knockdown in neuroblasts.

• When analyzing a possible role of Sdc in fGSCs, quantification of germline stem cells and gH2Av levels in control nosGal4 and nos>Sdc RNAi germaria should be done. In addition, it is not clear to me whether Sdc is in fact expressed in fGSCs.*

• *

• As mentioned in comments to reviewer 1, we will add a panel in figure S4 to show the low Sdc expression in fGSCs. We will also clarify in the text that we do not see any H2Av staining in the fGSCs (thus, there is nothing to quantify in this case).

* The authors should show presence of Sdc in neuroblasts.*

• Yes, we agree, as also mentioned in comments to reviewer 1.

Reviewer #2 (Significance (Required)): *In general, although this work reveals that elimination of Sdc affects different aspects of intestinal and neural stem cell behavior, including cell survival, cell production, nuclear shape, nuclear lamina or DNA damage, their contribution to stem cell loss and interactions between them have not been analyzed in detail. The role of the basement membrane in stem cell behavior has been extensively studied. In particular, the role of syndecan in stem cell regulation has been primarily confined to cancer, muscle, neural and hematopoietic stem cells. Thus, the study here presented could extend the role of Sdc to intestinal stem cells and could potentially reveals a conserved role for Sdc in neural stem cell behavior. However, the problem with the data mentioned above, hinders the assessment of the significance of this work. *

• We thank the reviewer for their assessment and are glad that they also find that our study provides novel connections between Syndecan and the regulation of intestinal and neural stem cell behaviors. To strengthen our conclusions, we will include additional experiments or amend the text, as indicated above.

Reviewer #3* (Evidence, reproducibility and clarity (Required)): ** Peer-review: The transmembrane protein Syndecan regulates stem cell nuclear properties and cell maintenance.

In this work, the authors investigate the role of the transmembrane protein Syndecan (Sdc) in nuclear organisation and stem cell maintenance. Theys show that Sdc knockdown in intestinal stem cells (ISCs) results in a reduction of the ISC pool as well as of their progeny. They hypothesise that these ISCs might get eliminated via cell death, however, expression of the apoptotic inhibitor DIAP1 only rescued ISC loss by 50%. Hence, they suggest that apoptosis can not account for the total decrease in ISCs observed upon Sdc loss. ISCs depleted from Sdc exhibited abnormal cytoplasmic and nuclear morphologies. As Sdc has previously been implicated in the abscission machinery in mammalian cultured cells, they tested if Sdc could be playing a similar role in the abscission of ISCs. However, ISCs were capable of undergoing cytokinesis. Next, they tested if Sdc depletion could be altering the linkage between the plasma membrane and the nucleus mediated by the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex. However, individual knockdowns of the different components of the complex did not disrupt the nuclear morphology to the same extent as Sdc knockdown, suggesting that Sdc function may be independent of the LINC complex. Finally, they observed that Sdc-depleted ISCs exhibited DNA damage, suggesting that Sdc may play a role in DNA protection. The authors next tested if Sdc played similar roles in other stem cell types such as the female germline stem cells (fGSCs) and larval neural stem cells (NSCs). While Sdc depletion appeared dispensable for fGSC maintenance, it prolonged NSC divisions and altered the nuclear morphology of NSCs. Upon further investigations, they observed that the NSC's nuclear envelope was disrupted upon division, hence causing defects in the nuclear size ratio of NSC and their progeny. This study provides with interesting findings in the field and proves a new role for Sdc in the regulation of intestinal and neural stem cell maintenance. I would recommend this manuscript to be accepted if the authors address the following comments.

__Major comments: __ 1. In Figure 2 A-B, Sdc RNAi should ideally have a UAS control transgene to match the number of UAS being expressed to that of Sdc RNAi, DIAP1. Otherwise, it is plausible that reduced RNAi expression of Sdc RNAi, DIAP1 animals is the cause of the partial rescue. Staining against cell death markers such as Dcp-1 or TUNEL might also quantify the number of cells undergoing cell death in each of the genotypes. *

• As mentioned in comments to reviewer 2 (point 1), it is difficult to label apoptotic cells in the fly gut. However, we could set up an additional control to test that the partial rescue observed upon DIAP1 expression is not a result of Gal4 dilution.

• " These phenotypes were observed both with and without DIAP1 expression (Figure 2C), indicating that these cell shapes are not caused by apoptosis."Misleading, as DIAP overexpression in Sdc knockdown background only rescued apoptosis by 50%. Hence, it is possible that those cells undergoing morphological defects, protrusions and blebbing might still undergo death - also considering those morphological changes are typically observed in apoptotic cells...Therefore, to rule apoptosis out, these cells should be shown to be negative for cell death markers. *

• We agree, however, it is difficult to label apoptotic cells. We think that the quantification of shape and area (as suggested by reviewer 2, point 1) will clearly show that the cell shapes resulting from Sdc depletion are not caused by apoptosis.

• Show if Sdc is expressed in fGSCs - the lack of phenotype caused by Sdc knockdown might be due to lack of expression of Sdc.*

• As mentioned in comments to reviewers 1&2, we will add a panel in figure S4 to show the low sdc expression in fGSCs.

• "After confirming the presence of Sdc in neuroblasts (data not shown)."Data should be shown. It would be of great interest for researchers if you showed a staining of different brain cell types (NBs, glia, neurons) and the Sdc expression patterns.*

• As mentioned in comments to reviewers 1&2, we will add a panel in figure 4 to show sdc expression in NBs and the overall expression pattern.

• You show how Slc-depleted NBs have disrupted nuclear morphologies. However, does Slc KD in NB lineages affect their ability to self-renew and generate differentiated progeny? Is the number of NBs and of their progeny cells altered as it is for ISCs?*

• We propose to knockdown Sdc in NBs and quantify brain size in 3rd instar larvae to test if the ability to generate progeny is affected.

• Does protection against DNA damage in an Slc knockdown background prevent the defects observed with the single knockdown and ISC elimination?*

• This is a good question, and we should emphasize this point in the discussion. However, because of the multiple routes of DNA damage response, and the multiple lines needed to explore this connection, we feel that investigating this question is beyond this project.

• Any idea the similarities between ISC and NBs that can account for why Sdc knockdown has effects in those systems, while no effect was observed in the germ cells?*

• Besides the differences in expression level, we speculate that GSCs may have a different nuclear / lamina architecture which might reflect differences in how GSCs control the physical integrity of their nuclei. It is also possible that the differences observed between tissues reflect the way stem cells connect to their microenvironment. Notably, fGSCs rely extensively on E-Cadherin mediated adhesion with neighbouring cells, and it is possible that contact with the extracellular matrix is dispensable. We will consider these possibilities in the discussion.

Minor comments:* ** 8. Lamina invaginations, for example in Figure 3 A, could be indicated with an arrow for easier detection. *

• Thanks for this suggestion, we will amend the figure.

Specify the type and location of NB imaged during live cell experiments.

• The NBs were imaged in the brain lobes, and we did not distinguish between type I and II NBs. We will add a sentence in the method section to clarify.

*Reviewer #3 (Significance (Required)): Expertise: Drosophila stem cells *

• Many thanks for the constructive comments.

Author response:

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

Reviewer #1 (Public Review):

(1) The data strongly suggest that iron depletion in urine leads to conditional essentiality of some genes. It would be informative to test the single gene deletions (Figure 3G) for growth in urine supplemented with iron, to determine how many of those genes support growth in urine due to iron limitation.

We appreciate this suggestion. We have now included this suggested experiment as a new panel (Figure 5G).

(2) Line 641. The authors raise the intriguing possibility that some mutants can "cheat" by benefitting from the surrounding cells that are phenotypically wild-type. Growing a fepA deletion strain in urine, either alone or mixed with wild-type cells, would address this question. Given that other mutants may be similarly "masked", it is important to know whether this phenomenon occurs.

We thank the reviewer for this suggestion but believe that this would be very difficult to ascertain in K. pneumoniae as several redundant iron uptake systems exist. This would require significantly more time to construct sequential/combinatorial iron-uptake mutants to exactly determine this “cheating” and “masking” phenomenon and such work is beyond the scope of the current study.

(3) In cases where there are disparities between studies, e.g., for genes inferred to be essential for serum resistance, it would be informative to test individual deletions for genes described as essential in only one study.

We thank the reviewer for this suggestion, and we agree that deleting conditionally essential genes (i.e. serum resistance) could help identify discrepancies in methodology with other studies but this is beyond the scope of this study. Furthermore, we do not have these other strains readily available to us and importing these strains into Australia is challenging due to the strict import/quarantine laws.

Reviewer #1 (Recommendations For The Authors)

(4) Line 529. Why was 50 chosen as the read count threshold?

This was chosen as the minimum threshold needed to exclude essential genes from the comparative analysis, as these can contribute false positive results where a change from, for example, 2 to 5 reads between conditions is considered a >2-fold change. We have updated the manuscript text to highlight this: “were removed from downstream analysis to exclude confounding essential genes and minimize the effect of stochastic mutant loss” (line 539

(5) The titles for Figure 5 and Figure 6 appear to be switched.

Thank you, we have now corrected this error.

(6) Line 381. "Forty-six of these regions contain potential open reading frames that could encode proteins". How is a potential ORF defined?

This was based on submitting the selected 145bp regions to BLASTx using default parameters and listing the top hit (if one was found). We have now edited the manuscript text to make this clearer. (Line 394)

(7) Two previous TnSeq studies looking at Escherichia coli and Vibrio cholerae suggest that H-NS can prevent transposon insertion, leading to false positive essentiality calls. Is there any evidence of this phenomenon here? A/T content could be used as a proxy for H-NS occupancy.

We thank the reviewer for this point and also agree that H-NS or other DNA-binding proteins could indeed lead to false-positive essentiality calls using TraDIS. Based on this, we have now included a sentence in the conclusion section mentioning this methodological caveat (Line 631). We believe that A/T content could potentially be used as a proxy for H-NS occupancy,

Reviewer #2 (Recommendations For The Authors):

(1) The authors may wish to reformat the manuscript by decanting a number of panels and figures as supplementary material. These include the panels related to the description of TraDIS (for example Fig 1D, 1E, 1F. 1G, Fig 2A, Fig 3C, 3D, 3E, 3F, Fig 5C, Fig 6D). This is a well-established method.

We thank the reviewer for this suggestion but believe that these panels allow the methodology and resulting insertion plots to be more followable and allow other researchers, of varying expertise, to better understand this functional genetic screen technique.

(2) The authors need to indicate how relevant the strain they have probed is. Is it a good reference strain of the KpI group?

This is a great suggestion and we have now included a new figure illustrating the genetic context and relatedness of K. pneumoniae ECL8 within the KpI phylogroup (New Figure 3).

(3) The authors need to provide an extensive comparison between the data obtained and those reported testing other Klebsiella strains. A Table identifying the common and different genes, as well as a figure, may suffice. I would encourage authors to compare also their data against E. coli and Salmonella. For example, igaA seems to be not essential in Kebsiella although data indicates it is in Salmonella.

We thank the reviewer for their comment and appreciate that our data could be extended and compared to other relevant Enterobacteriaceae members. However, we believe this is beyond the scope of this study as the focus is more on K. pneumoniae.

(4) None of the mutants tested further are complemented. Without these experiments, it cannot be rigorously claimed that these loci play any role in the phenotypes investigated.

We agree that complementation is an important tenet for validation of mutant gene phenotypes to specific gene loci, in this case wbbY has already been complemented and believe complementation for an already known molecular mechanism would be redundant. Please refer to our response in point 6.

We complemented isolated transposon mutants hns7::Tn5 and hns18::Tn5 with a mid-copy IPTG inducible . We observed a slight increase in serum susceptibility but not full rescue of the WT phenotype (i.e. serum susceptibility). We suspect that the imperfect rescue of the serum-resistance phenotype observed could be due to the expression levels and copy number of the complement hns plasmid used. As hns is a known global regulator its possible pleiotropic role is complex as many aspects of stress response, metabolism or capsule could be affected in Klebsiella (doi.org/10.1186/1471-2180-6-72, doi.org/10.3389/fcimb.2016.00013). We have now included in the text our efforts in complementation and have included a new supplementary figure (Figure S11).

(5) The contribution of siderophores to survival in urine is not conclusively established. Authors may wish to test the transcription of relevant genes, and to assess whether the expression is fur dependent in urine. Also, authors may wish to identify the main siderophore needed for survival in urine by probing a number of mutants; this will allow us to assess whether there is a degree of selection and redundancy.

We thank the reviewer for their comment and agree siderophore uptake is important. We have now included an additional panel (Figure 5G) interrogating the importance of iron-uptake genes grown in urine which is iron limited. We do appreciate that further experiments looking into the Fur regulon and siderophore biosynthesis would be interesting but believe this is outside the scope of this study.

(6) The role of wbbY is intriguing, pointing towards the importance of high molecular weight O-polysaccharide. In this mutant background, the authors need to assess whether the expression of the capsule, and ECA is affected. Authors need also to complement the mutant. Which is the mechanism conferring resistance?

We thank the reviewer for their comment and would like to mention that wbbY has already been shown to play a role in LPS profile/biosynthesis and serum-resistance (10.3389/fmicb.2014.00608 ). Furthermore, blast analysis shows that the wbbY gene between the NTUH-K2044 (strain used in aforementioned study) and ECL8 shares 100% sequence identity and also shares lps operon structure. Hence, we do not find it pertinent to complement this mutant as we believe its molecular mechanism has already been established. We have now in the text more prominently highlighted the results of this study and how our screen was robust enough to also identify this gene for serum resistance.

(7) hns and gnd mutants most likely will have their capsule affected. The authors need to assess whether this is the case. Which is the mechanism conferring resistance?

As mentioned in point 6, we believe that the serum resistance phenotype is attributable to the LPS phenotype. Previous studies have listed hns and gnd mutants would likely have differences in capsule but due to hns being pleiotropic and gnd being intercalated/adjacent to the LPS/O-antigen biosynthesis it would be difficult to exactly delineate which cellular surface structure is involved.

(8) The conclusion section can be shortened significantly as much of the text is a repetition of the results/discussion section.

We thank the reviewer for their suggestion and have made edits to limit repetition in the conclusion section.

Reviewer #3 (Public Review):

Below I include several comments regarding potential weaknesses in the methodology used:

• The study was done with biological duplicates. In vitro studies usually require 3 samples for performing statistical robust analysis. Thus, are two duplicates enough to reach reproducible results? This is important because many genes are analyzed which could lead to false positives. That said, I acknowledge that genes that were confirmed through targeted mutagenesis led to similar phenotypic results. However, what about all those genes with higher p and q values that were not confirmed? Will those differences be real or represent false positives? Could this explain the differences obtained between this and other studies?

We thank the reviewer for their comment and apologize for the confusion, data were only pooled for the statistical analysis of gene essentiality. Here, two technical replicates of the input library were sequenced and the number of insertions per gene quantified (insertion index scores). These replicates had a correlation coefficient of r2 = 0.955, and the insertions per gene data were pooled to give total insertions index scores to predict gene essentiality. For conditional analyses (growth in urine or serum), replicate data were not combined. As mentioned previously, differences between this and other studies could also be attributed to inherent genomic differences or due to differences in experimental methodology, computational approaches, or the stringency of analysis used to categorize these genes.

• Two approaches are performed to investigate genes required for K. pneumoniae resistance to serum. In the first approach, the resistance to complement in serum is investigated. And here a total of 356 genes were identified to be relevant. In contrast, when genes required for overall resistance to serum are studied, only 52 genes seem to be involved. In principle, one would expect to see more genes required for overall resistance to serum and within them identify the genes required for resistance to complement. So this result is unexpected. In addition, it seems unlikely that 356 genes are involved in resistance to complement. Thus, is it possible false positives account for some of the results obtained?

We thank the reviewer for their comment and do believe false positives may account for some of the identified genes. Specifically, to the large contrast in genes, we believe this is due to the methodology as alluded to in our conclusion section. For overall resistance to serum, we used a longer time point (180 min exposure) where fewer surviving mutants are recovered hence fewer overall genes will be identified, whereas strains with short killing windows will have more (i.e. complement-mediated killing, 90 minute exposure).

Reviewer #3 (Recommendations For The Authors):

• In Figure 4 it is shown that genes important for growth in urine include several that are required for enterobactin uptake. Moreover, an in vitro experiment shows that the complementation of urine with iron increases K. pneumoniae growth. It would have been informative to do a competition experiment between the WT and Fep mutants in urine supplemented with iron. This could demonstrate that the genes identified are only necessary for conditions in which iron is in limiting concentrations and confirm that the defect of the mutants is not due to other characteristics of urine.

We appreciate this suggestion. We have now included a new panel (Figure 5G) addressing the supplementation of iron in urine for these select mutants.

• Considering the results section, the title for Figure 6 seems to be more appropriate for Figure 5.

Thank you, this has now been corrected.

Other points:

• Line 44: treat instead of treating

Thank you, this has now been corrected.

• Line 63: found that only 3 genes played a role instead of "found only 3 genes played a role"

Thank you, this has now been corrected.

• Line 105: is there any reason for only using males? Since UTIs are frequent in women? Why not use urine from women volunteers?

Due to accessibility of willing volunteers and human ethic application processes, only male samples were available. We are currently undertaking further studies to understand how male and female urine influences growth of uropathogens.

• Line 105: since the urine was filter-sterilized, maybe the authors can comment that another point that is missing in urine - and that it may be important to study - will be the presence of the urine microbiome and how this affects growth of K. pneumoniae.

We again thank the reviewer for this comment and have now edited the manuscript discussing how the absence of urine microbiome could affect growth (Line 659). As an aside, future studies in our lab are interested in looking at the role of commensal/microbiome co-interactions for essentiality/pathogenesis using TraDIS.

• Line 116: I understand that the 8 healthy volunteers combined males and females

Thank you, we have now edited this methods line to make this clearer.

• Line 120: incubate in serum 90 min and 180 RPM shaking: any reasons for using these conditions, any reference supporting these conditions?

Thank you for pointing this out, we were mirroring a previous K. pneumoniae serum-resistance study (doi.org/10.1128/iai.00043-).

• Line 156: space after the dot.

Thank you, we have now corrected this in the manuscript.

• Line 164: resulting reads were mapped to the K. pneumoniae: what are the parameters used for mapping (e.g. % of identity...)?

Thank you for bringing this to our attention, we have now included in our manuscript that we used the default parameters of BWA-MEM for mapping for minimum seed length (default -k =20bp exact match)

• Line 180: it will be good to upload to a repository the In-house scripts used or indicate the link beside the reference for those scripts.

Our scripts are derived from the pioneering TraDIS study (doi: 10.1101/gr.097097.109). We are currently still optimizing our scripts and intend to upload these to be publicly available. However, in the meantime we are more than happy to share them with other parties upon request.

• Line 191: why were genes classified as 12 times more likely to be situated in the left mode? Any particular reason for using this threshold?

We opted for a more-stringent threshold for classifying essential genes, in keeping with previous and comparable studies (doi.org/10.1371/journal.pgen.1003834).

• Line 209: do you mean Q-value of <0.05 instead of >0.05 ? How is this Q value is calculated, and which specific tests are applied?

Thank you for pointing out this Q value error, we have now corrected this in the manuscript. These values were generated using the biotradis tradis_comparison.R script which uses the EdgeR package. For further reading please see DOI: 10.1093/bioinformatics/btp616. The Q-values are from P values corrected for multiple testing by the Benjamini-Hochberg method.

• Line 212: again, which type of test is used? What about the urine growth analysis? The same type of tests were applied?

Thank you for bringing this to our attention, we have now indicated in the referenced method section the use of which package for which datasets (i.e. or serum). Line 212 refers to our use of the AlbaTraDIS package, which builds on the biotradis toolkit, to identify gene commonalities/differences in the selected growth conditions again using multiple testing by the Benjamini-Hochberg methods. For further reading, please refer to DOI: 10.1371/journal.pcbi.1007980

• Line 226: do the authors mean Sanger sequencing instead of SangerSanger sequencing?

Thank you, we have now corrected this in the manuscript.

• Line 239: does the WT strain contain another marker for differentiating this strain from the mutant? Or is the calculation of the number of WT CFUs done by subtracting the number of CFUs in media with antibiotics from the total number of CFUs in media without antibiotics? The former will be a more accurate method.

The calculation was based on the latter assumption, “number of WT CFUs done by subtracting the number of CFUs in media with antibiotics from the total number of CFUs in media without antibiotics”. We have now updated the methods section to make this clearer.

• Line 266: can you indicate approximately how many CFUs you have in this OD?

Thank you, we have now also indicated an approximate CFU for this mentioned OD600 (OD600 1 = 7 × 108 cells).

• Line 309: besides indicating Figure 1D please indicate here Dataset S1 (the table where one can see the list of essential and non-essential genes). This table is shown afterwards but I think it will be more appropriate to show it at the begging of the section.

Thank you, we have now taken on this recommendation and have now edited the manuscript to also indicate Dataset S1 earlier.

• Table 3. regarding the comparison of essential genes between different strains. I think it will be more clear if a Venn diagram was drawn including only genes that have homologs in all the studied strains (i.e. defining the core genome essentially).

We would like to thank the reviewer for suggesting a venn diagram and have now removed Table 3 which has been replaced with a new Figure 3.

• Line 461: replicates were combined for downstream analyses? But are replicates combined for doing the statistical analysis? If so, how is the statistical analysis performed? How is it taken into account the potential variability in the abundance in each library? An r of 0.9 is high but not perfect.

Technical replicates of the sequenced input library were combined following identification of a correlation coefficient of r2 = 0.955, for the calculation of insertion index scores used in gene essentiality analysis. While r2 = 0.955 is not perfect, discrepancies here can be attributed to higher variance in insertion index scores when sampling small genes, as these are represented by fewer insertions and the stochastic absence of a single insertion event has a greater effect on the overall IIS. Replicate data were not pooled for statistical analysis of mutant fitness (growth in urine and serum).

• Line 487: is there any control strain containing the kanamycin gene in a part of the genome that does not affect the growth of K. pneumoniae? This could be used to show that having the kanamycin gene does not provide any defect in urine growth.

We thank the reviewer for this suggestion but argue that introduction of the kanamycin gene into each unique loci may result in various levels of gene fitness that would be incomparable to a single control strain. Instead, we culture the ECL8 mutant library in urine and ensure that its kinetics are comparable to the wildtype. As the library contains thousands of kanamycin cassettes uniquely positioned across most of the genome with no observable growth defect, we do not anticipate the presence or expression of the cassette to have an appreciable impact.

• Line 569: in the methodology it was indicated that control cells were incubated in PBS for the same amount of time. I think this is an important control that is not cited in the results section. Please can you indicate?

We apologise for this misunderstanding due to how the methodology was written. The experiment did not sequence the PBS incubated samples as this was solely used a check for viability of the used K. pneumoniae ECL8 stock solution.

• Line 597: "Mutants in igaA are enriched in our experiments". Can you show this data?

We have now included this as a supplementary (Figure S11A)

• Line 615: when doing this calculation, I guess the authors take into account only genes that are also present in the other strains.

That is correct, we were aiming to highlight the high conservation of “essential genes” among all the selected strains.

• Line 627: why surprisingly? Because is too low. Then indicate.

Thank you, we have now edited this sentence to indicate that.

• Figure 4: please, for clarity, can you indicate the meaning of the colors in the figure itself besides indicating it in the figure legend?

Thank you, we have now included a color legend in these figure panels for clarity.

Author response:

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

eLife assessment

The authors build upon prior data implicating the secreted peptidoglycan hydrolase SagA produced by Enterococcus faecium in immunotherapy. Leveraging new strains with sagA deletion/complementation constructs, the investigators reveal that sagA is non-essential, with sagA deletion leading to a marked growth defect due to impaired cell division, and sagA being necessary for the immunogenic and anti-tumor effects of E. faecium. In aggregate, the study utilizes compelling methods to provide both fundamental new insights into E. faecium biology and host interactions and a proof-of-concept for identifying the bacterial effectors of immunotherapy response.

We thank the Reviewers for their positive feedback on our manuscript. We also appreciate their helpful comments/critiques and have revised the manuscript as indicated below.

Public Reviews:

Reviewer #1 (Public Review):

Klupt, Fam, Zhang, Hang, and colleagues present a novel study examining the function of sagA in E. faecium, including impacts on growth, peptidoglycan cleavage, cell separation, antibiotic sensitivity, NOD2 activation, and modulation of cancer immunotherapy. This manuscript represents a substantial advance over their prior work, where they found that sagA-expressing strains (including naturally-expressing strains and versions of non-expressing strains forced to overexpress sagA) were superior in activating NOD2 and improving cancer immunotherapy. Prior to the current study, an examination of sagA mutant E. faecium was not possible and sagA was thought to be an essential gene.

The study is overall very carefully performed with appropriate controls and experimental checks, including confirmation of similar densities of ΔsagA throughout. Results are overall interpreted cautiously and appropriately.

I have only two comments that I think addressing would strengthen what is already an excellent manuscript.

In the experiments depicted in Figure 3, the authors should clarify the quantification of peptidoglycans from cellular material vs supernatants. It should also be clarified whether the sagA need to be expressed endogenously within E. faecium, and whether ambient endopeptidases (perhaps expressed by other nearby bacteria or recombinant enzymes added) can enzymatically work on ΔsagA cell wall products to produce NOD2 ligands?

We mentioned in the main text that peptidoglycan was isolated from bacterial sacculi and digested with mutanolysin for LC-MS analysis. We have now also included “mutanolysin-digested” sacculi in the Figure 3 legend as well.

We have added the following text “We next evaluated live bacterial cultures with mammalian cells to determine their ability to activate the peptidoglycan pattern recognition receptor NOD2” and “our analysis of these bacterial strains” to indicate live cultures were evaluated for NOD2 activation.

We have also added the following text “Our results also demonstrated that while many enzymes are required for the biosynthesis and remodeling of peptidoglycan in E. faecium, SagA is essential for generating NOD2 activating muropeptides ex vivo.”

In the murine experiments depicted in Figure 4, because the bacterial intervention is being performed continuously in the drinking water, the investigators have not distinguished between colonization vs continuous oral dosing of the mice peptidoglycans. While I do not think additional experimentation is required to distinguish the individual contributions of these 2 components in their therapeutic intervention, I do think the interpretation of their results should include this perspective.

We have added the following text “We note that by continuous oral administration in the drinking water, live E. faecium and soluble muropeptides that are released into the media during bacterial growth may both contribute to NOD2 activation in vivo.” and revised the following text “Nonetheless, these results demonstrate SagA is not essential for E. faecium colonization, but required for promoting the ICI antitumor activity through NOD2 in vivo.

Reviewer #2 (Public Review):

Summary:

The gut microbiome contributes to variation in the efficacy of immune checkpoint blockade in cancer therapy; however, the mechanisms responsible remain unclear. Klupt et al. build upon prior data implicating the secreted peptidoglycan hydrolase SagA produced by Enterococcus faecium in immunotherapy, leveraging novel strains with sagA deleted and complemented. They find that sagA is non-essential, but sagA deletion leads to a marked growth defect due to impaired cell division. Furthermore, sagA is necessary for the immunogenic and anti-tumor effects of E. faecium. Together, this study utilizes compelling methods to provide fundamental new insights into E. faecium biology and host interactions, and a proof-of-concept for identifying the bacterial effectors of immunotherapy response.

Strengths:

Klupt et al. provide a well-written manuscript with clear and compelling main and supplemental figures. The methods used are state-of-the-art, including various imaging modalities, bacterial genetics, mass spectrometry, sequencing, flow cytometry, and mouse models of immunotherapy response. Overall, the data supports the conclusions, which are a valuable addition to the literature.

Weaknesses:

Only minor revision recommendations were noted.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

General comments - the number/type of replicates and statistics are missing from some of the figure panels. Please be sure to add these throughout - all main figure panels should have replicates. I've also noted some specific cases below.

Abstract - sagA is non-essential, need to edit text at "essential functions".

This change has been made.

"small number of mutations" - specify how many in the text.

We revised the text. “Small number” is changed to “11”.

"under control of its native promoter" - what was the plasmid copy number? It looks clearly overexpressed in Figure 1d despite using a native promoter, although it's a bit hard to know for sure without a loading control.

pAM401 has p15A origin of replication, therefore the plasmid copy number ~20-30 copies (Lutz R. et al Nucleic Acids Res. 1997). Total protein was visualized by Stain-Free™ imaging technology (BioRad) and serves as protein loading control and has been relabeled accordingly.

"decrease levels of small muropeptides" - the asterisks are missing from Figure 3a.

Green asterisks for peaks 2, 3, 7 and purple asterisks for peaks 13, 14 were added.

The use of "Com 15 WT" in the figures is confusing - just replace it with "wt" and specify the strain in the text. Presumably, all of the strains are on the Com 15 background.

“Com15 WT” was replaced to “WT” in figures and main text.

Change 1d to 1b so that the panels are in order (reading left to right and then top to bottom).

Figure 1 legend is missing a number of replicates and statistics for 1a.

Number of replicates were added.

Figure 1b - it's unclear to me what to look at here, could add arrows indicating the feature or interest and expand the relevant text.

Arrows pointing to cell clusters were added.

Figure 1d - what is "stain free"? It would be preferable to show a loading control using an antibody against a constitutive protein to allow for normalization of the loading control.

Stain-Free Imaging technology (BioRad) utilizes gel-containing trihalo compound to make proteins fluorescent directly in the gel with a short photoactivation, allowing the immediate visualization of proteins at any point during electrophoresis and western blotting. Stain-Free total protein measurement serves as a reliable loading control comparable to Coomassie Blue Staining. This has been relabeled a “Total protein” in the Figure and Stain-free imaging technology is noted in the legend.

ED Figure 1 - representative of how many biological replicates?

Legends are updated.

ED Figure 2a - I would replace this with a table, it's not necessary to show the strip images. Also, please specify the number of replicates per group.

Additional Extended Data Table 2 was added.

ED Figure 2b - This data was not that convincing since the sagA KO has a marked growth defect and the time points are cut off too soon to know if growth would occur later. The MIC definition is potentially misleading. Should specific a % growth cutoff (i.e. <10% of vehicle control) and the metric used (carrying capacity or AUC). Then assign MIC to the tested concentration, not a range. The empty vector also seems to impact MIC, which is concerning and complicates the interpretation. Specify the number of replicates and add statistics. Given these various concerns, I might suggest removing this figure, as it doesn't really add much to the story.

We appreciate this comment from the Reviewer, but believe this data is helpful for paper and have included longer time points for the growth data. The definition of MIC for ED Fig. 2b has been included in the legend.

Figure 2 - specify the type of replicate. Number of cells? Number of slices? Number of independent cultures?

For Cryo-ET experiments single bacterial cultures were prepared. Number of cells and slices for analysis are indicated in the legend. Legends are updated.

Figure 4e - missing the water group, was it measured?

Water (αPD-L1) group was not included in immune profiling of tumor infiltrating lymphocytes (TILs) experiment, as we have previously demonstrated limited impact on ICI anti-tumor activity and T cell activation in this setting (Griffin M et al Science 2021).

Figure 4d - is this media specific to your strains? If not, qPCR may be a better method using strain-specific primers.

Yes, HiCrome™ Enterococcus faecium agar plates (HIMEDIA 1580) are selective for Enterococcus species, moreover the agar is chromogenic allowing to identify E. faecium as yellow colonies among other Enterococcus species.

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

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Reply to the reviewers

RC-2023-02105R: Brunetta et al.,

IF1 is a cold-regulated switch of ATP synthase to support thermogenesis in brown fat

We are happy to submit our revised manuscript after considering the suggestions made by reviewers. The comments were overall positive, and the changes requested were mostly editorial. We have, nevertheless, added new experiments as quality controls. These experiments did not affect the main conclusions of our work. In addition, we also included two in vivo experimental models of gain and loss-of-function, to further address the physiological relevance of IF1 in BAT thermogenesis. We believe with these additional experiments, quality controls as well as in vivo models, our study has improved considerably. We hope our efforts will be appreciated by the reviewers and we make ourselves available to answer any further questions.

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

Summary: In the present manuscript, the authors present data in support of their primary discovery that "IF1 controls UCP1-dependent mitochondrial bioenergetics in brown adipocytes". The opening figure convincingly demonstrates that IF1 expression is cold-exposure dependent. They then go on to show that loss of IF1 has functional consequences that would be predicted based on IF1's know role as a regulator of ATP hydrolysis by CV. They go on to make a few additional claims, succinctly detailed in the Discussion section. Specific claims include the following: 1) IF1 is downregulated in cold-adapted BAT, allowing greater hydrolytic activity of ATP synthase by operating in the reverse mode; 2) when IF1 is upregulated in brown adipocytes in vitro mitochondria unable to sustain the MMP upon adrenergic stimulation, 3) IF1 ablation in brown adipocytes phenocopies the metabolic adaptation of BAT to cold, and 4) IF1 overexpression blunts mitochondrial respiration without any apparent compensator response in glycolytic activity. The claims described above are well supported by the evidence. The manuscript is very well written, figures are clear and succinct. Overall, the quality of the work is very high. Given that IF1 is implicated across many fields of study, the novel discovery of IF1 as a regulator of brown adipose mitochondrial bioenergetics will be of significance across several fields. That said, a few areas of concern were apparent. Concerns are detailed in the "Major" and "Minor" comments section below. Additional experiments do not appear to be required, assuming the authors adequately acknowledge the limitations of the study and either remove or qualify speculative claims.

Major Comments:

1. The authors convincingly demonstrate that IF1 expression is specifically down-regulated in BAT upon cold-exposure. These data strongly implicate a role for IF1 in BAT bioenergetics, a major claim of the authors and a novel finding herein. Additional major strengths of the paper, which provide excellent scientific rigor include the use of both loss of function and gain of function approaches for IF1. In addition, the mutant IF1 experiments are excellent, as they convincingly show that the effects of IF1 are dependent on its ability to bind CV. RESPONSE: We thank the reviewer for the positive feedback on our work.

Regarding Figure 1 - Did the content of ATP synthase change? In figure 1A-B, the authors show that ATPase activity of CV is higher in cold-adapted mice. While this result could be due to a loss of IF1, it could also be due to a higher expression of CV. To control for this, the authors should consider blotting for CV, which would allow for ATPase activity to be normalized to expression.

RESPONSE: Thank you for this suggestion. We have now determined complex V subunit A in our experimental protocol. We found that cold exposure does not impact complex V protein levels. Given the importance of this control, we have now included it in Figure 1 (Please, see the revised version) alongside the IF1/complex V ratio. In addition, we have now performed WBs in the BAT from mice exposed for 3 and 7 days to thermoneutrality (~28°C). We found that IF1 is not reduced following whitening of BAT by this approach whilst UCP1 and other mitochondrial proteins are reduced. This set of data is now included in Figure 1I,K,L.

Regarding MMP generated specifically by ATP hydrolysis at CV, the reversal potential for ANT occurs at a more negative MMP than that of CV (PMID: 21486564). Because reverse transport of ATP (cytosol to matrix) via ANT will also generate a MMP, it is speculative to state that the MMP in the assay is driven by ATP hydrolysis at CV. It is possible and maybe even likely that the majority of the MMP is driven by ANT flux, which in turn limits the amount of ATP hydrolyzed by CV. Admittedly, it is very challenging to different MMP from ANT vs that from CV, thus the authors simply need to acknowledge that the specific contribution of ATP hydrolysis to MMP remains to be fully determined. That said, the fact that ATP-dependent MMP tracks with IF1 expression does certainly implicate a role for ATP hydrolysis in the process. The authors should consider including a discussion of the ambiguity of the assay to avoid confusion. A role for ANT likely should be incorporated in the Fig. 1J cartoon.

RESPONSE: Thank you for bringing the ANT contribution to MMP to our attention. The effects of ATP in the real-time MMP measurements were totally abolished by the addition of oligomycin in BAT-derived isolated mitochondria, thus suggesting dependency of complex V in this process. However, the assessment of MMP in intact cells is much more challenging given cytosolic vs. mitochondrial contribution to ATP pool, and ATP synthase vs. ANT reversal capacity depending on MMP. Nevertheless, we have addressed these points in the discussion section as well as added to our schematic cartoon in Figure 1m.

Regarding the lack of effect of IF1 silencing on MMP, it is possible that IF1 total protein levels are simply lower in cultured brown fat cells relative to tissue? The authors could consider testing this by blotting for IF1 and CV in BAT and brown fat cells. The ratio of IF1/ATP5A1 in tissue versus cells may provide some amount of mechanistic evidence as to their findings.

RESPONSE: We have now blotted for complex V and IF1 in both differentiated primary brown adipocytes and BAT homogenates derived from mice kept at room temperature (~22°C). We found the levels of complex V in primary brown adipocytes are higher than BAT homogenates. Therefore, IF1/complex V ratio is different between these two systems. This has indeed the potential to influence our gain and loss-of-function experiments. We have added these results alongside their interpretation in the revised manuscript.

The calculation of ATP synthesis from respiration sensitive to oligomycin has many conceptual flaws. Unlike glycolysis, where ATP is produced via substrate level phosphorylation, during OXPHOS, the stoichiometry of ATP produced per 2e transfer is not known in intact brown adipose cells. This is a major limitation of this "calculated ATP synthesis" approach that is beginning to become common. Such claims are speculative and thus likely do more harm than good. In addition to ANT and CV, there are many proton-consuming reactions driven by the proton motive force (e.g., metabolite transport, Ca2+ cycling, NADPH synthesis). Although it remains unclear how much proton conductance is diverted to non-ATP synthesis dependent processes, it seems highly likely that these processes contribute to respiratory demand inside living cells. Moreover, just as occurs with UCP1 in response to adrenergic stimuli, proton conductance across the various proton-dependent processes likely changes depending on the cellular context, which is another reason why using a fixed stoichiometry to calculate how much ATP is produced from oxygen consumption is so highly flawed. Maximal P/O values that are often used for NAD/FAD linked flux are generated using experimental conditions that favor near complete flux through the ATP synthesis system (supraphysiological substrate and ADP levels). The true P/O value inside living cells is likely to be lower.

RESPONSE: We agree with the reviewer regarding the limitations on calculating ATP production in intact cells based on respiration and proton flux. However, this was only one experiment on which we based our conclusions, as these were also supported by i.e. ATP/ADP ratio measurements and oxygen consumption using different substrates. Therefore, we do not rely exclusively on the ATP production estimative, rather we use this experiment to support complementary methodologies. Nevertheless, we have now better detailed our experimental protocol as well as acknowledged the limitations of the method, so the reader is aware of our procedure and its limitations. We hope the reviewer understands our motivation to perform these experiments and the contribution to our study.

Why are the results in Figure 3K expressed as a % of basal? Could the authors please normalize the OCR data to protein and/or provide a justification for why different normalization strategies were used between 3K and 3M?

RESPONSE: We apologize for the lack of consistency. We have now updated Figure 3 to show all the data in absolute values divided by protein content. This change does not affect the overall interpretation of the findings.

The authors claim that IF1 overexpression lowers ATP production via OXPHOS. However, given the major limitations of this assay (ass discussed above), these claims should be viewed as speculation. This needs to be addressed by the authors as a major limitation. The fact that the ATP/ADP levels did not change do not support of reduction in ATP production, as claimed in the title of Figure 4.

RESPONSE: The reduction in ATP levels and mitochondrial respiration (independent of the substrate offered) suggests a reduction in ATP production rather than an increase in ATP consumption. Moreover, the maintenance of ATP/ADP ratio suggests the existence of a compensatory mechanism to avoid cellular energy crises, which we interpreted as reduced metabolic activity of the cells. Nevertheless, we have now reworded our statements to address the limitations of the methods and our interpretation of the data.

In the discussion, the authors state "However, considering that IF1 inhibits F1-ATP synthase in a 1:1 stoichiometric ratio, the relatively higher expression of IF1 in BAT at room temperature could represent an additional inhibitory factor for ATP synthesis in this tissue." This does not appear to be correct. Although IF1 has been suggested to partially lower maximal rates of ATP synthesis rates, most of this evidence comes from over-expression experiments. According to the current understanding of IF1-CV interaction, the protein is expelled from the complex during rotation in favor of ATP synthesis (PMID: 37002198). It is far more likely that ATP synthesis is low in BAT mitochondria due to the low CV expression. Relative to heart and when normalized to mitochondrial content, CV expression in BAT mitochondria is about 10% that of heart (PMID: 33077793).

RESPONSE: We agree with the reviewer and removed this sentence.

The last sentence of the manuscript states, "Given the importance of IF1 to control brown adipocyte energy metabolism, lowering IF1 levels therapeutically might enhance approaches to enhance NST for improving cardiometabolic health in humans." This sentence seems at odds with the evidence that IF1 levels go up, not down, in human BAT upon cold exposure.

RESPONSE: In light of our new experiments, we have now updated our conclusions.

Minor Comments:

The term "anaerobic glycolysis" is used throughout. All experiments were performed under normoxic conditions, thus the correct term is "aerobic glycolysis.

RESPONSE: Thank you for this comment and we have replaced this term as suggested.

Only male mice were used in the study, could the authors please provide a justification for this?

RESPONSE: Given we devoted most of our efforts to the manipulation of IF1 in vitro, we have used the mouse model as a proof-of-principle on the impact of IF1 in adrenergic-induced thermogenesis. We have now included IF1 KO male and female mice to address the role of IF1 in adrenergic-induced thermogenesis. However, due to the limitation of material, we could only perform AAV in vivo gain-of-function in male mice, therefore, our results cannot be immediately transferred to both sexes, unfortunately.

Reviewer #1 (Significance (Required)):

Overall, the quality of the work is very high. Given that IF1 is implicated across many fields of study, the novel discovery of IF1 as a regulator of brown adipose mitochondrial bioenergetics will be of significance across several fields.

My expertise is in mitochondrial thermodynamics; thus, I do not feel there are any parts of the paper that I do not have sufficient expertise to evaluate.

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

Summary

The manuscript by Brunetta and colleagues conveys the message that the ATPase inhibitory factor 1 (IF1) protein, a physiological inhibitor of mitochondrial ATP synthase, is expressed in BAT of C57BL/6J mice. Moreover, upon cold-adaption of mice they report that the content of IF1 in BAT is downregulated to sustain the mitochondrial membrane potential (MMP) as a result of reverse functioning of the enzyme. In experiments of loss and gain of function of IF1 in cultured brown adipocytes and WT cells they further stress that IF1 silencing promotes metabolic reprogramming to an enhanced glycolysis and lipid oxidation, whereas IF1 overexpression blunts ATP production rendering a quiescent cellular state of the adipocytes.

RESPONSE: We appreciate the time the reviewer invested in our work. Please, see our responses below in a point-by-point manner.

Reviewer #2 (Significance (Required)):

Claims and conclusions:

I have been surprised by the claim that IF1 protein is expressed in BAT under basal conditions and that its expression is downregulated in the cold-adapted tissue. In a previously published work by Forner et al., (2009) Cell Metab 10, 324-335 (reference 43), using a quantitative proteomic approach, it is reported that the mitochondrial proteome of mouse BAT under basal conditions contains a low content of IF1 (at level comparable to the background of the analysis). Remarkably, in the same study they show that there is roughly a 2-fold increase in the content of IF1 protein in mitochondria of BAT at 4d and 24d of cold-adaptation of mice. In other words, just the opposite of what is being reported in the Brunetta study.

RESPONSE: We are aware of the inconsistencies between our findings and Forner et al. (2009). We would like to point out that we have determined IF1 levels in BAT in two separate cohorts with the same findings, and in a third cohort, we observed IF1 mRNA levels to be downregulated in a much shorter timeframe. Our functional analysis is line with this pattern of regulation. A closer look at the supplementary table provided by Forner et al. (2009), shows that the increase in IF1 content following cold exposure is not supported and since we do not have further insight into the methods and analysis employed by the Forner et al. group, we believe a direct comparison should be avoided at the moment. Regarding the baseline levels of IF1 in BAT, the relatively high abundance of IF1 in BAT was also found by another independent group (https://doi.org/10.1101/2020.09.24.311076).

Importantly, the last paragraph of the discussion needs to be amended when mentioning the work of Forner et al. (ref.43). The mentioned reference studied changes in the mouse mitochondrial proteome not in human mitochondria, as it is stated in the alluded paragraph.

RESPONSE: We apologize for this overlook; we have now reworded our statement.

More puzzling are the western blots in Figures 1E, 1H, Supp. Fig. 1C, D were IF1 (ATP5IF1) is identified by a 17kDa band. However, in other Figures (Fig. 2, Fig. 3, Fig. 4, Supp Fig. 2) IF1 is identified by its well-known 12kDa band. What is the reason for this change in labeling of the IF1 band? The reactivity of the anti-IF1 antibody used? It has been previously documented that liver of C57BL/6J and FVB mouse strains do not express IF1 to a significant level when compared to heart IF1 levels (Esparza-Molto (2019) FASEB J. 33, 1836-1851). However, in Fig. 1E they show opposite findings, much higher levels of IF1 in liver than in heart as reveal by the 17kDa band. Moreover, in Fig. 1H they show the vanishing of the 17 kDa band under cold adaptation, which is not the migration of IF1 in gels as shown in their own figures (see Fig. 2, Fig. 3, Fig. 4, Supp Fig. 2). I am certainly reluctant to accept that the 17kDa band shown in Figures 1E, 1H, Supp. Fig. 1C, D is indeed IF1. Most likely it represents a non-specific protein recognized by the antibody in the tissue extracts analyzed. Cellular overexpression experiments of IF1 in WT1 cells (Fig. 2E) and primary brown adipocytes (Fig. 4B) also support this argument. Overall, I do not support publication of this study for the reasons stated above.

RESPONSE: We understand the concerns raised by the reviewer and apologize for the lack of details in our experimental procedures. While we used the same antibody in the study (Cell Sig. cat. Num. 8528, 1:500), we used two different types of gels. The difference in the molecular weight appearance of IF1 is likely through the migration of the protein in the agarose gel. By using custom-made gels, we observe the protein ~17kDa (Fig. 1 and 5), whereas by using commercial gels (Fig. 2, 3, and 4), we observe the protein closer to the predicted molecular weight (i.e. ~12kDa). Of note, gain and loss-of-function experiments, both in vivo as well as in vitro confirm this statement and the specificity of the antibody (Fig. 2, 3, 4, 5, Fig. EV2). In addition, when we ran a custom-made gel with primary BAT cells, we observed again the ~17kDa band (see Figure for the reviewer below). These experiments alongside the absence of other bands in the gels (see uncropped membranes in Supplementary Figure 1) make us conclude that the band we observe is indeed IF1. Nevertheless, we have now updated our methods section, so the reader is aware of our approaches. We hope the reviewer is satisfied with our additional experiments and editions throughout the manuscript.

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

Summary:

In this manuscript, Brunneta et al describe the role of IF1 in brown adipose tissue activation using in vivo and in vitro experimental models. They observed that cold adaptation promotes a reduction in IF1 expression and an increase in the reverse activity of mitochondrial ATPase or Complex V. Based on these results, the authors explore the contribution of IF1 in this metabolic pathway by modeling the thermogenic process in differentiated primary brown adipocytes. They silenced and overexpressed IF1 in culture and studied their adrenergic stimulation under norepinephrine.

Major comments:

The experiments are well explained and the manuscript flows very well. There are several comments that should be addressed.

RESPONSE: We thank the reviewer for the kind words regarding our work.

1. The authors measure ATP hydrolysis in isolated mitochondria from BAT in Figure 1. They observed that IF1 is decreased upon cold exposure and that ATP hydrolysis is increased. They assess protein levels of different OXPHOS proteins, including IF1 but not other proteins of Complex V (ATP5A) as they do in Figures 3 and 4. It is important to see that cold exposure only affects IF1 levels but not other proteins from Complex V. Does IF1/Complex V ratio change? RESPONSE: We thank the reviewer for this suggestion which was also raised by Reviewer #1. We have now measured complex V subunit A in our experimental protocol. We found that cold exposure does not impact complex V protein levels. Given the importance of this information, we have now included it in Figure 1 (Please, see the revised version) alongside the IF1/complex V ratio. In addition, we have now performed WBs in the BAT exposed for 3 and 7 days to thermoneutrality (~28°C) where we found that IF1 is not reduced following whitening of BAT by this approach whilst UCP1 and other mitochondrial proteins are reduced.

This set of data is now included in Figure 1I,K,L.

In Figure 2J, the drop in MMP is lower upon adrenergic stimulation than in Figure 2E. The same observation applies to other results when the reduction in MMP after NE addition is minimal. Why do the authors remove TMRM for the measurements of membrane potential? TMRM imaging is normally done in the presence of the dye in non-quenching mode. Treatments should be done prior to the addition of the dye and then TMRM should be added and left during the imaging analysis and measure in non-quenching mode. This might explain some of the above-mentioned points regarding the MMP data. Alternatively, if the dye is removed before the measurements, they should let the cells to adapt and so the dye equilibrates between mitochondria and cytosol. A more elegant method to measure membrane potential could be live-cell imaging. In addition, authors propose that mitochondrial membrane potential upon NE stimulation is maintained by reversal of ATP synthase. If this is the case, one would expect that addition of oligomycin in NE treated adipocytes would cause depolarization. However, in FigS2A this is not the case. Authors should comment on this in addition to considering more elegant approach to measure MMP.

RESPONSE: We apologize for the lack of details in the methods. All treatments (i.e., transfection and norepinephrine stimulation) were performed before the addition of TMRM. Indeed, this approach does not have the resolution compared to safranine in isolated mitochondria (Fig. 1D), which limits our interpretation regarding the dynamic role of IF1 on MMP in brown adipocytes. We have taken care to state the limitations of our method throughout the entire paper to avoid overinterpretation of our data. Regarding the removal of the dye before the measurements, our internal controls indicate that this procedure does not change the ability of our method to detect fluctuations in MMP (i.e., oligomycin and FCCP as internal controls). Nevertheless, as suggested by the reviewer, to test the time effect of the probe equilibrium (i.e., mitochondria versus cytosol) in our method, we loaded cells with TMRM 20 nM for 30 min and measured the fluorescence right after the removal of the probe/washing steps for another 10 min. We were not able to detect differences in the fluorescence in a time-dependent manner (see below). Therefore, we conclude the removal of TMRM does not influence the fluorescence of the probe in differentiated brown adipocytes.

+NE

-NE

In addition, we performed a similar experiment using TMRM in the quenching mode (200 nM), however, after the removal of TMRM, we added FCCP (1 mM) to the cells for 10 min under constant agitations at 37°C. This approach aimed to expel all TMRM that accumulated within the mitochondria in an MMP-dependent manner. Therefore, excluding the dynamic Brownian movement that we could have caused by the removal of the dye before the measurement mentioned by the reviewer. By doing this, we found the same effect of IF1 overexpression in the reduction of MMP in the presence of norepinephrine.

Protocol:

• Transfection (24h) on day 4 of differentiation + 24h just normal media

• 30 min norepinephrine 10 µM

• 200 nM TMRM on top of NE

• Washing step

• Add FCCP 1 µM for 10 min, and read (The aim here was to release all TMRM accumulated inside of mitochondria in a MMP-dependent manner)

In summary, the data suggests the removal of the dye from the cells does not influence the fluorescence of TMRM, therefore, enabling us to make conclusions regarding the biological effects of IF1 manipulation in the MMP of brown adipocytes. Regarding the reverse mode of ATP synthase and the absence of effects with oligomycin, given oligomycin inhibits both rotation of ATP synthase and even uncoupled brown adipocytes respond to oligomycin (i.e. reduction in O2 consumption), the prediction of lowering MMP in the presence of oligomycin due to inhibition of the reserve mode of ATP synthase is more complicated than anticipated. Nevertheless, we have now addressed this topic in the discussion section. Lastly, we generally observe a reduction in MMP around 10-25% in differentiated adipocytes upon NE treatment (30 minutes, 10mM). However, due to the differentiation state of the cells, MMP response from norepinephrine fluctuated from experiment to experiment. Therefore, we did not compare experiments performed on different days or batches, but only within the same differentiation batch to reduce variability.

In Figure 2, in the model of siIF1, there is baseline more phosphorylation of AMPK than in the scramble control (pAMPK). However, this is not the case of p-p38MAPK. Do the authors have any explanation for those differences in baseline activation of the stress kinases when IF1 is silenced? In the same experimental group, addition of NE seems to have more effect in the scrambled than in siIF1, but the plotted data does not reflect these differences. In contrast, increase in pAMPK upon NE is higher in IF1 overexpressing cells compared to EV (Figure 2H), but again this is not reflected in western blot quantification (Figure 2I).

RESPONSE: Although some differences in pAMPK in the treatments were observed as gathered by the representative blots, these changes were not confirmed later in different biological replicates, therefore, the overall effect of IF1 manipulation in pAMPK does not change. Given we used this approach as quality control for our experiments to guarantee norepinephrine treatment works, we removed the pAMPK data from the study and kept p38 as a marker of adrenergic signaling activation (please see revised Fig. 2 in the main file).

Does NE promote decrease of IF1 expression in control (siScramble and EV) adipocytes? The authors should test it and see whether it goes in the same direction as the observations derived from the experiments in cold exposed mice. This is very important point, as it could explain the lack of an additional effect of IF1 silencing on NE-induced depolarization (Figure 2E).

RESPONSE: We thank the reviewer for this suggestion. In line, with the in vivo data, acute NE treatment in differentiated brown adipocytes does not change IF1 mRNA and protein levels. We have now added this information and the corresponding interpretation to the updated manuscript.

Does NE promote decrease of IF1 expression in the scramble and EV adipocytes? The authors should test it and see whether it goes in the same direction as the observations derived from the experiments in cold exposed mice.

RESPONSE: As this question is the same as #4, we believe the reviewer may have erroneously pasted this here.

For MMP data in Fig2, they should include significance between non treated and NE-treated groups. They say: "While UCP1 ablation did not cause any effect on MMP upon adrenergic stimulation...", but NE caused (probably significant) depolarization in siUCP1, which seems even stronger than depolarization in EV. This is opposite to what you would expect. They also didn't confirm UCP1 silencing with western blot.

RESPONSE: We thank the reviewer for this suggestion. We have now included the expected statistical main effect of NE upon MMP. Although the effects of IF1 overexpression were blunted when Ucp1 was silenced, we indeed still observed the same degree of reduction in MMP in brown adipocytes. This finding has two possible explanations, one is the effectiveness of the silencing protocol, therefore, residual Ucp1 expression may still play a role in this experiment; second, other ATP-consuming processes are able to lower MMP in a UCP1-independent manner. We have added this information to the updated manuscript to make the reader aware of our findings as well as the limitations of the method. Unfortunately, we were not able to detect UCP1 protein levels due to technical issues. Given the effects of IF1 overexpression were blunted when Ucp1 was silenced, we believe this functional outcome is sufficient, alongside mRNA levels, to demonstrate the effectiveness of our silencing protocol.

It has been established that decreased expression of IF1 promotes increase in the reverse activity of Complex V, ATP hydrolytic activity. Increase in ATP hydrolysis also affects ECAR. The authors should consider this when calculating the contribution of ATP glycolysis versus ATP OXPHOS since the ATP hydrolysis is also playing a role in the ECAR increase. The data should be reinterpreted. ATP hydrolysis should be measured in the situation where IF1 is silenced and overexpressed. These measurements can be done in cells using the seahorse.

RESPONSE: The only differences we observed in MMP are in the presence of norepinephrine (i.e. UCP-1-dependent proton conductance), which is not present during the estimation of ATP production by Seahorse analysis. Nevertheless, we have now improved the description of our experimental protocol and limitations to estimate ATP production to make it as clear as possible to the reader. Lastly, given the addition of in vivo gain-of-function experiments, we have now determined the ATP hydrolytic activity in this model, which offers a better understanding of the in vivo modulation of IF1 levels affecting ATP synthase activity (reverse mode). We hope the reviewer understands our motivation to focus on the in vivo model of gain-of-function regarding ATP synthase activity.

The authors use GAPDH as loading control in western blots. They should use another protein since GAPDH is part of the intermediary metabolism and plays a role in glycolysis.

RESPONSE: We understand the concern of the reviewer regarding the use of GAPDH as a loading control for the studies of metabolism. However, as can be observed by the western blot images, GAPDH levels do not change in our experimental models, therefore, we feel confident that our loading is homogeneous throughout our gels.

The authors show that reduction of IF1 involves more lipid utilization. They should include more experiments showing the connection of the metabolic adaptation in the absence of IF1 and some lipid imaging.

RESPONSE: We appreciate this suggestion. We have now performed Oil Red O staining in differentiated adipocytes following ablation of IF1. However, we did not observe any effect on lipid accumulation in primary brown adipocytes following IF1 knockdown. Therefore, the effects of IF1 ablation on lipid mobilization are not due to lipid content or reflected in lipid accumulation. We have now added this new information to the manuscript (please, see the revised form Fig. EV3).

In the text, "Despite this adjustment of experimental conditions, we did not detect any effect of IF1 ablation on mitochondrial oxygen consumption (Supplementary Fig. 3A,B)", this is true for baseline, NE-driven and ATP-linked respiration, but what about maximal respiration? There is a huge increase in IF1 knockdown... They should explain these results.

RESPONSE: We perform this experiment to address the question of whether the lipid mobilization induced by norepinephrine would uncouple mitochondria in a UCP1-independent manner. Given the absence of effect between scrambled and IF1 ablated cells in mitochondrial respiration in the presence of norepinephrine and following the addition of oligomycin, we concluded no effect of lipolysis-induced UCP1-independent uncoupling. However, as observed by the reviewer and consistent with other data within the study, the interaction between lipid metabolism and IF1 knockdown seems to affect maximal electron transport chain activity, which although interesting, was not the focus of the present study. Nevertheless, we have now acknowledged these findings and a possible explanation for them in the revised manuscript.

In Figure 3K they present OCR as % of baseline, but in a similar experiment in Figire 4G it is OCR/protein, they should make the Y axis consistent across experiments.

RESPONSE: We apologize for this overlook. We have now edited all the axes and labels for consistency.

The graphical abstract is confusing. In BAT there are two populations of mitochondria, the cytosolic and the mitochondria attached to the lipid droplet, peridroplet mitochondria (PDM). Upon adrenergic stimulation, PDM leave the lipid droplet and lipolysis takes place. The authors propose that upon adrenergic stimulation, IF1 is reduced and there is lipid mobilization. The part of the scheme where it says "fully recruited" should be removed or rewritten, since adrenergic stimulation is not compatible with mitochondria recruitment around the lipid droplet.

RESPONSE: Thank you for this input. Given the addition of new experiments and interpretation, we have now redrawn the graphical abstract and addressed this topic in the discussion section.

The title should be rewritten to better reflect the research presented in the manuscript.

RESPONSE: Thank you for this input. Given the addition of new experiments, we have now rewritten the title accordingly.

Minor comments:

Some of the Y axis should be corrected. For example, in Figure 2J, L and M should say % of EV untreated, Similarly, in Figure 2E, it should say % of scramble untreated. In Figure 3N, the Y axis is misspelled. All the Y axis referring to percentages should have the same scale for comparison purposes.

RESPONSE: Thank you for the proofreading. We have now edited the scales and labels to keep consistency.

The authors should describe better the results corresponding to Figure 2. There is a lot of information and they should improve the description pertaining the connection between the different pieces of data relating the different signaling pathways that are shown. For westerns in this Figure, they should provide some rationale (one to two sentences in the results section) as to why they are checking the expression of pAMPK and p38-MAPK.

RESPONSE: We have now edited the description of our results to make them as clear as possible.

Here are some comments referring to the methods section:

For Complex V hydrolytic activity, the reaction buffer contains 10mM Na-azide. I guess this is to inhibit respiration, but wouldn't azide also inhibit complex V at this concentration?

RESPONSE: We thank the reviewer for this question. To test that, we performed complex V activity in buffers containing or not 10 mM sodium azide. As demonstrated below, the presence of sodium azide in the buffer does not influence complex V activity in two different tissues with low and high complex V activity (BAT and heart, respectively).

Table 1. ATP synthase hydrolytic activity in the presence or absence of Na-azide.

BAT

Heart

+Na-azide

100 ± 43.01

100 ± 39.36

-Na-azide

82.6 ± 4.33

111.3 ± 43.32

+Na-azide + oligomycin

15.3 ± 4.32*

13.8 ± 14.01*

-Na-azide + oligomycin

14.2 ± 3.53*

11.9 ± 2.88*

Data presented as % of control (i.e. presence of Na-azide and absence of oligomycin) for both tissues independently. N = 2-3/condition. Statistical test: two-way ANOVA. * main effect of oligomycin (p In the mitochondrial isolation protocol, they say "mitochondria were centrifuged at 800g for 10min..." Will this speed pellet the mitochondria? I think this is a mistake in writing.

RESPONSE: We apologize for the lack of clarity. What was centrifuged at 800 g was the whole-tissue homogenate to discard cellular debris, before pelleting mitochondria at 5000 g. We have now corrected this mistake in the methods section.

For the safranin-O experiment, they don't mention mitochondrial substrate used, probably it's in the reference that they provide, but I think it should be included in the text.

RESPONSE: We did not use any substrate because our goal was to test the contribution of ATP synthase to mitochondrial membrane potential. For that, we inhibited proton movement within the ETC with antimycin A and through UCP1 with GDP (see Methods). We have now edited our Method’s description to make sure the reader is aware of our approach.

Reviewer #3 (Significance (Required)):

The manuscript is well written, and it flows well when reading. However, there are some additional experiments that need to be performed to reach the conclusions the authors claim.

RESPONSE: We thank the reviewer for the positive commentaries regarding our work and hope to have answered the open questions with the edits and new experiments.

The role of ATP hydrolysis in BAT thermogenesis is novel and interesting as it can sed some light onto potential approaches to promotes BAT activation.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

This is an interesting investigation into the activity of IF1 in brown adipocytes. The findings are innovative and the conclusion is well-supported by the data. The conclusion is in line with previous reports on IF1 activities in other cell types, particularly in terms of its regulation of FoF1-ATPase. The authors have executed an exceptional job in designing the study, preparing the figures, and writing the manuscript. Overall, this study significantly contributes to the understanding of IF1 activity in brown adipocytes and its role in thermogenesis.

RESPONSE: We thank the reviewer for the kind words. Please, find below our answers in a point-by-point manner.

Reviewer #4 (Significance (Required)):

The study demonstrates involvement of IF1 in regulating thermogenesis in brown adipocytes, which is a unique aspect not covered in existing literature. Advantage of the study is well-designed cellular studies. The major weakness is lack of proof of conclusion in vivo. There are a few minor concerns that should be addressed to further enhance quality of the manuscript.

RESPONSE: We have now included two in vivo models, whole-body IF1 KO mice and BAT-injected IF1 overexpression to test the role of IF1 in BAT biology. The whole dataset is included in the main manuscript, where we conclude the BAT IF1 overexpression partially suppresses b3-adrenergic induction of thermogenesis alongside a reduction (overall and UCP1 dependent) in mitochondrial oxygen consumption. Also, similar to our in vitro experiments, IF1 KO mice did not present any difference in adrenergic-stimulated oxygen consumption.

1. Current discussion does not mention the regulation of IF1 protein by the cAMP/PKA pathway. This point should be included to provide a comprehensive understanding of the regulatory mechanisms of IF1 protein. RESPONSE: Thank you for this suggestion. We have now added this topic to the discussion.

It has been reported that IF1 also influences the structure of mitochondrial crista. Considering the observed changes with IF1 knockdown, it would be valuable to discuss this activity in relation to the findings of the study.

RESPONSE: We discussed the implications of IF1 modulation in mitochondrial morphology in the revised manuscript.

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

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Reply to the reviewers

Manuscript number: RC-2023-02218R

Corresponding author(s): Steven, McMahon

1. General Statements [optional]

*We were pleased to receive the encouraging critiques and very much appreciate the Reviewer's specific comments and suggestions. In this revised version of our manuscript, we have made a number of substantive additions and modifications in response to these comments/suggestions. We hope you agree that the study is now improved to the point where it is suitable for publication. *

2. Point-by-point description of the revisions

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

Summary This study describes efforts to characterize differences in the roles of the two related human decapping factors Dcp1a and Dcp1b by assessing mRNA decay and protein associations in knockdown and knockout cell lines. The authors conclude that these proteins are non-redundant based on the observations that loss of DCp1a versus Dcp1b impacts the decapping complex (interactome) and the transcriptome differentially.

Major comments • While the experiments appear to be well designed and executed and the data of generally high quality, the conclusions are drawn without sufficient consideration for the fact that these two proteins form a heterotrimeric complex. The authors assume that there are distinct homotrimeric complexes rather than a single complex with both proteins in. Homotrimers may have new/different functions not normally seen when both proteins are expressed. Thus while it is acceptable to infer that the functions of these two proteins within the decapping complex are distinct, it is not clear that they act separately, or that complexes naturally exist without one or the other. A careful evaluation of the relative ratios of Dcp1a and b overall and in decapping complexes would be informative if the authors want to make stronger statements about the roles of these two factors.

RESPONSE: Thank you for this valuable comment. We have substantially edited the manuscript to incorporate these points. Examples include a detailed analysis of iBAQ values for the DDX6, DCP1a, and DCP1b interactomes (which now allows us to estimate the ratios of DCP1a and DCP1b in these complexes) and cellular fractionation to interrogate complex integrity (using Superose 6).

• The concept of buffering is not adequately introduced and the interpretation of observations that RNAs with increased half life do not show increased protein abundance - that Dcp1a/b are involved in transcript buffering is nebulous. In order to support this interpretation, the mRNA abundances (NOT protein abundances) should be assessed, and even then, there is no way to rule out indirect effects. RESPONSE: Thank you for this comment. In the revised version of the manuscript, we introduced the concept of transcript buffering at an earlier stage as one of the potential explanations for our findings. We were also able to use a new algorithm (grandR) to estimate half-lives and synthesis rates from our data. These new data add strength to the argument that DCP1a and DCP1b are linked to transcript buffering pathways.

• It might be interesting to see what happens when both factors are depleted to get an idea of the overall importance of each one.

RESPONSE: In our work we tried to emphasize the differences between the two paralogs. We believe that doing double knockout or knockdown would mask the distinct impacts of the paralogs. In data not included in this study, we have shown that cells lacking both DCP1a and DCP1b are viable. We did check PARP cleavage in the CRISPR generated cell pools of DCP1a KO, DCP1b KO, and the double KO. The WB measuring the PARP cleavage is shown in the supplemental material (Supplementary Material: Replicates)

• The algorithms etc used for data analysis should be included at the time of publication. Version number and settings used for SMART to define protein domains, and webgestalt should be indicated

RESPONSE: We apologize for this oversight. Version number and settings used for the webtools (SMART, Webgestalt) are now included. The analysis pipeline for half-lives and synthesis rates estimation as well as all the files and the code needed to generate the figures in the paper are available on zenodo (https://zenodo.org/records/10725429).

• Statistical analysis is not provided for the IP experiments, the number of replicates performed is not indicated and quantification of KD efficiency are not provided.

RESPONSE: The number of replicates performed in each experiment is now clearly indicated and quantifications of knockdown efficiency are provided (Supplemental Figure 3A and 3B, Figure 3A, Figure 3B).

• The possibility that the IP Antibody interferes with protein-protein interactions is not mentioned.

RESPONSE: Thank you for this comment. The revised manuscript includes a discussion of the antibody epitope location and the potential for impact on protein-protein interactions.

Minor comments • P4 - "This translational repression of mRNA associated with decapping can be reversed, providing another point at which gene expression can be regulated (21)" - implies that decapping can be reversed or that decapped RNAs are translated. I don't think this is technically true.

RESPONSE: There have been several studies that document the reversal of decapping. These findings are summarized in the following reviews.

Schoenberg, D. R., & Maquat, L. E. (2009). Re-capping the message. Trends in biochemical sciences, 34(9), 435-442.

Trotman, J. B., & Schoenberg, D. R. (2019). A recap of RNA recapping. Wiley Interdisciplinary Reviews: RNA, 10(1), e1504.

• P11 - how common is it for higher eukaryotes to have 2 DCP genes? *RESPONSE: Metazoans have 2 DCP1 genes. *

• Fig S1 - says "mammalian tissues" in the text but the data is all human. The statement that "expression analyses revealed that DCP1a and DCP1b have concordant rather than reciprocal expression patterns across different mammalian tissues (Supplemental Figure 1)" is a bit misleading as no evidence for correlation or anti-correlation is provided. Also co-expression is not strong support for the idea that these genes have non-redundant functions. Both genes are just expressed in all tissues - there's no evidence provided that they are concordantly expressed. In bone marrow it may be worth noting that one is high and the other low - i.e. reciprocal. *RESPONSE: We appreciate this comment. We have corrected the interpretation of the aforementioned dataset. We have also incorporated a more detailed discussion in the text of the paper. As the Reviewer pointed out, there are a subset of tissues where their expression appears to be reciprocal. *

• Fig 1A - it is not clear what the different colors mean. Does Sc DCP1 have 1 larger EVH or 2 distinct ones. Are the low complexity regions in Sc DCP2 the SLiMs. *RESPONSE: Thank you for this comment. We have corrected this ambiguity to reflect that Sc DCP1 has one EVH1 domain that is interconnected by a flexible hinge. The low-complexity regions typically contain short linear motifs (SLIMs), however, not all low-complexity regions have been verified to contain them. In the figure, only low-complexity regions are shown. The text of the paper refers only to verified SLIMs . *

• P11 - why were HCT116 cells selected? RESPONSE: HCT116 cells are an easily transfectable human cell line and have been widely used in biochemical and molecular studies, including studies of mRNA decapping (see references below). Since decapping is impacted by viral proteins we avoided the use of other commonly used cell models such as HEK293T or HeLa.

https://pubmed.ncbi.nlm.nih.gov/?term=decapping+hct116&sort=date&size=200

• Fig 1B - what are the asterisks by the RNA names? Might be worth noting that over-expression of DCP1b reduced IP of DCP1a. There's no quantification and no indication of the number of times this experiment was repeated. Data from replicates and quantification of the knockdown efficiency in each replicate would be nice to see. *RESPONSE: Thank you for this comment. Asterisks indicate that those bands were from a second gel, as DCP1a and DCP1b run at approximately the same molecular weight. We have now included a note in our figure legend to indicate this. The knockdown efficiency is provided (Figure 3 and Supplemental Figure 3). We also noted the number of replicas for each IP in figure 1. The replicas are provided as supplementary material (Supplementary Materials: Replicates). *

• Fig 1C/1D - why are there 3 bands in the DCP1a blot? Quantification of the IP bands is necessary to say whether there is an effect or not of over-expression/KO. RESPONSE: The additional bands in DCP1a blots are background. When we stained the whole blot for DCP1a, in cells which with complete DCP1a KO cells (clone A3), these bands still appear (Supplementary Material: Validation of the KO clones). Quantifications of the bands in the overexpression experiments is now provided.

• Fig 3 - is it possible that differences are due to epitope positions for the antibodies used for IP? RESPONSE: We do not believe so. DCP1a antibody binds roughly 300-400 residues on DCP1a, and DCP1b antibody binds around Val202. Antibodies therefore do not bind DCP1a or DCP1b low-complexity regions (which are largely responsible for interacting with the decapping complex interactome). Antibodies don't bind the EVH1 domains or the trimerization domain, which are needed for their interaction with DCP2 and each other.

• Fig 5A - the legend doesn't match the colors in the figure. It is not clear how the pRESPONSE: Thank you for this comment. We have corrected this issue in the revised version of the paper. High-confidence proteins are those with pRESPONSE: Thank you for this comment. We have corrected this issue in the revised version of the paper.*

• There are a few more recent studies on buffering that should be cited and more discussion of this in the introduction is necessary if conclusions are going to be drawn about buffering. *RESPONSE: We have included a discussion of transcript buffering in the introduction. *

• The heatmaps in figure 2 are hard to interpret. RESPONSE: To clarify the heatmaps, we included a more detailed description in the figure legends, have enlarged the heatmaps themselves, and have added more extensive labeling.

Reviewer #1 (Significance (Required)):

• Strengths: The experiments appear to be done well and the datasets should be useful for the field. • Limitations: The results are overinterpreted - different genes are affected by knocking down one or other of these two similar proteins but this does not really tell us all that much about how the two proteins are functioning in a cell where both are expressed. • Audience: This study will appeal most to a specialized audience consisting of those interested in the basic mechanisms of mRNA decay. Others may find the dataset useful. • This study might complement and/or be informed by another recent study in BioRXiv - https://doi.org/10.1101/2023.09.04.556219 • My field of expertise is mRNA decay - I am qualified to evaluate the findings within the context of this field. I do not have much experience of LC-MS-MS and therefore cannot evaluate the methods/analysis of this part of the study.

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

The authors provide evidence that Dcp1a and Dcp1b - two paralogous proteins of the mRNA decapping complex - may have divergent functions in a cancer cell line. In the first part, the authors show that interaction of Dcp2 with EDC4 is diminished upon depletion of Dcp1a but not affected by depletion of Dcp1b. The results have been controlled by overexpression of Dcp1b as it may be limiting factor (i.e. expression levels too low to compensate for depletion of Dcp1a reduced interaction with EDC3/4 while depletion of Dcp1b lead to opposite and increase interactions). They then defined the protein interactome of DDX6 in parental and Dcp1a or Dcp1b depleted cells. Here, the authors show some differential association with EDC4 again, which is along results shown in the first part. The authors further performed SLAM-seq and identified subsets of mRNA whose decay rates are common but also different upon depletion with Dcp1a and Dcp1b. Interestingly, it seems that Dcp1a preferentially targets mRNAs for proteins regulating lymphocyte differentiation. To further test whether changes in RNA decay rates are also reflected at the protein levels, they finally performed an MS analysis with Dcp1a/b depleted cells. However no significant overlap with mRNAs showing altered stability could be observed; and the authors suggested that the lack of congruence reflects translational repression.

Major comments: 1. While functional difference between Dcp1a and Dcp1b are interesting and likely true, there are overinterpretations that need correction or further evidence for support. Sentences like "DCP1a regulates RNA cap binding proteins association with the decapping complex and DCP1b controls translational initiation factors interactions (Figure 2E)" sound misleading. While differential association with proteins has been recognised with MS-data, it does not necessary implement an active process of control/regulation. To make the claim on 'control/regulation', and inducible system or introduction of mutants would be required.

RESPONSE: This set of comments were particularly useful in helping us refine the presentation of our findings. We have edited our manuscript to be more specific about the limits of our data.

1. The MS analysis is not clearly described in the text and it is unclear how authors selected high-confident proteins. The reader needs to consider the supplemental tables to find out what controls were used. Furthermore, the authors should show correlation plots of MS data between replicates. For instance, there seems to be limited correlation among some of the replicates (e.g. Dcp1b_ko3 sample, Fig. 2c). Any explanation in this variance?

*RESPONSE: We have now included a clear description of how all high-confidence proteins were selected in the Methods and Results sections. The revised manuscript also includes a more thorough description of the controls used and the number of replicates for individual experiments. The PCA plots have now been included where appropriate. The variance in this sample is likely technical. *

1. GO analysis for the proteome analysis should consider the proteome and not the genome as the background. The authors should also indicate the corrected P-values (multiple testing) FDRs.

*RESPONSE: Webgestalt uses a reference set of IDs to recognize the input IDs, and it does not use it for the background analysis in the classical sense. We repeated a subset of our proteome analyses using the 'genome-protein coding' as background and obtained the same result as in our original analysis. All ontology analyses now include raw p-values and/or FDRs when appropriate. *

1. Fig 2E. The figures display GO enrichments needs better explanation and additional data can be added. The enrichment ratio is not explained (is this normalised?) and p-values and FDRs, number of proteins in respective GO category should be added. *RESPONSE: More thorough explanations of the GO enrichments are now included. The supplemental data contains all p-values (raw and adjusted), as well as the number of proteins in each GO category. The Enrichment ratio is normalized and contains information about the number of proteins that are redundant in multiple groups. GO Ontology analyses are now displayed with p-values and/or FDR values, and in this case the enrichment ratio contains information regarding the number of proteins found in our input set and the number of expected proteins in the GO group. The network analysis shows the FDR values and the number of proteins found in the groups compared. *

Minor: 5. These studies were performed in a colorectal carcinoma cell line (HCT116). The authors should justify the choice of this specialised cell line. Furthermore, one wonders whether similar conclusions can be drawn with other cell lines or whether findings are specific to this cancer line.

RESPONSE: The study that is currently in pre-print in BioRxiv (https://doi.org/10.1101/2023.09.04.556219*) utilized HEK293Ts and found similar results to ours when examining the various relationships between the core decapping core members. *

1. Fig. 1B. It is unclear what DCP1b* refers to? There are bands of different size that are not mentioned by the authors - are those protein isoforms or what are those referring to? A molecular marker should be added to each Blots. Uncropped Western images and markers should be provided in the Supplement. *RESPONSE: The asterisk indicates that these images came from a second western blot gel (DCP1a and DCP1b have a similar molecular weight and cannot be probed on the same membrane). Uncropped western blot images and markers (as available) are provided in the supplement. *

2. MS data submitted to public repository with access. No. indicated in the manuscript.

RESPONSE: MS data is submitted as supplementary datasets to the paper. It contains the analyzed data as well as the LCMSMS output. We are in the process of submitting the raw LSMSMS data to a public repository.

Fig 3. A Venn Diagram displaying the overlap of identified proteins should be added. GO analysis should be done considering the proteome as background (as mentioned above).

*RESPONSE: A Venn diagram showing the overlap among the proteins identified is now included in the revised version. *

Reviewer #2 (Significance (Required)):

Overall, this is a large-scale integrative -omics study that suggest functional difference between Dcp1 paralogues. While it seems clear that both paralogous have some different functions and impact, there are overinterpretations in place and further evidence would to be provided to substantiate conclusions made in the paper. For instance, while the interactions with Dcp2/Ddx6 in the absence of Dcp1a,b with EDC4/3 may be altered (Fig. 1, 2), the functional implications of this changed associations remains unresolved and not further discussed. As such, it remains somehow disconnected with the following experiments and compromises the flow of the study. The observed differences in decay-rates for distinct functionally related sets of mRNAs is interesting; however, it remains unclear whether those are direct or rather indirect effects. This is further obscured by the absence of any correlation to changes in protein levels, which the authors interpreted as 'transcriptional buffering'. In this regard, it is puzzling how the authors can make a statement about transcriptional buffering? While this may be an interesting aspect and concept of the discussion, there is no primary data showing such a functional impact.

As such, the study is interesting as it claims functional differences between DCP1a/b paralogous in a cancer cell line. Nevertheless, I am not sure how trustful the MS analysis and decay measurements are as there is not further validation. It woudl be interesting if the authors could go a bit further and draw some hypothesis how the selectivty could be achieved i.e interaction with RNA-binding proteins that may add some specificity towards the target RNAs for differential decay. As such, the study remains unfortunately rather descriptive without further functional insight.

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

Review on "Non-redundant roles for the human mRNA decapping cofactor paralogs DCP1a and DCP1b" by Steven McMahon and co-workers mRNA decay is a critical step in the regulation of gene expression. In eukaryotes, mRNA turnover typically begins with the removal of the poly(A) tail, followed by either removal of the 5' cap structure or exonucleolytic 3'-5' decay catalyzed by the exosome. The decapping enzyme DCP2 forms a complex with its co-activator DCP1, which enhances decapping activity. Mammals are equipped with two DCP1 paralogs, namely DCP1a and DCP1b. Metazoans' decapping complexes feature additional components, such as enhancer of decapping 4 (EDC4), which supports the interaction between DCP1 and DCP2, thereby amplifying the efficiency of decapping. This work focuses on DCP1a and DCP1b and investigates their distinct functions. Using DCP1a- and DCP1a-specific knockdowns as well as K.O. cell lines, the authors find surprising differences between the DCP1 paralogs. While DCP1a is essential for the assembly of EDC4-containig decapping complexes and interactions with mRNA cap binding proteins, DCP1b mediates interactions with the translational machinery. Furthermore, DCP1a and DCP1b target different mRNAs for degradation, indicating that they execute non-overlapping functions. The findings reported here expand our understanding of mRNA decapping in human cells, shedding light on the unique contributions of DCP1a and DCP1b to mRNA metabolism. The manuscript tackles an interesting subject. Historically, the emphasis has been on studying DCP1a, while DCP1b has been deemed a functionally redundant homolog of DCP1a. Therefore, it is commendable that the authors have taken on this topic and, with the help of knockout cell lines, aimed to dissect the function of DCP1a and DCP1b. Despite recognizing the significance of the subject and approach, the manuscript falls short of persuading me. Following a promising start in Figure 1 (which still has room for improvement), there is a distinct decline in overall quality, with only relatively standard analyses being conducted. However, I do not want to give the authors a detailed advice on maximizing the potential of their data and presenting it convincingly. So, here are just a few key points for improvement: Figure 1C: Upon closer examination, a faint band is still visible at the size of DCP1a in the DCP1a knockout cells. Could this be leaky expression of DCP1a? The authors should provide an in-depth characterization of their cells (possibly as supplementary material), including identification of genomic changes (e.g. by sequencing of the locus) and Western blots with longer exposure, etc.

*RESPONSE: Thank you for this comment. The in-depth characterization of our cells is now included in the Supplementary Material. DCP1a KO cells and DCP1b KO cells indicated as single cell clones have been confirmed to have no DCP1a or DCP1b expression. In Figure 1D and Figure 3, polyclonal pool cells were used as indicated (only for DCP1a KO). *

Figure 2: It is great to see that the effects of the KOs are also visible in the DDX6 immunoprecipitation. However, I wonder if the IP clearly confirms that the KO cells indeed do not express DCP1a or DCP1b. In the heatmap in Figure 2B, it appears as if the proteins are only reduced by a log2-fold change of approximately 1.5? Additionally, Figure 2 shows a problem that persists in the subsequent figures. The visual presentation is not particularly appealing, and essential details, such as the scale of the heatmap in 2B (is it log2 fold?), are lacking.

*RESPONSE: The in-depth characterization of our cells is included in the Supplementary Materials and confirms the presence of single-cell clones where indicated. As noted above, only Figure 1D and Figure 3 used DCP1a KO pooled cells. The heatmap in Figure 2B is scaled by row using the pheatfunction in R studio. The actual data for the heatmap comes from protein intensities from the LC-MS/MS analysis. We have improved the visual presentation in the revised manuscript. *

Figure 3: I wonder why there are no primary data shown here, only processed GO analyses. Wouldn't one expect that DCP2 interacts mainly with DCP1a, but less with DCP1b? Is this visible in the data? Moreover, such analyses are rather uninformative (as reflected in the GO terms themselves, for instance, "oxoglutarate dehydrogenase complex" doesn't provide much meaningful insight). The authors should rather try to derive functional and mechanistic insights from their data.

RESPONSE: We have now revised this Figure to include primary data as well as the IP of DCP1a in DCP1b KO cells (single cell clones) and the IP of DCP1b in DCP1a KO cells (pooled cells). We identified EDC3 in the high-confidence protein pool. The EDC3:DCP1a interaction is enhanced in DCP1b KO cells. We also found that the EDC3:DCP1b interaction is less abundant in DCP1a KO cells. This is consistent with our data in Figures 1 and 2. DCP2 was not identified in the interactomes of either DCP1a or DCP1b. This is not unusual as DCP2 is highly flexible and the association between DCP1s with DCP2 is transient and facilitated by other proteins.

In Fig. 4 the potential of the approach is not fully exploited. Firstly, I would advocate for omitting the GO analyses, as, in my opinion, they offer little insight. Again, crucial information is missing to assess the results. While 75 nt reads are mentioned in the methods, the sequencing depth remains unspecified. Figure 4b should be included in the supplements. Furthermore, I strongly recommend concentrating on insights into the mechanisms of DCP1a and DCP1b-containing complexes. E.g. what characteristics distinguish DCP1a and DCP1b-dependent mRNAs? Are these targets inherently unstable? Why are they degraded? Are they known decapping substrates?

*RESPONSE: Thank you for this comment. We have now revised this figure and have included information about sequencing depth and other pertinent information. We have been able to use a newly available algorithm (grandR) and were able to estimate half-lives and synthesis rates. This is a significant addition to the paper. We were also able to compare significantly impacted mRNAs (by DCP1a or DCP1b loss) to the established DCP2 target list. *

In general, I suggest the authors revise the manuscript with a focus on the potential readers. Reduce Gene Ontology (GO) analyses and heatmaps, and instead, incorporate more analyses regarding the molecular processes associated with the different decapping complexes.

*RESPONSE: We removed selected GO analyses and heatmaps from the main body of the manuscript (included as Supplementary Figures instead). For our LC-MS/MS datasets, we added iBAQ analyses of the DDX6 IP, DCP1a IP, and DCP1b IP in the control conditions. Cellular fractionation studies (using Superose 6 chromatography) were also added to the paper and allow us to interrogate decapping complex composition in more detail. The revised version of the manuscript includes a new 4SU labeling experiment (pulse-chase) as well as estimation of half-lives and synthesis rates in our conditions. Also included is relevant information about DCP1b transcriptional regulation. *

Reviewer #3 (Significance (Required)):

The manuscript in its current form could benefit from substantial revisions for it to be considered impactful for researchers in the field.

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

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Reply to the reviewers

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

I have trialled the package on my lab's data and it works as advertised. It was straightforward to use and did not require any special training. I am confident this is a tool that will be approachable even to users with limited computational experience. The use of artificial data to validate the approach - and to provide clear limits on applicability - is particularly helpful.

The main limitation of the tool is that it requires the user to manually select regions. This somewhat limits the generalisability and is also more subjective - users can easily choose "nice" regions that better match with their hypothesis, rather than quantifying the data in an unbiased manner. However, given the inherent challenges in quantifying biological data, such problems are not easily circumventable.

*

* I have some comments to clarify the manuscript:

1. A "straightforward installation" is mentioned. Given this is a Method paper, the means of installation should be clearly laid out.*

__This sentence is now modified. In the revised manuscript we now describe how to install the toolset and we give the link to the toolset website if further information is needed. __On this website, we provide a full video tutorial and a user manual. The user manual is provided as a supplementary material of the manuscript.

* It would be helpful if there was an option to generate an output with the regions analysed (i.e., a JPG image with the data and the drawn line(s) on top). There are two reasons for this: i) A major problem with user-driven quantification is accidental double counting of regions (e.g., a user quantifies a part of an image and then later quantifies the same region). ii) Allows other users to independently verify measurements at a later time.*

We agree that it is helpful to save the analyzed regions. To answer this comment and the other two reviewers' comments pointing at a similar feature, we have now included an automatic saving of the regions of interest. The user will be able to reopen saved regions of interest using a new function we included in the new version of PatternJ.

* 3. Related to the above point, it is highlighted that each time point would need to be analysed separately (line 361-362). It seems like it should be relatively straightforward to allow a function where the analysis line can be mapped onto the next time point. The user could then adjust slightly for changes in position, but still be starting from near the previous timepoint. Given how prevalent timelapse imaging is, this seems like (or something similar) a clear benefit to add to the software.*

We agree that the analysis of time series images can be a useful addition. We have added the analysis of time-lapse series in the new version of PatternJ. The principles behind the analysis of time-lapse series and an example of such analysis are provided in Figure 1 - figure supplement 3 and Figure 5, with accompanying text lines 140-153 and 360-372. The analysis includes a semi-automated selection of regions of interest, which will make the analysis of such sequences more straightforward than having to draw a selection on each image of the series. The user is required to draw at least two regions of interest in two different frames, and the algorithm will automatically generate regions of interest in frames in which selections were not drawn. The algorithm generates the analysis immediately after selections are drawn by the user, which includes the tracking of the reference channel.

* Line 134-135. The level of accuracy of the searching should be clarified here. This is discussed later in the manuscript, but it would be helpful to give readers an idea at this point what level of tolerance the software has to noise and aperiodicity.

*

We agree with the reviewer that a clarification of this part of the algorithm will help the user better understand the manuscript.__ We have modified the sentence to clarify the range of search used and the resulting limits in aperiodicity (now lines 176-181). __Regarding the tolerance to noise, it is difficult to estimate it a priori from the choice made at the algorithm stage, so we prefer to leave it to the validation part of the manuscript. We hope this solution satisfies the reviewer and future users.

*

**Referees cross-commenting**

I think the other reviewer comments are very pertinent. The authors have a fair bit to do, but they are reasonable requests. So, they should be encouraged to do the revisions fully so that the final software tool is as useful as possible.

Reviewer #1 (Significance (Required)):

Developing software tools for quantifying biological data that are approachable for a wide range of users remains a longstanding challenge. This challenge is due to: (1) the inherent problem of variability in biological systems; (2) the complexity of defining clearly quantifiable measurables; and (3) the broad spread of computational skills amongst likely users of such software.

In this work, Blin et al., develop a simple plugin for ImageJ designed to quickly and easily quantify regular repeating units within biological systems - e.g., muscle fibre structure. They clearly and fairly discuss existing tools, with their pros and cons. The motivation for PatternJ is properly justified (which is sadly not always the case with such software tools).

Overall, the paper is well written and accessible. The tool has limitations but it is clearly useful and easy to use. Therefore, this work is publishable with only minor corrections.

*We thank the reviewer for the positive evaluation of PatternJ and for pointing out its accessibility to the users.

*

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

# Summary

The authors present an ImageJ Macro GUI tool set for the quantification of one-dimensional repeated patterns that are commonly occurring in microscopy images of muscles.

# Major comments

In our view the article and also software could be improved in terms of defining the scope of its applicability and user-ship. In many parts the article and software suggest that general biological patterns can be analysed, but then in other parts very specific muscle actin wordings are used. We are pointing this out in the "Minor comments" sections below. We feel that the authors could improve their work by making a clear choice here. One option would be to clearly limit the scope of the tool to the analysis of actin structures in muscles. In this case we would recommend to also rename the tool, e.g. MusclePatternJ. The other option would be to make the tool about the generic analysis of one-dimensional patterns, maybe calling the tool LinePatternJ. In the latter case we would recommend to remove all actin specific wordings from the macro tool set and also the article should be in parts slightly re-written.

*

We agree with the reviewer that our initial manuscript used a mix of general and muscle-oriented vocabulary, which could make the use of PatternJ confusing especially outside of the muscle field. To make PatternJ useful for the largest community, we corrected the manuscript and the PatternJ toolset to provide the general vocabulary needed to make it understandable for every biologist. We modified the manuscript accordingly.

* # Minor/detailed comments

# Software

We recommend considering the following suggestions for improving the software.

## File and folder selection dialogs

In general, clicking on many of the buttons just opens up a file-browser dialog without any further information. For novel users it is not clear what the tool expects one to select here. It would be very good if the software could be rewritten such that there are always clear instructions displayed about which file or folder one should open for the different buttons.*

We experienced with the current version of macOS that the file-browser dialog does not display any message; we suspect this is the issue raised by the reviewer. This is a known issue of Fiji on Mac and all applications on Mac since 2016. We provided guidelines in the user manual and on the tutorial video to correct this issue by changing a parameter in Fiji. Given the issues the reviewer had accessing the material on the PatternJ website, which we apologize for, we understand the issue raised. We added an extra warning on the PatternJ website to point at this problem and its solution. Additionally, we have limited the file-browser dialog appearance to what we thought was strictly necessary. Thus, the user will experience fewer prompts, speeding up the analysis.

*

## Extract button

The tool asks one to specify things like whether selections are drawn "M-line-to-M-line"; for users that are not experts in muscle morphology this is not understandable. It would be great to find more generally applicable formulations. *

We agree that this muscle-oriented vocabulary can make the use of PatternJ confusing. We have now corrected the user interface to provide both general and muscle-specific vocabulary ("center-to-center or edge-to-edge (M-line-to-M-line or Z-disc-to-Z-disc)").*

## Manual selection accuracy

The 1st step of the analysis is always to start from a user hand-drawn profile across intensity patterns in the image. However, this step can cause inaccuracy that varies with the shape and curve of the line profile drawn. If not strictly perpendicular to for example the M line patterns, the distance between intensity peaks will be different. This will be more problematic when dealing with non-straight and parallelly poised features in the image. If the structure is bended with a curve, the line drawn over it also needs to reproduce this curve, to precisely capture the intensity pattern. I found this limits the reproducibility and easy-usability of the software.*

We understand the concern of the reviewer. On curved selections this will be an issue that is difficult to solve, especially on "S" curved or more complex selections. The user will have to be very careful in these situations. On non-curved samples, the issue may be concerning at first sight, but the errors go with the inverse of cosine and are therefore rather low. For example, if the user creates a selection off by 5 degrees, which is visually obvious, lengths will be affected by an increase of only 0.38%. The point raised by the reviewer is important to discuss, and we therefore added a paragraph to comment on the choice of selection (lines 94-98) and a supplementary figure to help make it clear (Figure 1 - figure supplement 1).*

### Reproducibility

Since the line profile drawn on the image is the first step and very essential to the entire process, it should be considered to save together with the analysis result. For example, as ImageJ ROI or ROIset files that can be re-imported, correctly positioned, and visualized in the measured images. This would greatly improve the reproducibility of the proposed workflow. In the manuscript, only the extracted features are being saved (because the save button is also just asking for a folder containing images, so I cannot verify its functionality). *

We agree that this is a very useful and important feature. We have added ROI automatic saving. Additionally, we now provide a simplified import function of all ROIs generated with PatternJ and the automated extraction and analysis of the list of ROIs. This can be done from ROIs generated previously in PatternJ or with ROIs generated from other ImageJ/Fiji algorithms. These new features are described in the manuscript in lines 120-121 and 130-132.

*

## ? button

It would be great if that button would open up some usage instructions.

*

We agree with the reviewer that the "?" button can be used in a better way. We have replaced this button with a Help menu, including a simple tutorial showing a series of images detailing the steps to follow by the user, a link to the user website, and a link to our video tutorial.

* ## Easy improvement of workflow

I would suggest a reasonable expansion of the current workflow, by fitting and displaying 2D lines to the band or line structure in the image, that form the "patterns" the author aims to address. Thus, it extracts geometry models from the image, and the inter-line distance, and even the curve formed by these sets of lines can be further analyzed and studied. These fitted 2D lines can be also well integrated into ImageJ as Line ROI, and thus be saved, imported back, and checked or being further modified. I think this can largely increase the usefulness and reproducibility of the software.

*

We hope that we understood this comment correctly. We had sent a clarification request to the editor, but unfortunately did not receive an answer within the requested 4 weeks of this revision. We understood the following: instead of using our 1D approach, in which we extract positions from a profile, the reviewer suggests extracting the positions of features not as a single point, but as a series of coordinates defining its shape. If this is the case, this is a major modification of the tool that is beyond the scope of PatternJ. We believe that keeping our tool simple, makes it robust. This is the major strength of PatternJ. Local fitting will not use line average for instance, which would make the tool less reliable.

* # Manuscript

We recommend considering the following suggestions for improving the manuscript. Abstract: The abstract suggests that general patterns can be quantified, however the actual tool quantifies specific subtypes of one-dimensional patterns. We recommend adapting the abstract accordingly.

*

We modified the abstract to make this point clearer.

* Line 58: Gray-level co-occurrence matrix (GLCM) based feature extraction and analysis approach is not mentioned nor compared. At least there's a relatively recent study on Sarcomeres structure based on GLCM feature extraction: https://github.com/steinjm/SotaTool with publication: *https://doi.org/10.1002/cpz1.462

• *

We thank the reviewer for making us aware of this publication. We cite it now and have added it to our comparison of available approaches.

* Line 75: "...these simple geometrical features will address most quantitative needs..." We feel that this may be an overstatement, e.g. we can imagine that there should be many relevant two-dimensional patterns in biology?!*

We have modified this sentence to avoid potential confusion (lines 76-77).

• *

• Line 83: "After a straightforward installation by the user, ...". We think it would be convenient to add the installation steps at this place into the manuscript. *

__This sentence is now modified. We now mention how to install the toolset and we provide the link to the toolset website, if further information is needed (lines 86-88). __On the website, we provide a full video tutorial and a user manual.

* Line 87: "Multicolor images will give a graph with one profile per color." The 'Multicolor images' here should be more precisely stated as "multi-channel" images. Multi-color images could be confused with RGB images which will be treated as 8-bit gray value (type conversion first) images by profile plot in ImageJ. *

We agree with the reviewer that this could create some confusion. We modified "multicolor" to "multi-channel".

* Line 92: "...such as individual bands, blocks, or sarcomeric actin...". While bands and blocks are generic pattern terms, the biological term "sarcomeric actin" does not seem to fit in this list. Could a more generic wording be found, such as "block with spike"? *

We agree with the reviewer that "sarcomeric actin" alone will not be clear to all readers. We modified the text to "block with a central band, as often observed in the muscle field for sarcomeric actin" (lines 103-104). The toolset was modified accordingly.

* Line 95: "the algorithm defines one pattern by having the features of highest intensity in its centre". Could this be rephrased? We did not understand what that exactly means.*

We agree with the reviewer that this was not clear. We rewrote this paragraph (lines 101-114) and provided a supplementary figure to illustrate these definitions (Figure 1 - figure supplement 2).

* Line 124 - 147: This part the only description of the algorithm behind the feature extraction and analysis, but not clearly stated. Many details are missing or assumed known by the reader. For example, how it achieved sub-pixel resolution results is not clear. One can only assume that by fitting Gaussian to the band, the center position (peak) thus can be calculated from continuous curves other than pixels. *

Note that the two sentences introducing this description are "Automated feature extraction is the core of the tool. The algorithm takes multiple steps to achieve this (Fig. S2):". We were hoping this statement was clear, but the reviewer may refer to something else. We agree that the description of some of the details of the steps was too quick. We have now expanded the description where needed.

* Line 407: We think the availability of both the tool and the code could be improved. For Fiji tools it is common practice to create an Update Site and to make the code available on GitHub. In addition, downloading the example file (https://drive.google.com/file/d/1eMazyQJlisWPwmozvyb8VPVbfAgaH7Hz/view?usp=drive_link) required a Google login and access request, which is not very convenient; in fact, we asked for access but it was denied. It would be important for the download to be easier, e.g. from GitHub or Zenodo.

*

We are sorry for issues encountered when downloading the tool and additional material. We thank the reviewer for pointing out these issues that limited the accessibility of our tool. We simplified the downloading procedure on the website, which does not go through the google drive interface nor requires a google account. Additionally, for the coder community the code, user manual and examples are now available from GitHub at github.com/PierreMangeol/PatternJ, and are provided as supplementary material with the manuscript. To our knowledge, update sites work for plugins but not for macro toolsets. Having experience sharing our codes with non-specialists, a classical website with a tutorial video is more accessible than more coder-oriented websites, which deter many users.

* Reviewer #2 (Significance (Required)):

The strength of this study is that a tool for the analysis of one-dimensional repeated patterns occurring in muscle fibres is made available in the accessible open-source platform ImageJ/Fiji. In the introduction to the article the authors provide an extensive review of comparable existing tools. Their new tool fills a gap in terms of providing an easy-to-use software for users without computational skills that enables the analysis of muscle sarcomere patterns. We feel that if the below mentioned limitations could be addressed the tool could indeed be valuable to life scientists interested in muscle patterning without computational skills.

In our view there are a few limitations, including the accessibility of example data and tutorials at sites.google.com/view/patternj, which we had trouble to access. In addition, we think that the workflow in Fiji, which currently requires pressing several buttons in the correct order, could be further simplified and streamlined by adopting some "wizard" approach, where the user is guided through the steps.

*As answered above, the links on the PatternJ website are now corrected. Regarding the workflow, we now provide a Help menu with:

1. __a basic set of instructions to use the tool, __
2. a direct link to the tutorial video in the PatternJ toolset
3. a direct link to the website on which both the tutorial video and a detailed user manual can be found. We hope this addresses the issues raised by this reviewer.

*Another limitation is the reproducibility of the analysis; here we recommend enabling IJ Macro recording as well as saving of the drawn line ROIs. For more detailed suggestions for improvements please see the above sections of our review. *

We agree that saving ROIs is very useful. It is now implemented in PatternJ.

We are not sure what this reviewer means by "enabling IJ Macro recording". The ImageJ Macro Recorder is indeed very useful, but to our knowledge, it is limited to built-in functions. Our code is open and we hope this will be sufficient for advanced users to modify the code and make it fit their needs.*

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

Summary In this manuscript, the authors present a new toolset for the analysis of repetitive patterns in biological images named PatternJ. One of the main advantages of this new tool over existing ones is that it is simple to install and run and does not require any coding skills whatsoever, since it runs on the ImageJ GUI. Another advantage is that it does not only provide the mean length of the pattern unit but also the subpixel localization of each unit and the distributions of lengths and that it does not require GPU processing to run, unlike other existing tools. The major disadvantage of the PatternJ is that it requires heavy, although very simple, user input in both the selection of the region to be analyzed and in the analysis steps. Another limitation is that, at least in its current version, PatternJ is not suitable for time-lapse imaging. The authors clearly explain the algorithm used by the tool to find the localization of pattern features and they thoroughly test the limits of their tool in conditions of varying SNR, periodicity and band intensity. Finally, they also show the performance of PatternJ across several biological models such as different kinds of muscle cells, neurons and fish embryonic somites, as well as different imaging modalities such as brightfield, fluorescence confocal microscopy, STORM and even electron microscopy.

This manuscript is clearly written, and both the section and the figures are well organized and tell a cohesive story. By testing PatternJ, I can attest to its ease of installation and use. Overall, I consider that PatternJ is a useful tool for the analysis of patterned microscopy images and this article is fit for publication. However, i do have some minor suggestions and questions that I would like the authors to address, as I consider they could improve this manuscript and the tool:

*We are grateful to this reviewer for this very positive assessment of PatternJ and of our manuscript.

* Minor Suggestions: In the methodology section is missing a more detailed description about how the metric plotted was obtained: as normalized intensity or precision in pixels. *

We agree with the reviewer that a more detailed description of the metric plotted was missing. We added this information in the method part and added information in the Figure captions where more details could help to clarify the value displayed.

* The validation is based mostly on the SNR and patterns. They should include a dataset of real data to validate the algorithm in three of the standard patterns tested. *

We validated our tool using computer-generated images, in which we know with certainty the localization of patterns. This allowed us to automatically analyze 30 000 images, and with varying settings, we sometimes analyzed 10 times the same image, leading to about 150 000 selections analyzed. From these analyses, we can provide with confidence an unbiased assessment of the tool precision and the tool capacity to extract patterns. We already provided examples of various biological data images in Figures 4-6, showing all possible features that can be extracted with PatternJ. In these examples, we can claim by eye that PatternJ extracts patterns efficiently, but we cannot know how precise these extractions are because of the nature of biological data: "real" positions of features are unknown in biological data. Such validation will be limited to assessing whether a pattern was found or not, which we believe we already provided with the examples in Figures 4-6.

* The video tutorial available in the PatternJ website is very useful, maybe it would be worth it to include it as supplemental material for this manuscript, if the journal allows it. *

As the video tutorial may have been missed by other reviewers, we agree it is important to make it more prominent to users. We have now added a Help menu in the toolset that opens the tutorial video. Having the video as supplementary material could indeed be a useful addition if the size of the video is compatible with the journal limits.

* An example image is provided to test the macro. However, it would be useful to provide further example images for each of the three possible standard patterns suggested: Block, actin sarcomere or individual band.*

We agree this can help users. We now provide another multi-channel example image on the PatternJ website including blocks and a pattern made of a linear intensity gradient that can be extracted with our simpler "single pattern" algorithm, which were missing in the first example. Additionally, we provide an example to be used with our new time-lapse analysis.

* Access to both the manual and the sample images in the PatternJ website should be made publicly available. Right now they both sit in a private Drive account. *

As mentioned above, we apologize for access issues that occurred during the review process. These files can now be downloaded directly on the website without any sort of authentication. Additionally, these files are now also available on GitHub.

* Some common errors are not properly handled by the macro and could be confusing for the user: When there is no selection and one tries to run a Check or Extraction: "Selection required in line 307 (called from line 14). profile=getProfile( ;". A simple "a line selection is required" message would be useful there. When "band" or "block" is selected for a channel in the "Set parameters" window, yet a 0 value is entered into the corresponding "Number of bands or blocks" section, one gets this error when trying to Extract: "Empty array in line 842 (called from line 113). if ( ( subloc . length == 1 ) & ( subloc [ 0 == 0) ) {". This error is not too rare, since the "Number of bands or blocks" section is populated with a 0 after choosing "sarcomeric actin" (after accepting the settings) and stays that way when one changes back to "blocks" or "bands".*

We thank the reviewer for pointing out these bugs. These bugs are now corrected in the revised version.

* The fact that every time one clicks on the most used buttons, the getDirectory window appears is not only quite annoying but also, ultimately a waste of time. Isn't it possible to choose the directory in which to store the files only once, from the "Set parameters" window?*

We have now found a solution to avoid this step. The user is only prompted to provide the image folder when pressing the "Set parameter" button. We kept the prompt for directory only when the user selects the time-lapse analysis or the analysis of multiple ROIs. The main reason is that it is very easy for the analysis to end up in the wrong folder otherwise.

* The authors state that the outputs of the workflow are "user friendly text files". However, some of them lack descriptive headers (like the localisations and profiles) or even file names (like colors.txt). If there is something lacking in the manuscript, it is a brief description of all the output files generated during the workflow.*

PatternJ generates multiple files, several of which are internal to the toolset. They are needed to keep track of which analyses were done, and which colors were used in the images, amongst others. From the user part, only the files obtained after the analysis All_localizations.channel_X.txt and sarcomere_lengths.txt are useful. To improve the user experience, we now moved all internal files to a folder named "internal", which we think will clarify which outputs are useful for further analysis, and which ones are not. We thank the reviewer for raising this point and we now mention it in our Tutorial.

I don't really see the point in saving the localizations from the "Extraction" step, they are even named "temp".

We thank the reviewer for this comment, this was indeed not necessary. We modified PatternJ to delete these files after they are used.

* In the same line, I DO see the point of saving the profiles and localizations from the "Extract & Save" step, but I think they should be deleted during the "Analysis" step, since all their information is then grouped in a single file, with descriptive headers. This deleting could be optional and set in the "Set parameters" window.*

We understand the point raised by the reviewer. However, the analysis depends on the reference channel picked, which is asked for when starting an analysis, and can be augmented with additional selections. If a user chooses to modify the reference channel or to add a new profile to the analysis, deleting all these files would mean that the user will have to start over again, which we believe will create frustration. An optional deletion at the analysis step is simple to implement, but it could create problems for users who do not understand what it means practically.

* Moreover, I think it would be useful to also save the linear roi used for the "Extract & Save" step, and eventually combine them during the "Analysis step" into a single roi set file so that future re-analysis could be made on the same regions. This could be an optional feature set from the "Set parameters" window. *

We agree with the reviewer that saving ROIs is very useful. ROIs are now saved into a single file each time the user extracts and saves positions from a selection. Additionally, the user can re-use previous ROIs and analyze an image or image series in a single step.

* In the "PatternJ workflow" section of the manuscript, the authors state that after the "Extract & Save" step "(...) steps 1, 2, 4, and 5 can be repeated on other selections (...)". However, technically, only steps 1 and 5 are really necessary (alternatively 1, 4 and 5 if the user is unsure of the quality of the patterning). If a user follows this to the letter, I think it can lead to wasted time.

*

We agree with the reviewer and have corrected the manuscript accordingly (line 119-120).

• *

*I believe that the "Version Information" button, although important, has potential to be more useful if used as a "Help" button for the toolset. There could be links to useful sources like the manuscript or the PatternJ website but also some tips like "whenever possible, use a higher linewidth for your line selection" *

We agree with the reviewer as pointed out in our previous answers to the other reviewers. This button is now replaced by a Help menu, including a simple tutorial in a series of images detailing the steps to follow, a link to the user website, and a link to our video tutorial.

* It would be interesting to mention to what extent does the orientation of the line selection in relation to the patterned structure (i.e. perfectly parallel vs more diagonal) affect pattern length variability?*

As answered to reviewer 1, we understand this concern, which needs to be clarified for readers. The issue may be concerning at first sight, but the errors grow only with the inverse of cosine and are therefore rather low. For example, if the user creates a selection off by 3 degrees, which is visually obvious, lengths will be affected by an increase of only 0.14%. The point raised by the reviewer is important to discuss, and we therefore have added a comment on the choice of selection (lines 94-98) as well as a supplementary figure (Figure 1 - figure supplement 1).

* When "the algorithm uses the peak of highest intensity as a starting point and then searches for peak intensity values one spatial period away on each side of this starting point" (line 133-135), does that search have a range? If so, what is the range? *

We agree that this information is useful to share with the reader. The range is one pattern size. We have modified the sentence to clarify the range of search used and the resulting limits in aperiodicity (now lines 176-181).

* Line 144 states that the parameters of the fit are saved and given to the user, yet I could not find such information in the outputs. *

The parameters of the fits are saved for blocks. We have now clarified this point by modifying the manuscript (lines 186-198) and modifying Figure 1 - figure supplement 5. We realized we made an error in the description of how edges of "block with middle band" are extracted. This is now corrected.

* In line 286, authors finish by saying "More complex patterns from electron microscopy images may also be used with PatternJ.". Since this statement is not backed by evidence in the manuscript, I suggest deleting it (or at the very least, providing some examples of what more complex patterns the authors refer to). *

This sentence is now deleted.

* In the TEM image of the fly wing muscle in fig. 4 there is a subtle but clearly visible white stripe pattern in the original image. Since that pattern consists of 'dips', rather than 'peaks' in the profile of the inverted image, they do not get analyzed. I think it is worth mentioning that if the image of interest contains both "bright" and "dark" patterns, then the analysis should be performed in both the original and the inverted images because the nature of the algorithm does not allow it to detect "dark" patterns. *

We agree with the reviewer's comment. We now mention this point in lines 337-339.

* In line 283, the authors mention using background correction. They should explicit what method of background correction they used. If they used ImageJ's "subtract background' tool, then specify the radius.*

We now describe this step in the method section.

*

Reviewer #3 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. Being a software paper, the advance proposed by the authors is technical in nature. The novelty and significance of this tool is that it offers quick and simple pattern analysis at the single unit level to a broad audience, since it runs on the ImageJ GUI and does not require any programming knowledge. Moreover, all the modules and steps are well described in the paper, which allows easy going through the analysis.
• Place the work in the context of the existing literature (provide references, where appropriate). The authors themselves provide a good and thorough comparison of their tool with other existing ones, both in terms of ease of use and on the type of information extracted by each method. While PatternJ is not necessarily superior in all aspects, it succeeds at providing precise single pattern unit measurements in a user-friendly manner.
• State what audience might be interested in and influenced by the reported findings. Most researchers working with microscopy images of muscle cells or fibers or any other patterned sample and interested in analyzing changes in that pattern in response to perturbations, time, development, etc. could use this tool to obtain useful, and otherwise laborious, information. *

We thank the reviewer for these enthusiastic comments about how straightforward for biologists it is to use PatternJ and its broad applicability in the bio community.

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

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors present an ImageJ Macro GUI tool set for the quantification of one-dimensional repeated patterns that are commonly occurring in microscopy images of muscles.

Major comments

In our view the article and also software could be improved in terms of defining the scope of its applicability and user-ship. In many parts the article and software suggest that general biological patterns can be analysed, but then in other parts very specific muscle actin wordings are used. We are pointing this out in the "Minor comments" sections below. We feel that the authors could improve their work by making a clear choice here. One option would be to clearly limit the scope of the tool to the analysis of actin structures in muscles. In this case we would recommend to also rename the tool, e.g. MusclePatternJ. The other option would be to make the tool about the generic analysis of one-dimensional patterns, maybe calling the tool LinePatternJ. In the latter case we would recommend to remove all actin specific wordings from the macro tool set and also the article should be in parts slightly re-written.

Minor/detailed comments

Software

We recommend considering the following suggestions for improving the software.

File and folder selection dialogs

In general, clicking on many of the buttons just opens up a file-browser dialog without any further information. For novel users it is not clear what the tool expects one to select here. It would be very good if the software could be rewritten such that there are always clear instructions displayed about which file or folder one should open for the different buttons.

Extract button

The tool asks one to specify things like whether selections are drawn "M-line-to-M-line"; for users that are not experts in muscle morphology this is not understandable. It would be great to find more generally applicable formulations.

Manual selection accuracy

The 1st step of the analysis is always to start from a user hand-drawn profile across intensity patterns in the image. However, this step can cause inaccuracy that varies with the shape and curve of the line profile drawn. If not strictly perpendicular to for example the M line patterns, the distance between intensity peaks will be different. This will be more problematic when dealing with non-straight and parallelly poised features in the image. If the structure is bended with a curve, the line drawn over it also needs to reproduce this curve, to precisely capture the intensity pattern. I found this limits the reproducibility and easy-usability of the software.

Reproducibility

Since the line profile drawn on the image is the first step and very essential to the entire process, it should be considered to save together with the analysis result. For example, as ImageJ ROI or ROIset files that can be re-imported, correctly positioned, and visualized in the measured images. This would greatly improve the reproducibility of the proposed workflow. In the manuscript, only the extracted features are being saved (because the save button is also just asking for a folder containing images, so I cannot verify its functionality).

? button

It would be great if that button would open up some usage instructions.

Easy improvement of workflow

I would suggest a reasonable expansion of the current workflow, by fitting and displaying 2D lines to the band or line structure in the image, that form the "patterns" the author aims to address. Thus, it extracts geometry models from the image, and the inter-line distance, and even the curve formed by these sets of lines can be further analyzed and studied. These fitted 2D lines can be also well integrated into ImageJ as Line ROI, and thus be saved, imported back, and checked or being further modified. I think this can largely increase the usefulness and reproducibility of the software.

Manuscript

We recommend considering the following suggestions for improving the manuscript. Abstract: The abstract suggests that general patterns can be quantified, however the actual tool quantifies specific subtypes of one-dimensional patterns. We recommend adapting the abstract accordingly.

Line 58: Gray-level co-occurrence matrix (GLCM) based feature extraction and analysis approach is not mentioned nor compared. At least there's a relatively recent study on Sarcomeres structure based on GLCM feature extraction: https://github.com/steinjm/SotaTool with publication: https://doi.org/10.1002/cpz1.462

Line 75: "...these simple geometrical features will address most quantitative needs..." We feel that this may be an overstatement, e.g. we can imagine that there should be many relevant two-dimensional patterns in biology?!

Line 83: "After a straightforward installation by the user, ...". We think it would be convenient to add the installation steps at this place into the manuscript.

Line 87: "Multicolor images will give a graph with one profile per color." The 'Multicolor images' here should be more precisely stated as "multi-channel" images. Multi-color images could be confused with RGB images which will be treated as 8-bit gray value (type conversion first) images by profile plot in ImageJ.

Line 92: "...such as individual bands, blocks, or sarcomeric actin...". While bands and blocks are generic pattern terms, the biological term "sarcomeric actin" does not seem to fit in this list. Could a more generic wording be found, such as "block with spike"?

Line 95: "the algorithm defines one pattern by having the features of highest intensity in its centre". Could this be rephrased? We did not understand what that exactly means.

Line 124 - 147: This part the only description of the algorithm behind the feature extraction and analysis, but not clearly stated. Many details are missing or assumed known by the reader. For example, how it achieved sub-pixel resolution results is not clear. One can only assume that by fitting Gaussian to the band, the center position (peak) thus can be calculated from continuous curves other than pixels.

Line 407: We think the availability of both the tool and the code could be improved. For Fiji tools it is common practice to create an Update Site and to make the code available on GitHub. In addition, downloading the example file (https://drive.google.com/file/d/1eMazyQJlisWPwmozvyb8VPVbfAgaH7Hz/view?usp=drive_link) required a Google login and access request, which is not very convenient; in fact, we asked for access but it was denied. It would be important for the download to be easier, e.g. from GitHub or Zenodo.

Significance

The strength of this study is that a tool for the analysis of one-dimensional repeated patterns occurring in muscle fibres is made available in the accessible open-source platform ImageJ/Fiji. In the introduction to the article the authors provide an extensive review of comparable existing tools. Their new tool fills a gap in terms of providing an easy-to-use software for users without computational skills that enables the analysis of muscle sarcomere patterns. We feel that if the below mentioned limitations could be addressed the tool could indeed be valuable to life scientists interested in muscle patterning without computational skills.

In our view there are a few limitations, including the accessibility of example data and tutorials at sites.google.com/view/patternj, which we had trouble to access. In addition, we think that the workflow in Fiji, which currently requires pressing several buttons in the correct order, could be further simplified and streamlined by adopting some "wizard" approach, where the user is guided through the steps. Another limitation is the reproducibility of the analysis; here we recommend enabling IJ Macro recording as well as saving of the drawn line ROIs. For more detailed suggestions for improvements please see the above sections of our review.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Bell et al. provide an exhaustive and clear description of the diversity of a new class of predicted type IV restriction systems that the authors denote as CoCoNuTs, for their characteristic presence of coiled-coil segments and nuclease tandems. Along with a comprehensive analysis that includes phylogenetics, protein structure prediction, extensive protein domain annotations, and an in-depth investigation of encoding genomic contexts, they also provide detailed hypotheses about the biological activity and molecular functions of the members of this class of predicted systems. This work is highly relevant, it underscores the wide diversity of defence systems that are used by prokaryotes and demonstrates that there are still many systems to be discovered. The work is sound and backed-up by a clear and reasonable bioinformatics approach. I do not have any major issues with the manuscript, but only some minor comments.

Strengths:

The analysis provided by the authors is extensive and covers the three most important aspects that can be covered computationally when analysing a new family/superfamily: phylogenetics, genomic context analysis, and protein-structure-based domain content annotation. With this, one can directly have an idea about the superfamily of the predicted system and infer their biological role. The bioinformatics approach is sound and makes use of the most current advances in the fields of protein evolution and structural bioinformatics.

Weaknesses:

It is not clear how coiled-coil segments were assigned if only based on AF2-predicted models or also backed by sequence analysis, as no description is provided in the methods. The structure prediction quality assessment is based solely on the average pLDDT of the obtained models (with a threshold of 80 or better). However, this is not enough, particularly when multimeric models are used. The PAE matrix should be used to evaluate relative orientations, particularly in the case where there is a prediction that parts from 2 proteins are interacting. In the case of multimers, interface quality scores, such as the ipTM or pDockQ, should also be considered and, at minimum, reported.

A description of the coiled-coil predictions has been added to the Methods. For multimeric models, PAE matrices and ipTM+pTM scores have been included in Supplementary Data File S1.

Reviewer #2 (Public Review):

Summary:

In this work, using in-depth computational analysis, Bell et al. explore the diverse repertoire of type IV McrBC modification-dependent restriction systems. The prototypical two-component McrBC system has been structurally and functionally characterised and is known to act as a defence by restricting phage and foreign DNA containing methylated cytosines. Here, the authors find previously unanticipated complexity and versatility of these systems and focus on detailed analysis and classification of a distinct branch, the so-called CoCoNut, named after its composition of coiled-coil structures and tandem nucleases. These CoCoNut systems are predicted to target RNA as well as DNA and to utilise defence mechanisms with some similarity to type III CRISPR-Cas systems.

Strengths:

This work is enriched with a plethora of ideas and a myriad of compelling hypotheses that now await experimental verification. The study comes from the group that was amongst the first to describe, characterize, and classify CRISPR-Cas systems. By analogy, the findings described here can similarly promote ingenious experimental and conceptual research that could further drive technological advances. It could also instigate vigorous scientific debates that will ultimately benefit the community.

Weaknesses:

The multi-component systems described here function in the context of large oligomeric complexes. Some of the single chain AF2 predictions shown in this work are not compatible, for example, with homohexameric complex formation due to incompatible orientation of domains. The recent advances in protein structure prediction, in particular AlphaFold2 (AF2) multimer, now allow us to confidently probe potential protein-protein interactions and protein complex formation. This predictive power could be exploited here to produce a better glimpse of these multimeric protein systems. It can also provide a more sound explanation for some of the observed differences amongst different McrBC types.

Hexameric CnuB complexes with CnuC stimulatory monomers for Type I-A, I-B, I-C, II, and III-A CoCoNuT systems have been modeled with AF2 and included in Supplementary Data File S1, albeit without the domains fused to the GTPase N-terminus (with the exception of Type I-B, which lacks the long coiled-coil domain fused to the GTPase and was modeled with its entire sequence). Attempts to model the other full-length CnuB hexamers did not lead to convincing results.

Recommendations for the authors:

Reviewing Editor:

The detailed recommendations by the two reviewers will help the authors to further strengthen the manuscript, but two points seem particularly worth considering: 1. The methods are barely sketched in the manuscript, but it could be useful to detail them more closely. Particularly regarding the coiled-coil segments, which are currently just statists, useful mainly for the name of the family, more detail on their prediction, structural properties, and purpose would be very helpful. 2. Due to its encyclopedic nature, the wealth of material presented in the paper makes it hard to penetrate in one go. Any effort to make it more accessible would be very welcome. Reviewer 1 in particular has made a number of suggestions regarding the figures, which would make them provide more support for the findings described in the text.

A description of the techniques used to identify coiled-coil segments has been added to the Methods. Our predictions ranged from near certainty in the coiled-coils detected in CnuB homologs, to shorter helices at the limit of detection in other factors. We chose to report all probable coiled-coils, as the extensive coiled-coils fused to CnuB, which are often the only domain present other than the GTPase, imply involvement in mediating complex formation by interacting with coiled-coils in other factors, particularly the other CoCoNuT factors. The suggestions made by Reviewer 1 were thoughtful and we made an effort to incorporate them.

Reviewer #1 (Recommendations For The Authors):

I do not have any major issues with the manuscript. I have however some minor comments, as described below.

• The last sentence of the abstract at first reads as a fact and not a hypothesis resulting from the work described in the manuscript. After the second read, I noticed the nuances in the sentence. I would suggest a rephrasing to emphasize that the activity described is a theoretical hypothesis not backed-up by experiments.

This sentence has been rephrased to make explicit the hypothetical nature of the statement.

• In line 64, the authors rename DUF3578 as ADAM because indeed its function is not unknown. Did the authors consider reaching out to InterPro to add this designation to this DUF? A search in interpro with DUF3578 results in "MrcB-like, N-terminal domain" and if a name is suggested, it may be worthwhile to take it to the IntrePro team.

We will suggest this nomenclature to InterPro.

• I find Figure 1E hard to analyse and think it occupies too much space for the information it provides. The color scheme, the large amount of small slices, and the lack of numbers make its information content very small. I would suggest moving this to the supplementary and making it instead a bar plot. If removed from Figure 1, more space is made available for the other panels, particularly the structural superpositions, which in my opinion are much more important.

We have removed Figure 1E from the paper as it adds little information beyond the abundance and phyletic distribution of sequenced prokaryotes, in which McrBC systems are plentiful.

• In Figure 2, it is not clear due to the presence of many colorful "operon schemes" that the tree is for a single gene and not for the full operon segment. Highlighting the target gene in the operons or signalling it somehow would make the figure easy to understand even in the absence of the text and legend. The same applies to Supplementary Figure 1.

The legend has been modified to show more clearly that this is a tree of McrB-like GTPases.

• In line 146, the authors write "AlphaFold-predicted endonucelase fold" to say that a protein contains a region that AF2 predicts to fold like an endonuclease. This is a weird way of writing it and can be confusing to non-expert readers. I would suggest rephrasing for increased clarity.

This sentence has been rephrased for greater clarity.

• In line 167, there is a [47]. I believe this is probably due to a previous reference formatting.

Indeed, this was a reference formatting error and has been fixed.

• In most figures, the color palette and the use of very similar color palettes for taxonomy pie charts, genomic context composition schemes, and domain composition diagrams make it really hard to have a good understanding of the image at first. Legends are often close to each other, and it is not obvious at first which belong to what. I would suggest changing the layouts and maybe some color schemes to make it easier to extract the information that these figures want to convey.

It seemed that Figure 4 was the most glaring example of these issues, and it has been rearranged for easier comprehension.

• In the paragraph that starts at line 199, the authors mention an Ig-like domain that is often found at the N-terminus of Type I CoCoNuTs. Are they all related to each other? How conserved are these domains?

These domains are all predicted to adopt a similar beta-sandwich fold and are found at the N-terminus of most CoCoNuT CnuC homologs, suggesting they are part of the same family, but we did not undertake a more detailed sequenced-based analysis of these regions.

We also find comparable domains in the CnuC/McrC-like partners of the abundant McrB-like NxD motif GTPases that are not part of CoCoNuT systems, and given the similarity of some of their predicted structures to Rho GDP-dissociation inhibitor 1, we suspect that they have coevolved as regulators of the non-canonical NxD motif GTPase type. Our CnuBC multimer models showing consistent proximity between these domains in CnuC and CnuB GTPase domains suggest this could indeed be the case. We plan to explore these findings further in a forthcoming publication.

• In line 210, the authors write "suggesting a role in overcrowding-induced stress response". Why so? In >all other cases, the authors justify their hypothesis, which I really appreciated, but not here.

A supplementary note justifying this hypothesis has been added to Supplementary Data File S1.

• At the end of the paragraph that starts in line 264, the authors mention that they constructed AF2 multimeric models to predict if 2 proteins would interact. However, no quality scores were provided, particularly the PAE matrix. This would allow for a better judgement of this prediction, and I would suggest adding the PAE matrix as another panel in the figure where the 3D model of the complex is displayed.

The PAE matrix and ipTM+pTM scores for this and other multimer models have been added to Supplementary Data File S1. For this model in particular, the surface charge distribution of the model has been presented to support the role of the domains that have a higher PAE in RNA binding.

• In line 306, "(supplementary data)" refers to what part of the file?

This file has been renamed Supplementary Table S3 and referenced as such.

• In line 464, the authors suggest that ShdA could interact with CoCoNuTs. Why not model the complex as done for other cases? what would co-folding suggest?

As we were not able to convincingly model full-length CnuB hexamers with N-terminal coiled-coils, we did not attempt modeling of this hypothetical complex with another protein with a long coiled-coil, but it remains an interesting possibility.

• In line 528, why and how were some genes additionally analyzed with HHPred?

Justification for this analysis has been added to the Methods, but briefly, these genes were additionally analyzed if there were no BLAST hits or to confirm the hits that were obtained.

• In the first section of the methods, the first and second (particularly the second) paragraphs are extremely long. I would suggest breaking them to facilitate reading.

This change has been made.

• In line 545, what do the authors mean by "the alignment (...) were analyzed with HHPred"?

A more detailed description of this step has been added to the Methods.

• The authors provide the models they produced as well as extensive supplementary tables that make their data reusable, but they do not provide the code for the automated steps, as to excise target sequence sections out of multiple sequence alignments, for example.

The code used for these steps has been in use in our group at the NCBI for many years. It will be difficult to utilize outside of the NCBI software environment, but for full disclosure, we have included a zipped repository with the scripts and custom-code dependencies, although there are external dependencies as well such as FastTree and BLAST. In brief, it involves PSI-BLAST detection of regions with the most significant homology to one of a set of provided alignments (seals-2-master/bin/wrappers/cog_psicognitor). In this case, the reference alignments of McrB-like GTPases and DUF2357 were generated manually using HHpred to analyze alignments of clustered PSI-BLAST results. This step provided an output of coordinates defining domain footprints in each query sequence, which were then combined and/or extended using scripts based on manual analysis of many examples with HHpred (footprint_finders/get_GTPase_frags.py and footprint_finders/get_DUF2357_frags.py), then these coordinates were used to excise such regions from the query amino acid sequence with a final script (seals-2-master/bin/misc/fa2frag).

Reviewer #2 (Recommendations For The Authors):

(1) Page 4, line 77 - 'PUA superfamily domains' could be more appropriate to use instead of "EVE superfamily".

While this statement could perhaps be applied to PUA superfamily domains, our previous work we refer to, which strongly supports the assertion, was restricted to the EVE-like domains and we prefer to retain the original language.

(2) Page 5. lines 128-130 - AF2 multimer prediction model could provide a more sound explanation for these differences.

Our AF2 multimer predictions added in this revision indeed show that the NxD motif McrB-like CoCoNuT GTPases interact with their respective McrC-like partners such that an immunoglobulin-like beta-sandwich domain, fused to the N-termini of the McrC homologs and similar to Rho GDP-dissociation inhibitor 1, has the potential to physically interact with the GTPase variants. However, we did not probe this in greater detail, as it is beyond the scope of this already highly complex article, but we plan to study it in the future.

(3) Page 8, line 252 - The surface charge distribution of CnuH OB fold domain looks very different from SmpB (pdb3iyr). In fact, the regions that are in contact with RNA in SmpB are highly acidic in CoCoNut CnuH. Although it looks likely that this domain is involved in RNA binding, the mode of interaction should be very different.

We did not detect a strong similarity between the CnuH SmpB-like SPB domain and PDB 3IYR, but when we compare the surface charge distribution of PDB 1WJX and the SPB domain, while there is a significant area that is positively charged in 1WJX that is negatively charged in SPB, there is much that overlaps with the same charge in both domains.

The similarity between SmpB and the SPB domain is significant, but definitely not exact. An important question for future studies is: If the domains are indeed related due to an ancient fusion of SmpB to an ancestor of CnuH, would this degree of divergence be expected?

In other words, can we say anything about how the function of a stand-alone tmRNA-binding protein could evolve after being fused to a complex predicted RNA helicase with other predicted RNA binding domains already present? Experimental validation will ultimately be necessary to resolve these kinds of questions, but for now, it may be safe to say that the presence of this domain, especially in conjunction with the neighboring RelE-like RTL domain and UPF1-like helicase domain, signals a likely interaction with the A-site of the ribosome, and perhaps restriction of aberrant/viral mRNA.

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

The manuscript by Barba-Aliaga and colleagues describe a potential function of eIF5A for the control of TIM50 translation. The authors showed that in temperature-sensitive mutants of eIF5A several mitochondrial proteins are decreased including OXPHOS subunits, proteins of the TCA cycle and some components of protein translocases. Some precursor proteins appear to localize into the cytosol. As consequent of mitochondrial dysfunction, the expression of some stress components is induced. The idea is that eIF5A ribosome-stalling of the proline-rich Tim50 of the TIM23 complex and thereby controls mitochondrial protein set-up.

The findings are potentially interesting. However, some control experiments are required to substantiate the findings.

1. To support their conclusion the authors should show whether Tim50 levels are affected in the eIF5A-ts mutants used. Tim50 protein half-life is approximately 9.6 h (Christiano et al, 2014), which makes difficult to measure large differences in new protein synthesis upon eIF5A depletion. However, we used different approaches to show that reduction in eIF5A provokes a reduction in Tim50 protein levels and synthesis. 1) The steady-state levels of Tim50 protein (genomic HA-tagged version) are shown by western blotting analysis in Fig. S4B and confirm a significant drop of approximately 20% in the tif51A-1 mutant at restrictive temperature. 2) The use of a construct in which Tim50 is fused to a nanoluciferase reporter under the control of a tetO7 inducible promoter shows a significant 3-fold reduction in Tim50 protein synthesis in the tif51A-1 mutant compared to wild-type (Fig. 4C). In addition, the protein synthesis time is calculated and indicates that it takes the double time for the tif51A-1 strain to synthesize Tim50 protein than the wild-type (Fig. 4E). 3) The expression of a FLAG-TIM50-GFP version under a GAL inducible system also shows a significant reduction in Tim50 protein synthesis in the two eIF5A temperature-sensitive strains (Fig. S4C). 4) The proteomic analysis performed at 41ºC showed a 20% reduction in Tim50 protein levels in the two eIF5A temperature-sensitive strains, although not being statistically significant (Table S1). Furthermore, TIM50 mRNA levels were determined by RT-qPCR across all the experiments mentioned to confirm that the low levels of Tim50 protein were not due to decreased transcription or increased mRNA degradation. 5) An additional experiment of polysome profiling has been included in Fig. R1 (Figure for Reviewers) showing a higher TIM50 mRNA abundance at low polysomal fractions and a lower mRNA abundance at heavy polysomal fractions upon eIF5A depletion. This indicates that the TIM50 mRNA abundance is significantly shifted to earlier fractions and translation of Tim50 is reduced in the tif51A-1 mutant at restrictive temperature but not at permissive temperature. Altoghether, all these experiments confirm a significant reduction of Tim50 protein levels upon eIF5A depletion and conclusions are supported on these results.

How are the levels of TOM and TIM23 subunits?

Response: Our proteomic analysis shows that the protein levels of Tom70 and Tom20 receptor subunits of the TOM complex are significantly decreased in the two eIF5A temperature-sensitive strains (Table S1). These results are in agreement with the polysome profiling results, where it is seen a significant reduction of TOM70 and TOM20 mRNAs in the heavy polysomal fractions while a significant increase of these mRNAs is observed in the light fractions of eIF5A-depleted cells (Fig. 2C and Fig. S2D). Apart from Tim50, no other proteins of the Tim23 translocase complex were detected in the proteomic analysis.

Furthermore, how are the levels of the Tim50 variant that lack the proline residues? Is the stability or function of Tim50 affected by these mutations?

Although we did not specifically analysed the Tim50ΔPro protein levels, a quantification of the Tim50ΔPro fluorescent signal has been performed to address this matter and is shown in Fig. R2 and mentioned in the corresponding Results section. Results indicate that the Tim50 variant lacking the proline residues has similar protein levels to the wild-type version and therefore, it is tempting to say that its stability should also be similar. However, if Reviewers consider this to be essential for publishing, additional experiments using cycloheximide could be conducted in order to better assess the stability and half-life of this Tim50 version.

Additionally, functional levels of Tim50ΔPro protein is shown by the fact that wild-type cells carrying this Tim50 protein version as the only copy of Tim50 grew well in glycerol media, where Tim50 is essential for the mitochondrial function (Fig. 5A). However, we suspect that Tim50ΔPro is a bit less efficient protein since a double mutant tif51A-1 Tim50ΔPro shows even reduced growth than the single tif51A-1 mutant (Fig. 5A). This information also responds to the comments made by Reviewer #2.

How specific is the effect of eIF5A on Tim50? Is there any other mitochondrial substrate of eIF5A? It is not so clear to the reviewer why the authors focused on Tim50.

Response: eIF5A has been shown to be necessary for the translation of mRNA codons encoding for consecutive prolines and, consequently, lack of eIF5A causes ribosome stalling in these polyproline motifs (Gutierrez et al., 2013; Pelechano and Alepuz, 2017; Schuller et al., 2017). In our manuscript we showed: 1) using an artificial tetO7-TIM50-nanoLuc genomic construct we demonstrate that the synthesis of Tim50 protein (measured as appearance of luciferase activity upon induction of tetO promoter) is significantly reduced by 3-fold under eIF5A depletion only when Tim50 contains the stretch of 7 consecutive prolines (Fig. 4A-D); 2) genomic Tim50-HA and plasmid FLAG-TIM50-GFP protein levels are significantly reduced upon eIF5A depletion (Fig. S4B); 3) calculation of the time for translation elongation of Tim50 mRNA shows that this time is double in cells with eIF5A depletion than in cells containing normal eIF5A levels (Fig. 4E); and 4) analysis of published ribosome profiling data shows a precipitous drop-off in ribosome density exactly where the stretch of polyprolines is located in Tim50 (540-561bp) upon eIF5A depletion but not in the control strain (Fig. 4F). This result is indicative of ribosome stalling at Tim50 polyproline motif upon eIF5A depletion. Altogether, our results strongly support a direct and specific role of eIF5A in Tim50 protein synthesis. However, as we discuss in relation to Fig. 5 and in the Discussion section, Tim50 does not seem to be the only mitochondrial substrate of eIF5A, since recovery of Tim50 protein synthesis does not rescue the growth of eIF5A mutants under respiratory conditions. In this line, we have added further data pointing to ribosome stalling for other co-translationally inserted mitoproteins which are potential substrates of eIF5A (Table S6). Accordingly, this has also been included in the Discussion section. This information also responds to the comments made by Reviewer #4.

Our focus on Tim50 in this manuscript resides in that we found a global downregulation of mitochondrial protein synthesis (Fig.1 and 2) in parallel to the accumulation of mitochondrial precursor proteins in the cytoplasm and induction of the mitoCPR response (Fig.3). All these data were pointing to a mitochondrial protein import defect. Since Tim50 is an essential component of the Tim23 translocase complex, its protein levels are reduced in eIF5A mutants and Tim50 contains a polyproline motif, all these data were pointing towards a Tim50-dependent effect in mitochondrial protein import upon eIF5A depletion, which we addressed in the manuscript.

Figure 1A: Which tif51A strain was used?

Response: The proteomic analysis was performed with tif51A-1 and tif51A-3 temperature-sensitive strains (see Table S1) and Fig.1A shows the average of the values obtained for the two mutants (proteins detected as down-regulated in these two samples and from 3 different biological replicates). This is now clarified in the Figure 1A legend. A similar approach was also followed in Pelechano and Alepuz, 2017. Additionally, the ratios between the protein level in the temperature-sensitive mutant respect wild-type for each protein and for each eIF5A mutant are also shown in Table S1. This information also responds to the comments made by Reviewer #2.

Figure 1C: The authors should show the steady state levels of some OXPHOS/TCA components to confirm the findings of Figure 1A.

Response: Proteomic findings have been confirmed for several proteins. The steady state levels of Por1 and Hsp60 proteins were investigated by western blotting (Figs. 1C,D) and results show a significant down-regulation on the two eIF5A temperature-sensitive strains at 41ºC, which confirms the findings of Fig. 1A. Additionally, we have included the same experiment performed at 37ºC (Fig. S1E), which also confirms the same conclusion.

Furthermore, the steady-state levels of Tim50 protein were also investigated by western blotting (Fig. S4B), and results also showed a significant down-regulation in the tif51A-1 mutant at restrictive temperature (37ºC), compared to wild-type. This result also confirms the findings of Fig. 1A.

However, if Reviewers consider that additional confirmation for OXPHOS/TCA proteins to be essential for publishing, additional experiments could be conducted to assess the protein levels of other OXPHOS/TCA proteins.

The manuscript contains several quantifications. However, central information like number of repeats or whether a standard deviation or S.E.M. is depicted are missing.

Response: Clear information on the number of repeats, type of graphical representation and statistical analysis is now included for all figures in the corresponding figure legends and also detailed in the Materials and Methods section. This information also responds to the comments made by Reviewer #2.

Figure 3: The authors propose that precursor form aggregates outside mitochondria. To assess the data, a quantification should address in how many cells are protein aggregates.

Response: The quantification of cytoplasmic Yta12 aggregates is now included in Fig.3E, which shows significant differences between the tif51A-1 mutant and the wild-type strain. In addition, quantification of cytosolic Tim50 aggregates was already included in Fig. 4H, which also shows significant differences between the tif51A-1 mutant and the wild-type strain. These two figures include the individual values from three biological replicates (at least 150 cells were analyzed), mean, standard deviation and statistical analysis.

Do the observed aggregated proteins interact with Hsp104? recycled?

Response: Yes, the cytoplasmic mitochondrial precursor aggregated proteins co-localize with Hsp104 as shown in Fig. 3I for Cyc1 and in Fig. 4J for Tim50. The quantification of Cyc1 and Tim50 co-localization with Hsp104 is shown in Fig S5D.

Significance

See above

Reviewer #2

Evidence, reproducibility and clarity

The authors report here novel findings concerning the role of eIF5A in mediating protein import to mitochondria in the model eukaryote Saccharomyces cerevisiae. It was previously known from structural and other studies that the translation factor eIF5A binds to the E-site of stalled ribosomes to help promote peptide bond formation. It was inferred by ribosome footprinting and reporter studies assessing the impact of eIF5A depletion that eIF5A is particularly needed to translate several specific amino acid motifs including polyproline stretches. However additional target sequences are known.

Here a proteomics approach reveals clear evidence that mitochondrially targeted proteins are impacted by temperature sensitive mutations in eIF5A that deplete the factor, including those without polyprolines. The authors then use a range of molecular and cell biology to focus on the role of mitochondrial signal sequences/mitochondrial protein import and the mitochondrial stress response, before highlighting a role for poly-prolines in Tim50, a major mitochondrial protein import factor. Consistent with the ribosome footprinting done previously it is shown that a stretch of 7 prolines limit its translation when eIF5A is depleted and studies shown here are consistent with the idea that this has wider consequences for mitochondrial protein import and hence translation/stability of other proteins. However improved Tim50 translation alone, by eliminating the poly-proline motif, is not sufficient to overcome all consequences of eIF5A depletion for mitochondrial protein import and for viability, suggesting a wider role.

In general the text flows nicely, this could be a study that explains why a large number of mitochondrially targeted proteins are impacted by depletion of eIF5A in yeast. As the poly Pro sequence in Tim50 is not conserved in higher eukaryotes it is unclear how this observation will scale to other systems, but it provides an example of how studies in a relatively simple system can trace wide-spread impact of the loss of one component of a central pathway-here protein synthesis to altered translation of a key component of another process-mitochondrial protein import. Given that eIF5A and its hypusine modifying enzymes are mutated in rare human disorders, it is likely there will be interest in this study.

However, while the conclusions may be justified, there are significant deficiencies in how the experiments have been analysed and presented in this version of the manuscript that impact every figure shown, coupled with deficiencies in the methods section that all need to be addressed. Thus, we have here the basis of what should be a very interesting paper here, but there is a lot of work to do to remedy perceived weaknesses. It may be that the overall conclusions are entirely sound and appropriate, but I suspect that performing the statistics in less biased ways may change some of the significant differences claimed. Some explanations concerning how data analyses were conducted and the reasons for specific analysis decisions being made would also improve the narrative. These points are expanded on below.

All the edits suggested here are aimed at improving the rigor of reporting in this study. Depending on how they are answered some may become major issues, or they could all be minor.

1 Figure 1 shows proteomic data for response to heat shock at 41{degree sign}C. In the text it is made clear that two different temperature sensitive missense alleles the 51A-1 and 51A-3 were analysed, but the single volcano plot in Figure 1A does not say whether it is reporting one of these experiments compared to WT (which one) or some other analysis (ie have data from the 2 mutants been amalgamated somehow?). I would assume only one, but which one, and why only one plot? How different is the other experiment? Why does the Figure title say the experiment is an eIF5A deletion when it is not this?

Response: The data shown in Figure 1A corresponds to the average values obtained in the proteomic analysis for the two temperature-sensitive mutants tif51A-1 and tif51A-3 (with data for each mutant obtained from 3 different biological replicates). Highly reproducible proteomic results and similar between the two mutants were obtained (see in Fig. S1A the MDS-plot showing all replicates for each strain and condition studied in the proteomic analysis). In addition, the proteomic data showing the protein 41°C/25°C ratio for each eIF5A temperature-sensitive mutant with respect to wild-type is shown in the Table S1. This is now clarified in the Figure 1A legend. A similar approach using the mean values of the two mutants was followed in the analysis of ribosome footprintings made in Pelechano and Alepuz, 2017. Additionally, the ratios between the protein level in the temperature-sensitive mutant respect wild-type for each protein and for each eIF5A mutant are also shown in Table S1. This information also responds to the comments made by Reviewer #1.

Reviewer #2 is right with his/her comment and there was a mistake in the Fig.1 title. Now it is corrected and written “depletion” instead of the wrong “deletion”.

2 Why were the experiments shown in Figure 1 done at 41{degree sign}C when all other experiments are done at 37{degree sign}C? This experimental difference is ignored in the text and no comparison of the impact of 37 vs 41 is made anywhere in the manuscript. For example it would be straightforward to perform a comparison of eIF5A depletion (by western blot), polyribosome profiles, strain growth/inhibition at both temperatures.

Response: Our aim carrying out a proteomic experiment after 4 hours of incubation of the temperature-sensitive strains at 41°C was to get a more profound depletion of the eIF5A protein, which is very abundant and stable at normal conditions, in order to get clear proteomic results. The proteomic results were pointing to a reduction in the levels of many mitochondrial proteins, corroborating previous results obtained in murine embryonic fibroblasts upon depletion of active eIF5A conditions (https://doi.org/10.1016/j.cmet.2019.05.003). From this starting point we tried to find out the molecular mechanism involved and all the rest of experiments are done with temperature sensitive eIF5A mutants under restrictive temperature of 37°C that is the most common conditions used in yeast by us and others, and in which wild-type yeast cells still grow vigorously.

In our previous manuscript version, the depletion of eIF5A after growing the cells at 41ºC for 4 h was shown in Fig. 1C. These data has been expanded and we have now included in Fig. S1E a western blotting analysis that shows the depletion of eIF5A after incubating the cells at 37ºC and 41 ºC for 4 h (Fig. S1E). The steady state level of the mitochondrial Por1 protein was investigated by western blotting (Figs. 1C,D) and results show a significant down-regulation in the two eIF5A temperature-sensitive strains at 41ºC. We have now included the same experiment performed at 37ºC (Fig. S1E), which also confirms the same conclusion. In addition, following Reviewer #2 suggestions, growth of the wild-type and tif51A-1 strains was tested by serial drop assays conducted at 25ºC, 37ºC and 41ºC and results confirm that both 37ºC and 41ºC temperatures impair the growth of the tif51A-1 strain but not the wild-type (Fig.S1B). The new information included in Figure S1 is now explained in the Results section. This information also responds to the comments made by Reviewer #4.

3 Western blot quantification. In Figure 1D and E the authors present western blot quantification. However they have chosen to normalise every panel to the signal in lane 1. This means that there is no variation at all in that sample as every replicate is =1. This completely skews the statistical assumptions made (because there will be variation in that sample) and effectively invalidates all the statistics shown. An appropriate approach to use is to normalise the signal in each lane to the mean signal across all lanes in a single blot. That way if all are identical they remain at 1, but importantly variation across all samples is captured. This should be done to the loading controls as well before working out ratios or performing any statistical analyses.

Response: Following Reviewer #2 suggestions we have changed the normalization methodology for the Western blots and we have now normalized the signal in each lane to the mean signal across all lanes in each single blot, and do so also for the loading controls. We have conducted this analysis in every western blotting experiment shown in the manuscript (Figs. 1D, S4B and S4C) and statistical analyses have been performed again to capture variation across all samples. In addition, this is also included in the Materials and Methods section (“Western blotting” subsection). Results obtain are similar to previous ones but we agree that this new approach improves the data presentation.

For this type of experiment it is more appropriate to use Anova than a T-test. This advice applies to every western data analysis figure in the whole manuscript and so all associated statistics need to be done again from the original quantification values. If T-test is justified then a correction for multiple hypothesis testing should be applied.

Response: After reviewing a large number of publications analysing similar data, and also following the recommendations of our statistical department, we have retained the statistics used in our previous version (with the new data normalisation as explained above, following the recommendations of Reviewer #2). This is because for each western blot figure shown, we have performed experiments with two different biological samples, wild-type cells and eIF5A mutant cells, and compared results for a single variable (Por1 protein level; eIF5A protein level or Hsp60 protein level) using three or more biological replicates. In this context, we compare the mean of the protein levels obtained from the biological replicate for two groups: wild-type and eIF5A mutant. Therefore, we believe that the statistical T-test is more appropriate. However, we could repeat the statistic if it is finally considered more appropriate.

In all bar chart figures in addition to showing the mean and SD, each replicate value should be shown (eg as done in Fig 2C). Graphpad allows individual points to be plotted easily.

Response: All Figures along the manuscript now include individual values from each replicate, in addition to showing the mean, SD and statistical analysis. All figure legends have been corrected accordingly.

5 Figure 2. Polysome profiles. The impact of translation elongation stalls on global polysome profiles is complex, but a global run off is highly unlikely. Stalls later in the coding region would be anticipated to cause an increase in ribosome density as more ribosomes accumulate (like cars queueing held at a red light). However where a stall is early in a longer ORF, for example at a signal sequence, then there is less opportunity for ribosomes to join and so for those mRNAs moving to lighter points in the gradient may be observed. This may also cause knock on effects on AUG clearance and initiation which the authors appear to see as there may be an increased 60S peak in the traces shown. Are there differences in overall -low vs high polysomes, the traces shown suggest there may be? Discussion of these points is merited in the results section given the subsequent qPCR experiment.

Response: The comments made by the Reviewer #2 are very interesting and we have made changes accordingly. First, we now show in Fig. 2A,B and Fig.S2B,C the quantification of polysomal and monosomal fractions in wild-type and tif51A-1 mutants at permissive and restrictive temperatures. It can be appreciated that there is no impact on global polysomal and monosomal fractions under eIF5A depletion. This result does not support a global stall at 3’ region of the ORF, because then an increase in polysomal fractions should be detected; nor a global stall at the 5’ region of the ORF, because then a decrease in polysomal fractions should be detected. However, with respect to individual mRNAs, our data show a significant reduction in the heavier polysomal fractions and a significant increase in lighter polysomal fractions for mRNAs encoding mitochondrial proteins, while no significant changes were observed for mRNAs encoding cytoplasmic proteins (Fig. 2C and Fig. S2D-I). These results could be interpreted as a result of ribosome stalls in the 5’ ORF regions, for example at the signal sequence, according to Reviewer #2 comments.

We have now introduced this comment in the Results and Discussion sections.

Figure 2 qPCR. Using qPCR to analyse RNA levels across polysome gradients is tricky for multiple reasons including that the total RNA level varies across fractions that can impact recovery efficiencies following precipitation of gradient fractions. Often investigators use a spike in control to act as a normalising factor. Here it is completely unclear what analysis was done because details are not stated anywhere. How were primers optimized, was amplification efficiency determined? Or are they assumed to be 100%, which they will not be? A detailed description or reference to a study where that is written is needed.

Response: The RNA extraction and analyses by RT-qPCR of the mRNA levels in the polysomal gradients was done as in previous studies of our lab (Romero et al. Sci Rep. 2020;10(1):233. doi: 10.1038/s41598-019-57132-0; Ramos-Alonso et al. PLoS Genet. 2018;14(6):e1007476. doi: 10.1371/journal.pgen.1007476; van Wijlick et al. PLoS Genet. 2016;12(10):e1006395. doi: 10.1371/journal.pgen.1006395; Garre et al., 2012 Mol Biol Cell. ;23(1):137-50. doi: 10.1091/mbc.E11-05-0419.). Three independent replicates were analyzed and results were reproducible and statistically significant, as shown in Fig. S2. Total RNA was extracted from each fraction using the SpeedTools Total RNA Extraction kit (Biotools B&M Labs). In the first replicate a spike in RNA control (Phenylalanine) was added and tested that no significant differences in the results were obtained when using or not the spike in control (see below Figure R3 for referees). mRNA relative values are always obtained from qPCR using a calibrating efficiency standard curve for each pair of oligos, after the initial set up of the qPCR for this specific pair of oligos. Therefore, slight differences in amplification efficiencies for each oligo pair are taken into account. More details about qPCR are now included in the Materials and Methods section (“Polyribosome profile analysis” subsection) and one additional reference is also included for the processing of polysomal gradient fractions.

It would be helpful to state how long CDS are for these mRNAs and where 2-3/2-8 cut off made is what for determining what is 'short' vs 'long' and the scientific basis for selecting 2-3 vs 2-8, why 8? Were M fractions also used in qPCR, they appear to be ignored in the analysis as currently presented?

Response: The CDS lengths of the mRNAs analyzed by polysome profiling and other important features are now included in new Table S5. We decided to classify as short length mRNAs those with a length below 600 bp, while mRNAs with lengths above 600 bp were classified as long length mRNAs. This classification was made on the basis of specific mRNA profiles obtained by qPCR analysis. mRNAs with short lengths behaved similarly and we selected 2n-3n fractions since the main polysomal peak under normal conditions appeared among 4n-5n fractions. In this line, long length mRNAs also behaved similarly between them, and we selected 2n to 8n fractions since the main polysomal peak under normal conditions appeared right after the 8n fraction. This information is now included in the Results and Materials and Methods sections.

Regarding the use of the Monosomal fractions, yes, they were used as it can be seen in Fig. S2 which includes the distribution in Monosomal (M), lighter (2n-3n/2n-8n) or heavier (n>3/n>8, P) polysomal fractions. In the polysomal profiles we can be see that depletion of eIF5A causes a reduction in the amount of mitochondrial mRNAs in the heavier fractions and a corresponding increase in the amount of mRNAs in the lighter polysomal fractions, while no significant changes are found in the monosomal fractions. Therefore, the statistically significant change in the heavier/lighter polysomal fraction ratio is indicative of the translation down-regulation and these ratios are shown in Fig. 2C. As the Reviewer #2 commented in point 5, the change in mRNA distribution to lighter polysomal fractions may be indicative or ribosome stalling at the 5’ ORF region, compatible with a stall at the mitochondrial target signal (MTS), and this discussion is now included in the Results and Discussion section.

Which transcripts studied here encode proteins with signal sequences? As Signal sequence pauses early in translation should impact ribosome loading this is potentially important here as discussed above.

Response: Yes, we agree with Reviewer #2 that this information may be relevant according to the hypothesis of ribosome stall at the MTS. Therefore, a score value of probability of harbouring an MTS presequence (Fukasawa et al., 2015) is now included in Table S5 for each of the mRNAS analyzed by polysome profiling. The discussion of this point has also been included in the Results and Discussion sections.

While it has been shown that SRP recognition is able to slow and even arrest translation of ER signal recognition peptides, there is currently no known direct SRP like correlate for mitochondrial signal sequences. We are therefore unaware of literature showing that mitochondrial signal sequences pause translation in a manner similar to ER signal sequences. We have previously found that downstream translational slowing is important for mitochondrial mRNA targeting (Tsuboi et al 2020, Arceo et al 2022), but we believe that to be distinct to what the Reviewer #2 is addressing.

Figures 3-5. Microscopy. The false green color images in Figure 3B do not show up well. They may be better shown in grayscale, with only the multiple overlays in color.

Response: False color for fluorescent microscopy images are widely used because they help to visualize the results to the readers and also facilitate the interpretation of multiple overlays. The use of false color is also suggested by Reviewer #4.

Figure 3C should show the data spread for all 150 cells and normalise differently as discussed above for westerns. I do not believe that all 150 WT cells have exactly the same GFP intensity, which is what the present plot claims.

Response: As answered to point 3 made by this Reviewer, now all figures, including Fig. 3C, are made with Graphpad and scatter plot with all individual points plotted, additionally to showing the mean, SD and statistical analysis. Results correspond to three independent experiments and show a statistically significant difference in Pdr5-GFP intensity signal between wild-type and tif51A-1 mutant. Figure legend has been corrected accordingly.

For panels 3D-F image quantification should be shown so that the variation across a population is clear. Eg in violin plots, or showing every point. It should be clear what proportion of cells have GFP aggregates and what the variation in number of granules is.

Response: The quantification of cytoplasmic Yta12 aggregates is now included in Fig.3E, which shows significant differences between the tif51A-1 mutant and the wild-type strain. Results show the individual values from three independent experiments with a minimum of 150 cells counted. We used a bar graph in which the values (% of cells with 0, 1, 2 or 3 aggregates) for each independent experiment are shown together with the mean, SD and statistical analysis. Figure legend has been corrected accordingly. This information also responds to the comments made by Reviewer #1.

Figure 4H has no error bars.

Response: New Fig.4H now shows the individual values of each of the three independent replicates, mean and error bars (SD). Figure legend has been corrected accordingly.

Figure 5C normalises 2 WTs to 1 as in Figure 3C. Both would be better as violin plots.

Response: Results in Fig. 5C are now shown using Graphpad and scatter plot in which all individual values are plotted (not normalized wild-type to 1), and also mean, SD and statistical significance. Results correspond to three independent replicates with the fluorescence intensity measured in more than 150 cells.

Figure 5D/E shows 37{degree sign}C data only. Do tif51A-1 cells have aggregates at 25{degree sign}C?There are no error bars in Figure 5E or any indication of how many cells/replicates were quantified.

Response: Figures 5D and 5E only show data at 37ºC since there are no Tim50-GFP aggregates, nor aggregates of other mitochondrial proteins, in tif51A-1 mutants at 25ºC, as shown in Fig. S3C-F and Fig. S5C.

New Fig. 5E shows individual values from each of the three independent experiments, mean, SD and statistical significance. Results correspond to the measurement of Tim50 protein aggregates in more than 150 cells. Figure legend has been corrected accordingly.

There are no sizing bars on any of the micrographs.

Response: Now, all sets of microscopy figures contain a size bar and this is indicated in the corresponding Figure legend.

The methods states that all quantification was done using ImageJ, but there is no detail given about how this was done. There are lots of ways to use ImageJ.

Response: A detailed description of the quantifications made using ImageJ is now included in the Materials and Methods section (“Fluorescent microscopy and analysis” subsection).

Figure 4. Luciferase assay. It is clear that there are differences in Tim50 vs Tim50∆7pro signal over time from the primary plots. It is not clear why the quantification plots on the right are from 2 selected time points. It is more typical to calculate the rate of increase in RLU per min in the linear portion of the plot, for these examples it would be approximately 30-40 mins.

Response: As luciferase mRNA level is also increasing with time, the total amount of luciferase protein will increase exponentially. At some point mRNA levels will reach a steady state and for a brief period there could be a linear portion of RLU increase, but that will be different for each condition and reporter as ribosome quality control can have a direct impact on mRNA half-life. We have instead chosen two time points to show that statistical differences in Tim50 protein expression upon eIF5A depletion are not dependent on the time point chosen. We have also included the full data plots for readers to view the raw data.

Figure 4F. The text on p6 states Fig 4F is evidence of RQC induction. This is an overstatement. There are no data presented relating to RQC.

Response: Ribosome-associated quality control (RQC) is a mechanism by which elongation-stalled ribosomes are sensed in the cell, and then removed from the stall site by ribosomal subunit dissociation. This is the definition of RQC. With high levels of RQC this will cause a drop in ribosome density downstream of the stall site because of ribosome removal. While we would agree that most studies do not show actual buildup of ribosomes at ribosome stalls, and removal after the stall, we do. Our ribosome profiling analysis shows in vivo distribution of ribosome density across the TIM50 mRNA in wild-type and upon eIF5A depletion. We show that in the eIF5A depletion the ribosome density is similar to wild-type for the first ~200 bp, then there is a buildup of ribosomes for ~300 bps up to the stretch of polyproline residues, indicative of slowed ribosome movement. This slowed ribosome movement is further supported by our translation duration measurements in Fig. 4E. Then the transcript is almost completely devoid of ribosomes after the stretch of proline residues, indicating the ribosomes are removed at the proline stretch. This combination of ribosome stalling (Fig. 4E,4F) and subsequent ribosome removal (Fig 4F) is the textbook definition of RQC, so we indicate this as evidence for RQC.

Figure 5G. It is not clear to this reviewer why the CYC1 reporter is impacted by Tim50∆pro at 25{degree sign}C. Can the authors comment?

Response: This is also not clear to us, however, no differences are seen with and without eIF5A depletion, supporting the interpretation that Cyc1 translation is not affected by eIF5A depletion when Tim50 protein levels are restored in the Tim50∆pro strain. However, in order to clarify this point, we propose, if it is considered necessary, to remake the Tim50∆pro CYC1 reporter strain.

Does ∆pro impact Tim50 function or is there possibly some other off target impact of integrating the reporter in this strain?

Response: As answered to Reviewer #1 in her/his point 1, the functionality of Tim50ΔPro is shown by the fact that wild-type cells carrying this Tim50 protein version as the only copy of Tim50 grew well in glycerol media, where Tim50 is essential for the mitochondrial function (Fig. 5A). However, we suspect that Tim50ΔPro is a bit less efficient protein since a double mutant tif51A-1 Tim50ΔPro shows even reduced growth than the single tif51A-1 mutant (Fig. 5A). We do not expect off target impact in this Tim50ΔPro strains, although we cannot exclude this 100%, as in any other yeast strain obtained by transformation.

Significance

Strengths and Limitations:

Strengths are that the study uses a wide range of molecular approaches to address the questions and that the results present a clear story.

Limitations are that the poly-proline residues identified in yeast Tim50 are not conserved through to humans, so the direct relevance to higher organisms is unclear. However there are many more poly-proline proteins in human genes than in yeast and there are rare genetic conditions affecting eIF5A and its hypusination

Advance. provides a clear link between dysregulation of eIF5A, Tim50 expression and wider impact on mitochondria.

Audience. Scientists interested in protein synthesis, mitochondrial biology and clinicians investigating rare human disorders of eIF5A and hypusination.

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

eIF5A is required to mediate efficient translation elongation of some amino-acid sequences like polyproline motifs, and eIF5A depletion was reported to impair mitochondrial respiration functions, decreasing mitochondrial protein levels. In this study, Barba-Aliaga et al. showed that eIF5A is important for the translation of the Pro-repeat containing protein, Tim50, an essential subunit of the TIM23 complex, the presequence translocase in the mitochondrial inner membrane. eIF5A ts mutants caused ribosome stalling of Tim50 mRNA on the mitochondrial surface at non-permissive temperature, and the removal of the Pro-repeat from Tim50 (Tim50-delta7Pro mutant) made its translation independent of eIF5A. However, the replacement of endogenous Tim50 with Tim50-delta7Pro did not recover the cell growth defects of eIF5A ts mutant on respiration medium at semi-permissive temperature, suggesting that Tim50 is not the only reason for the global mitochondrial defects caused by defective eIF5A.

(1) I am wondering why the authors mainly used the eIF5A ts mutant strains instead of the eIF5A degron strain since, for example, the decrease in the level of Tim50 was only marginal (Fig. EV4A).

Response: eIF5A is a very abundant protein and with high stability (SGD data: 273594 molecules/cell in YPD and 9.1 h protein half-life). We have used temperature-sensitive strains, tif51A-1, instead of eIF5A-degron because eIF5A is depleted much quicker in the first than the second system. As it can be seen in Schuller et al., Mol Cell. 2017;66(2):194-205.e5. doi: 10.1016/j.molcel.2017.03.003, with the eIF5A-degron system the addition of auxin was made in parallel to a transcriptional shut off using GAL promoter to express eIF5A-degron, changing the media from galactose to glucose and incubating the cells for 10 hours. With our approach using temperature-sensitive proteins, almost full depletion (without affecting viability, see Li et al., Genetics 2014; 197(4):1191-200 doi: 10.1534/genetics.114.166926) can be done after 4-6 h incubation at 37ºC or 4 h incubation at 41ºC (Fig. 1C and Fig. S1E, almost no signal is detected by western blotting). Therefore, we chose to use eIF5A depletion with temperature-sensitive yeast strains to achieve stronger protein depletion with shorter times and avoid secondary effects. In addition, the two eIF5A temperature-sensitive strains used in this study have been widely used by us and others (Pelechano and Alepuz, 2017; Zanelli and Valentini, 2005; Zanelli et al., 2006; Dias et al., 2008; Muñoz-Soriano et al., 2017; Rossi et al., 2014; Li et al., 2014; Xiao et al., 2024).

(2) To show that the compromised translation of Tim50 in the absence of functional eIF5A causes defects in the mitochondrial protein import by clogging the import channels, the authors should directly observe the accumulation of the precursor forms of several matrix-targeting proteins by immunoblotting. In this sense, the results in Fig. 1C for Hsp60 do not fit the interpretation of import channel clogging.

Response: We did not see precursor mitochondrial proteins by Western blot upon eIF5A depletion possibly because: 1) the mature protein form is more abundant and stable; 2) the precursor mito-protein appears in cytoplasmic aggregates and this may not be easily extracted during preparation of proteins for Western blot analysis. In the work by Weidberg and Amon, 2018, who described the mitoCPR response; Krämer et al., 2023, who described mitostores; and others (Wrobel et al., 2015; Boos et al., 2019) the authors use extreme over-expression of mitoproteins or mutations in essential proteins for mitochondrial biogenesis to induce clogging of translocases and accumulation of precursors in the cytosol. However, we are using and detecting proteins at their physiological levels, expressed under their native promoters, what may explain why we do not detect precursor mito-proteins. We are using what we believe to be a much more physiologically relevant system, where we use endogenous expression of mitochondrially imported proteins. Yet we see similar transcriptional induction of mitoCPR targets (CIS1, PDR5, PDR15) and mislocalization of mitochondrial proteins to Hsp104 marked aggregates (MitoStores).

(3) The authors speculated in the Discussion section that import defects caused by compromised translation of Tim50 could cause down-regulation of translation through prolonged mitochondrial stress. However, this lacks experimental evidence.

Response: We do see that depletion of eIF5A causes import defects through Tim50 and correlates with the down-regulation of translation of mRNAs encoding mitoproteins as shown in Fig. 2C and Fig. S2. In these figures it can be seen that mito-mRNAs move from heavier to lighter polysomal fractions upon eIF5A depletion, indicating that less ribosomes are bound to these mRNAs. Importantly, synthesis of Cyc1 and Cox5A mitochondrial proteins is recovered when TIM50 gene is replaced by an eIF5A-translation independent TIM50ΔPro gene, arguing in favor of a translation defect caused by eIF5A depletion through the collapse of import systems produced by the ribosome stalling in TIM50 mRNA.

As discussed by Reviewer #2 and in our answers to his/her points 5 and 6, the reduction in the number of ribosomes bound to mito-mRNAs upon eIF5A depletion may be a consequence of the stall of ribosomes after the mRNA 5’ coding region encoding the MTS. This discussion has now been introduced in the Discussion section. This information also responds to the comments made by Reviewer #2.

(4) The authors stated that human Tim50 does not have Pro-repeat motif, but how about other organisms (like other fungi species)? Is the present observation specific only to S. cerevisiae?

Response: We have now included a sequence alignment of the Tim50 protein sequences of different yeast species (Saccharomyces cerevisiae, Candida albicans, Candida glabrata, Candida lipolytica, Schizzosaccharomyces pombe, Schizzosaccharomyces jamonicus), mouse and human (Fig. S4A). The resulting alignment shows that S. cerevisiae is the only organism presenting the seven consecutive proline residues. Still, C. albicans and C. glabrata conserve five consecutive prolines while C. lipolytica conserves five non-consecutive prolines. Furthermore, S. pombe and S. jamonicus, and mouse and human, conserve three and four non-consecutive prolines respectively. This means that the observations presented in this manuscript could be extended to other fungi species as well since most of the proline residues are conserved and are predicted to behave as eIF5A-dependent motifs for translation. Moreover, the described eIF5A-dependent tripeptide motif PDP is found in humans, mice and S. pombe at the Tim50 region where we found the PPP motif inducing ribosome stalling in S. cerevisiae (Fig S4A). This may confer eIF5A-dependent ribosome stall since as we showed in our previous ribosome footprinting (Pelechano et al., 2017), this PDP motif causes a similar high ribosome stall as the PPP motif. This discussion has now been introduced in the Results and Discussion sections.

(5) Two references in the text are marked with "?", which should be corrected.

Response: We thank you the Reviewer #3 for noticing this, references have been corrected in the text.

__Reviewer #3 (Significance (Required)): __

The essence of this work, the role of eIF5A in the efficient translation of Pro-repeat containing Tim50 (Figs. 4 and 5), is important and worth publication. However, the results of the effects of defective eIF5A on the levels and localization of mitochondrial proteins (Figs.1-3) can be even deleted to make clear the point of this work.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

The manuscript submitted by Barba-Aliaga et al. aims to understand on the molecular level how eIF5A influences mitochondrial function. elF5A promotes translation elongation at stretches prone to translational stalling like e.g. polyproline sequence. The finding that eIF5a influences mitochondrial function has been previously reported by the same group and by others. In this context, it was suggested that eIF5a promotes translation of N-terminal mitochondrial targeting signals. Here, the authors propose an alternative mechanism and suggest that "eIF5a directly controls mitochondrial protein import through alleviation of ribosome stalling along TIM50 mRNA." Using luciferase reporter assay, the authors indeed convincingly show that the speed of Tim50 translation is dependent on the presence of functional TIF51A, the major eIF5a in yeast, and that this dependence comes from the presence of the polyproline stretch in Tim50. The rest of the manuscript is unfortunately less clear and it is very hard, if not impossible, to sort out direct from secondary effects and compensations. The authors use proteomics, biochemical methods, RNAseq and fluorescence microscopy to analyze the temperature sensitive tif51A mutant but the conditions used in the manuscript are non-consistent between various experiments presented, in respect to the medium, temperature, preculture condition and the length of treatment used.

Response: We do not agree with this Reviewer #4 appreciation. We used different molecular approaches to investigate different questions. Indeed, this is one of the Strengths that is highlighted by Reviewer 2 as it reads above: “Strengths are that the study uses a wide range of molecular approaches to address the questions and that the results present a clear story.” All the experiments presented in the manuscript, apart from proteomics analysis (Fig. 1), have been performed in the same conditions respect to the medium (SGal), temperature (25ºC/37ºC), preculture condition (SGal, 25ºC) and length of treatment used (4 h of depletion at 37ºC). This is already clearly specified in every Figure legend along the whole manuscript and also in the Materials and Methods section. In addition, individual values from each replicate, mean, standard deviation and statistical tests are shown for every Figure in the manuscript. Therefore, we do believe that conditions are consistent between experiments and conclusions are made based on different experiments and different scientific approaches.

We agree with Reviewer #4 in that depletion of eIF5A protein in the temperature sensitive tif51A-1 mutant was done in the proteomic at 41°C for 4 h, whereas in the rest of experiments depletion is made at 37°C for 4 h. As answered to Reviewer #2 (see answer to point 2), stronger depletion conditions were used to get clear proteomic results, and in order to compare both temperatures we have added now some controls showing eIF5A depletion and growth of tif51A-1 mutant at 41°C and 37°C; importantly, we also show the reduction in mito-protein levels upon eIF5A depletion at 37°C (Fig. S1B and E).

In some cases, the genetic background of the yeast strains and plasmids used are also unclear (e.g. pYES2-pGAL-FLAG-TIM50-GFP-URA3 - based on the provided description, TIM50 was inserted between FLAG and GFP tags; if so, mitochondrial targeting signal of Tim50 would be masked making its import into mitochondria impossible).

Response: We do not agree with this appreciation. The genetic background of the yeast strains is always the same along the whole manuscript (BY4741 background) and is clearly specified in Table S2. In this line, all the information regarding the plasmids used can be found at Table S3 and plasmids construction is extensively detailed in the Materials and Methods section (“Yeast strains, plasmids, and growth conditions” subsection).

Regarding the pYES2-pGAL-FLAG-TIM50-GFP-URA3 plasmid and as already mentioned in the text, we only used this plasmid to analyze by western blotting the protein synthesis of Tim50 independently of its subcellular localization. Our results (Fig. S4C) confirm that the synthesis defect of this Tim50 version upon eIF5A depletion is only due to the presence of the polyproline region. Importantly, we did not make any conclusion regarding import defects or protein localization based on these results.

I have no doubt that upon exposure of tif51A cells to 41{degree sign}C for 4h cells initiate a number of cellular responses including mitoCPR and formation of MitoStores, however, I don´t think that the authors convincingly show that these are initiated by reduced levels of Tim50 - on the contrary, the authors show that levels of Tim50 are actually not significantly changed. This can hardly be reconciled with the model proposed. In addition, should the effect of Tif51A on mitochondria primarily be due to its effect on Tim50, then Tim50deltaPro should rescue the phenotype of tif51a mutant but it didn´t; if anything, it made it worse (see Fig 5A - the double mutant grows worse than the single ones). Furthermore, expression of Cyc1 luciferase reporter is reduced in Tim50deltaPro strain even at permissive temperature, Figure 5G. Since cytochrome c is not a substrate of the presequence pathway this again points to the secondary effects that are being observed.

Response: We believe that our main results, summarized next and all performed at 37°C, do show that translation defects in TIM50 mRNA are the cause of the mitoCPR induction and formation of MitoStores. First, Tim50 protein levels are significantly reduced upon eIF5A depletion, as shown in Fig. S4A and S4B. Although being statistically significant, we agree that the reduction in Tim50 protein level is quantitatively low. This can be explained by the high stability of Tim50 protein, with a half-life of approximately 9.6 h (Christiano et al, 2014), which makes it more difficult to measure large differences in new protein synthesis. This is why we additionally used an accurate and quantitative test for showing the eIF5A-dependency for TIM50 mRNA translation: the fusion of the TIM50 DNA sequence to a TetO7-inducible nLuc reporter, which allows to monitor the appearance of new Tim50 protein and to estimate the translation elongation rate (Fig.4C-E). The ribosome stalling at TIM50 mRNA provoked by eIF5A depletion, where this mRNA is located at the mitochondrial surface to promote the import of nascent Tim50 protein during translation (Fig. S5B), may cause by itself the clogging of the protein import system even though yields only a slight reduction in total Tim50 cellular protein. Second, as Reviewer #4 pointed with our model, Tim50deltaPro should rescue the phenotype of tif51A-1 mutant and it does it: no mitoCPR induction and no mito-protein cytoplasmic aggregation are observed (Fig. 5D-F). Moreover, no differences in Cyc1- and Cox5a-nanoLuc synthesis are observed in the tif51A-1 Tim50ΔPro strain between depletion and not depletion conditions (Fig. 5E). These results strongly suggest that the mitochondrial protein import defects (and consequently the mitoCPR induction and mito-protein cytoplasmic aggregation) caused by eIF5A depletion are a consequence of ribosome stalling during TIM50 mRNA translation. However, Reviewer #4 is right in that mitochondrial respiration and growth in glycerol are not restored in the tif51A-1 Tim50ΔPro strain, even though Tim50 protein levels have been restored under eIF5A depletion conditions. As we discuss in the manuscript, we expect that there are additional mitochondrial proteins as targets of eIF5A, such as Yta12 and/or others. We have added further data pointing to ribosome stalling and RQC for other cotranslationally inserted mitochondrial proteins (Table S6). Accordingly, this has also been included in the Discussion section. However, the identification and study of these other mitochondrial targets goes beyond the aim of our current study.

Minor comments

1. Page 1, mitochondrial proteins cross do not the intermembrane space through Tom40 but rather the outer membrane Response: We think the Reviewer #4 misunderstood the sentence because we are saying exactly what he/she states: mitoproteins cross the outer membrane to the intermembrane space through Tom40. Thus our sentence is:

“Usually, mitoproteins contact the central receptor Tom20 and cross to the intermembrane space (IMS) through Tom40, the β-barrel pore-forming subunit.”

Therefore, we kept the sentence.

Page 4, ATP1 is present in the matrix and not the inner membrane

Response: This has been corrected. We thank the Reviewer for pointing this.

The citations are missing at several places - they are left as "?"

Response: References have been corrected in the text.

It would be nice if microscopy images were colored in magenta and cyan, rather than red and green, to make them accessible to a wider audience.

Response: Green and red colors for fluorescent microscopy images are widely used in high-impact journals, especially when showing mitochondrial proteins and mitochondrial marker Su9-mCherry (Hughes et al., 2016, eLife, doi: 10.7554/eLife.13943; Kakimoto et al., 2018, Scientific Reports, doi: 10.1038/s41598-018-24466-0; Kreimendahl et al, 2020, BMC Biology, doi: 10.1186/s12915-020-00888-z). However, if the Reviewers think this is essential for publication, microscopy images can be colored in magenta and cyan instead.

Formally speaking, Tim50 is not per se a translocase, it is either a component of the translocase or, more precisely, a receptor of the translocase. Similarly, Tom20 and Tom70 are not membrane transporters but rather receptors of the TOM complex.

Response: We have changed the title and text to be more precise in the description of the components of the mitochondrial import systems as suggested by Reviewer #4.

Reviewer #4 (Significance (Required)):

This is a potentially interesting story, however, the conditions used for the analysis of the temperature sensitive mutants were either too harsh or these mutants are in general impossible to control, making the manuscript, in my opinion, unfortunately too premature for publication.

Response: We do not agree with the Reviewer #4 opinion, all experiments were done at 37ºC except the proteomic analysis that it is also confirmed further for Tim50 and Por1 proteins at 37ºC. We want to stress that we show all experiments with at least three biological replicates, individual values for each measurement are included now in the graphics as recommended by Reviewer #2, and the mean, SD and statistical tests are included. We make conclusions based in statistical significant differences along the manuscript. The temperature-sensitive yeast mutants used show reproducible analysis, they behave as expected in the controlled conditions used and they have been widely used in our lab and others (Pelechano and Alepuz, 2017; Zanelli and Valentini, 2005; Zanelli et al., 2006; Dias et al., 2008; Muñoz-Soriano et al., 2017; Rossi et al., 2014; Li et al., 2014; Xiao et al., 2024).

Author Response

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

eLife assessment

This work provides a valuable contribution and assessment of what it means to replicate a null study finding, and what are the appropriate methods for doing so (apart from a rote p-value assessment). Through a convincing re-analysis of results from the Reproducibility Project: Cancer Biology using frequentist equivalence testing and Bayes factors, the authors demonstrate that even when reducing 'replicability success' to a single criterion, how precisely replication is measured may yield differing results. Less focus is directed to appropriate replication of non-null findings.

Reviewer #1 (Public Review):

Summary:

The goal of Pawel et al. is to provide a more rigorous and quantitative approach for judging whether or not an initial null finding (conventionally with p ≥ 0.05) has been replicated by a second similarly null finding. They discuss important objections to relying on the qualitative significant/non-significant dichotomy to make this judgment. They present two complementary methods (one frequentist and the other Bayesian) which provide a superior quantitative framework for assessing the replicability of null findings.

Strengths:

Clear presentation; illuminating examples drawn from the well-known Reproducibility Project: Cancer Biology data set; R-code that implements suggested analyses. Using both methods as suggested provides a superior procedure for judging the replicability of null findings.

Weaknesses:

The proposed frequentist and the Bayesian methods both rely on binary assessments of an original finding and its replication. I'm not sure if this is a weakness or is inherent to making binary decisions based on continuous data.

For the frequentist method, a null finding is considered replicated if the original and replication 90% confidence intervals for the effects both fall within the equivalence range. According to this approach, a null finding would be considered replicated if p-values of both equivalences tests (original and replication) were, say, 0.049, whereas would not be considered replicated if, for example, the equivalence test of the original study had a p-value of 0.051 and the replication had a p-value of 0.001. Intuitively, the evidence for replication would seem to be stronger in the second instance. The recommended Bayesian approach similarly relies on a dichotomy (e.g., Bayes factor > 1).

Thanks for the suggestions, we now emphasize more strongly in the “Methods for assessing replicability of null results” and “Conclusions” sections that both TOST p-values and Bayes factors are quantitative measures of evidence that do not require dichotomization into “success” or “failure”.

Reviewer #2 (Public Review):

Summary:

The study demonstrates how inconclusive replications of studies initially with p > 0.05 can be and employs equivalence tests and Bayesian factor approaches to illustrate this concept. Interestingly, the study reveals that achieving a success rate of 11 out of 15, or 73%, as was accomplished with the non-significance criterion from the RPCB (Reproducibility Project: Cancer Biology), requires unrealistic margins of Δ > 2 for equivalence testing.

Strengths:

The study uses reliable and shareable/open data to demonstrate its findings, sharing as well the code for statistical analysis. The study provides sensitivity analysis for different scenarios of equivalence margin and alfa level, as well as for different scenarios of standard deviations for the prior of Bayes factors and different thresholds to consider. All analysis and code of the work is open and can be replicated. As well, the study demonstrates on a case-by-case basis how the different criteria can diverge, regarding one sample of a field of science: preclinical cancer biology. It also explains clearly what Bayes factors and equivalence tests are.

Weaknesses:

It would be interesting to investigate whether using Bayes factors and equivalence tests in addition to p-values results in a clearer scenario when applied to replication data from other fields. As mentioned by the authors, the Reproducibility Project: Experimental Philosophy (RPEP) and the Reproducibility Project: Psychology (RPP) have data attempting to replicate some original studies with null results. While the RPCB analysis yielded a similar picture when using both criteria, it is worth exploring whether this holds true for RPP and RPEP. Considerations for further research in this direction are suggested. Even if the original null results were excluded in the calculation of an overall replicability rate based on significance, sensitivity analyses considering them could have been conducted. The present authors can demonstrate replication success using the significance criteria in these two projects with initially p < 0.05 studies, both positive and non-positive.

Other comments:

• Introduction: The study demonstrates how inconclusive replications of studies initially with p > 0.05 can be and employs equivalence tests and Bayesian factor approaches to illustrate this concept. Interestingly, the study reveals that achieving a success rate of 11 out of 15, or 73%, as was accomplished with the non-significance criterion from the RPCB (Reproducibility Project: Cancer Biology), requires unrealistic margins of Δ > 2 for equivalence testing.

• Overall picture vs. case-by-case scenario: An interesting finding is that the authors observe that in most cases, there is no substantial evidence for either the absence or the presence of an effect, as evidenced by the equivalence tests. Thus, using both suggested criteria results in a picture similar to the one initially raised by the paper itself. The work done by the authors highlights additional criteria that can be used to further analyze replication success on a case-by-case basis, and I believe that this is where the paper's main contributions lie. Despite not changing the overall picture much, I agree that the p-value criterion by itself does not distinguish between (1) a situation where the original study had low statistical power, resulting in a highly inconclusive non-significant result that does not provide evidence for the absence of an effect and (2) a scenario where the original study was adequately powered, and a non-significant result may indeed provide some evidence for the absence of an effect when analyzed with appropriate methods. Equivalence testing and Bayesian factor approaches are valuable tools in both cases.

Regarding the 0.05 threshold, the choice of the prior distribution for the SMD under the alternative H1 is debatable, and this also applies to the equivalence margin. Sensitivity analyses, as highlighted by the authors, are helpful in these scenarios.

Thank you for the thorough review and constructive feedback. We have added an additional “Appendix C: Null results from the RPP and EPRP” that shows equivalence testing and Bayes factor analyses for the RPP and EPRP null results.

Reviewer #3 (Public Review):

Summary:

The paper points out that non-significance in both the original study and a replication does not ensure that the studies provide evidence for the absence of an effect. Also, it can not be considered a "replication success". The main point of the paper is rather obvious. It may be that both studies are underpowered, in which case their non-significance does not prove anything. The absence of evidence is not evidence of absence! On the other hand, statistical significance is a confusing concept for many, so some extra clarification is always welcome.

One might wonder if the problem that the paper addresses is really a big issue. The authors point to the "Reproducibility Project: Cancer Biology" (RPCB, Errington et al., 2021). They criticize Errington et al. because they "explicitly defined null results in both the original and the replication study as a criterion for replication success." This is true in a literal sense, but it is also a little bit uncharitable. Errington et al. assessed replication success of "null results" with respect to 5 criteria, just one of which was statistical (non-)significance.

It is very hard to decide if a replication was "successful" or not. After all, the original significant result could have been a false positive, and the original null-result a false negative. In light of these difficulties, I found the paper of Errington et al. quite balanced and thoughtful. Replication has been called "the cornerstone of science" but it turns out that it's actually very difficult to define "replication success". I find the paper of Pawel, Heyard, Micheloud, and Held to be a useful addition to the discussion.

Strengths:

This is a clearly written paper that is a useful addition to the important discussion of what constitutes a successful replication.

Weaknesses:

To me, it seems rather obvious that non-significance in both the original study and a replication does not ensure that the studies provide evidence for the absence of an effect. I'm not sure how often this mistake is made.

Thanks for the feedback. We do not have systematic data on how often the mistake of confusing absence of evidence with evidence of absence has been made in the replication context, but we do know that it has been made in at least three prominent large-scale replication projects (the RPP, RPEP, RPCB). We therefore believe that there is a need for our article.

Moreover, we agree that the RPCB provided a nuanced assessment of replication success using five different criteria for the original null results. We emphasize this now more in the “Introduction” section. However, we do not consider our article as “a little bit uncharitable” to the RPCB, as we discuss all other criteria used in the RPCB and note that our intent is not to diminish the important contributions of the RPCB, but rather to build on their work and provide constructive recommendations for future researchers. Furthermore, in response to comments made by Reviewer #2, we have added an additional “Appendix B: Null results from the RPP and EPRP” that shows equivalence testing and Bayes factor analyses for null results from two other replication projects, where the same issue arises.

Reviewer #1 (Recommendations For The Authors):

The authors may wish to address the dichotomy issue I raise above, either in the analysis or in the discussion.

Thank you, we now emphasize that Bayes factors and TOST p-values do not need to be dichotomized but can be interpreted as quantitative measures of evidence, both in the “Methods for assessing replicability of null results” and the “Conclusions” sections.

Reviewer #2 (Recommendations For The Authors):

Given that, here follow additional suggestions that the authors should consider in light of the manuscript's word count limit, to avoid confusing the paper's main idea:

2) Referencing: Could you reference the three interesting cases among the 15 RPCB null results (specifically, the three effects from the original paper #48) where the Bayes factor differs qualitatively from the equivalence test?

We now explicitly cite the original and replication study from paper #48.

3) Equivalence testing: As the authors state, only 4 out of the 15 study pairs are able to establish replication success at the 5% level, in the sense that both the original and the replication 90% confidence intervals fall within the equivalence range. Among these 4, two (Paper #48, Exp #2, Effect #5 and Paper #48, Exp #2, Effect #6) were initially positive with very low p-values, one (Paper #48, Exp #2, Effect #4) had an initial p of 0.06 and was very precisely estimated, and the only one in which equivalence testing provides a clearer picture of replication success is Paper #41, Exp #2, Effect #1, which had an initial p-value of 0.54 and a replication p-value of 0.05. In this latter case (or in all these ones), one might question whether the "liberal" equivalence range of Δ = 0.74 is the most appropriate. As the authors state, "The post-hoc specification of equivalence margins is controversial."

We agree that the post hoc choice of equivalence ranges is a controversial issue. The margins define an equivalence region where effect sizes are considered practically negligible, and we agree that in many contexts SMD = 0.74 is a large effect size that is not practically negligible. We therefore present sensitivity analyses for a wide range of margins. However, we do not think that the choice of this margin is more controversial for the mentioned studies with low p-values than for other studies with greater p-values, since the question of whether a margin plausibly encodes practically negligible effect sizes is not related to the observed p-value of a study. Nevertheless, for the new analyses of the RPP and EPRP data in Appendix B, we have added additional sensitivity analyses showing how the individual TOST p-values and Bayes factors vary as a function of the margin and the prior standard deviation. We think that these analyses provide readers with an even more transparent picture regarding the implications of the choice of these parameters than the “project-wise” sensitivity analyses in Appendix A.

4) Bayes factor suggestions: For the Bayes factor approach, it would be interesting to discuss examples where the BF differs slightly. This is likely to occur in scenarios where sample sizes differ significantly between the original study and replication. For example, in Paper #48, Exp #2 and Effect #4, the initial p is 0.06, but the BF is 8.1. In the replication, the BF dramatically drops to < 1/1000, as does the p-value. The initial evidence of 8.1 indicates some evidence for the absence of an effect, but not strong evidence ("strong evidence for H0"), whereas a p-value of 0.06 does not lead to such a conclusion; instead, it favors H1. It would be interesting if the authors discussed other similar cases in the paper. It's worth noting that in Paper #5, Exp #1, Effect #3, the replication p-value is 0.99, while the BF01 is 2.4, almost indicating "moderate" evidence for H0, even though the p-value is inconclusive.

We agree that some of the examples nicely illustrate conceptual differences between p-values and Bayes factors, e.g., how they take into account sample size and effect size. As methodologists, we find these aspects interesting ourselves, but we think that emphasizing them is beyond the scope of the paper and would distract eLife readers from the main messages.

Concerning the conceptual differences between Bayes factors and TOST p-values, we already discuss a case where there are qualitative differences in more detail (original paper #48). We added another discussion of this phenomenon in the Appendix C as it also occurs for the replication of Ranganath and Nosek (2008) that was part of the RPP.

5) p-values, magnitude and precision: It's noteworthy to emphasize, if the authors decide to discuss this, that the p-value is influenced by both the effect's magnitude and its precision, so in Paper #9, Exp #2, Effect #6, BF01 = 4.1 has a higher p-value than a BF01 = 2.3 in its replication. However, there are cases where both p-values and BF agree. For example, in Paper #15, Exp #2, Effect #2, both the original and replication studies have similar sample sizes, and as the p-value decreases from p = 0.95 to p = 0.23, BF01 decreases from 5.1 ("moderate evidence for H0") to 1.3 (region of "Absence of evidence"), moving away from H0 in both cases. This also occurs in Paper #24, Exp #3, Effect #6.

We appreciate the suggestions but, as explained before, think that the message of our paper is better understood without additional discussion of more general differences between p-values and Bayes factors.

6) The grey zone: Given the above topic, it is important to highlight that in the "Absence of evidence grey zone" for the null hypothesis, for example, in Paper #5, Exp #1, Effect #3 with a p = 0.99 and a BF01 = 2.4 in the replication, BF and p-values reach similar conclusions. It's interesting to note, as the authors emphasize, that Dawson et al. (2011), Exp #2, Effect #2 is an interesting example, as the p-value decreases, favoring H1, likely due to the effect's magnitude, even with a small sample size (n = 3 in both original and replications). Bayes factors are very close to one due to the small sample sizes, as discussed by the authors.

We appreciate the constructive comments. We think that the two examples from Dawson et al. (2011) and Goetz et al. (2011) already nicely illustrate absence of evidence and evidence of absence, respectively, and therefore decided not to discuss additional examples in detail, to avoid redundancy.

7) Using meta-analytical results (?): For papers from RPCB, comparing the initial study with the meta-analytical results using Bayes factor and equivalence testing approaches (thus, increasing the sample size of the analysis, but creating dependency of results since the initial study would affect the meta-analytical one) could change the conclusions. This would be interesting to explore in initial studies that are replicated by much larger ones, such as: Paper #9, Exp #2, Effect #6; Goetz et al. (2011), Exp #1, Effect #1; Paper #28, Exp #3, Effect #3; Paper #41, Exp #2, Effect #1; and Paper #47, Exp #1, Effect #5).

Thank you for the suggestion. We considered adding meta-analytic TOST p-values and Bayes factors before, but decided that Figure 3 and the results section are already quite technical, so adding more analyses may confuse more than help. Nevertheless, these meta-analytic approaches are discussed in the “Conclusions” section.

8) Other samples of fields of science: It would be interesting to investigate whether using Bayes factors and equivalence tests in addition to p-values results in a clearer scenario when applied to replication data from other fields. As mentioned by the authors, the Reproducibility Project: Experimental Philosophy (RPEP) and the Reproducibility Project: Psychology (RPP) have data attempting to replicate some original studies with null results. While the RPCB analysis yielded a similar picture when using both criteria, it is worth exploring whether this holds true for RPP and RPEP. Considerations for further research in this direction are suggested. Even if the original null results were excluded in the calculation of an overall replicability rate based on significance, sensitivity analyses considering them could have been conducted. The present authors can demonstrate replication success using the significance criteria in these two projects with initially p < 0.05 studies, both positive and non-positive.

Thank you for the excellent suggestion. We added an Appendix B where the null results from the RPP and EPRP are analyzed with our proposed approaches. The results are also discussed in the “Results” and “Conclusions” sections.

9) Other approaches: I am curious about the potential impact of using an approach based on equivalence testing (as described in https://arxiv.org/abs/2308.09112). It would be valuable if the authors could run such analyses or reference the mentioned work.

Thank you. We were unaware of this preprint. It seems related to the framework proposed by Stahel W. A. (2021) New relevance and significance measures to replace p-values. PLoS ONE 16(6): e0252991. https://doi.org/10.1371/journal.pone.0252991

We now cite both papers in the discussion.

10) Additional evidence: There is another study in which replications of initially p > 0.05 studies with p > 0.05 replications were also considered as replication successes. You can find it here: https://www.medrxiv.org/content/10.1101/2022.05.31.22275810v2. Although it involves a small sample of initially p > 0.05 studies with already large sample sizes, the work is currently under consideration for publication in PLOS ONE, and all data and materials can be accessed through OSF (links provided in the work).

Thank you for sharing this interesting study with us. We feel that it is beyond the scope of the paper to include further analyses as there are already analyses of the RPCB, RPP, and EPRP null results. However, we will keep this study in mind for future analysis, especially since all data are openly available.

11) Additional evidence 02: Ongoing replication projects, such as the Brazilian Reproducibility Initiative (BRI) and The Sports Replication Centre (https://ssreplicationcentre.com/), continue to generate valuable data. BRI is nearing completion of its results, and it promises interesting data for analyzing replication success using p-values, equivalence regions, and Bayes factor approaches.

We now cite these two initiatives as examples of ongoing replication projects in the introduction. Similarly as for your last point, we think that it is beyond the scope of the paper to include further analyses as there are already analyses of the RPCB, RPP, and EPRP null results.

Reviewer #3 (Recommendations For The Authors):

I have no specific recommendations for the authors.

Thank you for the constructive review.

Reviewing Editor (Recommendations For the Authors):

I recognize that it was suggested to the authors by the previous Reviewing Editor to reduce the amount of statistical material to be made more suitable for a non-statistical audience, and so what I am about to say contradicts advice you were given before. But, with this revised version, I actually found it difficult to understand the particulars of the construction of the Bayes Factors and would have appreciated a few more sentences on the underlying models that fed into the calculations. In my opinion, the provided citations (e.g., Dienes Z. 2014. Using Bayes to get the most out of non-significant results) did not provide sufficient background to warrant a lack of more technical presentation here.

Thank you for the feedback. We added a new “Appendix C: Technical details on Bayes factors” that provides technical details on the models, priors, and calculations underlying the Bayes factors.

Author response:

eLife assessment

In this valuable study, Kumar et al., provide evidence suggesting that the p130Cas drives the formation of condensates that sprout from focal adhesions to cytoplasm and suppress translation. Pending further substantiation, this study was found to be likely to provide previously unappreciated insights into the mechanisms linking focal adhesions to the regulation of protein synthesis and was thus considered to be of broad general interest. However, the evidence supporting the proposed model was incomplete; additional evidence is warranted to substantiate the relationship between p130Cas condensates and mRNA translation and establish corresponding functional consequences.

We thank the Elife editorial team for their positive assessment of the broad significance of our manuscript. We fully agree that the functional consequences need to be explored in more detail. We feel that many of the criticisms are valid points that are not easily addressed via available tools, thus, should be considered limitations of present approaches. We hope that readers appreciate that identification of a new class of liquid-liquid phase separations calls for much more work to fully explore their characteristics, regulation and function, which will likely advance many areas of cell biology and perhaps even medicine.

Reviewer #1 (Public Review):

Summary:

The authors demonstrated the phenomenon of p130Cas, a protein primarily localized at focal adhesions, and its formation of condensates. They identified the constituents within the condensates, which include other focal adhesion proteins, paxillin, and RNAs. Furthermore, they proposed a link between p130Cas condensates and translation.

Strengths:

Adhesion components undergo rapid exchange with the cytoplasm for some unclear biological functions. Given that p130Cas is recognized as a prominent mechanical focal adhesion component, investigating its role in condensate formation, particularly its impact on the translation process, is intriguing and significant.

We thank the reviewer for recognizing the functional significance of the work.

Weaknesses:

The authors identified the disordered region of p130Cas and investigated the formation of p130Cas condensate. They attempted to demonstrate that p130Cas condensates inhibit translation, but the results did not fully support this assertion. There are several comments below:

(1) Despite isolating p130Cas-GFP protein using GFP-trap beads, the authors cannot conclusively eliminate the possibility of isolating p130Cas from focal adhesions. While the characterization of the GFP-tagged pulls can reveal the proteins and RNAs associated with p130Cas, they need to clarify their intramolecular mechanism of localization within p130Cas droplets. Whether the protein condensates retain their liquid phase or these GFP-p130Cas pulls represent protein aggregate remains uncertain.

We agree, the isolation from cell lysates does not distinguish between focal adhesions and cytoplasmic LLPS. We note that p130Cas in focal adhesions also appears to be in LLPS. But there are no methods available to isolate them separately. We acknowledge this is a limitation of the study.

(2) The authors utilized hexanediol and ammonium acetate to highlight the phenomenon of p130Cas condensates. Although hexanediol is an inhibitor for hydrophobic interactions and ammonium acetate is a salt, a more thorough explanation of the intramolecular mechanisms underlying p130Cas protein-protein interaction is required. Additionally, given that the size of p130Cas condensates can exceed >100um2, classification is needed to differentiate between p130Cas condensates and protein aggregation.

Ammonium acetate, which works by promoting hydrophobic interactions and weak Van der Waals forces, has been widely used in phase separation studies to change ionic strength without altering intracellular pH. Conversely, hexanediol weakens hydrophobic/ Van der Walls interactions that commonly mediate phase separation of IDRs. In the case of p130Cas, the multiple tyrosines and within the scaffolding domain are obvious targets. If the reviewer is asking us to resolve the detailed hydrophobic interactions within the scaffolding domain, this is far beyond the scope of the current paper.

Protein aggregates are defined by their characteristics (e.g irreversibility, departure from spherical) not by size. Older, larger droplets remain circular and show slower but still measurable rates of exchange. Moreover, droplets are essentially absent after trypsinizing and replating cells. All these results argue against aggregates.

(3) The connection between p130Cas condensates and translation inhibition appears tenuous. The data only suggests a correlation between p130Cas expression and translation inhibition. Further evidence is required to bolster this hypothesis.

The optogenetic experiment shows that triggering LLPS by dimerizing p130Cas results in inhibition of translation. This is a causal not a correlative experiment. The reviewer may be thinking that dimerizing p130Cas could stimulate focal adhesion signaling, activating FAK or a src family kinase or other signals. However, none of these signals has been linked to inhibition of cell growth or migration. Thus, we agree that this is a limitation but consider it a low probability mechanism.

Reviewer #2 (Public Review):

Summary:

In this article, Kumar et al., report on a previously unappreciated mechanism of translational regulation whereby p130Cas induces LLPS condensates that then traffic out from focal adhesion into the cytoplasm to modulate mRNA translation. Specifically, the authors employed EGFP-tagged p130Cas constructs, endogenous p130Cas, and p130Cas knockouts and mutants in cell-based systems. These experiments in conjunction with various imaging techniques revealed that p130Cas drives assembly of LLPS condensates in a manner that is largely independent of tyrosine phosphorylation. This was followed by in vitro EGFP-tagged p130Cas-dependent induction of LLPS condensates and determination of their composition by mass spectrometry, which revealed enrichment of proteins involved in RNA metabolism in the condensates. The authors excluded the plausibility that p130Cas-containing condensates co-localize with stress granules or p-bodies. Next, the authors determined mRNA compendium of p130Cas-containing condensates which revealed that they are enriched in transcripts encoding proteins implicated in cell cycle progression, survival, and cell-cell communication. These findings were followed by the authors demonstrating that p130Cas-containing condensates may be implicated in the suppression of protein synthesis using puromycylation assay. Altogether, it was found that this study significantly advances the knowledge pertinent to the understanding of molecular underpinnings of the role of p130Cas and more broadly focal adhesions on cellular function, and to this end, it is likely that this report will be of interest to a broad range of scientists from a wide spectrum of biomedical disciplines including cell, molecular, developmental and cancer biologists.

Strengths:

Altogether, this study was found to be of potentially broad interest inasmuch as it delineates a hitherto unappreciated link between p130Cas, LLPS, and regulation of mRNA translation. More broadly, this report provides unique molecular insights into the previously unappreciated mechanisms of the role of focal adhesions in regulating protein synthesis. Overall, it was thought that the provided data sufficiently supported most of the authors' conclusions. It was also thought that this study incorporates an appropriate balance of imaging, cell and molecular biology, and biochemical techniques, whereby the methodology was found to be largely appropriate.

We thank reviewer for this positive assessment.

Weaknesses:

Two major weaknesses of the study were noted. The first issue is related to the experiments establishing the role of p130Cas-driven condensates in translational suppression, whereby it remained unclear whether these effects are affecting global mRNA translation or are specific to the mRNAs contained in the condensates. Moreover, some of the results in this section (e.g., experiments using cycloheximide) may be open to alternative interpretation. The second issue is the apparent lack of functional studies, and although the authors speculate that the described mechanism is likely to mediate the effects of focal adhesions on e.g., quiescence, experimental testing of this tenet was lacking.

We appreciate the reviewer’s insights. Assessing translational inhibition for specific genes rather than global measurement of translation is an important direction for future work.

Regarding the cycloheximide experiments, we are unsure what the reviewer means. We used it as a control for puromycin labeling but this is a very standard approach. It seems more likely that the question concerns Fig 5G, where we used it to sequester mRNAs on ribosomes to deplete from other pools. In this case, p130cas condensates decrease after 2 minutes. The reviewer may be suggesting that this effect could be due to blocked translation per se and loss of short-lived proteins. We acknowledge that this is possible but given the very rapid effect (2 min), we think it unlikely.

Lastly, we agree with the reviewer that further functional studies in quiescence or senescence are warranted; however, these are extensive, open-ended studies and we will not be able to include them as part of the current paper.

Author response:

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

eLife assessment

In this valuable study, the authors investigate the transcriptional landscape of tuberculous meningitis, revealing important molecular differences contributed by HIV co-infection. Whilst some of the evidence presented is compelling, the bioinformatics analysis is limited to a descriptive narrative of gene-level functional annotations, which are somewhat basic and fail to define aspects of biology very precisely. Whilst the work will be of broad interest to the infectious disease community, validation of the data is critical for future utility.

We appreciate with eLife’s positive assessment, although we challenge the conclusion that we ‘fail to define aspects of biology very precisely’. Our stated objective was to use bioinformatics tools to identify the biological pathways and hub genes associated with TBM pathogenesis and the eLife assessment affirms we have investigated ‘the transcriptional landscape of tuberculous meningitis’. To more precisely define aspects of the biology will require another study with different design and methods.

Reviewer #1 (Public Review):

Summary:

Tuberculous meningitis (TBM) is one of the most severe forms of extrapulmonary TB. TBM is especially prevalent in people who are immunocompromised (e.g. HIV-positive). Delays in diagnosis and treatment could lead to severe disease or mortality. In this study, the authors performed the largest-ever host whole blood transcriptomics analysis on a cohort of 606 Vietnamese participants. The results indicated that TBM mortality is associated with increased neutrophil activation and decreased T and B cell activation pathways. Furthermore, increased angiogenesis was also observed in HIV-positive patients who died from TBM, whereas activated TNF signaling and down-regulated extracellular matrix organisation were seen in the HIV-negative group. Despite similarities in transcriptional profiles between PTB and TBM compared to healthy controls, inflammatory genes were more active in HIV-positive TBM. Finally, 4 hub genes (MCEMP1, NELL2, ZNF354C, and CD4) were identified as strong predictors of death from TBM.

Strengths:

This is a really impressive piece of work, both in terms of the size of the cohort which took years of effort to recruit, sample, and analyse, and also the meticulous bioinformatics performed. The biggest advantage of obtaining a whole blood signature is that it allows an easier translational development into a test that can be used in the clinical with a minimally invasive sample. Furthermore, the data from this study has also revealed important insights into the mechanisms associated with mortality and the differences in pathogenesis between HIV-positive and HIV-negative patients, which would have diagnostic and therapeutic implications.

Weaknesses:

The data on blood neutrophil count is really intriguing and seems to provide a very powerful yet easy-to-measure method to differentiate survival vs. death in TBM patients. It would be quite useful in this case to perform predictive analysis to see if neutrophil count alone, or in combination with gene signature, can predict (or better predict) mortality, as it would be far easier for clinical implementation than the RNA-based method. Moreover, genes associated with increased neutrophil activation and decreased T cell activation both have significantly higher enrichment scores in TBM (Figure 9) and in morality (Figure 8). While I understand the basis of selecting hub genes in the significant modules, they often do not represent these biological pathways (at least not directly associated in most cases). If genes were selected based on these biologically relevant pathways, would they have better predictive values?

We conducted a sensitivity analysis including blood neutrophil as a potential predictor in the multivariate Cox elastic-net regression model for important predictor selection (Table S14). In this analysis, all six selected important predictors (genes and clinical risk factors) identified in the original analysis (Table S13) were also selected, together with blood neutrophil number. Additionally, we evaluated the predictive value of blood neutrophil alone, which demonstrated poor performance, with an optimism-corrected AUC of 0.63 for all TBM, 0.67 for HIV-negative TBM, and 0.70 for HIV-positive TBM. Even when combined with identified gene signatures, blood neutrophil did not improve the overall performance of predictive model (optimism-corrected AUC of 0.79 for all TBM, 0.76 for HIV-negative TBM, and 0.80 for HIV-positive). These results indicate that identified hub genes exhibit better predictive values compared to blood neutrophil alone or in combination. These findings have been incorporated into our manuscript results.

To test whether pathway representative genes have better predictive values than hub genes, we included all these genes in the analysis for important predictor selection. Pathway representative genes comprised ANXA3 and CXCR2 representing neutrophil activation and IL1b representing acute inflammatory response. We observed that all hub genes (MCEMP1, NELL2, ZNF354C, and CD4) consistently emerged as the most important genes with the highest selection in the models, compared to the rest, in both the HIV-negative TBM and HIV-positive TBM cohorts. Additionally, these identified hub genes were still selected when testing together with other hub genes representing relevant biological pathways associated with TBM mortality, such as CYSTM1 involved in neutrophil activation, TRAF5 involved in NF-kappa B signaling pathway, CD28 and TESPA1 involved in T cell receptor signaling. These results show that selected genes based on known biologically relevant pathways did not give better predictive values than the identified hub genes in the significant modules.

Reviewer #2 (Public Review):

Summary:

This manuscript describes the analysis of blood transcriptomic data from patients with TB meningitis, with and without HIV infection, with some comparison to those of patients with pulmonary tuberculosis and healthy volunteers. The objectives were to describe the comparative biological differences represented by the blood transcriptome in TBM associated with HIV co-infection or survival/mortality outcomes and to identify a blood transcriptional signature to predict these outcomes. The authors report an association between mortality and increased levels of acute inflammation and neutrophil activation, but decreased levels of adaptive immunity and T/B cell activation. They propose a 4-gene prognostic signature to predict mortality.

Strengths:

Biological evaluations of blood transcriptomes in TB meningitis and their relationship to outcomes have not been extensively reported previously.

The size of the data set is a major strength and is likely to be used extensively for secondary analyses in this field of research.

Weaknesses:

The bioinformatic analysis is limited to a descriptive narrative of gene-level functional annotations curated in GO and KEGG databases. This analysis cannot be used to make causal inferences. In addition, the functional annotations are limited to 'high-level' terms that fail to define biology very precisely. At best, they require independent validation for a given context. As a result, the conclusions are not adequately substantiated. The identification of a prognostic blood transcriptomic signature uses an unusual discovery approach that leverages weighted gene network analysis that underpins the bioinformatic analyses. However, the main problem is that authors seem to use all the data for discovery and do not undertake any true external validation of their gene signature. As a result, the proposed gene signature is likely to be overfitted to these data and not generalisable. Even this does not achieve significantly better prognostic discrimination than the existing clinical scoring.

As explained in response to the eLife assessment, our objective was to use bioinformatics tools to identify the biological pathways and hub genes associated with TBM pathogenesis. We agree that ‘This analysis cannot be used to make causal inferences’: that would require different study design and approaches. The proposed gene signature has higher AUC values than the existing clinical model alone or in combination with clinical risk factors (Table 4). We agree that independent validation of the gene signature will be a crucial next step for future utility. We have performed qPCR in another sample set, and have added these results in the revision (Table 4 and supplementary figure S8)

Reviewer #1 (Recommendations For The Authors):

I have a few additional comments most of which are relatively minor:

(1) Can the authors please clarify if all the PTB cases are also HIV-negative?

This has been added to the methods section.

(2) For Table 1, can the authors please list the total number of patients with microbiologically confirmed TB regardless of the methods used? And for the two TBM groups, was the positive microbiology based on CSF findings?

The total number of patients with microbiologically confirmed TB was presented in Table 2 in definite TBM group, which was microbiologically confirmed TB diagnosed using microscopy, culture, and Xpert testing in cerebrospinal fluid (CSF) samples. We have updated the note in Table 2 to provide clarity on the definition.

(3) How was the discovery and validation set selected? Was it based on randomisation?

We randomly split TBM data into two datasets, a discovery cohort (n=142) and a validation cohort (n=139) with a purpose to ensure reproducibility of data analysis. We described this in the methods section.

(4) Line 107 can be better clarified by stating that the overall 3-month mortality rate is 21.7% for TBM regardless of HIV status.

Thank you, we have restated this sentence in the results section.

(5) The authors stated that samples were collected at enrolment when patients would have received less than 6 days of anti-tubercular treatment. Is there information on the median and IQR on the number of days that the patients would have received Rx, especially between the groups? Did the authors control for this variable when analysing for DEGs?

One of criteria to enroll participants in LAST-ACT and ACT-HIV trials is that they must receive less than 6 consecutive days of two or more drugs active against M. tuberculosis. However, the information of the days that the patients would have received Rx was not recorded and we could not control this variable when performing differential expression analysis for DEGs. This has been clarified further in the methods section: ‘The samples were taken at enrollment, when patients could not have received more than 6 consecutive days of two or more drugs active against M. tuberculosis.’

(6) I am a little bit concerned with the reads mapping accuracy (57%) to the human genome, which is fairly low. Did the authors investigate the reasons behind this low accuracy?

Thank you. It was indeed a typo. We have corrected it in the results section.

(7) On Tables S2-S4, can the authors please clarify what the last column (labelled as "B") shows?

Tables S2-S4 now have been changed to S3-S5. We have updated the legend of these tables to provide clarification regarding the meaning of the last column.

Reviewer #2 (Recommendations For The Authors):

If the authors wish to revise their manuscript, I suggest the following amendments:

(1) Provide a consort diagram for the selection of samples included in the present analysis (from parent study cohorts), allocation to test and validation splits for bioinformatics analysis, and outcomes.

We have provided our consort diagram in supplementary Figure S10.

(2) Provide details of inclusion criteria for pulmonary TB cohort, and how samples from this cohort were selected for inclusion in the present analysis. Please clarify whether this cohort excluded HIV-positive participants by design or by chance.

The inclusion criteria for the pulmonary TB cohort were described in the methods section. Due to the very low prevalence of HIV in this prospective observational study, HIV-positive participants were excluded. We have clarified in the amended manuscript that the pulmonary TB cohort only included HIV-negative participants.

(3) Baseline characteristics of HIV-positive participants (Table 1) should include CD4 count, HIV viral load, and whether anti-retroviral therapy was naïve or experienced.

We have included pre-treatment CD4 cell count, information on anti-retroviral therapy, and HIV viral load data in Table 1, as well as described these information in the results section.

(4) I note that the TBM samples were derived from RCTs of adjunctive steroid therapy, but not stratified in the present analysis by treatment arm allocation. Clearly, this may affect the survival/mortality outcomes that are the central focus of this manuscript. Therefore, they should be included in the models for differential gene expression analysis and prognostic signature discovery. To do so, the authors may need to wait until they are able to unblind the trial metadata.

With permission from the trial investigators, we were able to adjust the analyses for treatment with corticosteroids. The investigators remained blind to the allocation and we have not reported any direct effects of corticosteroids on outcome – such an analysis could only be done once the LAST-ACT trial has been reported (which won’t be until the end of 2024). Treatment outcome and effect were blinded by extracting only the fold change difference between survival and death in the linear regression model, in which gene expression was outcome and survival and treatment were covariates.

(5) I understood from the methods (lines 460-461) that batch correction of the RNAseq data was necessary. However, it is not clear how the samples were batched. PCA of the transcriptomes before and after batch correction with batch and study group labels should be provided. I would also advocate for a sensitivity analysis to check the robustness of the main findings without batch correction. I assume Fig2A represents batch-corrected data, but this is not clear.

We have now added information about the RNA sequencing batch and the batch correction approach, analyses and data visualizations utilized batch-corrected data in the methods section. We have also updated results related to batch correction in Fig. 2A and Supplementary Figure S9.

(6) I would encourage the authors to include a differential gene expression analysis to directly compare the transcriptome of TBM to that of pulmonary TB. I think it would add additional value to their focus on describing the transcriptome in TBM.

We thank for reviewer’s suggestion. Conducting differential gene expression analysis to compare the transcriptome of TBM with that of PTB is beyond the scope of this manuscript and we will examine this question separately.

(7) I don't really understand the purpose of splitting their data set into test and validation for the purposes of showing that WGCNA analysis is mostly reproduced in the two halves of the data. I would advocate that they scrap this approach to maximise the statistical power of their analysis in the descriptive work.

As mentioned in response to reviewer #1 in question #3, the purpose of splitting data is to ensure the reproducibility of the data analysis as suggested by Langfelder et al. (PMID: 21283776). This approach served two purposes: (i) to affirm the existence of functional modules in an independent cohort and (ii) to validate the association of interested modules or their hub genes with survival outcomes.

(8) The authors should soften the confidence in their interpretation of the GO/KEGG annotations of WGCNA modules. At least, they should include a paragraph that explicitly details the limitations of their analyses, including (i) the accuracy GO/KEGG annotations are not validated in this context (if at all), (ii) that none of the data can be used to make causal inferences and (iii) that peripheral blood assessments that are obviously impacted by changes in cellular composition of peripheral blood do not necessarily reflect immunopathogenesis at the site of disease - in fact if circulating cells are being recruited to the site of disease or other immune compartments, then quite the opposite interpretations may be true.

We appreciate the reviewer's comment. (i) In our analysis, we initially confirmed the existence of Weighted Gene Co-expression Network Analysis (WGCNA) modules in discovery cohort and validated the association of these modules with mortality outcomes in validation cohort. We then applied GO/KEGG annotations to define the biological functions involved in WGCNA modules. Finally, we performed Qusage analysis to directly test the association of top-hit pathways of each WGCNA module with mortality outcomes (see supplementary S6). This analysis approach helped to identify and validate modules and biological pathways associated with TBM mortality in this context, avoiding potential false positives in GO/KEGG annotations of WGCNA modules. (ii) We agree with the assessment that 'This analysis cannot be used to make causal inferences,' as that would require a different study design and approach. (iii) The focus of this study is to investigate the pathogenesis of TBM in the systemic immune system. We have highlighted this focus in the title and the aim of the manuscript.

(9) For the prognostic signature discovery and validation, I strongly recommend the authors include more robust validation. For example, to undertake an 80:20 split for sequential discovery (for feature selection and derivation of a prognostic model), followed by validation of a 'locked' model in data that made no contribution to discovery. In two separate sensitivity analyses. I also suggest they split their dataset (i) by treatment allocation in the RCT and (ii) by HIV status. In addition, their method for feature selection has to be clearer- precisely how they select hub genes from their WGCNA analysis as candidate predictors is not explained. Since this is such a prominent output of their manuscript, the results of this analysis should really be included in the main manuscript, and all performance metrics for discrimination should include confidence intervals.

Employing an 80:20 split for training and testing models is a good approach for an internal validation. However, we addressed the issue of overestimating the performance of a prognostic model by bootstrapping sampling approach proposed by Steyerberg et al. (PMID: 11470385). This approach has been proven to provide stable estimates with low bias. The overall model performance for discrimination, reported in our manuscript, was corrected for “optimism” to ensure internal validity. This adjustment was achieved through a 1000-times bootstrapping approach, which effectively accounted for estimation uncertainty. As such, there is no need to present confidence intervals for these metrics.

Moreover, in our revision, to confirm prognostic signatures independently, we have evaluated the predictive value of identified gene signatures using qPCR in another set of samples. The results have been added in Table 4, supplementary Figure S8 and the results section.

For the reasons given above (comment 4), we are unable to split our dataset by treatment allocation in this analysis. But as described, we have adjusted the analysis for corticosteroid treatment. Once the primary results of the LAST ACT trial have been published, we will examine the impact of corticosteroids on TBM pathophysiology and outcomes, seeking to better understand the mechanisms by which steroids have their therapeutic effects.

Given the difference in pathogenesis and immune response by HIV-coinfection, we stratified our analysis by HIV status. As the reviewer’s suggestion, we have provided additional details in the methods section regarding the selection of hub genes from associated WGCNA modules and the feature selection process for predictive modeling.

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

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Reply to the reviewers

__Below is our point-by-point reply to the reviewer's comments __

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

PNKP is one of critical end-processing enzymes for DNA damage repair, mainly base excision & single strand break repair, and double strand break repair to a certain extent. This protein has dual enzyme function: 3' phosphatase and 5' kinase to make DNA ends proper for ligation. It has been demonstrated that PTM of PNKP (e.g., S114, S126), particularly phosphorylation by either ATM or DNAPK, is important for PNKP function in DNA damage repair. The authors found a new phosphorylation site, T118, of PNKP which might be modified by CDK1 or 2 during S phase. This modification of phosphorylation is involved in maintenance and stability of the lagging strand, particularly Okazaki fragments. Loss of this phosphorylation could result in increased single strand gaps, accelerated speed of fork progression, and eventually genomic instability. And for this process, PNKP enzyme activity is not that important. And the authors concluded that PNKP T118 phosphorylation is important for lagging strand stability and DNA damage repair.

Major comments

In general, enzymes have protein interactions with its/their substrates. If PNKP is phosphorylated by either/both CDK1/2, the protein interaction between these would be expected. However, the authors did not provide any protein interactions in PNKP and CDKs. *Thank you for your suggestion. We will perform GFP-pulldown assays using cell extracts from HEK293 cells expressing GFP-WT-PNKP, GFP-T118A-PNKP. And then to confirm the interaction of PNKP and CDK1/2, we will blot with CDK1 and CDK2 antibodies. *

It is not clear how T118 phosphorylation is involved in DNA damage repair itself as the authors suggested. The data presenting the involvement of T118 phosphorylation in this mechanism are limited. This claim opens more questions than answers. CDK1/2 still phosphorylates T118 in this DNA damage repair process? What would happen to DNA damage repair in which PNKP involves outside of S phase in terms of T118 phosphorylation?

Thank you for your comment. We have investigated how T118 phosphorylation is important in DNA damage repair by several experiments. In figure S8, we tested SSB and DSB repair abilities of PNKP KO cells expressing PNKP T118A mutant, in which PNKP T118 phosphorylation has critical roles in both SSB and DSB repair pathways. Interestingly, the result of SSB repair assay (figure S8A & B) may indirectly indicate that T118 phosphorylation is important for SSB repair throughout cell cycle as these SSBs are instantly induced by IR exposure and recovered only for 30 mins that is presumably not enough time for cells to go through cell cycle. Along with the repair abilities, we also analyzed a recruitment kinetics/ability to DNA damage in PNKP T118A and T118D mutants using laser micro-irradiation assay in figure S9. This result indicates that the phosphorylation of PNKP at T118 is controlling its recruitment to at least laser-induced DNA damage sites. Moreover, we have analyzed recruitment of PNKP to a single-strand DNA gap structure, which mimics intermediates of some DNA repair pathways and incomplete Okazaki fragment maturation, using cell extracts from PNKP KO cells expressing PNKP T118A and T118D mutants and biochemical assay in figure 4H. This assay is much cleaner and shows that loss of T118 phosphorylation impairs PNKP recruitment to the ssDNA gap structure. We believe that these data sufficiently support our model that the phosphorylation of T118 on PNKP is involved in DNA repair in general. However, we agree with that we have not yet directly tested DNA repair ability of PNKP T118A in outside of S-phase. Therefore, in addition to these data, we will perform H2O2-induced SSB and IR-induced DSB repair assay using EdU (S phase) pulse labelling in PNKP KO cells expressing PNKP T118A mutant, then we will measure the ADP-ribose intensity and pH2AX foci in EdU negative cells (outside of S phase as the reviewer suggested).

Along the same line with #1/2 comments, the recruitment of PNKP to the damage sites is XRCC1 dependent. Is not clear whether PNKP recruitment to gaps on the lagging strand is XRCC1 independent or dependent. It might be interesting to examine (OPTIONAL)

*Thank you for an important suggestion. XRCC1 acts as a scaffold of PNKP and is required for recruitment of PNKP for canonical SSB repair, although we propose that PNKP is involved in two pathways in DNA replication: PARP1-XRCC1-dependent ssDNA gap filling pathway and Okazaki fragment maturation pathway working with FEN1. It is still important to address how XRCC1 is required for PNKP recruitment to the single-strand gaps on nascent DNA. Therefore, we will perform iPOND analysis in XRCC1 knock down + GFP-WT-PNKP expressed HEK293 cells. *

Minor comments

In results: 'Generation of PNKP knock out U2OS cell line' - In figure S2A; There are no data regarding diminishing the phosphorylation of g-H2AX.

Thank you for your suggestion. We will add pH2AX blot data in fig S2A (all reviewers requested).

• By showing data in figure S2B/C/D/E, the authors describe 'PNKP KO cells impaired the SSBs repair activity'. However, as the authors mentioned in this manuscript, PNKP could bind to either XRCC1 or XRCC4. Also for this experiment, IR had been applied, which induces DNA double strand breaks. Therefore, it is not certain that the authors' description is fully supported by these data presented. Perhaps, SSB inducing reagents should be used instead of IR.

In figure S2B/C/D/E, we used gamma-ray as IR source, which classified as low energy transfer irradiation. which mainly act as indirect effect to the DNA. It is estimated gamma-ray induce DNA damage as 60-80% SSBs and 20-40 % DSBs. We believe our results are reasonable. In addition to these results, we will perform poly-ADP-ribose assay with H2O2 treatment to more specifically assess SSBs repair activity.

• Is there any FACS analysis data to support the description of the last sentence 'especially the phosphorylation of PNKP T118, is required for S phase progression and proper cell proliferation'?

Thank you for your suggestion. We will add the FACS analysis data of cell cycle profiles in PNKP KO cells expressing GFP, GFP-PNKP WT, T118A.

In results: 'CDKs phosphorylate T118 of PNKP ~~~ replication forks'

• In figure 3A, Is there any change in total PNKP (both GFP-tagged & endogenous) level?

*Thank you for your suggestion. We agree with your comment. We will add the PNKP expression analysis in different cell cycle population in asynchronized and synchronized cells (G1, S, G2/M samples). *

In results: 'Phosphorylation of PNKP at T118 ~~~ between Okazaki fragments'

• In figure 4D, What happens in the ADP-ribose level, when T118D PNKP is expressed?

*Thank you for your suggestion. This is interesting question. We will perform ADP-ribosylation assay in PNKP KO cells and PNKP KO cells expressing PNKP WT and T118D, and add data of ADP-ribose levels in those cells. *

In results: 'PNKP is involved in postreplicative single-strand DNA gap-filling pathway'

• The description regarding data presented in figure 6 is not clear enough. These data might suggest that wildtype U2OS does not have SSB which is a substrate for S1 nuclease (except under FEN1i and PARPi treatment), whereas PNKP KO has SSB during both IdU and CIdU incorporation, so that S1 nuclease treatment dramatically reduces the speed of fork formation in PNKP KO cells. Also In figure 6B/C/D, adding an experimental group of PNKP KO with S1 nuclease + PARPi might help to understand the role of PNKP during replication better. Also these additional data could support the description in discussion 'Furthermore, PNKP is required for the PARP1-dependent single-strand gap-filling pathway ~~~ DNA gap structure'.

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*We agree with reviewer's comment and suggestion. Since this point is also raised by reviewer 3, we will add the rationale of the experiment and more detailed description about the results, which would substantially improve this manuscript. We will also revise our representation in text followed by the comment. In addition to revising the text, we will add experiment groups of PNKP KO with S1 nuclease with/without PARPi as the reviewer suggested. *

In results: 'Phosphorylation of PNKP at T118 is essential for genome stability'

• In figure S8C, Did you measure g-H2AX foci disappearance for later time point, such as 24 hrs after DNA damage? Is not clear whether non-phosphorylated PNKP at T118 inhibit DNA damage repair or make it slower? How does T114A-PNKP behave in this experimental condition? T114 is well known target of ATM/DNAPK for DDR & DSB repair.

Thank you for your suggestion. We agree with your point. It is very important to analyze whether T118A mutant shows delayed or total loss of DSB repair ability. We will add the measurement of pH2AX foci at 24 hrs after IR in PNKP KO cells expressing GFP, WT-PNKP, T118A-PNKP. Although the analysis of pS114 PNKP is previously reported (Segal-Raz et al., EMBO reports, 2011 and Zolner et al., Nucleic Acids Research, 2011), we will also perform pH2AX assay in PNKP KO cells expressing S114A-PNKP as a control.

The result shown in figure S9 should be described in the result section, not in the discussion section.

Thank you for your suggestion. This is a point also raised by Reviewer 3. Since we are going to re-consider the layout of the manuscript upon the planned revision (as reviewer 3 suggested), we will move these points to the appropriate result section from the discussion.

**Referees cross-commenting**

I could see a similar degree of positive tendency toward the manuscript. I agree with the comments and suggestions in additional experiments made by reviewers 2 and 3. Those suggestions will improve an impact of the manuscript in the DNA damage repair field.

Reviewer #1 (Significance (Required)):

Significance

The authors discovered new phosphorylation site (T118) of PNKP which is an important DNA repair protein. This modification seems to play a role in maintenance of the lagging strand stability in S phase. This discovery is something positive in DNA repair field to expand the canonical and non-canonical functions of DNA repair factors.

The data presented to support PNKP functions and T118 phosphorylation in S phase seem solid in general, yet it is not sure how much PNKP is critical in the Okazaki fragment maturation process which is known that several end processing enzymes (like FEN1, EXO1, DNA2 etc which leave clean DNA ends.) are involved.

These finding might draw good attentions from researchers interested broadly in cell cycle, DNA damage repair, replication, and possibly new tumor treatment.

My field and research interest: DNA damage response (including cell cycle arrest and programmed cell death), DNA damage repair (including BER, SSBR, DSBR)

Thank you very much for your positive comment. As you mentioned, there are several other end processing enzymes that seem to be involved in Okazaki fragment maturation, however, none of those enzymes is reported as a protein involved in the gap-filling pathway as well. Therefore, the role(s) of PNKP in DNA replication are very outstanding as PNKP could be involved in two separate pathways, Okazaki fragment maturation and a back-up gap-filling repair process. As you suggested, we will add several experiments such as iPOND experiments using XRCC1-depleted cells, analysis of DNA repair ability of PNKP T118A mutant throughout cell cycle and S1 nuclease DNA fiber assays in PNKP KO cells with/without PARP inhibitor treatment, to reveal how much PNKP is critical in the Okazaki fragment maturation. We believe that performing those experiments makes the conclusion and this manuscript more solid and convincing.

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

Polynucleotide kinase phosphatase (PNPK) participates in multiple DNA repair processes, where it acts on DNA breaks to generate 5'-phosphate and 3'-OH ends, facilitating the downstream activities of DNA ligases or polymerases.

This manuscript identifies a CDK-dependent phosphorylation site on threonine 118 in PNKP's linker region. The authors provide some convincing evidence that this modification is important to direct the activity of PNPK towards ssDNA gaps between Okazaki fragments during DNA replication. The authors monitored protein expression levels, enzymatic activity, the growth rate and replication fork speed, as well as the presence of ssDNA damage to make a comprehensive overview of the features of PNKP necessary for its function.

Overall, the conclusions are sufficiently supported by the results and this manuscript is relevant and of general interest to the DNA repair and genome stability fields. Some level of revision to the experimental data and text would help strengthen its message and conclusions.

Major points:

In an iPOND experiment the authors detect the wt PNKP and the T118 phosphorylated form at the forks and conclude that this phosphorylation promotes interaction with nascent DNA (Figure 3E). An informative sample to include here would have been the T118A mutant. Based on the model proposed, the prediction would be that it would not be associated with the forks, or at least, associated at reduced levels compared to the wt. *Thank you for your suggestion. We agree with your comment. We will add the iPOND analysis in PNKP KO cells expressing T118A mutant to confirm that pT118 is important for recruitment of PNKP at nascent DNA. *

The quality of the gels showing the phosphatase and kinase assays in Figure 5 could be improved to facilitate quantification of the results. The gel showing the phosphatase activity has a deformed band corresponding to K378A mutant. The gel showing the kinase activity seems to be hitting the detection limits, and the overall high background might influence the quantification of D171A mutant in the area of interest. The authors should provide a better quality of these gels, focusing on better separation (running them longer, eventually with a slightly increased electric current) and higher signal of the analyzed bands (longer incubation phosphatase/kinase prior to quenching or loading higher amount of DNA).

We agree with your suggestion. This phosphatase and kinase assay could be improved. We will perform this assay again followed by reviewer's suggestions.

The authors sometimes make statements like: "a slight increase, slightly increased, relatively high" without an evaluation of the statistical significance for the presented data. An example of such a statement is: "T118A mutant-expressing cells exhibited a marked delay in cell growth, which was not observed for S114A, although T122A, S126A, and S143A were slightly delayed," based on the figure 2E. A similar comment applies also to figures 4A, 5A, 5E. Whenever possible, the authors should include also an evaluation of the statistical significance in the statement.

Thank you for your suggestion. We will check manuscript and revise representation as reviewer's suggestion.

Minor revisions:

I could not find a gH2AX blot for figure S2A.

Thank you for your suggestion. We will add pH2AX blot data in fig S2A.

The authors established two PNKP-/- clones and supported it with sequencing and several functional observations However, the C-terminal antibody appears to detect lower-intensity bands (Figure 1A). Can authors comment on those bands?

Thank you for your comment. One possibility of this band is artificially recognized bands. To improve this problem, we will try electrophoresis for longer time to separate this band.

Why the S1 nuclease data on DNA fibers do not show the same level of epistasis with the Fen1i, as do those on ADP-ribosylation?

Because FEN1 dependent Okazaki fragment maturation and PARP1-XRCC1 dependent gap-filling pathway are different pathways, FEN1i and PARPi treatment resulted in an additive effect in S1 nuclease data in PNKP WT cells. To facilitate better understanding, we will add graphical scheme in figure 6 (a similar problem was raised by Reviewer 3 below) and revise the description of the result.

**Referees cross-commenting**

I agree with all the comments from the reviewers 1 and 3.

Reviewer #2 (Significance (Required)):

Significance:

The manuscript identifies a CDK phosphorylation site in a relevant DNA repair protein. The experiments on this part are elegant and convincing. It seems that this phosphorylation is important during DNA replication and there is some supporting evidence in this point, although not as robust, meaning that it is not clear whether this phosphorylation is controlling specifically the recruitment to Okazaki fragments, or a general role in DNA repair. Maybe if they see a reduced recruitment of the T118A mutant to the forks (iPOND experiment) this would further increase the impact.

This work will be relevant to the basic research, especially in the fields of DNA repair and DNA replication.

My expertise: DNA replication, genome stability, telomere biology.

Thank you very much for your positive comment. As you suggested, we will perform an iPOND assay using PNKP T118A mutant. In addition of the T118A iPOND assay, we will also analyze the DNA repair function of PNKP T118A mutant throughout cell cycle as reviewer 1 suggested. We believe that results of these experiments will pin down whether the phosphorylation of PNKP on T118 is controlling its recruitment to Okazaki fragments specifically or single-strand DNA gaps in general, and solidify the conclusion of the manuscript.

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

Tsukada and colleagues studied the role of PNKP phosphorylation in processing single-strand DNA gaps and its link to fork progression and processing of Okazaki fragments.

They generated two PNKP KO human clonal cell lines and described defects in cell growth, accumulation in S-phase, and faster fork progression. With some elegant experiments, they complement the KO cell lines with deletion and point mutants for PNKP, identifying a critical phosphorylation site (T118) in the linker regions, which is important for cell growth and DNA replication.

They show that phosphorylation of PNKP peaks in the mid-S phase. CDK1 and CDK2/ with Cyclin A2 are the two main CDK complexes responsible for this modification. With the IPOND experiment, the author shows that PNKP is recruited at nascent DNA during replication.

They described increased parylation activity in PNKP KO cells, and by using HU and emetin, they concluded that this increased activity depends on replication and synthesis of Okazaki fragments.

Interfering with Okazaki fragment maturation by FEN1 inhibition is epistatic with PNKP KO (and T118A) in influencing parylation activity in the S phase and fork progression. The authors try to understand by mutant complementation which of the two functions (Phosphatase vs Kinase) is important in processing OF, and they propose a primary role for the phosphatase activity of PNKP. They also show that T118 is important in controlling genome stability following different genotoxic stress. Finally, by coupling the measurement of fork progression with PARP/FEN1 inhibitors and S1 treatment, they propose a role of PNKP in the post-replicative repair of single-strand gaps due to unligated OF.

Here are my major points:

The authors use a poly ADP ribose deposition measurement to estimate SSB nick/gap formation. Even if PARP activity is strictly linked to SSB repair, ADP ribosylation does not directly estimate SSB/nick gap formation. In addition, in Figs S2A, B, and C, the authors use IR and PARG inhibition to measure poly-ADP ribosylation in WT and PNKP KO cells. IR produces both SSB and DSB. A better and cleaner experiment would be to directly measure SSB formation (with alkaline comet assay, for example) in combination with treatments that are known to mainly cause SSB (H2O2, or low doses of bleomycin). Thank you for your suggestion. The main purpose of this manuscript is to clarify the potential role of PNKP in DNA replication. Therefore, we generated PNKP KO human cells and figure S2 showed confirmation of function of established role of PNKP in SSBs and DSBs repair. In addition, previous our report published in EMBO Journal (Shimada et al., 2015), we showed SSBs and DSBs repair defect in PNKP KO MEF with comet assay (both alkaline and neutral) after IR and H2O2 treatment. In addition to those observations, we will also perform BrdU incorporation assay in PNKP WT and KO cells treated with H2O2. BrdU staining under an undenatured condition has now been commonly used and is a more direct method to detect ssDNA nick/gap formation. We believe that the importance of PNKP in SSB repair is sufficiently supported by all data such as previous comet assays in PNKP KO MEF cells and two SSB repair assays in human cells using ADP-ribose staining or BrdU incorporation, which will be provided in the revised manuscript.

The manuscript would benefit from substantially restructuring the figures' order and panels. Before starting the T118 part, the authors could create several figures to explain the main consequences of the loss of PNKP. A figure could be focused on DSB-driven genome instability (fig1 + fig S8 and S9). Then, a figure for the single-strand break and link to the S-phase. For example, by using data from Figure 6 and showing only WT vs PNKP KO +- Nuclease S1 (without FEN1 or PARP inhibitors), the authors could easily convince the readers that loss of PNKP leads to the accumulation of single-strand gaps. Only in the second part of the manuscript could they introduce all the T118 parts. Thank you for your suggestion. The layout of this manuscript makes reviewers feeling confusing. After performing all planned experiments, we will carefully re-consider the total layout of the revised manuscript.

I understand the use of a FEN1 inhibitor to link the PNKP KO phenotype to OF processing, but this drug does not either rescue or exacerbate any of the phenotypes described by the authors. It seems to have just an epistatic effect everywhere. So, what other conclusion can we have if not that PNKO has a similar effect to FEN1? I think that the presence of this inhibitor in many plots complicates the digestion of several figures a little bit. Maybe clustering the data in a different way (DMSO on one side FEN1i on the other) would help. Thank you for your suggestion. We agree that this data set is complicate. To facilitate better understanding, we will change organization of the data according to your suggestion and add graphical scheme in figure 6.

In terms of the other conclusion we can have from those experiments, the other conclusion is that PNKP might plays two important roles in DNA replication: Okazaki fragment maturation, which seems an epistatic effect with FEN1, and PARP1-XRCC1 dependent single-strand gap filling pathway, which is required for repairing single-strand gaps between Okazaki fragments when Okazaki fragment maturation pathway does not work properly (e.g., loss of FEN1 or PNKP). In figure 6D, we show that a double treatment of FEN1i and PARPi in PNKP WT cells with S1 nuclease treatment shows extensive amount of digested DNA fibers, although a single treatment of either FEN1i or PARPi in PNKP WT cells with S1 nuclease treatment leads to only limited amount of digested DNA fibers, which indicates that two pathways regulated by FEN1 or PARP are coordinately required for preventing eruption of ssDNA gaps in DNA replication. On the other hand, PNKP KO cells with S1 nuclease treatment cause extensive amount of digested DNA fibers even without FEN1i and PARP1i treatments, also it is not further increased by FEN1i and PARPi treatment. Those results indicate that PNKP itself is involved in two pathways mentioned above. Therefore, loss of PNKP has a similar phenotype with loss of FEN1 in terms of Okazaki fragment maturation, but also there is an additional effect in repairing ssDNA nicks/gaps, which is created in FEN1 loss condition, but FEN1 seems not dealing with it.

Fig S9 should be removed from the discussion. Additionally, the authors should consider whether they want to keep that piece of data in a manuscript that is already pretty dense. Why should we focus on additional linker residues and microirradiation data at the end of this manuscript? *Thank you for your suggestion. This is a point also raised by Reviewer 1. Since we are going to re-consider the layout of the manuscript upon the planned revision, we will move these points to the appropriate result section from the discussion. *

I suggest using a free AI writing assistant. I think this manuscript would substantially benefit from one. As a non-native English speaker, I personally use one of them and find it extremely useful. Thank you for your suggestion. Our manuscript was revised by a native speaker from an English correction company. However, for revised manuscript, we will discuss with native speakers as well as use a free AI writing assistant to improve the quality of the manuscript.

Minor points:

In Figure S1A, the author refers to P-H2AX, but I do not see this marker in the western blot. Thank you for your suggestion. We will add pH2AX blot data in fig S2A.

**Referees cross-commenting**

I agree with all comments from reviewer 1 and 2.

Reviewer #3 (Significance (Required)):

This is an interesting paper with generally solid data and proper statistical analysis. The figures are pretty straightforward. Unfortunately, the manuscript is dry, and the reader needs help to follow the logical order and the rationale of the experiments proposed. This is also complicated by the enormous amount of data the authors have generated. The authors should improve their narrative, explaining better why they are performing the experiment and not simply referring to a previous citation. Reordering panels and figures would help in this regard. Overall, with some new experiments, tone-downs over strong claims and a better explanation of the rationale behind experiments the authors could create a fascinating paper.

Thank you very much for your positive comment about the data/analysis and the logic behind the experiments provided in the manuscript. We agree with that a manner and a structure of the manuscript could be improved by reordering figures, cutting down some redundant experiments, adding better explanation of the rationale behind experiments, and toning-down some claims. With rewriting the manuscript as stated above and performing several additional experiments suggested by the reviewers, we believe that the revised manuscript will be more convincing and fascinating.

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

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

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Reviewer #1:

Minor comments

• Is there any difference (except for PARGi exposure time?!) between figure S2B/C and S2D/E? Both data show increased ADP ribose after IR. It seems redundancy. Also it is hard to imagine that there is absolutely no sign of ADP ribose after IR w/o PARGi treatment (figure S2D).

Figure S2B/C show spontaneous single strand DNA breaks (SSBs) in PNKP KO cells, on the other hand, figure 2S/E show ectopic SSBs induced by IR exposure in PNKP KO cells. We believe these data help for readers to understand the effect of endo or exo damage in PNKP KO cells. Poly-ADP ribosylations are immediately removed from SSB sites after repair as demonstrated previously (Tsukada, et al., PLoS One 2019, Kalasova et al., Nucleic Acids Research, 2020), although not zero (low level), it is very difficult to detect without PARGi treatment.

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Legend for figure S3 - typo!

Thank you for your suggestion about typo. The legend for figure S3 is corrected as "Protein expression of PNKP mutants in U2OS cells".

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• In figure S3A/B, it is quite interesting that the PNKP antibody used for this analysis can detect all truncated and alanine substituted PNKP proteins. It might be helpful to indicate for other researchers which antibody used (Novus; epitope - 57aa to 189 aa or Abcam; epitope not revealed).

In S3A/B, Novus PNKP antibody was used for all blots. We indicated this in the figure legend as "PNKP antibody (Novus: NBP1-87257) was used for comparing expression levels of endogenous and exogenous PNKP".

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In results: 'PNKP phosphorylation, especially of T118 ~~~ proliferation'

• In the fork progression experiment (figure 2C), is there any statistical difference between D2 and D3/4 expressing cells?

*Thank you for your suggestion. We performed statistical analysis as the reviewer suggested. Statistical analysis shows that there are no significant differences between D2 and D3/D4. Meanwhile, there are significant differences between WT and D3(P- What is the basis of the description 'Since the linker region of PNKP is considered to be involved in fork progression'? Any reference?

This sentence was considered based on the above sentences "Furthermore, D2 mutant-expressing cells also showed an increased speed of the replication fork compared to WT and D1 mutant-expressing cells, although D3 and D4 showed mildly high-speed fork progression.". The D2 mutant lacks a whole linker region, which shows increased speed of DNA fiber in figure 2C. Therefore, we originally explained as the sentence above. We have revised the sentence to "Since these results may indicate the linker region of PNKP is involved in proper fork progression".

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• In figure 3B: pS114-PNKP (also pS15-p53) is DNA damage inducible. In this experiment, was DNA damage introduced? Roscovitine could hinder DNA repair process, but not inducing DNA damage itself.

Thank you for your suggestion. DNA damage induction was not applied in this experiment. We agree that this panel makes confusing. We think that endogenously S114-PNKP (also S15-p53) might be phosphorylated slightly but not significant, although this is not the scope of this manuscript. This result showing that phosphorylated-T118 is reduced by Roscovitine treatment maybe redundant as we also have a result of in vitro phosphorylation assay using several combinations of CDKs and Cyclin proteins, which is a cleaner experiment to prove which CDK/Cyclin complex is directly controlling the T118 phosphorylation. Since the manuscript already contains enough amount of data to support the conclusion (as reviewer 3 also stated), we removed those blots result from the panel to avoid complicating the conclusion.

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In results: 'Phosphatase activity of PNKP is ~~~ of Okazaki fragments'

• In figure 5C, any statistical analysis between WT-PNKP KO vs D171A-PNKP KO or K378A-PNKP KO has been done?

Thank you for your comment. Statistical analysis shows P *

In discussion, 'In contrast, the T118A mutants showed the absence of both SSBs and DSBs repair (Fig. S7) : figure S7 does not indicate what the authors describe.

Thank you for pointing out this. This should refer to figure S8 instead of figure S7. We have corrected this error.

In addition, the same sentence in discussion: No evidence demonstrate that 'the absence of both SSBs and DSBs repair', and the following sentence is not clear.

*This is same point with above. We have corrected this mis-referencing and revised the sentence to "In contrast, the T118A mutants showed the impaired abilities of both SSBs and DSBs repair (Fig. S8).". We also revised the following sentence to "However, residual SSBs due to impaired SSB repair ability (e.g., in PARPi-treated cells and T118A cells) sometimes cause DNA replication-coupled DSBs formation in S phase, and the phenotype in DSB repair assay of the T118A mutant may be caused by an accumulated formation of DNA replication-coupled DSBs. Future works will be needed to distinguish whether the T118 phosphorylation directly regulate PNKP recruitment to DSBs as well as SSBs." for better explanation of the result. *

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In discussion, 'Because both CDK1/cyclin A2 and CDK2/cyclin A2 are involved in PNKP phosphorylation, cyclin A2 is likely important for these activities': It is not clear what this description intends? Is 'cyclin A2' important in what stance?

This description is coming from Fig3C observation. Since both CDK1 and CDK2 activities are cyclin A2 dependent, we speculated cyclin A2 is important for CDK1/CDK2 dependent PNKP T118 phosphorylation. We revised the description to "Since both CDK1/Cyclin A2 and CDK2/Cyclin A2 phosphorylate T118 of PNKP, we speculated that PNKP T118 is phosphorylated in S phase to G2 phase in CDK1/Cyclin A2- and CDK2/Cyclin A2-dependent manner (Fig. 3B and C)".

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In discussion, 'This may be explained by the fact that mutations in the phosphorylated residue in the linker region are embryonic lethal': any reference to support this embryonic lethality?

Thank you for your suggestion. We agree with that this sentence is overwriting. We revise the sentence to "This observation may indicate that mutations in the phosphorylated residue (T118) in the linker region are potentially embryonic lethal due to the importance of T118 in DNA replication, which is revealed in the present study.".

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Reviewer #2:

Minor comments

Sometimes there are incorrect references to the figures in the discussion (e.g. FigS9A, B, and C, are called out instead of E, F and G), a similar issue is found 4 lines below in the same page.

Thank you for pointing out these errors. We checked the references in the discussion and corrected to the appropriate references.

Based on the data in Figure 3A the authors suggest that pT118-PNKP follows Cyclin A2 levels, but this does not appear very clearly in the gel, especially for the last point. Even though the results are convincing, the authors should rephrase the conclusions of Figure 3A to reflect better the results.

Thank you for your suggestion. We agree that this phrase is overwriting. We revised the conclusion to "pT118-PNKP was detected in asynchronized cells but increased particularly in the S phase, similar to Cyclin A2 expression levels, although the reduction of pT118, possibly dephosphorylation of T118, seems not as robust as the reduction of the Cyclin A2 expression level at the 12 hours time point. However, this effect was very weak during mitosis, suggesting that T118 phosphorylation plays a specific role in the S phase.".

I did not find a reference to what seems to be a relevant work in this topic: PMID: 22171004

Thank you for your suggestion. We have added the ref (Coquelle et al., PNAS, 2011) in Introduction section.

Reviewer #3:

Major comments

The authors should consider and discuss the potential role of PNKP KO outside of the S-phase. In Figure 4C, while it is clear that poly ADP ribosylation is higher in S-phase, the effects of PNKP KO and complementation by WT or T118A are equally present. This would be more immediate if comparison, fold change, and statistical significance calculation were done within the same cell cycle phase instead of between cell stages. This is also clear by IF in Figure 4B. How do the authors explain this? Thank you for your suggestion. We agree with reviewer's suggestion. We compared intensities of ADP-ribose between cell lines in same cell cycle rather than between different cell cycles in a same cell line and added the respective statistics in figure 4C. Also, we agree with that poly ADP-ribose intensity is changed outside of S phase between WT and T118A PNKP expressing PNKP KO cells. As shown in figure S8, PNKP pT118 is also involved in DNA repair. These results might reflect of PNKP function outside of S phase. We have added the sentence "Of note, PNKP−/−*cells and PNKP T118A cells showed markedly higher ADP-ribose intensity in outside the S phase as well, which indicate that PNKP and T118 may have an endogenous role to prevent SSBs formation in outside the S phase. Since FEN1 has been reported to function in R-loop processing, PNKP could also be involved in this process. Future studies of a role of PNKP in different cell cycle will be able to address this question." to discuss about the function of PNKP outside the S phase. We have added the ref (Cristini et al., Cell Reports, 2019, and Laverde et al., Genes, 2022). *

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In connection with the previous point, can the author provide the same quantification in Figure 4E also for G2/M and not only the S phase? This should give an estimate of the activity of FEN1 outside the S-phase. This is important because FEN1 has other functions apart from OF maturation, such as R loop processing (Cristini 2019; Laverde 2023) Thank you for your suggestion. Here attached is the data of ADP-ribose intensity in cells outside the S phase as you suggested. FEN1i treatment still induces increased ADP-ribose intensity in outside the S phase as well, although the difference between with/without FEN1i treatment is much smaller than that in S phase, indicating that FEN1 has other functions outside the S phase. This finding is very interesting. However, the function of FEN1 in outside the S phase is outside the scope of this manuscript. Therefore, we would like to not put this data in the manuscript to avoid complicating the conclusion (as reviewer 3 also suggested).

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Why does FEN1 inhibition induce a faster fork progression in Fig4 but not in Fig5 and Fig6? Yes, it does in figure 4 and figure 5. In PNKP WT cells, FEN1i-treated fibers (CldU) show an increased speed of forks compared to non-treated fibers (IdU). However, loss of PNKP and T118 phosphorylation themselves cause a faster fork progression even if without FEN1i treatment, therefore the difference of speeds of forks before/after FEN1i treatment in PNKP KO and T118A cells is disappeared as both fibers grow faster than intact fibers in normal cells. In regard to figure 6, as you mentioned in a latter comment about figure 6, the title of vertical axis of the graph showing CldU length should not be speeds of replication forks as those DNA fibers are potentially digested by S1 nuclease, which is modified in the revised manuscript. Even so, DNA fibers from FEN1i-treated cells (CldU) with S1 nuclease shows similar length with fibers from untreated cells with S1 nuclease, whereas FEN1 inhibitor treatment accelerates a speed of forks in general (figure 4 and figure 5, assays without S1 nuclease), indicating that FEN1i treatment induces remaining of some ssDNA nicks/gaps which are substrates of S1 nuclease.

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How do the authors explain the impaired DNA gap binding activity of the phospho-mimetic T118D? Thank you for your suggestion. We think that the appropriate timing of phosphorylation of PNKP T118 is important, while the phosphor-mimetic mutant T118D mimics consecutively phosphorylated situation that may result in incomplete complementation of PNKP function.

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I would like to see a representative fiber image from Fig 6. Additionally, in Figure 6, the author should not label the y-axis as CldU-fork speed. Nuclease S1 treatment destroys single-strand gaps (in vitro) and does not affect the fork speed (in vivo) Thank you for your suggestion. We have added a representative fiber image. We also agree with that CldU fork speed is not a right label of y-axis as CldU fibers are potentially digested by S1 nuclease. We changed the y-axis label to "CldU tract length [kb/min]" in figure 6.

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Figure 5E: both mutants (kinase vs phosphatase) increase polyADP ribose intensity, while the title of this figure only emphasizes the phosphatase activity. We agree with your comment. We have changed this subtitle to "Enzymatic activities of PNKP is important for the end-processing of Okazaki fragments".

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Minor comments

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The authors refer to Hoch Nature 2017 when referring to polyADP ribose IF + PARG inhibition. Should they not refer to Hanzlikova Mol Cell 2018?

Thank you for your suggestion. We have added the ref (Hanzlikova et al., Mol Cell 2018).

Statistical analysis should be performed on the cell cycle profile in Figure 1B * *

We performed statistical analysis to check whether there are significant differences of S phase population between WT and PNKP KO cells. There were significant differences between WT vs PNKP KO C1 (PThe authors should not refer to fork degradation or protection as a given fact without assessing it in these conditions. Thank you for your suggestion. We assume that this comment refers to the result section of figure 1 and figure 4. We have added a sentence "although future studies will be needed to investigate whether PNKP−/− cells has the fork protection phenotype" in the result section of figure 1. We have changed representation in the section according to the reviewer's suggestion in the result section of figure 4.*

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