Author response:

We would like to thank the eLife Editors and Reviewers for their positive assessment and constructive comments, and for the opportunity to revise our manuscript. We greatly appreciate the Reviewers’ recommendations and believe that they will further improve our manuscript.

In revising the manuscript, our primary focus will be enhancing the clarity surrounding testing procedures and addressing corrections for multiple comparisons. Additionally, we intend to offer more explicit information about the statistical tests employed, along with the details about the number of models/comparisons for each test. We will also include an extended discussion on potential limitations of the dopaminergic receptor mapping methods used, addressing the Reviewers’ comments relating to the quality of PET imaging with different dopaminergic tracers in mesiotemporal regions such as the hippocampus. While the code used for connectopic mapping is publicly available through the ConGrads toolbox, we will provide the additional code we have used for data processing and analysis, visualization of hippocampal gradients, and the cortical projections. The data used in the current study is not publicly available due to ethical considerations concerning data sharing, but can be shared upon reasonable request from the senior author. Additional plans include clarifying and discussing which findings were successfully replicated, and addressing Reviewers’ suggestions for using other openly available cohorts for replication, and implementing alternative coordinate systems to quantify connectivity change along gradients.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript "comparative transcriptomics reveal a novel tardigrade specific DNA binding protein induced in response to ionizing radiation" aims to provide insights into the mediators and mechanisms underlying tardigrade radiation tolerance. The authors start by assessing the effect of ionizing radiation (IR) on the tardigrade lab species, H. exemplaris, as well as the ability of this organism to recover from this stress - specifically, they look at DNA double and single-strand breaks. They go on to characterize the response of H. exemplaris and two other tardigrade species to IR at the transcriptomic level. Excitingly, the authors identify a novel gene/protein called TDR1 (tardigrade DNA damage response protein 1). They carefully assess the induction of expression/enrichment of this gene/protein using a combination of transcriptomics and biochemistry - even going so far as to use a translational inhibitor to confirm the de novo production of this protein. TDR1 binds DNA in vitro and co-localizes with DNA in tardigrades.

Reverse genetics in tardigrades is difficult, thus the authors use a heterologous system (human cells) to express TDR1 in. They find that when transiently expressed TDR1 helps improve human cell resistance to IR.

This work is a masterclass in integrative biology incorporating a holistic set of approaches spanning next-gen sequencing, organismal biology, biochemistry, and cell biology. I find very little to critique in their experimental approaches.

Strengths:

(1) Use of trans/interdisciplinary approaches ('omics, molecular biology, biochemistry, organismal biology)

(2) Careful probing of TDR1 expression/enrichment

(3) Identification of a completely novel protein seemingly involved in tardigrade radio-tolerance.

(4) Use of multiple, diverse, tardigrade species of 'omics comparison.

Weaknesses:

(1) No reverse genetics in tardigrades - all insights into TDR1 function from heterologous cell culture system.

(2) Weak discussion of Dsup's role in preventing DNA damage in light of DNA damage levels measured in this manuscript.

(3) Missing sequence data which is essential for making a complete review of the work.

Overall, I find this to be one of the more compelling papers on tardigrade stress-tolerance I have read. I believe there are points still that the authors should address, but I think the editor would do well to give the authors a chance to address these points as I find this manuscript highly insightful and novel.

We thank the reviewer for his comments.

We agree that it will be important to further investigate the role of Dsup in radio-tolerance. We briefly mentioned this point in the discussion (p14). Our findings show that tardigrades undergo DNA damage at levels roughly similar to radio-sensitive organisms and therefore support a major role for DNA repair in the maintenance of genome integrity after exposure to IR. Nevertheless, we believe that more precise quantification of DNA damage may still reveal a contribution of genome protection to radio-tolerance of tardigrades compared to radio-sensitive organisms. Dsup loss of function experiments in tardigrades would clearly be the best way to assess this possibility. In the absence of experiments directly addressing the function of Dsup, we prefer to refrain from drawing any firm conclusion on prevention of DNA damage by Dsup and thus to keep a more open position. In any case, as discussed in the text, we note that Dsup has only been reported in Hypsibioidea and other molecular players, such as TDR1, are likely involved in radio-tolerance in other tardigrade species.

The sequence data can be accessed at the NCBI SRA database with Bioproject ID PRJNA997229.

Reviewer #3 (Public Review):

Summary:

This paper describes transcriptomes from three tardigrade species with or without treatment with ionizing radiation (IR). The authors show that IR produces numerous single-strand and double-strand breaks as expected and that these are substantially repaired within 4-8 hours. Treatment with IR induces strong upregulation of transcripts from numerous DNA repair proteins including Dsup specific to the Hypsobioidea superfamily. Transcripts from the newly described protein TDR1 with homologs in both Hypsibioidea and Macrobiotoidea supefamilies are also strongly upregulated. They show that TDR1 transcription produces newly translated TDR1 protein, which can bind DNA and co-localizes with DNA in the nucleus. At higher concentrations, TDR appears to form aggregates with DNA, which might be relevant to a possible function in DNA damage repair. When introduced into human U2OS cells treated with bleomycin, TDR1 reduces the number of double-strand breaks as detected by gamma H2A spots. This paper will be of interest to the DNA repair field and to radiobiologists.

Strengths:

The paper is well-written and provides solid evidence of the upregulation of DNA repair enzymes after irradiation of tardigrades, as well as upregulation of the TRD1 protein. The reduction of gamma-H2A.X spots in U2OS cells after expression of TRD1 supports a role in DNA damage.

Weaknesses:

Genetic tools are still being developed in tardigrades, so there is no mutant phenotype to support a DNA repair function for TRD1, but this may be available soon.

We thank the reviewer for his comments.

Reviewer #4 (Public Review):

The manuscript brings convincing results regarding genes involved in the radio-resistance of tardigrades. It is nicely written and the authors used different techniques to study these genes. There are sometimes problems with the structure of the manuscript but these could be easily solved. According to me, there are also some points which should be clarified in the result sections. The discussion section is clear but could be more detailed, although some results were actually discussed in the results section. I wish that the authors would go deeper in the comparison with other IR-resistant eucaryotes. Overall, this is a very nice study and of interest to researchers studying molecular mechanisms of ionizing radiation resistance.

I have two small suggestions regarding the content of the study itself.

(1) I think the study would benefit from the analyses of a gene tree (if feasible) in order to verify if TDR1 is indeed tardigrade-specific.

(2) It would be appreciated to indicate the expression level of the different genes discussed in the study, using, for example, transcript per millions (TPMs).Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

We thank the reviewer for his comments.

(1) To identify TDR1 homologous sequences in non-tardigrade species, we conducted extensive homology searches using multiple homology-based approaches (Blastp and Diamond against the NCBI non-redundant protein sequences (nr) database and hmmsearch against the EBI reference proteomes), which failed to identify TDR1 homologs in non-tardigrade ecdysozoans, thus strongly supporting that TDR1 is indeed tardigrade-specific.

To be clearer in the manuscript, we now state the absence of hits for TDR1 in non-tardigrade ecdysozoans. Given the absence of homologs in non-tardigrade species, it is not possible to make a gene tree with non-tardigrade species.

(2) To further document expression levels (which were already available from the Tables in the initial submission), we added MAplots (representing log2foldchange and logNormalized read counts) in the supplementary materials (Supp Figure 3 and Supp Figure 8). These additional figures clearly document that the DNA repair genes discussed in the main text and TDR1 are highly expressed genes after IR and after Bleomycin treatment.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

We thank the reviewer for his comments.

(1) It has always seemed strange to me that tardigrades accumulate just as much DNA damage as any other organism when irradiated and yet their Dsup protein is supposed to shield and protect their DNA from damage. Perhaps this is an appropriate time for this idea to be reconsidered given the Dsup was NOT induced by IR in this study and the authors found that their animals incurred just as much damage as other biological systems. While Dsup is clearly not the focus of this manuscript, it is the protein most associated with tardigrade radio-tolerance and I would argue this new paper would call into question previous conclusions made about Dsup.

We agree that it will be important to further investigate the role of Dsup in radio-tolerance. We briefly mentioned this point in the discussion (p14). Our findings show that tardigrades undergo DNA damage at levels roughly similar to radio-sensitive organisms and therefore support a major role for DNA repair in the maintenance of genome integrity after exposure to IR. Nevertheless, we believe that more precise quantification of DNA damage may still reveal a contribution of genome protection to radio-tolerance of tardigrades compared to radio-sensitive organisms. Dsup loss of function experiments in tardigrades would clearly be the best way to assess this possibility. In the absence of experiments directly addressing the function of Dsup, we prefer to refrain from drawing any firm conclusion on prevention of DNA damage by Dsup and thus to keep a more open position. In any case, as discussed in the text, we note that Dsup has only been reported in Hypsibioidea and other molecular players, such as TDR1, are likely involved in radio-tolerance in other tardigrade species.

(2) While reverse genetics are difficult in tardigrades, they are not impossible, and RNAi can be used to good effect in these animals. In fact several authors on this manuscript have used RNAi to examine the necessity of genes in tardigrade stress tolerance in the past. Was an attempt made to RNAi TDR1? If not, why? With the large amount of work that the authors put into showing the sufficiency of TDR1 for increasing radiotolerance in cell culture, one would think looking at necessity in tardigrades would be of great interest. If RNAi was performed, what were the results? Even a negative result here is informative since a protein can be sufficient but not necessary for a function - if this were the case it would mean tardigrades have some redundant mechanism(s) for surviving radiation exposure beyond TDR1.

We have attempted RNAi experiments targeting TDR1 or a mix of DNA repair genes (including XRCC5) and examined response to a bleomycin treatment of 2 weeks. Unfortunately, we could not distinguish any difference between uninjected animals and animals injected with TDR1 dsRNAs , or the mix of DNA repair genes dsRNAs. We concluded that, bleomycin treatment, that we used because it is much easier to perform than irradiation, was perhaps not the best way to assay a potential impact of RNAi on survival since it required long term treatment for several days during which the effect of RNAi may have waned. Another attempt was therefore made injecting with TDR1 or control GFP dsRNAs and exposing animals to a 2000Gy IR treatment. We noticed that the viability was lower after injection with GFP dsRNAs than with TDR1 dsRNAs (likely due to problems we had with the injection needle during injections). The next day, animals were irradiated and we observed after 24h that animals injected with GFP dsRNAs exhibited higher lethality rates than animals injected with TDR1 dsRNAs or uninjected animals. We found that this set of experiments were not conclusive. Our current experimental set up will make it difficult to distinguish lethality due to injections from lethality due to potentially decreased resistance to IR. In particular, many key controls are difficult to make (in particular, we could not confirm the efficiency of target gene knockdown, as it is very challenging given the low amount of biological material available and the poor expression of these genes without irradiation). From a practical point of view, performing these experiments is thus very challenging. We nevertheless agree that, in future work, further experimentation is needed to examine the impact of knock-down by RNAi of TDR1 or of other genes such as DNA repair genes or Dsup, in tardigrade DNA repair and survival after IR. Gene knock-out with CRISPR-Cas9 is a very promising alternative to RNAi given that studies in mutant lines will eliminate the confounding effect of lethality due to injections.

(3) Regarding the U2OS experiments. I have several questions/points of clarification:

a. Were survival/proliferation levels tested or only H2AX foci? I think that showing decreased H2AX foci (fewer double-stranded breaks) correlates with higher survival rates would be important.

In the experiments reported in Figure 6, cells were transiently transfected with expression vectors and we did not examine the impact on survival rates. U2OS cells are resistant to high doses of Bleomycin and testing survival would require longer exposure at much higher concentrations (Buscemi et al, 2014, PMID: 25486478). In order to try and better address an impact on cell survival, we therefore generated populations of cells stably expressing the candidate tardigrade proteins fused to GFP. Despite trying different experiment conditions for treatment with Bleomycin, we could not detect a reproducibly significant benefit on cell survival for any of the tardigrade proteins tested, including RvDsup which was used as a positive control (since it was previously reported to improve cell survival in response to X-rays). One possibility is that the analysis should be performed in clones and not in populations of cells with heterogeneous expression levels of the tardigrade protein tested. For example, expression levels of the tardigrade protein needed to reduce the number of phospho-H2AX foci in response to DNA damage may interfere with cell division. We note that in the original Dsup paper, the benefit of RvDsup on cell survival was reported in specific transgenic clones. Experiments in different biological systems have also started to document toxic effects of RvDsup expression, illustrating the challenge, when performing experiments in heterologous systems, to achieve suitable expression levels of the tested protein. Trying to perform such a finer analysis, in our opinion, would go beyond the scope of our manuscript and will be best addressed in future studies. We are therefore careful in the text not to make any claim on the benefit of TDR1 expression on cell survival in response to Bleomycin in human cultured cells.

(b) From the methods I am a bit confused as to how the images were treated/foci quantified. With the automatic segmentation and foci identification, is this done through the entire Z-series or a single layer? If the latter then I am not sure the results are meaningful, since we do not know how many foci might be present in other layers of the nuclei analyzed. If the former, please clarify this in the method since it is a very important consideration.

We have acquired images throughout the entire Z-series and edited the text to make it more clear ; We now write: “ Z-stacks were maximum projected and analyzed with Zen Blue software (v2.3)...”. To limit the time needed for image analysis, we have generated an artificial image by projecting the entire Z-series into a single image and counted foci in that single maximum projection image. Although there are potential drawbacks, such as potentially only counting one focus when two foci are superposed along the Z axis, this approach overcomes the limitations of quantification from a single layer. We further ensured statistical robustness of the analysis by performing quantification from several independent fields of the labelled cells and several independent biological replicates (n>=3 as now specified in the legend of figure 6a).

(c) RvDsup reduced levels of HXA1 foci in these experiments, however, HeDsup was not found to be enriched in the transcriptomic analysis performed here. Was there a reason HeDsup was not used in the cell-based experiments? One could argue that RvDsup is from a different species of tardigrade, but it is a bit concerning that an ortholog of a protein found NOT to be induced by radiation exposure seems to perform as well (if not better) than some versions of TDR1.

RvDsup is the protein initially shown to increase survival of human HEK293 cells treated with X-rays and reduce the number of phospho-H2AX foci induced: it was therefore used as a positive control in our experiments. The sequence of HeDsup is only poorly similar to RvDsup (with 26% identity) and activity of HeDsup in cultured cells has not been reported before. We therefore believe that HeDsup is not well suited to provide a positive control for the experiments performed in our manuscript.

(d) From the methods, it seems that cells were treated with Bleomycin and then immediately fixed without any sort of recovery time. In this short timeframe, the presence of TDR1 appears to be enough to deal with a substantial amount of double-stranded breaks (as evidenced by the reduced number of HXA1 foci). Does this make sense? How quickly could one expect DNA repair machinery to make significant progress in resolving damaged DNA? This response seems much faster than what was observed in tardigrades. Perhaps the authors to comment on this.

Kinetic studies in human cells show extremely rapid repair of DNA double-strand breaks. Sensing of DNA double strand breaks by PARP proteins takes place within seconds after irradiation by IR (Pandey and Black, 2021, PMID: 33674152). NHEJ is then observed to take place by formation of 53BP1 foci within 15 minutes (Schultz et al, 2000, PMID: 11134068). The number of phospho-H2AX and 53BP1 foci peaks at 30 minutes and starts declining thereafter, showing that at a significant number of sites, DNA repair is proceeding very rapidly (by NHEJ). Although we are not aware of any studies of DNA repair kinetics in U2OS cells after addition of Bleomycin, DNA damage must be instantaneous and further take place during exposure to the drug in parallel to DNA repair, which would be expected to have similar kinetics than after irradiation with IR.

In our experiments, several mechanisms may be involved in reducing the number of phospho-H2AX foci induced by Bleomycin, such as DNA protection (for Dsup expression) or stimulation of DNA repair (for RNF146 expression). For TDR1, the molecular mechanism involved remains to be determined. Given our finding that TDR1 can form aggregates with DNA, an additional possibility is that clustering of phospho-H2AX foci is induced.

(4) I could not find the sequences of the TDR1 proteins studied here. I did find the cDNA sequence of HeTDR1 in the final supplementary file, but not the other TDR1 orthologs. In the place where it appeared the TDR1 sequences from other tardigrades should be there were very short segments of the HETDR1 sequence. All sequences of proteins used in this study should be easily accessible to the reader and reviewers as it is not possible to review this work without accessing the sequences.

Our apologies for the inappropriate documentation of TDR1 sequences in the original manuscript. As requested, we have now included the TDR1 sequences in the Supplementary Table 4.

(5) Likewise, the RNA sequence data is said to be deposited in NCBI under PRJNA997229, but I do not find this available on NCBI.

The RNA sequence data was deposited in NCBI under the indicated reference before submission of the manuscript. The data has now been released and is fully available on NCBI.

(6) A few typographical errors: e.g., Page 10 - sentence 4 has two periods ". ." or page 14 which has an open parenthesis that is not closed.

These typos have been corrected in the revised manuscript.

Reviewer #3 (Recommendations For The Authors):

We thank the reviewer for his comments.

In Figure 4C, what fraction of the 50 genes upregulated in all species and treatments are DNA repair genes? Is there any other notable commonality between these 50 genes? The bulk of upregulated genes are specific to a species and to treatment with IR or bleomycin. What fraction of DNA repair genes are specific to a species or treatment?

The results in Figure 4C on the 50 putative orthologous genes upregulated in all species and treatments are further detailed in supp Figure 10. The legend to supp Figure 10 now provides the requested information: 14/50 genes are DNA repair genes and the other notable commonality is that 21/50 are “stress response genes”. We did not further breakdown the analysis to evaluate the fraction of DNA repair genes specific to a species or treatment. It will be interesting to gather data in more species to hed light on the evolutionary history of DNA repair gene regulation in response to IR.

How does the suite of upregulated tardigrade DNA repair proteins after IR or bleomycin compare with DNA or repair proteins upregulated under similar treatments in human cells? Are they quantitatively or qualitatively different, or both?

There is a great wealth of studies documenting genes differentially expressed in human cells in response to IR (e.g. Borras-Fresneda et al, 2016, PMID: 27245205; Rieger and Chu, 2004, PMID: 15356296; Budwoeth et al, 2012, PMID: 23144912 ; Rashi-Elkeles et al, 2011, PMID: 21795128; Jen and Cheung, 2003, PMID: 12915489...). Upregulation of DNA repair and cell cycle genes is commonly found. However, the number of DNA repair genes induced is always very limited and fold stimulation very modest compared to the massive upregulation observed in tardigrades.

On page 14, please explain the acronym BER. Do the authors mean Base Excision Repair? Or something else?

As assumed by the reviewer, the acronym BER stands for Base Excision Repair. The acronym has been removed from the main text and replaced by the full name.

Reviewer #4 (Recommendations For The Authors):

We thank the reviewer for his comments.

Abstract:

The abstract is fine. What was hard to grasp at the beginning is why TDR1 gene was named that way. It should be clearer that this study decided to further focus on that gene, one of the most overexpressed gene after IR, with an unknown function. Then maybe introduce that it was found to be unique to tardigrade and to interact with DNA. Therefore, it was named TDR1.

Introduction:

The introduction has been modified according to the suggestions of Reviewer#4 below. One of the suggested references, Nicolas et al 2023 from the Van Doninck lab, was published while our manuscript was under review and cannot be considered as background information for our study.

1st paragraph:

The study is on tardigrades, I found it strange that the first paragraph is on D. radiodurans. I think it is fine to mention what is known in bacteria and eucaryotes but we should already know what will be the main topic in the first paragraph of the introduction. Some details about D. radiodurans seem less important and distracting from the main topic (3D conformation).

2nd paragraph:

When mentioning radio-resistant eurcaryotes the authors do not mention the larvae of the anhydrobiotic insect Polypedilum vanderplanki. Stating that the mechanisms of resistance are poorly characterized should perhaps be nuanced. There are some recent studies on D. radiodurans (Ujaoney et al., 2017) the insect P. vanderplanki (Ryabova et al., 2017), tardigrades (Kamilari et al., 2019), and rotifers (Nicolas et al., 2023, Moris et al., 2023). Perhaps these papers are worth indicating that if mechanisms are not elucidated yet, recent studies suggest some actors involved in their resistance. Regarding the sentence stating that DNA repair rather than DNA protection plays a predominant role in the radio-resistance of bdelloid rotifers should also be nuanced. Indeed, many chaperones, antioxidants were mentioned to play a role in the radio-resistance of bdelloid rotifers (Moris et al., 2023). The authors mentioned the reference Hespeels et al., 2023 which is not found in their list of references, I am not sure which paper they refer to. The last sentence of the second paragraph does not mean much. I am not sure what the authors want to state with this. Perhaps they should specify if they mean that the function of many other genes overexpressed after IR remains unknown.

Still, in the second paragraph, the authors focus on rotifers. They also do not mention what is known in the insect P. vanderplanki, which should be added. They still do not mention tardigrades. I think it is nice to first start with eucaryotes and then focus on tardigrades but as I mentioned before it would help to understand the aim of the paper if the first paragraph mentioned briefly the tardigrades and then could go into detail in the third paragraph.

3rd paragraph:

The sentence starting "with over 1400 species" best to remove from it "but they can differ in their resistance" and start the next sentence with that.

4th paragraph:

Very clear, we finally understand what is the focus of the manuscript.

5th paragraph:

Very clear. The authors should mention the names of the three studied species. Here, A. antarcticus is missing. The sentence "Further analyses in H. exemplaris... showed that TDR1 protein is present and upregulated". The authors should mention in which conditions the protein is upregulated. In that paragraph the authors mention phospho-H2AX: it might be good to introduce its functions before in the introduction (it is mentioned in the second sentence of the results: best to move it to the introduction).

Results:

There are a few sentences in this section which rather discuss the results than describe them. I think the manuscript might gain in quality if these interpretations of the results are moved into the discussion section. That would make the result section more concise and the discussion enriched.

For instance, I suggest to move these sentences into the discussion:

• "the finding of persistent DSBs in gonads at 72h.... likely explains...".

• "suggesting that (i) DNA synthesis..."

• " Phospho-H2AX....also suggested"

• "Moreover, expression of TDR1-GFP..., supporting the potential role of TDR1 proteins..."

• "our results suggest that RNF146 upreguation could contribute..."

• "AMNP gene g12777 was shown to increase...Based on our results, it is possible that..."

Interpretations mentioned here above were always introduced cautiously (-"suggesting that (i) DNA synthesis..." ; -" Phospho-H2AX....also suggested" ; -"Moreover, expression of TDR1-GFP..., supporting the potential role of TDR1 proteins..." ; -"our results suggest that RNF146 upreguation could contribute..." ). These cautious interpretations were usually important in deciding next steps of the work. We therefore believe it is important to mention these interpretations in the results section to clearly expose the milestones marking the progression of the study.

For some results, they were directly discussed in the results section for the sake of concision (for example -"the finding of persistent DSBs in gonads at 72h.... likely explains..."; -"AMNP gene g12777 was shown to increase...Based on our results, it is possible that..." ) since, in our opinion, there was no need to mention them again in the main discussion.

Some other parts could be good to be moved into the introduction:

• "Previous studies have indicated that irradiation with IR increases expression of Rad51,..." none of the actors involved in DNA repair are mentioned in the introduction. Also, change resistant into resistance

• "A. antarcticus ..., known for its resistant to high doses of UV....

We have moved these parts to the introduction as recommended.

It was in O. areolatus.... that the first demonstration..."

This piece of information is somewhat anecdotical. We choose to keep it it here in the results section. This information on the radio-resistance of the species P. areolatus is only relevant at this specific step of the study because it encouraged us to consider that P. fairbanksi, which we isolated fortuitously, would be a good model species for studying radio-resistance of tardigrades.

Here are some additional comments/suggestions on the result section:

1st section

• Remove the Gross et al., 2018 from the sentence "using confocal microscopy", it looks otherwise that these results are from their study, not yours.

We have changed the text to make it clear that this is indeed a finding of Gross et al which was previously made in non-irradiated tardigrades. We replicated this finding, which showed that the protocol was working appropriately, and that we could use this control result for comparison with irradiated animals. We apologize for this confusion.

The text now states: “Using confocal microscopy, we could detect DNA synthesis in replicating intestinal cells of control animals, as previously shown by (Gross et al. 2018).”

2nd section

• It is confusing what has been found induced by IR and/or by Bleomycin.

• I think it might help if the authors first present what is induced after IR, then write if it is similar after Bleomycin. Especially since they start to do it in the first paragraph of that section. However, they only mention TDR1 in the second paragraph dedicated to Bleomycin treatment which is confusing as it is also overexpressed after IR. It is also not clear if RNF146 is also induced by Bleomycin.

As recommended, the text presents first what is induced after IR and then what is induced by Bleomycin in the following paragraph. When reporting results with Bleomycin, we have provided a global assessment of what is common to both treatments in Supp Figure 3 and in Supp Table 3. In this figure, we also specifically highlighted several key genes of DNA repair induced by both treatments. These are also mentioned in the text (p8) to illustrate the point that many key DNA repair genes are common to both treatments. We have now added RNF146 to that list as recommended.

• Regarding TDR1, it is not clear when introduced in the text as "promising candidate" why it is the case. It is clear in the figures but perhaps the authors should explain why they chose these genes for further analyses: high log2foldchange and expression level for instance. Regarding that last comment, it would be interesting to have an idea about the expression level of the genes with high log2foldchange. In Figures 2, 3, and 4 the pvalue and log2foldchange are represented but not the expression level (ideally Transcript per Millions). These values would give an additional idea on the importance of that gene. While looking at the figures, it is unclear why you did not further characterize other genes with high log2foldchange (some with even hints of their function): the mentioned RNF146, macroH2A1 (not even mentioned in the results), some genes unannotated in the figures with likely unknown functions,

When selecting genes of interest, we did indeed take into account high expression levels. To more clearly document expression levels (which were already available from the Tables), we added MAplots (representing log2foldchange and logNormalized read counts) in the supplementary materials (Supp Figure 3 and Supp Figure 8).

• It is also unclear at that stage why you named it "Tardigrade DNA damage response protein", as it is characterized as DNA repair/damage proteins by specific GO id or is it based on your downstream analyses, I think it might be worth to quickly mention the reason of that name.

The name illustrates two points which were already characteristic at this point in time of the study i.e. 1) it is a tardigrade specific protein and 2) it is induced in response to DNA damage.

• Regarding the BLAST analyses the protein was searched in C. elegans, D. melanogaster and H. sapiens. Why only these three species? What were the threshold evalues used for these analyses. As mentioned in the main comment, it would be worth searching species phylogenetically close to tardigrades to verify if it is well-tardigrade specific. Did you try to make a gene tree, after looking for a conserved domain (using hmmersearch)?

As indicated in the methods section, the “Tardigrade-specific" annotation was determined by absence of hits after high-throughput alignment (with diamond using –ultrasensitive-option) on the NCBI nr database and absence of hits after blast search on C. elegans, D. melanogaster and H. sapiens proteomes as a complementary criterion (the latter blast search was primarily performed to enrich for functional annotations). Based on these criteria, TDR1 was annotated as “Tardigrade-specific”. As stated in the text, we also searched for TDR1 related sequences with 1) blastp (which is more sensitive than diamond) on the NCBI nr database and 2) HMMER on Reference Proteomes, and no hits were found among non-tardigrade ecdysozoans organisms, confirming TDR1 is specific to tardigrades. For Blast search for example, there were five hits in non-ecdysozoans organisms (two cephalochordates, one mollusc and two echinoderma). The blastp and HMMER results are now included in the revised supplementary material (Supp Table 5). These very few hits in species phylogenetically distant from tardigrades cannot be taken to support the existence of TDR1 genes outside tardigrades.

To be clearer in the manuscript, we now state the absence of hits for TDR1 in non-tardigrade ecdysozoans. Given the absence of homologs in non-tardigrade species, it is not possible to make a gene tree with non-tardigrade species.

• Page 9: "Proteins extracts from H. exemplaris... at 4h and 24h..." I think this sentence can be removed as this is mentioned again 2 paragraphs after: "...we conducted an unbiased proteome analysis... at 4h..." The log2foldchange threshold mentioned for the proteomic analyses is 0.3: why this threshold, was it chosen randomly?

This is threshold is commonly used when considering log2foldchange with the technology used in our study, an isobaric multiplexed quantitative proteomic strategy which is known to compress ratios (Hogrebe et al. 2018).

• Page 10:

It would be good for more clarity to indicate at the beginning of the new section which species were investigated after IR or Bleomycin treatment.

TDR1 homologs in the other tardigrade species were identified based on what? Best reciprocal hit?

As indicated in the methods section of the manuscript, we searched for homologs in other tardigrade species by BLAST. A best reciprocal hit approach was not performed to try to determine which homologs might be orthologs. In particular, most TDR1 homologs identified are known from transcriptome assemblies and high-contiguity genome assemblies are needed to more confidently identify orthology (using synteny). The results of the BLASTP search are now provided as supplementary material (Supp Table 5).

Preliminary experiments indicated that A. antarcticus and P. fairbanski survived exposure to 1000 Gy: is there a supplementary graph showing this?

We have corrected the text to avoid any confusion. We have not rigorously examined the dose-dependent survival of P. fairbanksi in response to irradiation. Text was changed to: “We found by visual inspection of animals after IR that A. antarcticus and P. fairbanksi readily survived exposure to 1000 Gy.”

• Page 11:

"A set of 50 genes was upregulated in the three species": please be precise if only after IR.

Done

These genes cannot be the same as they are from different species. Did the author mean that they are coding for similar proteins? It might be good to give some more details even if the supplementary figure is mentioned.

Obviously, these genes are putative orthologs. We have changed the text to:

” a set of 50 putative orthologous genes was upregulated in response to IR in all three species”

Discussion:

• General comment: the discussion is focused mainly on TDR1, it would be nice to also discuss the other results: DNA repair genes, RNF146.

A whole paragraph is devoted to discussion of results on DNA repair genes and RNF146. We have extended that discussion following on the suggestion of the reviewer. In particular, we have explicitly mentioned the apparent paradox that XRCC5 and XRCC6, which are among the most highly stimulated genes at the mRNA level, only display modest upregulation at the protein level. Although further studies would be needed to examine the mechanisms involved, we propose that upregulation of RNF146, whose human homolog has been shown to drive degradation of PARylated XRCC5 and XRCC6 proteins in response to IR (Kang et al. 2011), may be responsible for higher degradation rates and may thus counterbalance increased levels of protein synthesis.

• Pulse field electrophoresis would be nice to be performed. It has been used to assess DSBs in bdelloid rotifers, is it possible in tardigrades?

As stated in the discussion, we believe that it would be challenging to perform pulse field electrophoresis in tardigrades. However, if possible, these experiments would certainly bring invaluable information to complement our analysis of DNA damage induced by IR.

• "By comparative transcriptomics": please rephrase that sentence.

• Proteins acting early in DNA repair: I am not sure I understand this sentence. Actors as ligases act not at the beginning of the repair pathways.

Well noted. We have removed ligases from the list.

• It is confusing that the authors mention NHEJ and double-strand break repair pathways as different pathways. There are 2 main pathways to repair DBSs: NHEJ and HR. It would be nice to add a reference to the sentence "PARP proteins act as sensors of DNA damage etc."

A typo in the sentence gave rise to the misleading suggestion that NHEJ is not a double strand repair pathway. It has been corrected.

A reference has been added for PARP proteins.

• It would be nice if the authors can explain deeper their suggestion that degradation of DNA repair actors is essential for tardigrade IR resistance.

We have expanded this part of the discussion and hope that it is clearer.

“For XRCC5 and XRCC6, our studyestablished, by two independent methods, proteomics and Western blot analysies, that the stimulation at the protein level could be much more modest (6 and 20-fold at most (Supp Figure 6) than at the RNA level (420 and 90 fold respectively). This finding suggests that the abundance of DNA repair proteins does not simply increase massively to quantitatively match high numbers of DNA damages. Interestingly, in response to IR, the RNF146 ubiquitin ligase was also found to be strongly upregulated. RNF146 was previously shown to interact with PARylated XRCC5 and XRCC6 and to target them for degradation by the ubiquitin-proteasome system (Kang et al. 2011). To explain the lower fold stimulation of XRCC5 and XRCC6 at the protein levels, it is therefore tempting to speculate that, XRCC5 and XRCC6 protein levels (and perhaps that of other scaffolding complexes of DNA repair as well) are regulated by a dynamic balance of synthesis, promoted by gene overexpression, and degradation, made possible by RNF146 upregulation. Consistent with this hypothesis, we found that, similar to human RNF146 (Kang et al. 2011), He-RNF146 expression in human cells reduced the number of phospho-H2AX foci detected in response to Bleomycin (Figure 6).”

• Page 15: Please add a reference for the sentence "Functional analysis of promotor sequences in transgenic tardigrades etc."

The reference has been added to fix this omission.

Material and Methods:

Small comments:

• 40 μm mesh: space missing

• 100 μm mesh: space missing

• (for Bleomycin)): parenthesis missing

• remove "as indicated in the text"

• The investigated time points after radiation need to be clearly stated in the method section. It is also unclear in the IR and Bleomycin section which tardigrades were treated with what. Not all were treated with Bleomycin.

The small comments above have been fixed in the revised version of the manuscript.

• Page 21: please precise the coverage of the RNA sequencing

Statistics on mapping of RNAseq reads are now provided in Supp Table 10.

• Page 22: Was any read trimming performed? Anything about the quality check of the reads?

Trimming was conducted using trimmomatic (v0.39) and quality check using FastQC (v. ?) This information has been added to the Methods section.

• Were the analyses confirmed by a second approach: for instance, EdgeR? Deseq2 and EdgeR do not always have the same results. For more robust analyses it is advised to use both.

Differential transcriptome analyses were conducted with DESeq2 only. The robustness of our identification of differentially expressed genes in response to IR stems from performing comparative analyses in three different species, rather than from using two bioinformatics pipelines in a single species. We also note that benchmarking reported in the initial DEseq2 paper showed that identification of differentially expressed genes with large log fold changes (which, as reported in our manuscript, is characteristic of many DNA repair genes in response to IR) is very consistent between DEseq2 and EdgeR.

Figures:

• Figure 2: Legend vertical dotted line does not indicate log2foldchange value of 4 in all panels: it would be good to indicate for panels a and c as well.

Figure 2has been improved following on the suggestions of the reviewer. Dotted lines now show log2foldchange value of 2 in all panels (ie Fold Change of 4 as mentioned in the main text).

• Figure 2C: There are a few points with high log2foldchange which are not annotated: was it because nothing was found in the blast research? If yes, it would be good to indicate their functions. If not, it would be good to mention in the discussion that there are some genes with still unknown functions which might play an important role in the resistance of tardigrades to IR.

The few points which are not annotated in figure 2c can now be found in Supp Table 3 Some of them have no hit in Blast search, some others such as BV898_09662 or BV898_07145 have hits on DNA repair genes as RBBP8/CtIP or XRCC6 respectively but are not annnotated as such by eggnog in KEGG pathway.

• Figure 4C: Why not have included the response of P. fairbanski to bleomycin? I guess it was not done, but it is unclear in the results and methods sections.

P.fairbanksi response to bleomycin wasn’t assessed as we didn’t get enough animals to run the study. The method section has been modified to precise this point.

Author response:

Reviewer #1 (Public Review):

This study makes a substantial contribution to our understanding of the molecular evolutionary dynamics of microbial genomes by proposing a model that incorporates relatively frequent adaptive reversion mutations. In many ways, this makes sense from my own experience with evolutionary genomic data of microbes, where reversions are surprisingly familiar as evidence of the immense power of selection in large populations.

One criticism is the reliance on one major data set of B. fragilis to test fits of these models, but this is relatively minor in my opinion and can be caveated by discussion of other relevant datasets for parallel investigation.

We analyze data from 10 species of the Bacteroidales family, and we compare it to a dataset of Bacteroides fragilis. We have now added a reference to a recent manuscript from our group showing phenotypic alteration by reversion of a stop codon and further breaking of the same pathway through stop codons in other genes in Burkholderia dolosa on page 9, and have added a new analysis of codon usage in support of the reversion model on page 14.

We have chosen not to analyze other species as there are no large data sets with rigorous and evenly-applied quality control across scales. We anticipate the reversion model would be able to fit the data in these cases. We now note that this work remains to be done in the discussion.

Another point is that this problem isn't as new as the manuscript indicates, see for example https://journals.asm.org/doi/10.1128/aem.02002-20 .

Loo et al puts forward an explanation similar to the purifying model proposed by Rocha et al, which we refute here. Quoting from Loo et al: “Our results confirm the observation that nonsynonymous SNPs are relatively elevated under shorter time periods and that purifying selection is more apparent over longer periods or during transmission.” While there is some linguistic similarity between the weak purifying model and our model of strong local adaptation model and strong adaptive reversion, we believe that the dynamical and predictive implications suggested by the reversion model are an important conceptual leap and correction to the literature. We now cite Loo et al and additional works cited therein. We have updated the abstract, introduction, and discussion to further emphasize the distinction of the reversion model from previous models: namely the implication of the reversion model that long-time scale dN/dS hides dynamics.

Nonetheless, the paper succeeds by both developing theory and offering concrete parameters to illustrate the magnitudes of the problems that distinguish competing ideas, for example, the risk of mutational load posed in the absence of frequent back mutation.

Reviewer #2 (Public Review):

This manuscript asks how different forms of selection affect the patterns of genetic diversity in microbial populations. One popular metric used to infer signatures of selection is dN/dS, the ratio of nonsynonymous to synonymous distances between two genomes. Previous observations across many bacterial species have found dN/dS decreases with dS, which is a proxy for the divergence time. The most common interpretation of this pattern was proposed by Rocha et al. (2006), who suggested the excess in nonsynonymous mutations on short divergence times represent transient deleterious mutations that have not yet been purged by selection.

In this study, the authors propose an alternative model based on the population structure of human gut bacteria, in which dN is dominated by selective sweeps of SNPs that revert previous mutations within local populations. The authors argue that contrary to standard population genetics models, which are based on the population dynamics of large eukaryotes, the large populations in the human gut mean that reversions may be quite common and may have a large impact on evolutionary dynamics. They show that such a model can fit the decrease of dN/dS in time at least as well as the purifying selection model.

Strengths

The main strength of the manuscript is to show that adaptive sweeps in gut microbial populations can lead to small dN/dS. While previous work has shown that using dN/dS to infer the strength of selection within a population is problematic (see Kryazhimskiy and Plotkin, 2008, cited in the paper) the particular mechanism proposed by the authors is new to my knowledge. In addition, despite the known caveats, dN/dS values are still routinely reported in studies of microbial evolution, and so their interpretation should be of considerable interest to the community.

The authors provide compelling justification for the importance of adaptive reversions and make a good case that these need to be carefully considered by future studies of microbial evolution. The authors show that their model can fit the data as well as the standard model based on purifying selection and the parameters they infer appear to be plausible given known data. More generally, I found the discussion on the implications of traditional population genetics models in the context of human gut bacteria to be a valuable contribution of the paper.

Thank you for the kind words and appreciation of the manuscript.

Weaknesses

The authors argue that the purifying selection model would predict a gradual loss in fitness via Muller's ratchet. This is true if recombination is ignored, but this assumption is inconsistent with the data from Garud, et al. (2019) cited in the manuscript, who showed a significant linkage decrease in the bacteria also used in this study.

We now investigate the effect of recombination on the purifying selection model on page 8 and in Supplementary Figure S6. In short, we show that reasonable levels of recombination (obtained from literature r/m values) cannot rescue the purifying selection model from Muller’s ratchet when s is so low and the influx of new deleterious mutations is so high. We thank the reviewers for prompting this improvement.

I also found that the data analysis part of the paper added little new to what was previously known. Most of the data comes directly from the Garud et al. study and the analysis is very similar as well. Even if other appropriate data may not currently be available, I feel that more could be done to test specific predictions of the model with more careful analysis.

In addition to new analyses regarding recombination and compensatory mutations using the Garud et al data set, we have now added two new analyses, both using Bacteroides fragilis . First, we show that de novo mutations in Zhao & Lieberman et al dataset include an enrichment of premature stop codons (page 9). Second we show that genes expected to be under fluctuating selection in B. fragilis displays a significant closeness to stop codons, consistent with recent stop codons and reversions. We thank the reviewer for prompting the improvement.

Finally, I found the description of the underlying assumptions of the model and the theoretical results difficult to understand. I could not, for example, relate the fitting parameters nloci and Tadapt to the simulations after reading the main text and the supplement. In addition, it was not clear to me if simulations involved actual hosts or how the changes in selection coefficients for different sites was implemented. Note that these are not simply issues of exposition since the specific implementation of the model could conceivably lead to different results. For example, if the environmental change is due to the colonization of a different host, it would presumably affect the selection coefficients at many sites at once and lead to clonal interference. Related to this point, it was also not clear that the weak mutation strong selection assumption is consistent with the microscopic parameters of the model. The authors also mention that "superspreading" may somehow make a difference to the probability of maintaining the least loaded class in the purifying selection model, but what they mean by this was not adequately explained.

We apologize for leaving the specifics of the implementation from the paper and only accessible through the Github page and have corrected this. We have added a new section in the methods further detailing the reversion model and the specifics of how nloci and Tadapt (now tau_switch as of the edits) are implemented in the code.

The possibility for clonal interference is indeed included in the simulation. Switching is not correlated with transmissions in our main figure simulations (Figure 4a). When we run simulations in which transmission and selection are correlated, the results remain essentially the same, barring higher variance at lower divergences (new Figure S10). We have now clarified these points in the results, and have also better clarified the selection only at transmission model in the main results.

Reviewer #3 (Public Review):

The diversity of bacterial species in the human gut microbiome is widely known, but the extensive diversity within each species is far less appreciated. Strains found in individuals on opposite sides of the globe can differ by as little as handfuls of mutations, while strains found in an individual's gut, or in the same household, might have a common ancestor tens of thousands of years ago. What are the evolutionary, ecological, and transmission dynamics that established and maintain this diversity?

The time, T, since the common ancestor of two strains, can be directly inferred by comparing their core genomes and finding the fraction of synonymous (non-amino acid changing) sites at which they differ: dS. With the per-site per-generation mutation rate, μ, and the mean generation times roughly known, this directly yields T (albeit with substantial uncertainty of the generation time.) A traditional way to probe the extent to which selection plays a role is to study pairs of strains and compare the fraction of non-synonymous (amino acid or stop-codon changing) sites, dN, at which the strains differ with their dS. Small dN/dS, as found between distantly related strains, is attributed to purifying selection against deleterious mutations dominating over mutations that have driven adaptive evolution. Large dN/dS as found in laboratory evolution experiments, is caused by beneficial mutations that quickly arise in large bacterial populations, and, with substantial selective advantages, per generation, can rise to high abundance fast enough that very few synonymous mutations arise in the lineages that take over the population.

A number of studies (including by Lieberman's group) have analyzed large numbers of strains of various dominant human gut species and studied how dN/dS varies. Although between closely related strains the variations are large -- often much larger than attributable to just statistical variations -- a systematic trend from dN/dS around unity or larger for close relatives to dN/dS ~ 0.1 for more distant relatives has been found in enough species that it is natural to conjecture a general explanation.

The conventional explanation is that, for close relatives, the effects of selection over the time since they diverged has not yet purged weakly deleterious mutations that arose by chance -- roughly mutations with sT<1 -- while since the common ancestor of more distantly related strains, there is plenty of time for most of those that arose to have been purged.

Torrillo and Lieberman have carried out an in-depth -- sophisticated and quantitative -- analysis of models of some of the evolutionary processes that shape the dependence of dN/dS on dS -- and hence on their divergence time, T. They first review the purifying selection model and show that -- even ignoring its inability to explain dN/dS > 1 for many closely related pairs -- the model has major problems explaining the crossover from dN/dS somewhat less than unity to much smaller values as dS goes through -- on a logarithmic scale -- the 10^-4 range. The first problem, already seen in the infinite-population-size deterministic model, is that a very large fraction of non-synonymous mutations would have to have deleterious s's in the 10^-5 per generation range to fit the data (and a small fraction effectively neutral). As the s's are naturally expected (at least in the absence of quantitative analysis to the contrary) to be spread out over a wide range on a logarithmic scale of s, this seems implausible. But the authors go further and analyze the effects of fluctuations that occur even in the very large populations: ~ >10^12 bacteria per species in one gut, and 10^10 human guts globally. They show that Muller's ratchet -- the gradual accumulation of weakly deleterious mutations that are not purged by selection - leads to a mutational meltdown with the parameters needed to fit the purifying selection model. In particular, with N_e the "effective population size" that roughly parametrizes the magnitude of stochastic birth-death and transition fluctuations, and U the total mutation rate to such deleterious mutations this occurs for U/s > log(sN_e) which they show would obtain with the fitted parameters.

Torrillo and Lieberman promise an alternate model: that there are a modest number of "loci" at which conditionally beneficial mutations can occur that are beneficial in some individual guts (or other environmental conditions) at some times, but deleterious in other (or the same) gut at other times. With the ancestors of a pair of strains having passed through one too many individuals and transmissions, it is possible for a beneficial mutation to occur and rise in the population, only later to be reverted by the beneficial inverse mutation. With tens of loci at which this can occur, they show that this process could explain the drop of dN/dS from short times -- in which very few such mutations have occurred -- to very long times by which most have flipped back and forth so that a random pair of strains will have the same nucleotide at such sites with 50% probability. Their qualitative analysis of a minimally simple model of this process shows that the bacterial populations are plenty big enough for such specific mutations to occur many times in each individual's gut, and with modest beneficials, to takeover. With a few of these conditionally beneficial mutations or reversions occurring during an individuals lifetime, they get a reasonably quantitative agreement with the dN/dS vs dS data with very few parameters. A key assumption of their model is that genetically exact reversion mutations are far more likely to takeover a gut population -- and spread -- than compensatory mutations which have a similar phenotypic-reversion effect: a mutation that is reverted does not show up in dN, while one that is compensated by another shows up as a two-mutation difference after the environment has changed twice.

Strengths:

The quantitative arguments made against the conventional purifying selection model are highly compelling, especially the consideration of multiple aspects that are usually ignored, including -- crucially -- how Muller's ratchet arises and depends on the realistic and needed-to-fit parameters; the effects of bottlenecks in transmission and the possibility that purifying selection mainly occurs then; and complications of the model of a single deleterious s, to include a distribution of selective disadvantages. Generally, the author's approach of focusing on the simplest models with as few as possible parameters (some roughly known), and then adding in various effects one-by-one, is outstanding and, in being used to analyze environmental microbial data, exceptional.

The reversion model the authors propose and study is a simple general one and they again explore carefully various aspects of it -- including dynamics within and between hosts -- and the consequent qualitative and quantitative effects. Again, the quantitive analysis of almost all aspects is exemplary. Although it is hard to make a compelling guess of the number of loci that are subject to alternating selection on the needed time-scales (years to centuries) they make a reasonable argument for a lower bound in terms of the number of known invertible promoters (that can genetically switch gene expression on and off).

We are very grateful for the reviewer’s kind words and careful reading.

Weaknesses:

The primary weakness of this paper is one that the author's are completely open about: the assumption that, collectively, any of possibly-many compensatory mutations that could phenotypically revert an earlier mutation, are less likely to arise and takeover local populations than the exact specific reversion mutation. While detailed analysis of this is, reasonably enough, beyond the scope of the present paper, more discussion of this issue would add substantially to this work. Quantitatively, the problem is that even a modest number of compensatory mutations occurring as the environmental pressures change could lead to enough accumulation of non-synonymous mutations that they could cause dN/dS to stay large -- easily >1 -- to much larger dS than is observed. If, say, the appropriate locus is a gene, the number of combinations of mutations that are better in each environment would play a role in how large dN would saturate to in the steady state (1/2 of n_loci in the author's model). It is possible that clonal interference between compensatory and reversion mutations would result in the mutations with the largest s -- eg, as mentioned, reversion of a stop codon -- being much more likely to take over, and this could limit the typical number of differences between quite well-diverged strains. However, the reversion and subsequent re-reversion would have to both beat out other possible compensatory mutations -- naively less likely. I recommend that a few sentences in the Discussion be added on this important issue along with comments on the more general puzzle -- at least to this reader! -- as to why there appear to be so little adaptive genetic changes in core genomes on time scales of human lifetimes and civilization.

We now directly consider compensatory mutations (page 14, SI text 3.2, and Supplementary Figure 12). We show that as long as true reversions are more likely than compensatory mutations overall, (adaptive) nonsynonymous mutations will still tend to revert towards their initial state and not contribute to asymptotic dN/dS, and show that true reversions are expected in a large swath of parameter space. Thank you for motivating this improvement!

We note in the discussion that directional selection could be incorporated into the parameter alpha (assuming even more of the genome is deleterious) on page 16.

An important feature of gut bacterial evolution that is now being intensely studied is only mentioned in passing at the end of this paper: horizontal transfer and recombination of core genetic material. As this tends to bring in many more mutations overall than occur in regions of a pair of genomes with asexual ancestry, the effects cannot be neglected. To what extent can this give rise to a similar dependence of dN/dS on dS as seen in the data? Of course, such a picture begs the question as to what sets the low dN/dS of segments that are recombined --- often from genetic distances comparable to the diameter of the species.

We now discuss the effect of recombination on the purifying selection model on page 8 and in Supplementary Figure S6. In short, we now show that reasonable levels of recombination cannot rescue the purifying selection model from Muller’s ratchet when s is so low and the influx of new deleterious mutations is so high. We thank the reviewers for prompting this improvement

Author response:

Reviewer #1 (Public Review):

1) Napthylamine (1NA), an industrial reagent used in the manufacturing of dyes and pesticides is harmful to humans and the environment. In the current manuscript, the authors report the successful isolation of a Pseudomonas strain from a former naphthylamine manufacturing site that is capable of degrading 1NA. Using genetic and enzymatic analysis they identified the initial stages of 1NA degradation and the enzymes responsible for downstream processing of 1,2-dihydroxynapthalene and Salicylate. The authors determined the molecular structure of NpaA1, the first enzyme in the pathway responsible for glutamylation of 1NA. NpaA1 has a border substrate specificity compared to previously characterized enzymes involved in aromatic amine degradation. They carried out structural comparison of NpaA1 with glutamine synthase structures, alfa-fold models of similar enzymes and put forth hypothesis to explain the broad substrate specificity of NpaA1.

The manuscript is well written and easy to understand. The authors carried out careful genetic analysis to identify the genes/enzymes responsible for degradation of 1NA to catechol. They characterized the first enzyme in the pathway, NpaA1 which is responsible glutamylation of 1NA. and determined the molecular structure of apo-NpaA1, NpaA1 - AMPPNP complex and Npa1 - ADP - Met-Sox-P complex using X-ray crystallography.

The proposed mechanism of broad substrate specificity of NpaA1, however, is based on comparison of 1NA docked NpaA1 structure with St-GS (Glutamate synthase) and Alphafold2 predicted model of AtdA1 from an aniline degrading strain of Acinetobacter sp. Lack of molecular structure or mutational studies to back the proposed mechanism makes it difficult to agree with the proposed mechanism.

We appreciate your valuable comments. To further demonstrate that the structure of the aromatic amine binding tunnel and active pocket determines the broad substrate specificity of NpaA1, we have conducted additional experiments with several key residue mutants of the binding tunnel for naphthylamine and monocyclic aniline activities. The results provide a more detailed elucidation of the reasons for NpaA1's broad substrate specificity. Specific results and analyses are provided in the subsequent response.

Reviewer #2 (Public Review):

Microbial degradation of synthetic organic compounds is the basis of bioremediation. Biodegradation of 1NA has not been previously reported. The report describes a complete study of 1NA biodegradation by a new isolate Pseudomonas sp. strain JS3066. The study includes the enrichment and isolation of the 1NA-degrading bacterium Pseudomonas sp. strain JS3066, the identification of the genes and enzymes involved in 1NA degradation, and the detailed characterization of γ-glutamylorganoamide synthetase by using biochemical and structural analysis. In the discussion, the potential evolution of 1NA degradation pathway, the similarity and difference between γ-glutamylorganoamide synthetase and glutamine synthetase, and the significance were explained. The conclusions were well supported by the results presented.

We deeply appreciate the reviewer’s comments on the manuscript. We have responded to the recommendations one by one in the later section.

Author response:

Reviewer #1 (Public Review):

“… it remains unclear how ninein reduction causes bone defects …”

We have added several control experiments that permit us to conclude that osteoblast numbers remain unaltered in the ninein-knockout embryos, and that bone abnormalities in vivo are caused by fusion defects of osteoclast precursor cells, whereas the proliferation, viability, or the adhesion of these precursor cells remain unaffected. For details, please see our comments below.

“Discussion includes several unfounded potential mechanisms that really need to be thoroughly analyzed to gain a mechanistic understanding of the bone defects…”

The new data back up our claim of fusion defects as a cause for limited osteoclast function. We have re-written parts of the discussion, to take into account our new findings.

“Data showing normal osteoblasts in ninein-null mice was qualitative and requires further in-depth analysis and quantification of osteoblast …”

To address this point, quantification of osteoblast numbers in tibiae at E16.5 and E18.5 was performed in control and ninein-deleted mouse embryos. The data are presented in the new Figures 3G and J.

“In ninein knock-out mice, reduced TRAP+ve multinuclear cells were observed (Figure 6A and 6B). However, the magnitude of difference (about 5% decrease in multinucleated cells) is not consistent with the skeletal deformities reported in Figures 2-4, potentially suggesting the contribution of additional mechanisms.”

We agree that the difference appears to be small at first glance, but nevertheless it remains statistically significant (a more than three-fold difference). We would like to recall that these observations (Fig. 6A) were performed at E14.5, i.e. at a stage when no ossification has occurred yet. We are looking at the first fusion events of myeloid precursors, likely derived from the fetal liver, that colonize the area of the first bone to form, and small differences in the number of functional osteoclasts may account for different timing of ossification. We think that differences in osteoclast fusion also account for the premature appearance of ossification centers for other skeletal elements, at later time points during development.

“The fusion assay in Figure 6C needs further clarification. How was the syncytia perimeter defined to measure cell surface? The x-axis suggests that there are syncytia that contain up to 160 nuclei at day 3. How were the nuclei differentially stained and quantified?”

We provide now additional information on the experimental approach in the revised manuscript, on pages 16-17 (Materials and Methods). For information: high numbers of syncytial nuclei in cultures were also observed by other groups in the past (Tiedemann et al., 2017, Front Cell Dev Biol. 5:54). In addition, we performed new experiments and quantified the fusion of osteoclast precursors by staining for actin and nuclei (new Figure 7C). This allowed us to quantify several additional parameters related to cell fusion (as initially performed in Raynaud-Messina et al., 2018, PNAS, 115:E2556-E2565).

“Some text needs clarification. … What is the definition of "large syncytia"? Is the fusion index increase by day 5 diminished in later days? A graph of the syncytia size/ nuclei number or fusion index in the above-mentioned days will be helpful.”

Information on the definition of “large syncytia” is now provided on page 10 (1st paragraph). We added further experimental details on osteoclast size for days 3, 4, and 5 in the supplemental Figures 7A and B. Most importantly, we performed additional assays of the fusion index by quantifying syncytial versus non-syncytial nuclei in a semi-automated manner. The new data are presented in Figure 7C, and the methods are explained on page 17. Together with our new analysis of cell proliferation, cell viability, and cell adhesion (Figure 7C, D, suppl. Fig. 7C-G), we provide now solid evidence for a fusion defect at the origin of impaired formation of ninein del/del osteoclasts.

“Assessment of resorption was qualitative in Figure 6E and since the fusion deficiencies are transient, quantification of a corresponding resorption activity is needed. This should be described in the Materials and Methods section.”

Quantifications of the bone resorption activities are now provided in the new Figure 7E, and a reference for the methods is provided on page 16.

“Further experiments are needed to show connections between reduced centrosome clustering and reduced osteoclast formation as there is no evidence to date that suggest centrosome clustering is required for cell fusion. Multi-color live imaging and dynamic analysis can be used to determine if the ninein deficient cells show defective movement/migration/ fusion dynamics.”

We agree that it is an important question, and studying potential links between centrosomal microtubule organization and osteoclast fusion is an ongoing project of the team. However, we estimate that in order to obtain conclusive results this will require 1-2 additional years of research activity, and we intend to present this as a separate project in the future. At the current point of our investigation, we think that providing a solid link between ninein, osteoclast fusion, and controlled timing of ossification, as shown in this manuscript, represents valuable progress to understand previously published bone abnormalities in patients with ninein mutations.

“Quantification of the % of multinucleated osteoclasts that contain clustered and dispersed centrosomes is needed.”

New quantification experiments on centrosome clustering are now provided in Figure 8H. These quantifications demonstrate that the potential of centrosome clustering is almost completely lost in osteoclasts without ninein.

Reviewer #2 (Public Review):

“Based on the decrease in the number of osteoclasts (Fig 5E, G, and also per coverslip after 2 days in culture), the authors suggest that the loss of ninein impacts osteoclast proliferation. First, proliferation can be directly quantified using Ki67 staining or EdU incorporation. Second, other interpretations are also plausible and can also be experimentally tested. These include less adhesion and attachment of the mutants to the coverslips, but perhaps more relevant in vivo is cell death of the ninein mutant osteoclasts. It has been established that the loss of centrosome function activates p53- dependent cell death and osteoclasts might be a vulnerable cell population. Quantifying p53 immunoreactivity and/or cell death in osteoclasts might help clarify the phenotype of osteoclast reduction.”

In response to the reviewers, we have performed a series of new experiments that include

1) A careful analysis of the fusion index, using a semi-automated approach, indicating significant differences in the fusion of precursor cells into osteoclasts (Fig. 7C).

2) We have repeated the quantification of cell numbers prior to fusion and find variations between samples from different mice (also among mice of the same genotype), but we see on average comparable cell adhesion between samples from control mice and ninein-del/del mice. The data are provided in the supplemental Figure 7F. Moreover, we have quantified the expression of three main beta-integrins at the surface of control and ninein del/del osteoclast precursors (suppl. Fig. 7G), without detecting significant differences. Altogether, these data suggest the cell adhesion is comparable for the two genotypes.

3) We have addressed the question of altered cell proliferation, by performing flow cytometry experiments and by quantifying the different cell cycle stages (Fig. 7D), and by quantifying Ki67 expression (suppl. Fig. 7C). We see no significant differences between samples from control and ninein-del/del mice.

4) We have addressed the question of cell death, by performing Annexin V staining and flow cytometry (suppl. Fig. 7D), and by immunoblotting for cleaved caspase 3 and PARP (suppl. Fig. 7E). These experiments reveal no significant differences between the control and ninein del/del samples. Our data permit us to exclude cell death as a likely cause for the reduction of fused osteoclasts in the absence of ninein.

Overall, the new experiments show that the defects in osteoclast formation from ninein-deleted samples are due to defects in cell fusion, but not in cell proliferation, cell adhesion or viability.

Reviewer #3 (Public Review):

“The authors put much emphasis on the centrosome in the Introduction session. However, it was not until Figure 7 did they show abnormal centriole clustering in osteoclasts. The introduction should include more background on osteoclast and osteoblast balance during skeletal development.”

To address this, we included more background on the role of osteoclasts and osteoblasts in the revised introduction (page 4).

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Results showing reactivation for near and far items separately are now included in Fig. 5 and convincingly suggest a simultaneous reactivation. For me, the open question remaining (see public) review is the degree to which the methods used here to show clustered vs sequential reactivation are mutually exclusive; and if the pre-selection of a time window of peak reactivation (based on all future items) biases the analyses towards clustered reactivation. The discussion would benefit from a brief discussion of these issues.

We have added a brief discussion of the issues. However, we want to clarify a minor point of the public review: While our interpretation implies that replay and reactivation are probably mutually exclusive within a single retrieval event, it does not imply that strategies cannot vary within different retrieval events of the same participant. Nevertheless, we want to address this raised concern (that is, if we understand correctly, that replay events that are contained within the time window of the reactivation analysis could not be distinguished by the chosen methods) and have added it to the discussion.

The corresponding sentence reads:

“[…] Finally, we want to acknowledge that by selecting a time window for the clustered reactivation we cannot distinguish very fast replay events (<=30ms) from clustered reactivation if they are contained exactly within the specific reactivation analysis time window..

Reviewer #2 (Recommendations For The Authors):

Figure 5D shows the difference scores between near vs. distant items for learning and retrieval. Similar to Figure 5 from the first version of your paper, the difference score does not show whether reactivation of the near vs. distant items change from learning to retrieval. You could show this change in a 2 (near vs. distant) x 2 (learning vs. retrieval) box plot (corresponding to Figure 5A).

We have added the requested plot as supplement 9 and referred to it in the figure description. However comparing absolute, raw probabilities between different blocks is tricky, as baseline probabilities are varying over time (e.g. due to shift in distance to sensors), therefore, differential reactivation might be better suited as it is a relative measure to compare between blocks.

At the end of the results section, you state: "On average, differential reactivation probability increased from pre to post resting state (Figure 5D).". I would suggest providing some statistical comparison and the corresponding values.

We have calculated and added respective p-value statistics of a T-Test and reported that the increase is only descriptive and not statistically significant.

Author response:

We thank both the reviewers for their thorough reading of our manuscript and insightful suggestions. We thank the editors for their assessment of our article. We will submit a revised manuscript that addresses several comments and include a point-by-point response to the reviewers.

(1) With respect to how our data compares with previously published datasets, we will provide a table comparing cell numbers. Study differences such as read depth, strain of animals used (including pigmented vs albino), method of cell isolation (including drug exposure), and number of cells profiled raise a significant impediment to integration with previously published datasets. We would like to highlight that ours is the first SEC single cell study that uses pigmented mouse eyes on C57BL/6J background. Integrating with the albino mouse data (Thompson et al. 2021) hindered pathway analyses possibly due to the variable drop out of genes across studies that was likely impacted by differences in method of cell isolation and increased representation of stress response genes in their dataset. We also attempted an integrated analysis with published mouse data (Van Zyl et al. 2020) but did not obtain additional meaningful information due to their low SEC numbers.

(2) The reviewers commented that our integration of single cell and single nuc data should be done with caution: we agree and had given careful consideration to the integration process. We will demonstrate the contribution of different samples and datasets to show how our datasets have integrated.

(3) To address the purity of bulk RNA seq, we will add more details for isolation of SECs for bulk seq. The markers to distinguish the three cell types were informed by immunofluorescence. Using these markers, we performed FACS using gates that were well separated. We have provided a heatmap with hierarchical clustering based on Euclidean distance of the EC subtypes (Figure 1B) analyzed by bulk RNA seq in addition to number of DE genes between subtypes.

(4) To address the immunostaining of NPNT and CCL21A, since both our antibodies are derived from the same species (goat), a co-labeling wasn’t possible. To be prudent, we used adjacent sections, flat-mounts, and RNAscope and provided further evidence of the anterior/posterior “bias” in supplemental figures. Although we agree on its importance, work with human tissue will be a focus of future work.

(5) Regarding the reviewer’s comments on substructure and that profiling may still not be comprehensive, we agree that further even more comprehensive studies are still needed. Profiling more cells will determine the robustness of the detected cell state difference and will help to resolve the cause of substructure within clusters as due to either lack of completely comprehensive profiling of cell types/states or more stochastic differences. We will add a comment to the discussion.

Author response:

Reviewer #1:

The phenomenon of stress-inducible mutagenesis in bacterial evolution remains a topic of heated debate. Consequently, the emergence of genetically encoded resistance may stem from either microevolution or the dissemination of pre-existing variants from polyclonal infections under drug pressure. We believe that the Introduction presents both of these hypotheses in a balanced manner to elucidate the rationale behind our mutation accumulation investigations.

While we acknowledge the well-known existence of phenotypic antibiotic resistance, it's worth noting that conclusions regarding mutation rates are often drawn from fluctuation assays without confirmation of genetic-level changes. This discrepancy persists despite fluctuation assays accounting for both phenotypic and genotypic alterations. Combining genome sequencing with fluctuation assays underscores the importance of making this distinction.

Thank you for the suggestion regarding improving the figures; we will incorporate these changes accordingly in the revised version. Additionally, we will address the rationale for using sub-lethal doses of antibiotics and compare our results with the referenced papers.

Reviewer #2:

Thank you for acknowledging the values of the manuscript and for the insightful suggestions for improvement. We agree on the necessity to directly connect the mutation accumulation experiments with the tolerance assay, and we have already initiated additional experiments to integrate into a revised version.

We also agree with and have been aware of the notion that cell death affects the calculation of the mutation rate. However, the error in the estimation of the generation time leads to an overestimation of the mutation rate, which, in our case, reinforces the conclusion that no discernible increase in mutation rate occurs in our mutation accumulation experiment. In the revised version, we aim to address i) the source of variation in cell death degree and ii) its influence on calculations.

The SNPs identified from the lineages of each treatment are compiled in the "unique muts.xls" file within the Figshare document bundle we included with the manuscript. We regret not providing a detailed reference to this in the manuscript; instead, the Figshare files were merely mentioned under the Data Availability section (No. 6) without specifics. As advised, we will create a supplementary table containing this data.

Reviewer #3:

Thank you for appreciating the manuscript's merits and for the instructive suggestions (also articulated in the specific comments). We agree that we should show the data on reduced colony growth on agar plates to demonstrate that the drug concentrations used in the study are relevant. We will include this in the revised version, as well as changes in response to all specific comments.

We acknowledge that the observed upregulation of DNA repair enzymes and the low mutation rates under drug pressure represent correlative data. Therefore, we opted against presenting the qPCR results as a mechanistic explanation. In the manuscript, we carefully stated: "The observed upregulation of the relevant DNA repair enzymes might account for the low mutation rate even under drug pressure." We did not establish a mechanistic link or emphasize the repair activation in the title, abstract, or discussion. We recognize the necessity for a new series of targeted experiments to provide mechanistic explanations. In this paper, our aim is to convincingly demonstrate that antibiotic pressure did not induce the occurrence of new adaptive mutations.

Author response:

eLife assessment

This paper presents a valuable optimization algorithm for determining the spatio-temporal organization of chromatin. The algorithm identifies the polymer model that best fits population averaged Hi-C data and makes predictions about the spatio-temoral organization of specific genomic loci such as the oncogenic Myc locus. While the algorithm will be of value to biologists and physicists working in the field of genome organization, the provided methodological details and evidence are incomplete to fully substantiate the conclusions. In particular, the following would be beneficial: analysis of single-cell data, the inclusion of loci beyond Myc, testing the dependence of results on the chosen parameters, providing more details on CTCF occupancy at loop anchors, and better substantiating the claim about predictions of single-cell heterogeneity.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors of this study aim to use an optimization algorithm approach, based on the established Nelder-Mead method, to infer polymer models that best match input bulk Hi-C contact data. The procedure infers the best parameters of a generic polymer model that combines loop-extrusion (LE) dynamics and compartmentalization of chromatin types driven by weak biochemical affinities. Using this and DNA FISH, the authors investigate the chromatin structure of the MYC locus in leukemia cells, showing that loop extrusion alone cannot explain local pathogenic chromatin rearrangements. Finally, they study the locus single-cell heterogeneity and time dynamics.

Strengths:

• The optimization method provides a fast computational tool that speeds up the parameter search of complex chromatin polymer models and is a good technical advancement.

• The method is not restricted to short genomic regions, as in principle it can be applied genome-wide to any input Hi-C dataset, and could be potentially useful for testing predictions on chromatin structure.

Weaknesses:

(1) The optimization is based on the iterative comparison of simulated and Hi-C contact matrices using the Spearman correlation. However, the inferred set of the best-fit simulation parameters could sensitively depend on such a specific metric choice, questioning the robustness of the output polymer models. How do results change by using different correlation coefficients?

This is an important question. We have tested several metrics in the process of building the fitting procedure. We will showcase side-by-side comparisons of the fitting results obtained using these different metrics in an upcoming version of the preprint.

(2) The best-fit contact threshold of 420nm seems a quite large value, considering that contact probabilities of pairs of loci at the mega-base scale are defined within 150nm (see, e.g., (Bintu et al. 2018) and (Takei et al. 2021)).

This is a good point. Unfortunately, there is no established standard distance cutoff to map distances to Hi-C contact frequency data. Indeed, previous publications have used anywhere between 120 nm to 500 nm (see e.g. (Cardozo Gizzi et al. 2019), (Cattoni et al. 2017) , (Mateo et al. 2019), (Hafner et al. 2022), (Murphy and Boettiger 2022), (Takei et al. 2021), (Fudenberg and Imakaev 2017) , (Wang et al. 2016), (Su et al. 2020), (Chen et al. 2022), (Finn et al. 2019)). We will include a supplementary table in the upcoming revised preprint listing these values to demonstrate the lack of consensus. This large variation could reflect different chromatin compaction levels across distinct model systems, and different spatial resolutions in DNA FISH experiments performed by different labs. The variance in the threshold choice is also likely partially explained by Hi-C experimental details, e.g. the enzyme used for digestion, which biases the effective length scale of interactions detected (Akgol Oksuz et al. 2021). Among commonly used restriction enzymes, HindIII has a relatively low cutting frequency which results in a lower sensitivity to short-range interactions; on the other hand, MboI has a higher cutting frequency which results in a higher sensitivity to short-range interactions (Akgol Oksuz et al. 2021). Because the Hi-C data we used for the Myc locus in (Kloetgen et al. 2020) was generated using HindIII, we chose a distance cutoff close to the larger end of published values (420 nm).

(3) In their model, the authors consider the presence of LE anchor sites at Hi-C TAD boundaries. Do they correspond to real, experimentally found CTCF sites located at genomic positions, or they are just assumed? A track of CTCF peaks of the considered chromatin loci would be needed.

We apologize this was not clear. The LE anchor sites in the simulation model were chosen because they correspond to experimental CTCF sites and ChIP-seq peaks located at the corresponding genomic positions. Representative CTCF ChIP-seq tracks from (Kloetgen et al. 2020) will be added to figure 2 in the revised preprint version to emphasize this point.

(4) In the model, each TAD is assigned a specific energy affinity value. Do the different domain types (i.e., different colors) have a mutually attractive energy? If so, what is its value and how is it determined? The simulated contact maps (e.g., Figure 2C) seem to allow attractions between different blocks, yet this is unclear.

Sorry this was not explicit. The attraction energy between a pair of monomers in the simulation is determined using the geometric mean of the affinities of the two monomers. This applies to both monomers within the same domain and in different domains. This detail will be clarified in the upcoming revised preprint.

(5) To substantiate the claim that the simulations can predict heterogeneity across single cells, the authors should perform additional analyses. For instance, they could plot the histograms (models vs. experiments) of the TAD2-TAD4 distance distributions and check whether the models can recapitulate the FISH-observed variance or standard deviation. They could also add other testable predictions, e.g., on gyration radius distributions, kurtosis, all-against-all comparison of single-molecule distance matrices, etc,.

We agree that heterogeneity prediction is a key advantage of the simulations. We do note that the histograms (models vs. experiments) of the TAD2-TAD4 distance distributions measured by FISH were plotted in Fig. 3C as empirical cumulative probability distributions (as is standard in the field), side by side with the simulation predictions. Simulations indeed recapitulate the variance observed by FISH. We also had emphasized this important point in the main text: “Importantly, not just the average distances, but the shape of the distance distribution across individual cells closely matches the predictions of the simulations in both cell types, further confirming that the simulations can predict heterogeneity across cells.”

(6) The authors state that loop extrusion is crucial for enhancer function only at large distances. How does that reconcile, e.g., with Mach et al. Nature Gen. (2022) where LE is found to constrain the dynamics of genomically close (150kb) chromatin loci?

This is an interesting question. In (Mach et al. 2022), the authors tracked the physical distance between two fluorescent labels positioned next to either anchor of a ~150 kb engineered topological domain using live-cell imaging. They found that abrogation of the loop anchors by ablation of the CTCF binding motifs, or knock-down of the cohesin subunit Rad21 resulted in increased physical distance between the loci. HMM Modeling of the distance over time traces suggests that the increased distance resulted from rarer and shorter contacts between the anchors. While this might seem at odds with the results of Fig. 4L, we note a key difference between the loci. While (Mach et al. 2022) observed the dynamics of the distance separating two CTCF loop anchors, in our model only the MYC promoter is proximal to a loop anchor, while the position of the second locus is varied, but remains far from the other anchor. The deletion of the CTCF sites at both anchors in (Mach et al. 2022) indeed results in a lowered sensitivity of the physical distance to Rad21 knock-down, reminiscent of the results of Fig. 4L in our work. This result demonstrates that loop extrusion disruption disproportionately impacts distances between loci close to loop anchors, consistent with Hi-C results (Rao et al. 2017; Nora et al. 2017). We therefore believe that the models in our work and (Mach et al. 2022) are not at odds, but simply reflect that loop extrusion perturbations impact distances between loop anchors the most. Enhancer-Promoter loops are generally distinct from CTCF-mediated loops (Hsieh et al. 2020, 2022). While (Mach et al. 2022) represents a landmark study in our understanding of the dynamics of genomic folding by loop extrusion, we therefore believe that the locus we chose here - which matches the endogenous MYC architecture - may more accurately represent Enhancer-Promoter dynamics than a synthetic CTCF loop. To better articulate the similarities between model predictions and differences between the two loci, we will simulate a locus matching that of (Mach et al. 2022) in the upcoming revised preprint, and test the sensitivity of contact frequency and duration to in silico cohesin knock-down. This will also serve to extend the generality of our conclusions to different categories of genomic architectures, and the text will be clarified accordingly.

Reviewer #2 (Public Review):

Summary:

The authors Fu et al., developed polymer models that combine loop extrusion with attractive interactions to best describe Hi-C population average data. They analyzed Hi-C data of the MYC locus as an example and developed an optimization strategy to extract the parameters that best fit this average Hi-C data.

Strengths:

The model has an intuitive nature and the authors masterfully fitted the model to predict relevant biology/Hi-C methodology parameters. This includes loop extrusion parameters, the need for self-interaction with specific energies, and the time and distance parameters expected for Hi-C capture.

Weaknesses:

(1) We are no longer in the age in which the community only has access to population average Hi-C. Why was only the population average Hi-C used in this study?

Can single-cell data: i.e. single-cell Hi-C/Dip-C data or chromatin tracing data (i.e. see Tan et al Science 2018 - for Dip-C, Bintu et al Science 2018, Su et al Cell 2020 for chromatin tracing, etc.) or even 2 color DNA FISH data (used here only as validation) better constrain these models? At the very least the simulations themselves could be used to answer this essential question.

I am expecting that the single-cell variance and overall distributions of distances between loci might better constrain the models, and the authors should at least comment on it.

We agree that it is possible to recapitulate single-cell Hi-C or chromatin tracing data with simulations, and that these data modalities have a superior potential to constrain polymer models because they provide an ensemble of single allele structures rather than population-averaged contact frequencies. However, these data remain out of reach for most labs compared to Hi-C. Our goal with this work was to provide an approachable method that anyone interested could deploy on their locus of choice, and reasoned that Hi-C currently remains the data modality available to most. We envision this strategy will help reach labs beyond the small number of groups expert in single cell chromatin architecture, and thus hopefully broaden the impact of polymer simulations in the chromatin organization field.

Nevertheless, we do agree that the comparison of single-cell chromatin architectures to simulations is a fertile ground for future studies. We will include a brief discussion of the potential of single-cell architectures in an upcoming version of the manuscript.

(2) The authors claimed "Our parameter optimization can be adapted to build biophysical models of any locus of interest. Despite the model's simplicity, the best-fit simulations are sufficient to predict the contribution of loop extrusion and domain interactions, as well as single-cell variability from Hi-C data. Modeling dynamics enables testing mechanistic relationships between chromatin dynamics and transcription regulation. As more experimental results emerge to define simulation parameters, updates to the model should further increase its power." The focus on the Myc locus in this study is too narrow for this claim. I am expecting at least one more locus for testing the generality of this model.

We note that we used two distinct loci in the study, the MYC locus in leukemia vs T cells (Figs. 2-3) and a representative locus in experiments comparing WT CTCF with a mutant that leads to loss of a subset of CTCF binding sites (Fig. 1L). To further demonstrate generality, we will add to the upcoming revised preprint a demonstration of the simulation fitting to other loci acquired in different cell types.

Akgol Oksuz, Betul, Liyan Yang, Sameer Abraham, Sergey V. Venev, Nils Krietenstein, Krishna Mohan Parsi, Hakan Ozadam, et al. 2021. “Systematic Evaluation of Chromosome Conformation Capture Assays.” Nature Methods 18 (9): 1046–55.

Bintu, Bogdan, Leslie J. Mateo, Jun-Han Su, Nicholas A. Sinnott-Armstrong, Mirae Parker, Seon Kinrot, Kei Yamaya, Alistair N. Boettiger, and Xiaowei Zhuang. 2018. “Super-Resolution Chromatin Tracing Reveals Domains and Cooperative Interactions in Single Cells.” Science 362 (6413). https://doi.org/10.1126/science.aau1783.

Cardozo Gizzi, Andrés M., Diego I. Cattoni, Jean-Bernard Fiche, Sergio M. Espinola, Julian Gurgo, Olivier Messina, Christophe Houbron, et al. 2019. “Microscopy-Based Chromosome Conformation Capture Enables Simultaneous Visualization of Genome Organization and Transcription in Intact Organisms.” Molecular Cell 74 (1): 212–22.e5.

Cattoni, Diego I., Andrés M. Cardozo Gizzi, Mariya Georgieva, Marco Di Stefano, Alessandro Valeri, Delphine Chamousset, Christophe Houbron, et al. 2017. “Single-Cell Absolute Contact Probability Detection Reveals Chromosomes Are Organized by Multiple Low-Frequency yet Specific Interactions.” Nature Communications 8 (1): 1753.

Chen, Liang-Fu, Hannah Katherine Long, Minhee Park, Tomek Swigut, Alistair Nicol Boettiger, and Joanna Wysocka. 2022. “Structural Elements Facilitate Extreme Long-Range Gene Regulation at a Human Disease Locus.” bioRxiv. https://doi.org/10.1101/2022.10.20.513057.

Finn, Elizabeth H., Gianluca Pegoraro, Hugo B. Brandão, Anne-Laure Valton, Marlies E. Oomen, Job Dekker, Leonid Mirny, and Tom Misteli. 2019. “Extensive Heterogeneity and Intrinsic Variation in Spatial Genome Organization.” Cell 176 (6): 1502–15.e10.

Fudenberg, Geoffrey, and Maxim Imakaev. 2017. “FISH-Ing for Captured Contacts: Towards Reconciling FISH and 3C.” Nature Methods 14 (7): 673–78.

Hafner, Antonina, Minhee Park, Scott E. Berger, Elphège P. Nora, and Alistair N. Boettiger. 2022. “Loop Stacking Organizes Genome Folding from TADs to Chromosomes.” bioRxiv. https://doi.org/10.1101/2022.07.13.499982.

Hsieh, Tsung-Han S., Claudia Cattoglio, Elena Slobodyanyuk, Anders S. Hansen, Xavier Darzacq, and Robert Tjian. 2022. “Enhancer-Promoter Interactions and Transcription Are Largely Maintained upon Acute Loss of CTCF, Cohesin, WAPL or YY1.” Nature Genetics 54 (12): 1919–32.

Hsieh, Tsung-Han S., Claudia Cattoglio, Elena Slobodyanyuk, Anders S. Hansen, Oliver J. Rando, Robert Tjian, and Xavier Darzacq. 2020. “Resolving the 3D Landscape of Transcription-Linked Mammalian Chromatin Folding.” Molecular Cell 78 (3): 539–53.e8.

Kloetgen, Andreas, Palaniraja Thandapani, Panagiotis Ntziachristos, Yohana Ghebrechristos, Sofia Nomikou, Charalampos Lazaris, Xufeng Chen, et al. 2020. “Three-Dimensional Chromatin Landscapes in T Cell Acute Lymphoblastic Leukemia.” Nature Genetics 52 (4): 388–400.

Mach, Pia, Pavel I. Kos, Yinxiu Zhan, Julie Cramard, Simon Gaudin, Jana Tünnermann, Edoardo Marchi, et al. 2022. “Cohesin and CTCF Control the Dynamics of Chromosome Folding.” Nature Genetics 54 (12): 1907–18.

Mateo, Leslie J., Sedona E. Murphy, Antonina Hafner, Isaac S. Cinquini, Carly A. Walker, and Alistair N. Boettiger. 2019. “Visualizing DNA Folding and RNA in Embryos at Single-Cell Resolution.” Nature 568 (7750): 49–54.

Murphy, Sedona, and Alistair Nicol Boettiger. 2022. “Polycomb Repression of Hox Genes Involves Spatial Feedback but Not Domain Compaction or Demixing.” bioRxiv. https://doi.org/10.1101/2022.10.14.512199.

Nora, Elphège P., Anton Goloborodko, Anne-Laure Valton, Johan H. Gibcus, Alec Uebersohn, Nezar Abdennur, Job Dekker, Leonid A. Mirny, and Benoit G. Bruneau. 2017. “Targeted Degradation of CTCF Decouples Local Insulation of Chromosome Domains from Genomic Compartmentalization.” Cell 169 (5): 930–44.e22.

Nuebler, Johannes, Geoffrey Fudenberg, Maxim Imakaev, Nezar Abdennur, and Leonid A. Mirny. 2018. “Chromatin Organization by an Interplay of Loop Extrusion and Compartmental Segregation.” Proceedings of the National Academy of Sciences of the United States of America 115 (29): E6697–6706.

Rao, Suhas S. P., Su-Chen Huang, Brian Glenn St Hilaire, Jesse M. Engreitz, Elizabeth M. Perez, Kyong-Rim Kieffer-Kwon, Adrian L. Sanborn, et al. 2017. “Cohesin Loss Eliminates All Loop Domains.” Cell 171 (2): 305–20.e24.

Su, Jun-Han, Pu Zheng, Seon S. Kinrot, Bogdan Bintu, and Xiaowei Zhuang. 2020. “Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin.” Cell 182 (6): 1641–59.e26.

Takei, Yodai, Shiwei Zheng, Jina Yun, Sheel Shah, Nico Pierson, Jonathan White, Simone Schindler, Carsten H. Tischbirek, Guo-Cheng Yuan, and Long Cai. 2021. “Single-Cell Nuclear Architecture across Cell Types in the Mouse Brain.” Science 374 (6567): 586–94.

Wang, Siyuan, Jun-Han Su, Brian J. Beliveau, Bogdan Bintu, Jeffrey R. Moffitt, Chao-Ting Wu, and Xiaowei Zhuang. 2016. “Spatial Organization of Chromatin Domains and Compartments in Single Chromosomes.” Science 353 (6299): 598–602.

Author response:

The authors express their gratitude to the reviewers for their insightful comments.

Reviewer #1: We are uncertain about the reference to an overjudgement of the recovery of spermatogonial stem cells, as we did not draw any conclusions on this in the current study. Additionally, we have received feedback mentioning the multitude and diversity of datasets as both a strength and a weakness. However, we would appreciate clarification on which datasets may have been insufficiently reviewed and how our selection of highlights may have introduced bias to the interpretation and conclusion of the study. It is important to note that we did not select any patients/ data; all patient data were incorporated into our results section. We acknowledge the need for clarification regarding our study population for the germ cell stainings. As stated in our Materials and Methods section, our current study population includes the cohort from our previous publication (Vereecke et al., 2020), supplemented by nine additional participants, totaling n=106 trans women. While Fig. 1C incorporates both previous and new data on germ cells, we understand the need to clarify this to avoid confusion. Additionally, we will include information on the Tanner stages of the trans women in our cohort (all G5), as well as details on the selection criteria for our controls and their Tanner stages. As briefly touched upon in the discussion, a marker such as delta-like homolog 1 would indeed be valuable to assess the presence of truly immature Leydig cells. Unfortunately, our attempts to optimize the immunofluorescence protocol for this marker were unsuccessful, resulting in a double staining instead of a triple staining for the Leydig cells. The suboptimal resolution of Fig.1 will be solved.

Reviewer #2 raises concerns regarding the suitability of rejuvenated testicular tissue for research purposes. However, we emphasize that this tissue source holds significant value. Although there is a wide availability of adult testicular tissue (coming from prostate cancer patients or vasectomy reversal patients), we are especially looking for alternatives for the scarce prepubertal/ pubertal tissue for research on in vitro spermatogenesis. While we acknowledge that transgender tissue with severe hyalinization or without spermatogonia may not be suitable for such research, the abundance of transgender tissue without these issues emphasizes the value of this tissue source.

Author response:

Reviewer #1

[…] it seems that the readout units are not operating in continuous time, and that interval discrimination relies in part on external information. Specifically, the readout units only look at the spike counts during the window delta_t_w.

In the first version of the review, the reviewer implied that each readout unit only receives input during a small window around the interval it represents. However, this is not the case. The small window that is depicted in Fig. 16 is a sliding window that is used to compute the states (i.e., an estimate of the instantaneous firing rate) at each point in time. The fact that the readout units indeed do operate in continuous time is apparent from Fig. 2A, showing the activity of all output units as a function of time: There is gradually changing activity with a peak at the represented interval. If each unit would only receive input during a window of a couple milliseconds, there would be a single peak of activity at the represented interval, and near-zero activity at any other time.

This misunderstanding has been cleared out in the current version of the review (see last paragraph of review #1).

Stimulus onset occurs at 1500 ms in order to allow the network to stabilize. Ideally, this value should be randomized across trials to ensure performance generalizes across initial states.

This is a valid point which we will address in the revision. However, we note that experimentation with different onset values did not change the dynamics of the network systematically in previous studies (i.e., Hass et al., 2022).

Why does StDev saturate? Is that because subjective time saturates as well?

Indeed, the two phenomena are closely related. In section “Deviations from the scalar property and the origin on Vierordt’s law”, we discuss that both is caused by the broadening of the tuning curves of the readout units (Fig 1A) as the longest time constants of the network are exceeded.

In the discussion, it would be nice to explain that dopaminergic modulation of subjective timing is not as universally observed as the linear psychophysical law or the scalar property, and I believe somewhat controversial (e.g., Ward, ..., Balsam, 2009).

We are thankful for this advice and will adapt the discussion accordingly in the revision. Still, we note that dopaminergic modulation of subjective timing is one of the more robust effects observed in several time perception experiments.

Reviewer #2:

(1) Lack of Empirical Data: […] The paper would benefit from quantitative and qualitative simulations of results from specific, large-sample studies to anchor the model's predictions in concrete empirical evidence.

While it is correct that this study does not attempt the replicate a concrete empirical study, we note that do compare the model's results with specific studies wherever possible. The comparison is done on the level of parameters of functional relationships: For the linear psychophysical law, we compare the slope and the indifference point of the model with those from experimental studies. For the scalar property, we compare the Weber fraction of the model to those computed from experiments. For dopaminergic modulation of subjective duration, no direct comparison with experimental data is possible, as the levels of modulation are estimated from in vitro experiments and cannot be directly compared with modulations in vivo. However, we discuss a range of qualitative observations in experiments that are reproduced (and explained) by the model.

The above arguments notwithstanding, one can discuss whether the presentation of the experimental results and the comparison with the simulations is appropriate, and we do plan to extend this presentation in a revision.

(2) Methodological Ambiguities: The training and testing procedures lack robust checks for generalization, leading to potential overfitting issues.

It is correct that formal checks for generalization, such as cross-validation protocols, are missing, and we will include them in the revision. However, as we obtained a mechanistic understanding of how the model tells time, we are confident that our results are not due to overfitting.

(3) Inadequate Visualization of Empirical Data: References to empirical data are vague and not directly visualized alongside model outputs. Future iterations should include empirical data, not general trends from psychophysics, in figures for a clear comparison.

As mentioned above, the comparison between simulation and empirical data will be extended in a revision. However, we argue that the “general trends”, namely adherence of the model to the often-reported psychophysical regularities, are of greater importance compared to the replication of, e.g. one specific slope of the linear psychophysical law, which does vary a lot between experiments.

(4) Limitations in Model Scope and Dynamics: […] Expanding the model limitations to consider isochronous pulse processing and the emergence of limit-cycle behaviors after prolonged stimulation would provide a more comprehensive understanding of the model's capabilities and limitations.

The current research focuses on the estimation of a single duration rather than the processing of sequences of durations. Sequence processing is a vast field, and it has been argued that it comprises different mechanisms compared to duration estimation. Thus, we feel that including sequences processing would be beyond the scope of the already quite extensive paper. However, we will discuss a possible extension of the model to sequence processing in the revision.

Additionally, the justification for using(N_{Poisson}\) as a proxy for more connections is unclear and warrants a more direct approach.

We considered different means to vary the noise input into the network, including changes in the number of connections. We ultimately chose to vary the firing rate of a fixed number of Poisson input neurons. As the sum of the firing rates of N independent Poisson neurons with the same f is simply N*f and the synaptic contributions from each spike also linearly add up, this is equivalent to adding more Poisson neurons and thus, more connections.

(5) Omissions and Redundancies: Certain omissions, such as the lack of a condition in Figure 7A or missing references to relevant models and reviews, detract from the paper's thoroughness.

The reviewer refers to a condition where everything is ablated except NMDA. We will include such a condition in the revision. Regarding missing references, the reviewer requests including references that focus on sequence processing. While the focus of the current work is on estimating a single duration rather than a sequence of durations (see above), we will include a review on this topic as an outlook on this possible extension of the model.

Moreover, some statements and terms like "internal clock" are used without a clear mechanistic definition within the model.

We are thankful for this advice and will adapt the revision accordingly.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Recommendations For The Authors):

In this revision the authors address some of the key concerns, including clarification of the balanced nature of the RL driven pitch changes and conducting analyses to control for the possible effects of singing quantity on their results. The paper is much improved but still has some sources of confusion, especially around Fig. 4, that should be fixed. The authors also start the paper with a statistically underpowered minor claim that seems unnecessary in the context of the major finding. I recommend the authors may want to restructure their results section to focus on the major points backed by sufficient n and stats.

Major issues.

(1) The results section begins very weak - a negative result based on n=2 birds and then a technical mistake of tube clogging re-spun as an opportunity to peak at intermittent song in the otherwise muted birds. The logic may be sound but these issues detract from the main experiment, result, analysis, and interpretation. I recommend re-writing this section to home in on, from the outset, the well-powered results. How much is really gained from the n=2 birds that were muted before ANY experience? These negative results may not provide enough data to make a claim. Nor is this claim necessary to motivate what was done in the next 6 birds. I recommend dropping the claim?

We thank the reviewer for the recommendation. We moved the information to the Methods.

(2) Fig. 4 is very important yet remains very confusing, as detailed below.

Fig. 4a. Can the authors clarify if the cohort of WNd birds that give rise to the positive result in Fig 4 ever experienced the mismatch in the absence of ongoing DAF reinforcement pre-deafening? Fig4a does nor the next clearly specifies this. This is important because we know that there are day timescale delays in LMAN-dependent bias away from DAF and consolidation into the HVC-RA pathway (Andalman and Fee, 2009). Thus, if birds experienced mismatch pre-deafening in the absence of DAF, then an earnly learning phase in Area X could be set in place. Then deafening occurs, but these weight changes in X could result in LMAN bias that expresses only days later -independent of auditory feedback. Such a process would not require an internal model as the authors are arguing for here. It would simply arise from delays in implementing reinforcement-driven feedback. If the birds in Fig 4 always had DAF on before deafening, then this is not an issue. But if the birds had hours of singing with DAF off before deafening, and therefore had the opportunity to associate DA error signals with the targeted time in the song (e.g. pauses on the far-from-target renditions (Duffy et al, 2022), then the return-to-baseline would be expected to be set in place independent of auditory feedback. Please clarify exactly if the pitch-contingent DAF was on or off in the WNd cohort in the hours before deafening. In Fig. 3b it looks like the answer is yes but I cannot find this clearly stated in the text.

We did not provide DAF-free singing experience to the birds in Fig. 4 before deafening. Thus, according to the reviewer, the concern does not apply.

Note that we disagree with the reviewer’s premise that there is ‘day timescale delay in LMAN-dependent bias away from DAF and consolidation into the HVC-RA pathway’. More recent data reveals immediate consolidation of the anterior forebrain bias without a night-time effect (Kollmorgen, Hahnloser, Mante 2020; Tachibana, Lee, Kai, Kojima 2022). Thus, the single bird in (Andalman and Fee 2009) seems to be somewhat of an outlier.

Hearing birds can experience the mismatch regardless of whether they experience DAF-free singing (provided their song was sufficiently shifted): even the renditions followed by white noise can be assessed with regards to their pitch mismatch, so that DAF imposes no limitation on mismatch assessment.

We disagree with their claim that no internal model would be needed in case consolidation was delayed in Area X. If indeed, Area X stores the needed change and it takes time to implement this change in LMAN, then we would interpret the change in Area X as the plan that birds would be able to implement without auditory feedback. Because pitch can either revert (after DAF stops) or shift further away (when DAF is still present), there is no rigid delay that is involved in recovering the target, but a flexible decision making of implementing the plan, which in our view amounts to using a model.

Fig 4b. Early and Late colored dots in legend are both red; late should be yellow? Perhaps use colors that are more distinct - this may be an issue of my screen but the two colors are difficult to discern.

We used colors yellow to red to distinguish different birds and not early and late. We modified the markers to improve visual clarity: Early is indicated with round markers and late with crosses.

Fig 4b. R, E, and L phases are only plotted for 4c; not in 4b. But the figure legend says that R, E and L are on both panels.

In Fig. 4b E and L are marked with markers because they are different for different birds. In Fig. 4c the phases are the same for all birds and thus we labeled them on top. We additionally marked R in Fig. 4b as in Fig. 4c.

Fig 4e. Did the color code switch? In the rest of Fig 4, DLO is red and WND is blue. Then in 4e it swaps. Is this a typo in the caption? Or are the colors switch? Please fix this it's very confusing.

Thank you for pointing out the typo in the caption. We corrected it.

The y axes in Fig 4d-e are both in std of pitch change - yet they have different ylim which make it visually difficult to compare by eye. Is there a reason for this? Can the authors make the ylim the same for fig 4d-e?.

We added dashed lines to clarify the difference in ylim.

Fig 4d-3 is really the main positive finding of the paper. Can the others show an example bird that showcases this positive result, plotted as in Fig 3b? This will help the audience clearly visualize the raw data that go into the d' analyses and get a more intuitive sense of the magnitude of the positive result.

We added example birds to figure 4, one for WNd and one for dLO.

Please define 'late' in Fig.4 legend.

Done

Minor

Define NRP In the text with an example. Is an NRP of 100 where the birds was before the withdrawal of reinforcement?

We added the sentence to the results:

"We quantified recovery in terms of 𝑵𝑹𝑷 to discount for differences in the amount of initial pitch shift where 𝑵𝑹𝑷 = 𝟎% corresponds to complete recovery and 𝑵𝑹𝑷 = 𝟏𝟎𝟎% corresponds pitch values before withdrawal of reinforcement (R) and thus no recovery."

Reviewer #3 (Recommendations For The Authors):

The use of "hierarchically lower" to refer to the flexible process is confusing to me, and possibly to many readers. Some people think of flexible, top-down processes as being _higher_ in a hierarchy. Regardless, it doesn't seem important, in this paper, to label the processes in a hierarchy, so perhaps avoid using that terminology.

We reformulated the paragraph using ‘nested processes’ instead of hierarchical processes.

In the statement "a seeming analogous task to re-pitching of zebra finch song, in humans, is to modify developmentally learned speech patterns", a few suggestions: it is not clear whether "re-pitching" refers to planning or feedback-dependent learning (I didn't see it introduced anywhere else). And if this means planning, then it is not clear why this would be analogous to "humans modifying developmentally learned speech patterns". As you mentioned, humans are more flexible at planning, so it seems re-pitching would _not_ be analogous (or is this referring to the less flexible modification of accents?).

We changed the sentence to:

"Thus, a seeming analogous task to feedback-dependent learning of zebra finch song, in humans, is to modify developmentally learned speech patterns."

Author response:

We would first like to thank the editor for considering our findings for publication in eLife. Furthermore, we thank the reviewers and editors for their encouraging reviews and for providing helpful and insightful comments.

Reviewer #1 (Public Review):

Summary:

The pituitary gonadotropins, FSH and LH, are critical regulators of reproduction. In mammals, synthesis and secretion of FSH and LH by gonadotrope cells are controlled by the hypothalamic peptide, GnRH. As FSH and LH are made in the same cells in mammals, variation in the nature of GnRH secretion is thought to contribute to the differential regulation of the two hormones. In contrast, in fish, FSH and LH are produced in distinct gonadotrope populations and may be less (or differently) dependent on GnRH than in mammals. In the present manuscript, the authors endeavored to determine whether FSH may be independently controlled by a distinct peptide, cholecystokinin (CCK), in zebrafish.

Strengths:

The authors demonstrated that the CCK receptor is enriched in FSH-producing relative to LH-producing gonadotropes, and that genetic deletion of the receptor leads to dramatic decreases in gonadotropin production and gonadal development in zebrafish. Also, using innovative in vivo and ex vivo calcium imaging approaches, they show that LH- and FSH-producing gonadotropes preferentially respond to GnRH and CCK, respectively. Exogenous CCK also preferentially stimulated FSH secretion ex vivo and in vivo.

Weaknesses:

The concept that there may be a distinct FSH-releasing hormone (FSHRH) has been debated for decades. As the authors suggest that CCK is the long-sought FSHRH (at least in fish), they must provide data that convincingly leads to such a conclusion. In my estimation, they have not yet met this burden. In particular, they show that CCK is sufficient to activate FSH-producing cells, but have not yet demonstrated its necessity. Their one attempt to do so was using fish in which they inactivated the CCK receptor using CRISPR-Cas9. While this manipulation led to a reduction in FSH, LH was affected to a similar extent. As a result, they have not shown that CCK is a selective regulator of FSH.

Our conclusion regarding the necessity of CCK signaling for FSH secretion is based on the following evidence:

(1) CCK-like receptors are expressed in the pituitary gland predominantly on FSH cells.

(2) Application of CCK to pituitaries elicits FSH cell activation and FSH release, and, to a lesser degree, activation of LH cells.

(3) Mutating the CCK-like receptor causes a decrease in fsh and lh mRNA synthesis.

(4) Mutating the CCK-like receptor gives rise to a phenotype which is identical to that caused by mutation of both lh and fsh genes in zebrafish.

(5) Mutating the FSH-specific CCK receptor in a different species of fish (medaka) also causes a complete shutdown of FSH production and phenocopies a fsh-mutant phenotype (Uehara et al, BioRxiv, DOI: 10.1101/2023.05.26.542428).

Taken together, we believe that this data strongly supports the conclusion that CCK is necessary for FSH production and release from the fish pituitary. Admittedly, the overlapping effects of CCK on both FSH and LH cells in zebrafish (evident in both our calcium imaging experiments and the KO phenotype) complicates the interpretation of the phenotype. We speculate that the effect of CCK on LH cells in zebrafish can be caused either by paracrine signaling within the gland or by the effects of CCK on higher levels of the axis. In our revised manuscript we will make sure to highlight the overlapping effects of CCK on LH cells rather than portray it as a selective activator of FSH cells.

Moreover, they do not yet demonstrate that the effects observed reflect the loss of the receptor's function in gonadotropes, as opposed to other cell types.

Although there is evidence for the expression of CCK receptor in other tissues, we do show a direct decrease of FSH and LH expression in the gonadotrophs of the pituitary of the mutant fish; taken together with its significant expression in FSH cells, it is the most reasonable and forward explanation for the mutant phenotype. Unfortunately, unlike in mice, technologies for conditional knockout of genes in specific cell types are not yet available for our model and cell types. However, in the revised manuscript we will add a supplementary figure describing the distribution of this receptor in other tissues.

It also is not clear whether the phenotypes of the fish reflect perturbations in pituitary development vs. a loss of CCK receptor function in the pituitary later in life. Ideally, the authors would attempt to block CCK signaling in adult fish that develop normally. For example, if CCK receptor antagonists are available, they could be used to treat fish and see whether and how this affects FSH vs. LH secretion.

While the observed gonadal phenotype of the KO (sex inversion) should have a developmental origin since it requires a long time to manifest, the effect of the KO on FSH and LH cells is probably more acute.

In the Discussion, the authors suggest that CCK, as a satiety factor, may provide a link between metabolism and reproduction. This is an interesting idea, but it is not supported by the data presented. That is, none of the results shown link metabolic state to CCK regulation of FSH and fertility. Absent such data, the lengthy Discussion of the link is speculative and not fully merited.

In the revised manuscript, we will address this comment by either providing data to link cck with metabolic status or tuning down the Discussion of this topic.

Also in the Discussion, the authors argue that "CCK directly controls FSH cells by innervating the pituitary gland and binding to specific receptors that are particularly abundant in FSH gonadotrophs." However, their imaging does not demonstrate innervation of FSH cells by CCK terminals (e.g., at the EM level).

Innervation of the fish pituitary does not imply a synaptic-like connection between axon terminals and endocrine cells. In fact, such connections are extremely rare, and their functionality is unclear. Instead, the mode of regulation between hypothalamic terminals and endocrine cells in the fish pituitary is more similar to "volume transmission" in the CNS, i.e. peptides are released into the tissue and carried to their endocrine cell targets by the circulation or via diffusion.

Moreover, they have not demonstrated the binding of CCK to these cells. Indeed, no CCK receptor protein data are shown.

Our revised manuscript will include detailed experiments showing the activation of the receptor by its ligand. Unfortunately, no antibody is available against this fish- specific receptor (one of the caveats of working with fish models); therefore, we cannot present receptor protein data.

The calcium responses of FSH cells to exogenous CCK certainly suggest the presence of functional CCK receptors therein; but, the nature of the preparations (with all pituitary cell types present) does not demonstrate that CCK is acting directly in these cells.

We agree with the reviewer that there are some disadvantages in choosing to work with a whole-tissue preparation. However, we believe that the advantages of working in a more physiological context far outweigh the drawbacks as it reflects the natural dynamics more precisely. Since our transcriptome data as well as our ISH staining, show that the CCK receptor is exclusively expressed on FSH cells, it is improbable that the observed calcium response is mediated via a different pituitary cell type.

Indeed, the asynchrony in responses of individual FSH cells to CCK (Figure 4) suggests that not all cells may be activated in the same way. Contrast the response of LH cells to GnRH, where the onset of calcium signaling is similar across cells (Figure 3).

The difference between the synchronization levels of LH and FSH cells activity stems from the gap-junction mediated coupling between LH cells that does not exist between FSH cells (Golan et al 2016, DOI: 10.1038/srep23777). Therefore, the onset of calcium response in FSH cells is dependent on the irregular diffusion rate of the peptide within the preparation, whereas the tight homotypic coupling between LH cells generates a strong and synchronized calcium rise that propagates quickly throughout the entire population; we will make sure this is clear in the final revision.

Finally, as the authors note in the Discussion, the data presented do not enable them to conclude that the endogenous CCK regulating FSH (assuming it does) is from the brain as opposed to other sources (e.g., the gut).

We agree with the reviewer that, for now, we are unable to determine whether hypothalamic or peripheral CCK are the main drivers of FSH cells. While the strong innervation of the gland by CCK-secreting hypothalamic neurons strengthens the notion of a hypothalamic-releasing hormone and also fits with the dogma of the neural control of the pituitary gland in fish (Ball, 1981; doi: 10.1016/0016-6480(81)90243-4.), more experiments are required to resolve this question.

Reviewer #2 (Public Review):

Summary:

This manuscript builds on previous work suggesting that the CCK peptide is the releasing hormone for FSH in fishes, which is different than that observed in mammals where both LH and FSH release are under the control of GnRH. Based on data using calcium imaging as a readout for stimulation of the gonadotrophs, the researchers present data supporting the hypothesis that CCK stimulates FSH- containing cells in the pituitary. In contrast, LH-containing cells show a weak and variable response to CCK but are highly responsive to GnRH. Data are presented that support the role of CCK in the release of FSH. Researchers also state that functional overlap exists in the potency of GnRH to activate FSH cells, thus the two signalling pathways are not separate.

The results are of interest to the field because for many years the assumption has been that fishes use the same signalling mechanism. These data present an intriguing variation where a hormone involved in satiation acts in the control of reproduction.

Strengths:

The strengths of the manuscript are that researchers have shed light on different pathways controlling reproduction in fishes.

Weaknesses:

Weaknesses are that it is not clear if multiple ligand/receptors are involved (more than one CCK and more than one receptor?). The imaging of the CCK terminals and CCK receptors needs to be reinforced.

Reviewer consultation summary:

• The data presented establish sufficiency, but not necessity of CCK in FSH regulation. The paper did not show that CCK endogenously regulates FSH in fish. This has not been established yet.

This is a very important comment, also raised by reviewer 1. To avoid repetition, please see our detailed response to the comment above.

• The paper presents the pharmacological effects of CCK on ex vivo preparations but does not establish the in vivo physiological function of the peptide. The current evidence for a novel physiological regulatory mechanism is incomplete and would require further physiological experiments. These could include the use of a CCK receptor antagonist in adult fish to see the effects on FSH and LH release, the generation of a CCK knockout, or cell-specific genetic manipulations.

As detailed in the responses to the first reviewer,we cannot conduct conditional, cell- specific gene knockout in our model.

• Zebrafish have two CCK ligands: ccka, cckb and also multiple receptors: cckar, cckbra and cckbrb. There is ambiguity about which CCK receptor and ligand are expressed and which gene was knocked out.

In the revised manuscript, we will clarify which of the receptors are expressed and which receptor is targeted. We will also provide data showing the specificity of the receptors (both WT and mutant) to the ligands.

• Blocking CCK action in fish (with receptor KO) affects FSH and LH. Therefore, the work did not demonstrate a selective role for CCK in FSH regulation in vivo and any claims to have discovered FSHRH need to be more conservative.

We agree with the reviewer that the overlap in the effect of CCK measured in the calcium activation of cells and in the KO model does not allow us to conclude selectivity. In this context, it is crucial to highlight that CCK-R exhibits high expression on FSH cells but not on LH cells. Therefore, the effect of CCK on LH cells is likely paracrine rather than solely endocrine. We will tone down our claims of selectivity in the revised manuscript.

• The labelling of the terminals with anti-CCK looks a lot like the background and the authors did not show a specificity control (e.g. anti-CCK antibody pre-absorbed with the peptide or anti-CCK in morphant/KO animals).

We will update the colors of the image for better clarity. Also, The same antibody had been previously used to mark CCK-positive cells in the gut of the red drum fish (K.A. Webb, Jr. 2010; DOI: https://doi.org/10.1016/j.ygcen.2009.10.010), where a control (pre-absorbed with the peptide) experiment had been conducted.

Author response:

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

The authors have addressed my comments. As a final minor point, regarding comment 2, these condensates are likely viscoelastic rather than purely viscous. It is prudent to indicate that the data may refer to an apparent viscosity.

We added the following text to the manuscript to highlight the viscoelastic nature of ELP condensates, and the relationship of reported values with the steady state viscosity. “It is worth noting that the reported values, although related, may not quantitatively represent the steady-state viscosity. This discrepancy arises from the slow relaxation timescale inherent in ELP condensates with viscoelastic properties.”

Author response:

We thank eLife and the reviewers for the thoughtful summary and valuable review of our manuscript. We largely agree with the summary and review and have provided our responses to the comments below. We believe BADGER is a significant new tool for identifying associated risk factors for complex diseases, and the associations we observed in the analysis provide insights into the genetic basis of Alzheimer's disease.

Reviewer #1 (Public Review):

The major aim of the paper was a method for determining genetic associations between two traits using common variants tested in genome-wide association studies. The work includes a software implementation and application of their approach. The results of the application of their method generally agree with what others have seen using similar AD and UKB data.

The paper has several distinct portions. The first is a method for testing genetic associations between two or more traits using genome-wide association tests statistics. The second is a python implementation of the method. The last portion is the results of their method using GWAS from AD and UK Biobank.

We thank the reviewer for the conclusion and positive comments.

Regarding the method, it seems like it has similarities to LDSC, and it is not clear how it differs from LDSC or other similar methods. The implementation of the method used python 2.7 (or at least was reportedly tested using that version) that was retired in 2020. The implementation was committed between Wed Oct 3 15:21:49 2018 to Mon Jan 28 09:18:09 2019 using data that existed at the time so it was a bit surprising it used python 2.7 since it was initially going to be set for end-of-life in 2015. Anyway, trying to run the package resulted in unmet dependency errors, which I think are related to an internal package not getting installed. I would expect that published software could be installed using standard tooling for the language, and, ideally, software should have automated testing of key portions.

We thank the reviewer for their comments. To clarify, the primary difference between our proposed method, BADGERS, and LDSC lies in their respective objectives and applications. LDSC is designed to estimate heritability and genetic correlations between traits by utilizing GWAS summary statistics, thereby aiding in the elucidation of the genetic architecture of complex traits and diseases. Conversely, BADGERS is specifically developed to explore causal relationships between risk factors, such as biomarkers, and diseases of interest. It employs genetic variants as variables to deduce causality, thereby addressing the challenges of confounding and reverse causation that are common in observational studies. Although BADGERS utilizes the LD reference panel derived from LDSC, the LD reference panel is used to obtain the predicted trait expression. The ultimate goal is to focus on linking biobank traits with Alzheimer’s disease and building causal relationships instead of identifying genetic architecture.

Regarding the technical aspects mentioned, we acknowledge the concerns about the use of Python 2.7 and the issues encountered during the package installation. We are in the process of updating the software to ensure compatibility with current versions of Python and to enhance the installation process with standard tooling and automated testing for a more user-friendly experience. We have provided tests for each portion of the software so the user can test if the software is working properly.

Regarding the main results, they find what has largely been shown by others using the same data or similar data, which add prima facie validity to the work The portions of the work dealing with AD subgroups, pathology, biomarkers, and cognitive traits of interest. I was puzzled why the authors suggested surprise regarding parental history and high cholesterol not associated with MCI or cognitive composite scores since the this would seem like the likely fallout of selection of the WRAP cohort. The discussion paragraph that started "What's more, environmental factors may play a big role in the identified associations." confused me. I think what the authors are referring to are how selection, especially in a biobank dataset, can induce correlations, which is not what I think of as an environmental effect.

We thank the reviewer very much for their comment. We're glad that our findings align with existing research using similar data, increasing the validity of our work and the proposed BADGER algorithm. Your point about the lack of association between parental history, high cholesterol, and mild cognitive impairment (MCI) or cognitive composite scores in the WRAP cohort is well-taken. We agree that the selection criteria of the WRAP cohort may influence these findings, as it consists of individuals with a specific risk profile for Alzheimer's disease. This selection could indeed mitigate the observed association between these factors and cognitive outcomes, which we initially found surprising.

Regarding the environmental factors, we appreciate your clarification and understand the confusion. Our intention was to discuss the potential for selection bias and confounding factors in biobank datasets for the identified associations, which might not necessarily be direct environmental effects.

Overall, the work has merit, but I am left without a clear impression of the improvement in the approach over similar methods. Likewise, the results are interesting, but similar findings are described with the data that was used in the study, which are over 5 years old at the time of this review.

We thank the reviewer a lot for their endorsement of the BADGER framework. We believe that our method, BADGER, improves on existing approaches by effectively linking genetic data with the detailed phenotypic information in biobanks and large disease GWAS. This enhances our ability to detect associations without needing individual-level data, offering clearer insights while reducing issues like reverse causality and confounding factors.

Even though the IGAP dataset is over five years old, it remains one of the largest publicly available datasets for Alzheimer’s Disease. Likewise, the UK biobank is one of the largest publicly available human traits datasets, which researchers continue to use. These datasets' continued utility demonstrates their value in the research community. Additionally, the versatility of the BADGER framework makes it suitable for future research investigating the relationship between human traits and various diseases using different datasets.

Reviewer #2 (Public Review):

Summary:

Yan, Hu, and colleagues introduce BADGERS, a new method for biobank-wide scanning to find associations between a phenotype of interest, and the genetic component of a battery of candidate phenotypes. Briefly, BADGERS capitalizes on publicly available weights of genetic variants for a myriad of traits to estimate polygenic risk scores for each trait, and then identify associations with the trait of interest. Of note, the method works using summary statistics for the trait of interest, which is especially beneficial for running in population-based cohorts that are not enriched for any particular phenotype (ie. with few actual cases of the phenotype of interest).

Here, they apply BADGERS on Alzheimer's disease (AD) as the trait of interest, and a battery of circa 2,000 phenotypes with publicly available precalculated genome-wide summary statistics from the UK Biobank. They run it on two AD cohorts, to discover at least 14 significant associations between AD and traits. These include expected associations with dementia, cognition (educational attainment), and socioeconomic status-related phenotypes. Through multivariate modelling, they distinguish between (1) clearly independent components associated with AD, from (2) by-product associations that are inflated in the original bivariate analysis. Analyses stratified according to APOE inclusion show that this region does not seem to play a role in the association of some of the identified phenotypes. Of note, they observe overlap but significant differences in the associations identified with BADGERS and other Mendelian randomization (MR), hinting at BADGERS being more powerful than classical top variant-based MR approaches. They then extend BADGERS to other AD-related phenotypes, which serves to refine the hypotheses about the underlying mechanisms accounting for the genetic correlation patterns originally identified for AD. Finally, they run BADGERS on a pre-clinical cohort with mild cognitive impairment. They observe important differences in the association patterns, suggesting that this preclinical phenotype (at least in this cohort) has a different genetic architecture than general AD.

We thank the reviewer a lot for the conclusion and positive comments.

Strengths:

BADGERS is an interesting new addition to a stream of attempts to "squeeze" biobank data beyond pure association studies for diagnosis. Increasingly available biobank cohorts do not usually focus on specific diseases. However, they tend to be data-rich, opening for deep explorations that can be useful to refine our knowledge of the latent factors that lead to diagnosis. Indeed, the possibility of running genetic correlation studies in specific sub-settings of interest (e.g. preclinical cohorts) is arguably the most interesting aspect of BADGERS. Classical methods like LDSC or two-sample MR capitalize on publicly available summary statistics from large cohorts, or having access to individual genotype data of large cohorts to ensure statistical power. Seemingly, BADGERS provides a balanced opportunity to dissect the correlation between traits of interest in settings with small sample size in which other methods do not work well.

We thank the reviewer a lot for the conclusion and positive comments.

Weaknesses:

However, the increased statistical power is just hinted, and for instance, they do not explore if LDSC would have identified these associations. Although I suspect that is the case, this evidence is important to ensure that the abovementioned balance is right. Finally, as discussed by the authors, the reliance on polygenic risk scoring necessarily undermines the causality evidence gained through BADGERS. In this sense, BADGERS provides an alternative to strict instrumental-variable based analysis, which can be particularly useful to generate new mechanistic hypotheses.

We thank the reviewer a lot for the comments. We understand the importance of comparing BADGER to other methods. The comparison with LDSC, while not directly relevant to BADGER’s causal inference aims, is indeed an interesting aspect to consider for future studies. In this paper, we focused on comparing BADGER with Mendelian Randomization (MR), which shares its causal inference objective.

As a result, BADGERS identified a total of 48 traits that reached Bonferroni-corrected statistical significance. In contrast, MR-IVW only identified nine traits with Bonferroni-corrected statistical significance. Among these nine traits, seven were also identified by BADGERS. This demonstrates that BADGER holds higher power in detecting causal relationships.

Regarding the use of polygenic risk scoring, we agree that it holds challenges in directly inferring causality. While BADGERS offers an innovative way to explore genetic correlations and can help generate new hypotheses about disease mechanisms, it does not replace the causal inferences that can be drawn from instrumental-variable-based analyses. Instead, it should be viewed as a complementary tool that can illuminate potential genetic relationships and guide further causal investigations.

In summary, after 15 years of focus on diagnosis that would require having individual access to large patient cohorts, BADGERS can become an excellent tool to dig into trait heterogeneity, especially if it turns out to be more powerful than other available methodologies.

We thank the reviewer a lot for the conclusion and positive comments.

Author response:

We thank the reviewers and editors for their time and effort reviewing and improving this manuscript. We also thank them for their support.

Following the guidelines received by eLife we submit here the preliminary author’s response to the Public review with our planned changes to the manuscript.

Reviewer 1.

Comment 1. Issue on cross-reactivities of MafB antibodies.

We are confident that our description of MafB V1 interneurons is correct despite some cross-reactivity with one of the antibodies used. We test all antibodies we use, and unfortunately, we found an inverse relationship between sensitivity and specificity with the two MafB antibodies used in this study. We chose for quantification the one with highest sensitivity, despite the presence of some cross-reactivity in interneurons other than the dorsal and ventral (Renshaw) V1 populations we focus on. The dorsal and ventral (Renshaw) V1 populations we describe here are also reactive with the more specific antibody (although with lower sensitivity) and both are neatly labeled in a MafB-GFP reporter mouse as described in Figure 3. We will add an image to the supplement with MafB-GFP V1 Interneurons at P5 showing the immunoreactivity of both MafB antibodies as suggested by the reviewer. We agree with the reviewer that this will give further support to the characterization of these populations by either immunocytochemical or genetic means at P5.

Unfortunately, we cannot show lack of immunoreactivity for MafB antibodies in MafB GFP/GFP knockout mice at P5 because MafB global KOs die at birth as a result of respiratory failure. This is due to removal of inhibitory interneurons in brainstem centers critical for respiration (Blanchi at al. 2003 MafB deficiency causes defective respiratory rhythmogenesis and fatal central apnea at birth. Nat Neurosci. 6(10):1091-100. doi: 10.1038/nn1129. PMID: 14513037). This is why we used tissues from late embryos for testing antibody specificity in KO spinal cords. We will make this clearer in the text.

Comment 2. Overlap of V1 clades with lineage labeled Foxp2-V1s at P5.

We collected the data requested by the reviewer for P5 Foxp2-V1 interneurons and this will be added to an updated version of this figure. In comparison to the results with the OTP mouse, we only found marginal overlap at P5 with Renshaw cells, Pou6f2, and Sp8 V1s in our genetic intersection to label Foxp2-V1s. We apologize for not showing the data. We will make this clearer.

Reviewer 2.

Comment 1. Paper VERY hard to read.

We will make every effort to make the paper more readable by moving methodological discussions to supplementary materials. We strive to keep our methods as rigorous, clean, and replicable as possible, and that sometimes requires lengthy explanations of the details and reasoning behind our approaches. We will make sure this does not distract from the principal scientific messages we want to convey. We agree with the reviewer that these should be emphasized over methodological detail, and we will correct any mistakes in the text that lead to confusion. Thank you for pointing out this problem that we hope to correct in a new version. Why focus on Foxp2 V1s? We focus in the Foxp2 population for several reasons: 1) This is the largest population of V1s, and it is the one with a close spatial association to motoneurons, in particular limb motoneurons; 2) Given previous results (Benito-Gonzalez and Alvarez, 2012, cited in bibliography) it likely includes many reciprocal inhibitory interneurons; 3) We do not have the mice for studying the Pou6f2 (or Sp8) population, but similar studies are now being carried out in the Bikoff lab.

Comment 2. Lack of functional studies.

Functional studies are currently being carried out, both during development of limb function in postnatal mice as well as in adult animals. These studies required the creation of several new animal models and reagents. As with the present manuscript, we thoroughly characterize all animals and methods. This takes time and space. These studies are beyond the goals and length of the current manuscript, but we agree with the reviewer that these are the critical next experiments that need to be performed. We are now finalizing studies on the role of Foxp2-V1 interneurons in the postnatal development of limb coordination and validating approaches for silencing them in the adult while also optimizing behavioral assays and recordings. The data presented here on Foxp2-V1 interneuron heterogeneity and relations with limb motoneurons gives the necessary context for raising stronger hypotheses and aiding in the interpretation of future results in functional studies.

Synapse counts.

We respectfully disagree with the reviewer’s comments on our synapse density estimates. To fully explain the reasons and prevent any ambiguity, we need to focus on detailed methodological aspects. We apologize for the lengthy response. Two major issues were raised:

(1) Focus on the cell body.

The issue pointed by the reviewer of potential synapses in distal dendrites from V1 subgroups not projecting proximally was already discussed in the text. The reason we focus on the cell body is because 1) it is not feasible to study the full dendritic arbor of so many different types of motoneurons and 2) it allows us to identify V1 subpopulations that likely exert stronger modulation of motoneuron firing by targeting the proximal somatodendritic membrane. The fact that synaptic organization on motoneurons is similar on cell bodies and proximal dendrites (first 100 µm) suggests that inputs from V1 clades other than Renshaw cells are likely further away, and therefore there is limited benefit to include analyses of proximal dendrites in these data. Additionally, dendrites would be difficult to consistently follow in Chat immunostained tissue. We are currently using novel viral approaches to obtain labeling of single motoneurons and their full dendritic trees for more in depth dendritic analyses in the mouse. The classical method based on single cell in vivo intracellular labeling using micropipettes is presently very low yield in the adult mouse. We are experienced with detailed single motoneuron dendritic arbor analyses in cat and rat motoneurons (Alvarez et al. 1997 Cell-type specific organization of glycine receptor clusters in the mammalian spinal cord. J Comp Neurol. 379(1):150-70; Alvarez et al., 1998 Distribution of 5-hydroxytryptamine-immunoreactive boutons on alpha-motoneurons in the lumbar spinal cord of adult cats. J Comp Neurol. 393(1):69-83; Rotterman et al., 2014. Normal distribution of VGLUT1 synapses on spinal motoneuron dendrites and their reorganization after nerve injury. J Neurosci. 34(10):3475-92. doi: 10.1523/JNEUROSCI.4768-13.2014). Based on this experience, we do not believe it is feasible to include similar analyses to compare all motor columns throughout 6 segments of the spinal cord in this study. We agree with the reviewer that these are important data sets that need to be collected and they are planned for future experiments. These analyses will address different questions than the ones posed and answered in our current manuscript.

(2) Number of motoneurons analyzed.

We disagree with the reviewer assessment that our conclusions might be biased because of the numbers of motoneurons analyzed. We sampled a total of 295 motoneurons in 5 different mice (117 LMC/HMC, 99 MMC, and 79 PGC motoneurons), and we used stringent methods for synapse detection. Due to a technical error, Mouse 3 lacked data in upper lumbar and Th13, but all other mice included data in almost all motor columns and segments. We disagree with the characterization that these are small samples. For full transparency, all motoneurons analyzed were identified in Figure 6D. Each of the nearly 300 motoneuron cell bodies was carefully reconstructed through several optical planes to obtain an accurate estimate of synapse density. More automatic methods in current use in the literature sometimes analyze larger samples, but our methods are designed to avoid methodological biases inherent to these automatic methods. We do not use image thresholding to extract synaptic contacts because they lack accuracy identifying single synapses. Thus, estimates using this technique frequently refer to coverage, not synapse density. In addition, it is hard to keep threshold criteria consistent across multiple optical planes to analyze enough section thickness to estimate a motoneuron surface. This is because tissue light diffraction alters thresholding levels continuously across optical planes. Thus, many authors present data as linear densities across a perimeter (in a single plane) measuring many cells in one field in one plane. We avoid cell body linear densities (or coverage) because they bias counts towards larger synapses that have higher probability of being present at any single confocal plane. Moreover, estimates along a surface reduces synapse sampling variability and better estimate synaptic coverage compared to estimates derived from analyzing single cross-sections. We also confirm each genetically labeled varicosity as a likely synapse by accumulation of VGAT. In this manner we restrict our counts to synaptic boutons and not axons or intervaricose regions. Previously, we used bassoon to show the accuracy of our methods (Wootz et al. 2013 Alterations in the motor neuron-Renshaw cell circuit in the Sod1(G93A) mouse model. J Comp Neurol. 521(7):1449-69. doi: 10.1002/cne.23266). That means that our densities are true synaptic densities, which are difficult to extract from automatic methods that estimate fluorescence coverage over larger samples of somatic profiles but fail to individualize synapses and frequently bias results. These bulk methods introduce significant confounds in data interpretation: Is higher coverage due to bigger synapses or more synapses? Do threshold structures represent true synapses or also include axons? To what extent does sub- or over-thresholding in different planes affect identification of structures in contact with the motoneuron surface? We avoid all these problems. Not surprisingly, a nested ANOVA demonstrated consistent significant differences among motor columns and segments.

In summary, while more automatic methods allow larger samples, they disregard true synaptic densities and are based on thresholding methods with high variability in different motoneurons, optical planes and histological sections, thereby they require much larger numbers of motoneurons to overcome their many biases and sources of error. This is not our case. Our sample size is large enough considering the accuracy of our methods and data quality. This is demonstrated by consistency in statistical results across motor columns in different segments and mice.

Comment 3. Possibility of anterograde transsynaptic labeling from primary afferents infected with rabies virus.

This is a fair question that we did not clearly explain. The reviewer compares our results with those of Pimpinella et al., 2022. The methods used are different. To obtain anterograde tracing, these authors used Cre lines to achieve high levels of expression of TVA and RV glycoprotein in specific subtypes of sensory neurons including proprioceptors. Then EnVa-coated Rabies virus was injected directly inside the spinal cord for cell-type specificity. This method transynaptically labeled in the anterograde direction interneurons receiving inputs from specific types of sensory afferents, but the method does not have the muscle specificity required in our analyses. In our case, we used intramuscular injections at P5 of AAV1-G for transcomplementation with Rabies virus delta G injected in the same muscles later, at P15. In previous studies in which we used the RV-delta G virus without AAV1G, we analyzed motoneuron and primary afferent infection rates and found both to be considerably reduced with injection age. In our hands, there is almost no RV infection of primary afferents when Rabies virus is injected i.m. at P15, but there is some limited motoneuron infection remaining (that we used to our advantage in this paper to avoid primary afferent and developmental confounds).

Unfortunately, these methodological studies are presently communicated only in abstract form (GomezPerez et al., 2015 and 2016; Program Nos. 242.08 and 366.06). Therefore, we will add to the supplementary information some images from serial sections to those illustrated in the paper and that will show a few “start” LG motoneurons that remained labeled at this survival time point and the lack of any dorsal horn primary afferent labeling. This is consistent with our yet unpublished data that is based on a larger number of animals and more extensive time courses.

Comment 4. Temporal resolution of birth-dating.

We agree with the reviewer, and that is the reason we explicitly discuss that temporal resolution is not perfect (we also add a few more caveats that affect temporal resolution beyond the reviewers’ comments). However, the method is good enough to differentiate temporal sequences of neurogenesis with close to 12-hour resolution, once enough animals are analyzed to compensate for methodological temporal overlaps. That is the reason for our Figure 1D.

Reviewer 3

Comment 1. Text is too long and main message buried in technical details.

We agree and similar to our response to the first comment of Reviewer 2, we will revise the writing to make it more straightforward while moving some of the information on methods and technical discussion to supplementary materials. As demonstrated by reviewer 2 comments, methodological discussions are still important to best interpret the data presented in this paper.

Author response:

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

Public Reviews:

Reviewer #2 (Public Review):

In this revised manuscript Aguillon and collaborators convincingly demonstrating that CLK is required for free-running behavioral rhythms under constant conditions in the Cnidarian Nematostella. The results also convincingly show that CLK impacts rhythmic gene expression in this organism. This original work thus demonstrates that CLK was recruited very early during animal evolution in the circadian clock mechanism to optimize behavior and gene expression with the time-of-day. The manuscript could still benefit from some improvements so that it is more accessible for a wide readership.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Aguillon and collaborators have deeply revised, and in the progress significantly improved the presentation of their interesting results with the first Cnidarian circadian gene mutant. Results are now very convincingly demonstrating that CLK is required for free-running behavioral rhythms under constant conditions. The results also now more convincingly show that CLK impact rhythmic gene expression, although interpretation of the transcriptomics data is not straightforward. I think there is still improvements that are needed to make the manuscript more accessible. We authors need to keep in mind that a broad audience will read their report, not just chronobiologists. I have listed below several issues that I think should be addressed, and some editing suggestions.

General comment to Editor and Reviewers:

We are genuinely grateful to both reviewers and editors about all the feedback which helped us to make the best of our data, to question our analysis to the point we redefined our approach and end up with a great article we are proud of it. Only the name of authors is visible on the article, and considering how much the reviewing system help to improve the research it seems almost unfair. As such, we thank all of you and really appreciate the new eLife system. Bravo all.

Abstract:

(1) Line 40" It should read "transcript levels" instead of "transcription". There is no measurement of transcription rates in this manuscript, only mRNA levels.

Modified accordingly.

(2) Line 41: the authors mention "constant light". Does this refer to previous work? Their data in Figure 4 were in constant darkness, not in LL.

Modified accordingly.

(3) Line 46 and throughout the manuscript, the allelic nomenclature is not standard. 1-/- seems to indicate there are two different alleles. Since the allele might not be a null, I would suggest simply using 1/1, or perhaps delta/delta since the mutation results in a truncates CLK.

NvClk1-/- became NvClkΔ/Δ. Except in the .xls supplementary table were the mutant kept the NvClk-/- nomenclature. It is not possible to replace only part of a word with a different font, here generating delta sign would require to do it one by one.

(4) The last sentence of the abstract needs to be rephrased, as it suggests that CLK evolved to maintain circadian rhythms under constant conditions. Constant conditions very rarely exist on Earth, and thus cannot be an evolutionary driving force. Different explanations have been proposed on why a self-sustained clock is the evolutionary solution to timekeeping, but the purpose of the clock and of clock genes is not to maintain oscillations in constant conditions. Actually, this sentence conflicts with the title.

Modified to: the Clock gene has evolved in cnidarians to sustain 24-hour rhythmic physiology and behavior in absence of diel environmental conditions. From my actual understanding, you are right, the purpose of clock gene is not to maintain oscillation in constant conditions (this is simply the result of the experiment), but to synchronize the physiology to the day/night rhythm, and surely to sustain 24h oscillations in case the environment challenges the perception of the diel cues. The DD or LL is just an artificial experimental design to reveal the endogenous time-keeping pacemaker.

Results:

(1) Line 148 and elsewhere in the MS: I would not use the word "lower" or "higher" to qualify acrophases. I would suggest advanced/delayed or earlier/later.

Modified accordingly.

(2) Line 157-9: The introductory sentence does not clearly present the rationale for the 6/6 experiments.

We modified the paragraph accordingly: The presence of a 24-hour rhythm of NvClkΔ/Δ polyps under LD conditions could be attributed to either a direct light-response or the partial functioning of the circadian clock due to the nature of the mutation….

(3) At the end of the behavior section, or perhaps at the end of each paragraph in this section, it would be helpful to have a summary of the results and more clearly explain their interpretation. The authors need to guide the readers, particularly non-chronobiologist, so that they can understand what the really neat data that were obtained mean. For example, what does it mean that the acrophase is different between mutant and wild-type, why are Clk mutants rhythmic under LD12/12 or 6/6, etc.

We added a conclusion sentence to help non-specialist to understand each result.

(4) Line 172 and elsewhere" "true rhythmic genes" sounds odd to me. Either they are, or they are not rhythmic.

Modified to “rhythmic genes.”

(5) Paragraph starting with line 184: I do not follow what is important about the number of genes per time cluster. What does it tell us, beyond the simple fact that less genes are rhythmic in the Clk mutants?

We rewrote the result paragraph to make it clearer why we performed this clustering analysis. This clustering analysis became Extended Data Fig.2 with modification of the figures (see my comments in your review about Figure 3).

(6) Line 197: The authors need to explain what they saw with circadian clock genes and their expression in CLk mutants. In some case, amplitude increased in LD. This surprising observation deserves some explanations. "Complex regulatory effect" is too vague.

We replaced the vague “complex regulatory effect” by a more thorough description of the figure 3.a.

(7) Line 198-203: Again, help the reader understand the significance of these observations.

We rewrote the paragraph to help the reader to better understand the significance of these observations.

Discussion:

(1) Line 236-40. Careful with the use of -/-, which implies that an allele is a null. The first CLk mutants in mammals and flies, which the authors refer to. were actually dominant negatives.

I went over the citations we used for this paragraph and this first mutation in fly dClkar is null, no dominant negative. Flies are still rhythmic in the dark. Unless there is an older mutation? However, you right the first mutation identified in mouse was a dominant-negative with loss of rhythmicity, while the gene deletion did not show any effect on the behavior, suggesting compensation by a paralog. I removed two references which were not relevant to the discussion.

(2) Line 265-268 are not very clear. Do the authors mean that the lack of overlap for non-cricadian pacemaker genes is because of different experimental conditions? What would be those differences? It is reassuring that the Leach/Reitzel study and the present share pacemaker genes as rhythmic, but it is also surprising that there is almost no overlap beyond these genes. How robust are those other rhythms compared to circadian clock genes?

We revised this paragraph and raised major points regarding the raising condition of our polyps between labs and their potential genetic differences which could explain these differences.

(3) Line 270. I am not sure "compensation" is the right word, since there is no overlap between the rhythmic genes in mutants under LD and wild-type under either LD or DD. Also, saying on line 273 that the transcriptional pattern is not fully reproduced is a rather striking understatement, given the absence of rhythm gene overlap

We rewrote the paragraph accordingly. We replaced by “alternative way to drive rhythmicity under LD condition”.

(4) Line 279. The authors mention the possibility of false positives. Based on the FDR, is there more rhythmic genes than by chance?

The possibility of false-positive is a risk to consider when you do not perform multiple-testing. We added within the results paragraph the number of rhythmic genes identified with BH.Q or p.adj. which both are the multiple testing for each algorithm (RAIN and JTK) we used.

(5) Line 279-82. The references to the Ray study is rather obscure. What is the point the authors are trying to make here?

Eventually, we removed the reference from this article and modify the paragraph of the discussion. Indeed, the discussion around the Ray study did not gave an interesting direction to discuss our results and analysis approach.

(6) Line 284: define BHQ and p.adj

Defined and referenced.

(7) The way Lines 283-288 are worded do not provide a good rationale for how transcriptional rhythms were analyzed. The idea to combine two different approaches (JTK and RAIN) to be selective with rhythmicity was great. The authors need however to justify these choices in a more convincing manner. The goal is to detect rhythmic genes in a reliable manner, irrespective of the number of rhythmic genes observed Also, explaining the choice of methodology belongs to the result section.

We explained our choice of methodology and moved it to the result section as suggested.

(8) Line 292-3. There are known mechanisms that explain how transcriptional time clusters are generated. In particular, the use of interlocked feedback loop with antiphase peaks of transcriptions is well documented. Actually, it seems to me the clustering shown in Fig 4 might hint at such a mechanism.

Indeed you are right the clustering shown in Fig 3 (former Fig 4) revealed such mechanism.

Figures:

Figure 2: Define relative amplitude

We added the definition of the relative amplitude within the results. If this is what you asked for?

Figure 3: Some of the cycles look odd (first row of graphs in panel C). Why would the first and last data point be so different in three of these graphs?

We decided to modify this figure as we realized it was not informative and not objective enough, as we selected among multiple patterns few “representatives”. In the new figure we combined the cluster analysis to the behavior. Thus, readers can now pick a cluster according to a specific behavior activity level (or ZT/CT) and reach in supp. Table 4 the “genes of potential interest”. However generally speaking this figure does not explain more the consequences of the mutation, so we moved it into the Extended data Fig.2

Figure4: define the color coding in the correlation panels (blue to red)

These values from -1 to 1 are the Pearson correlation values. Now indicated on the figure with the color coding legend.

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.

Author response:

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

eLife assessment

The authors have made important contributions to our understanding of the pathogenesis of erectile dysfunction (ED) in diabetic patients. They have identified the gene Lbh, expressed in pericytes of the penis and decreased in diabetic animals. Overexpression of Lbh appears to counteract ED in these animals. The authors also confirm Lbh as a potential marker in cavernous tissues in both humans and mice. While solid evidence supports Lbh's functional role as a marker gene, further research is needed to elucidate the specific mechanisms by which it exerts its effects. This work is of interest to those working in the fields of ED and angiogenesis.

Public Reviews:

Reviewer #1 (Public Review):

In this study, the researchers aimed to investigate the cellular landscape and cell-cell interactions in cavernous tissues under diabetic conditions, specifically focusing on erectile dysfunction (ED). They employed single-cell RNA sequencing to analyze gene expression patterns in various cell types within the cavernous tissues of diabetic individuals. The researchers identified decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in several cell types, including fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. They also discovered a newly identified marker, LBH, that distinguishes pericytes from smooth muscle cells in mouse and human cavernous tissues. Furthermore, the study revealed that pericytes play a role in angiogenesis, adhesion, and migration by communicating with other cell types within the corpus cavernosum. However, these interactions were found to be significantly reduced under diabetic conditions. The study also investigated the role of LBH and its interactions with other proteins (CRYAB and VIM) in maintaining pericyte function and highlighted their potential involvement in regulating neurovascular regeneration. Overall, the manuscript is well-written and the study provides novel insights into the pathogenesis of ED in patients with diabetes and identifies potential therapeutic targets for further investigation.

Comments on revised version:

For Figure 4, immunofluorecent staining of LBH following intracavernous injections with lentiviruses is required to justify overexpression and tissue specificity.

We agree with this claims. Therefore, we have performed the immunofluorecent staining of LBH in cavernous tissues after infection with LBH O/E lentiviruses. And we found the LBH expression is significantly decreased in DM or DM+NC groups, however, after infection with LBH O/E lentiviruses, the LBH expression is significantly increased, shown as Supplementary Fig. 10. (Please see revised ‘Result’ and ‘Supplementary Fig. 10’)

Reviewer #3 (Public Review):

Bae et al. described the key roles of pericytes in cavernous tissues in diabetic erectile dysfunction using both mouse and human single-cell transcriptomic analysis. Erectile dysfunction (ED) is caused by dysfunction of the cavernous tissue and affects a significant proportion of men aged 40-70. The most common treatment for ED is phosphodiesterase 5 inhibitors; however, these are less effective in patients with diabetic ED. Therefore, there is an unmet need for a better understanding of the cavernous microenvironment, cell-cell communications in patients with diabetic ED, and the development of new therapeutic treatments to improve the quality of life.

Pericytes are mesenchymal-derived mural cells that directly interact with capillary endothelial cells (ECs). They play a vital role in the pathogenesis of erectile function as their interactions with ECs are essential for penile erection. Loss of pericytes has been associated with diabetic retinopathy, cancer, and Alzheimer's disease and has been investigated in relation to the permeability of cavernous blood vessels and neurovascular regeneration in the authors' previous studies. This manuscript explores the mechanisms underlying the effect of diabetes on pericyte dysfunction in ED. Additionally, the cellular landscape of cavernous tissues and cell type-specific transcriptional changes were carefully examined using both mouse and human single-cell RNA sequencing in diabetic ED. The novelty of this work lies in the identification of a newly identified pericyte (PC)-specific marker, LBH, in mouse and human cavernous tissues, which distinguishes pericytes from smooth muscle cells. LBH not only serves as a cavernous pericyte marker, but its expression level is also reduced in diabetic conditions. The LBH-interacting proteins (Cryab and Vim) were further identified in mouse cavernous pericytes, indicating that these signaling interactions are critical for maintaining normal pericyte function. Overall, this study demonstrates the novel marker of pericytes and highlights the critical role of pericytes in diabetic ED.

Comments on revised version:

Bae and colleagues substantially improved the data quality and revised their manuscript "Pericytes contribute to pulmonary vascular remodeling via HIF2a signaling". While these revisions clarify some of the concerns raised, others remain. In my view, the following question must be addressed.

In my prior question on #3, I completely disagree with the statement that "identified cells with pericyte-like characteristics in the walls of large blood vessels". The staining that authors provided for LBH, was clearly stained for SMCs, not pericytes. Per Fig 2E, the authors are correct that LBH is colocalized with SMA+ cells( SMCs). However, the red signal from LBH clearly stains endothelial cells. In the rest of 2E and 2D, LBH is CD31- and their location suggests LBH stained for SMCs in the Aorta, Kidney vasculature, Dorsal vein, and Dorsal Artery.

We respect the reviewer's comments and provide further justification for the reviewer's concerns. We first performed double staining of LBH and CD31 on dorsal artery and dorsal vein tissues. We found that LBH-expressing cells are completely different from CD31-expressing cells (Figrue 2D, indicated by arrows, and Supplementary Fig. 10A) and that expression is higher in veins than in arteries. This is consistent with previous understanding. In addition, in the double staining of LBH and α-SMA, we also found that there was no overlap between LBH-expressing cells and α-SMA-expressing smooth muscle cells in the cavernosum tissues, but there was some overlap in dorsal artery and dorsal vein (Figrue 2E, indicated by arrows). This may indicate that LBH is expressed slightly different types of blood vessels. This requires further experiments to prove in the future. In addition, to avoid confusion among other readers. We modify our previous discussion regarding the identification of cells with pericyte-like characteristics in the walls of large blood vessels. We removed the associated immunofluorescence staining in the aorta and kidneys replaced them with dorsal artery and dorsal vein (Please see revised ‘Result’ and ‘Figure 2’ and ‘Supplementary Fig. 10A’)

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:

eLife assessment

Unlocking the potential of molecular genetic tools (optogenetics, chemogenetics, sensors, etc.) for the study of systems neuroscience in nonhuman primates requires the development of effective regulatory elements for cell-type specific expression to facilitate circuit dissection. This study provides a valuable building block, by carefully characterizing the laminar expression profile of two viral vectors, one designed for general GABA+ergic neurons and the second for parvalbumin+ cell-type selective expression in the marmoset primary visual cortex. The authors provide solid evidence for the first enhancer S5E2 and incomplete evidence for the second one, h56D. This study contributes to our understanding of these tools but is limited by the understandably small number of animals used.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Federer et al. tested AAVs designed to target GABAergic cells and parvalbumin-expressing cells in marmoset V1. Several new results were obtained. First, AAV-h56D targeted GABAergic cells with >90% specificity, and this varied with serotype and layer. Second, AAV-PHP.eB.S5E2 targeted parvalbumin-expressing neurons with up to 98% specificity. Third, the immunohistochemical detection of GABA and PV was attenuated near viral injection sites.

Strengths:

Vormstein-Schneider et al. (2020) tested their AAV-S5E2 vector in marmosets by intravenous injection. The data presented in this manuscript are valuable in part because they show the transduction pattern produced by intraparenchymal injections, which are more conventional and efficient.

Our manuscript additionally provides detailed information on the laminar specificity and coverage of these viral vectors, which was not investigated in the original studies.

Weaknesses:

The conclusions regarding the effects of serotype are based on data from single injection tracks in a single animal. I understand that ethical and financial constraints preclude high throughput testing, but these limitations do not change what can be inferred from the measurements. The text asserts that "...serotype 9 is a better choice when high specificity and coverage across all layers are required". The data presented are consistent with this idea but do not make a strong case for it.

We are aware of the limitations of our results on the AAV-h56D. We agree with the Reviewer that a single injection per serotype does not allow us to make strong statements about differences between the 3 serotypes. Therefore, in the revised version of the manuscript we will temper our claims about such differences and use more caution in the interpretation of these data. Despite this weakness, we feel that these data still demonstrate high efficiency and specificity across cortical layers of transgene expression in GABA cells using the h56D promoter, at least with two of the 3 AAV serotypes we tested. We feel that in itself this is sufficiently useful information for the primate community, worthy of being reported. Due to cost, time and ethical considerations related to the use of primates, we chose not to perform additional experiments to determine precise differences among serotypes. Thus, for example, while it is possible that had we replicated these experiments, serotype 7 would have proven equally efficient and specific as the other two serotypes, we felt answering this question did not warrant additional experiments in this precious species.

A related criticism extends to the analysis of injection volume on viral specificity. Some replication was performed here, but reliability across injections was not reported. My understanding is that individual ROIs were treated as independent observations. These are not biological replicates (arguably, neither are multiple injection tracks in a single animal, but they are certainly closer). Idiosyncrasies between animals or injections (e.g. if one injection happened to hit one layer more than another) could have substantial impacts on the measurements. It remains unclear which results regarding injection volume or serotype would hold up had a large number of injections been made into a large number of marmosets.

For the AAV-S5E2, we made a total of 7 injections (at least 2 at the same volume), all of which, irrespective of volume, resulted in high specificity and efficiency for PV interneurons. Our conclusion is that larger volumes are slightly less specific, but the differences are minimal and do not warrant additional injections. Additionally, all of our injections involved all cortical layers, and the ROIs we selected for counts encompassed reporter protein expression across all layers. To provide a better sense of the reliability of the results across injections, in the revised version of the manuscript we will provide a supplementary table with results for each injection case separately.

Reviewer #2 (Public Review):

This is a straightforward manuscript assessing the specificity and efficiency of transgene expression in marmoset primary visual cortex (V1), for 4 different AAV vectors known to target transgene expression to either inhibitory cortical neurons (3 serotypes of AAV-h56D-tdTomato) or parvalbumin (PV)+ inhibitory cortical neurons in mice. Vectors are injected into the marmoset cortex and then postmortem tissue is analyzed following antibody labeling against GABA and PV. It is reported that: "in marmoset V1 AAV-h56D induces transgene expression in GABAergic cells with up to 91-94% specificity and 80% efficiency, depending on viral serotype and cortical layer. AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86-90% efficiency."

These claims are largely supported but slightly exaggerated relative to the actual values in the results presented. In particular, the overall efficiency for the best h56D vectors described in the results is: "Overall, across all layers, AAV9 and AAV1 showed significantly higher coverage (66.1{plus minus}3.9 and 64.9%{plus minus}3.7)". The highest coverage observed is just in middle layers and is also less than 80%: "(AAV9: 78.5%{plus minus}9.1; AAV1: 76.9%{plus minus}7.4)".

In the abstract, we indeed summarize the overall data and round up the decimals, and state that these parentages are upper bound and that they vary by serotype and layer, while in the Results we report the detailed counts with decimals. To clarify this, in the revised version of the Abstract we will change 80% to 79% and emphasize even more clearly the dependence on serotype and layer. We will amend this sentence of the Abstract as follows: “We show that in marmoset V1 AAV-h56D induces transgene expression in GABAergic cells with up to 91-94% specificity and 79% efficiency, but this depends on viral serotype and cortical layer.”

For the AAV-PHP.eB-S5E2 the efficiency reported in the abstract ("86-90%) is also slightly exaggerated relative to the results: "Overall, across all layers coverage ranged from 78%{plus minus}1.9 for injection volumes >300nl to 81.6%{plus minus}1.8 for injection volumes of 100nl."

Indeed, the numbers in the Abstract are upper bounds, for example efficiency in L4A/B with S5E2 reaches 90%. To further clarify this important point, in the revised abstract we will state ”AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86-90% efficiency, depending on layer”.

These data will be useful to others who might be interested in targeting transgene expression in these cell types in monkeys. Suggestions for improvement are to include more details about the vectors injected and to delete some comments about results that are not documented based on vectors that are not described (see below).

Major comments:

Details provided about the AAV vectors used with the h56D enhancer are not sufficient to allow assessment of their potential utility relative to the results presented. All that is provided is: "The fourth animal received 3 injections, each of a different AAV serotype (1, 7, and 9) of the AAV-h56D-tdTomato (Mehta et al., 2019), obtained from the Zemelman laboratory (UT Austin)." At a minimum, it is necessary to provide the titers of each of the vectors. It would also be helpful to provide more information about viral preparation for both these vectors and the AAVPHP.eB-S5E2.tdTomato. Notably, what purification methods were used, and what specific methods were used to measure the titers?

We thank the Reviewer for this comment. In the revised version of the manuscript, we will provide a Table with titers of each viral vector injected as well as more information regarding viral preparation methods. In fact, the methods for viral preparation and purification are detailed in the original publications so we feel it may be sufficient to cite the original papers?

The first paragraph of the results includes brief anecdotal claims without any data to support them and without any details about the relevant vectors that would allow any data that might have been collected to be critically assessed. These statements should be deleted. Specifically, delete: "as well as 3 different kinds of PV-specific AAVs, specifically a mixture of AAV1-PaqR4-Flp and AAV1-h56D-mCherry-FRT (Mehta et al., 2019), an AAV1-PV1-ChR2-eYFP (donated by G. Horwitz, University of Washington)," and delete "Here we report results only from those vectors that were deemed to be most promising for use in primate cortex, based on infectivity and specificity. These were the 3 serotypes of the GABA-specific pAAV-h56D-tdTomato, and the PV-specific AAVPHP.eB-S5E2.tdTomato." These tools might in fact be just as useful or even better than what is actually tested and reported here, but maybe the viral titer was too low to expect any expression.

This data is indeed anecdotal, and while we could delete it from the manuscript, as suggested by the Reviewer, we feel it could be useful information for the scientific community. It could prevent other labs from wasting resources, animals and time, particularly, as some of these vectors have been reported to be selective and efficient in the primate cortex, which we have not been able to confirm. We made several injections in several animals of those vectors that failed either to infect a sufficient number of cells or turned out to be poorly specific. Therefore, the negative results have been consistent. But we agree with the Reviewer that our negative results could have depended on factors such as titer. In the revised version of the manuscript, we will provide a supplementary Methods section in which we will report the specifics of the vectors that failed in our hands (i.e. number of injections made in how many animals, volumes, survival time, and titers).

Based on the description in the Methods it seems that no antibody labeling against TdTomato was used to amplify the detection of the transgenes expressed from the AAV vectors. It should be verified that this is the case - a statement could be added to the Methods.

That is indeed the case. We used no immunohistochemistry to enhance the reporter proteins as this was unnecessary. The native / non-emplified tdT signal was strong.

Reviewer #3 (Public Review):

Summary:

Federer et al. describe the laminar profiles of GABA+ and of PV+ neurons in marmoset V1. They also report on the selectivity and efficiency of expression of a PV-selective enhancer (S5E2). Three further viruses were tested, with a view to characterizing the expression profiles of a GABA-selective enhancer (h56d), but these results are preliminary.

Strengths:

The derivation of cell-type specific enhancers is key for translating the types of circuit analyses that can be performed in mice - which rely on germline modifications for access to cell-type specific manipulation - in higher-order mammals. Federer et al. further validate the utility of S5E2 as a PV-selective enhancer in NHPs.

Additionally, the authors characterize the laminar distribution pattern of GABA+ and PV+ cells in V1. This survey may prove valuable to researchers seeking to understand and manipulate the microcircuitry mediating the excitation-inhibition balance in this region of the marmoset brain.

Weaknesses:

Enhancer/promoter specificity and efficiency cannot be directly compared, because they were packaged in different serotypes of AAV.

The three different serotypes of AAV expressing reporter under the h56D promoter were only tested once each, and all in the same animal. There are many variables that can contribute to the success (or failure) of a viral injection, so observations with an n=1 cannot be considered reliable.

This is an important point that was also brought up by the Reviewer 1, which we thoroughly addressed in our comments. For clarity and convenience, we copied our response to Reviewer 1 below:.

We are aware of the limitations of our results on the AAV-h56D. We agree with the Reviewer that a single injection per serotype does not allow us to make strong statements about differences between the 3 serotypes. Therefore, in the revised version of the manuscript we will temper our claims about such differences and use more caution in the interpretation of these data. Despite this weakness, we feel that these data still demonstrate high efficiency and specificity across cortical layers of transgene expression in GABA cells using the h56D promoter, at least with two of the 3 AAV serotypes we tested. We feel that in itself this is sufficiently useful information for the primate community, worthy of being reported. Due to cost, time and ethical considerations related to the use of primates, we chose not to perform additional experiments to determine precise differences among serotypes. Thus, for example, while it is possible that had we replicated these experiments, serotype 7 would have proven equally efficient and specific as the other two serotypes, we felt answering this question did not warrant additional experiments in this precious species.

The language used throughout conflates the cell-type specificity conferred by the regulatory elements with that conferred by the serotype of the virus.

In the revised version of the manuscript we will correct ambiguous language.

Author response:

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

General responses to the weaknesses of this work:

The two reviewers mentioned two major weaknesses of this work:

(1) The one unexplained step in this intricately described mechanism is how HSCB functions to promote TACC3 degradation. It appears that the proteasome is involved since MG-132 reverses the effect of HSCB deficiency, but no other details are provided. Does HSCB target TACC3 for ubiquitination somehow? Future studies will be required to understand this portion of the mechanism.

We totally agree that the detailed mechanisms through which HSCB promotes TACC3 degradation should be clarified. We tried to find the ubiquitin ligases involved in this regulatory process but could not identify such a key protein so far. We also investigated whether HSCB itself is a ubiquitin ligase but found that the protein does not possess this activity. We therefore consider this weakness another limitation of this research and have added one sentence to the penultimate paragraph of the Discussion section to address this issue.

(2) This study only uses cell models. The significance of this work may be broadened by further studies using animal models.

We totally agree that in vivo models should be adopted to validate the major findings of this study. As we stated in the penultimate paragraph of the Discussion section, we did not have access to biological samples from the patient harboring the HSCB mutation. Additionally, HSCB constitutive knockout mice died during the embryonic stage, while conditional knockout did not cause embryonic death but resulted in almost no erythroid cells in the bone marrow. Therefore, we were not able to further validate our findings in in vivo models.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• Figure 3A - Should include FOG1 on the total cell lysate blots to show if total FOG1 is changing or only the cytoplasmic/nuclear ratio. This is shown later but would be good to include here.

We would like to thank the reviewer for the nice suggestion. We have added the blots for total FOG1 to updated Figure 3A as requested.

• Figures 3C and 4F - Should include the qPCR results from control cultures on the graphs (EPO + CRISPR NC and shNC, respectively).

We would like to thank the reviewer for the good suggestion. We have added the control groups for all qPCR assays to the updated figures throughout the study.

• Figure 4 - The addition of genetic manipulation of TACC3 to confirm its role in the cytoplasmic retention of FOG1 and failed erythroid differentiation in HSCB-deficient cells would strengthen the conclusions of this figure.

We would like to thank the reviewer for the good suggestion. We initially tried to knock down TACC3 expression through siRNAs to confirm its role in the cytoplasmic retention of FOG1. However, we found that siRNAs that worked well in untreated K562 and erythroid progenitor cells as well as several other cell lines had poor efficiency of knocking down gene expression upon HSCB deficiency. This happened not only to siRNAs targeting TACC3, but also to those targeting several other genes. Interestingly, gene overexpression plasmids worked especially well in HSCB-deficient cells. We were not able to explain these phenomena and chose to use an inhibitor of TACC3 to study its functional implications in this research.

• Text should be added to discuss the implications of this work for the lineage-specifying function of GATA-1. There are papers by John Crispino and Alan Cantor/Stu Orkin using the FOG-binding mutant of GATA-1 that implicate FOG1-dependent GATA-1 activity as Meg/Ery specifying, whereas FOG1-independent GATA-1 activity promotes mast cell or eosinophil fate. This work suggests that GATA1-expressing myeloid progenitors where FOG1 is kept cytoplasmic (no EPO signaling) would be driven towards the mast cell fate.

We would like to thank the reviewer for the valuable suggestion. We have added a new paragraph in the Discussion section of the updated manuscript to discuss the implication of this work for the lineage-specifying function of GATA-1.

Reviewer #2 (Recommendations For The Authors):

Minor comments:

(1) In the model provided in Figure 7H, HSCB and FOG1 bind TACC3 simultaneously. However based on the data provided in Figure 6B and other figures, it seems that their interactions are more likely to be mutually exclusive. Is there a possibility that, besides inducing the degradation of TACC3, the binding of HSCB can inhibit the interaction between TACC3 and FOG1?

We would like to thank the reviewer for the insightful comment. According to the data presented in the updated Figure 5F, TACC3 can simultaneously bind with HSCB and FOG1 in E 2-day HSCs. That is why we depict the simultaneous binding pattern in the model provided in Figure 7H. However, we agree that there is a possibility that the binding of HSCB can inhibit the interaction between TACC3 and FOG1 and have mentioned this possibility in the “Phosphorylation of HSCB by PI3K was necessary for its functionalization during human erythropoiesis” subsection of the “Results” section in the updated manuscript.

(2) Whether the decreased TACC3 protein abundance (Figure 5D) during erythroblast differentiation is mainly due to the effect of HSCB. Can silencing of HSCB block this reduction?

We would like to thank the reviewer for the great question. We have analyzed the protein abundance of TACC3 in HSCB-deficient hematopoietic stem cells induced for erythropoiesis for 0, 2 and 4 days and summarized the results as a new Figure 5E. According to the results, TACC3 protein abundance in HSCB-deficient hematopoietic stem cells exhibited no obvious change when the cells were induced for erythropoiesis for 0, 2 and 4 days. These results suggest that the decreased TACC3 protein abundance during early erythroblast differentiation was indeed due to the effect of HSCB. We only investigated the effect of HSCB on TACC3 abundance in early erythroid progenitors because, as shown in Figure 1, HSCB-deficient hematopoietic stem cells stopped differentiation at an early phase of their erythropoiesis. We have also mentioned these data in the “HSCB facilitated FOG1 nuclear translocation by binding with and mediating the proteasomal degradation of TACC3 upon activation of the EPO/EPOR signaling” subsection of the “Results” section in the updated manuscript.

(3) This study shows that HSCB can be phosphorylated by PI3K, and this modification is important for its role in regulating FOG1 distribution. Does the phosphorylation of HSCB also affect its function in ISC biogenesis?

We would like to thank the reviewer for the instructive question. We have analyzed the mitochondrial and cytosolic aconitase activities in wortmannin-treated K562 and E 2-day HSCs and their respective controls. The results have been summarized as a new Figure S5. According to the results, wortmannin treatment did not significantly affect mitochondrial and cytosolic aconitase activities. Therefore, it seems that HSCB phosphorylation does not affect its function in ISC biogenesis. We have also mentioned these data in the “Phosphorylation of HSCB by PI3K was necessary for its functionalization during human erythropoiesis” subsection of the “Results” section in the updated manuscript.

(4) The method of isolation of nuclear fraction needs to be provided in the "Materials and Methods" section.

We would like to thank the reviewer for the thoughtful suggestion. We have added the required information to the “Nuclear proteomics analysis” subsection of the "Materials and Methods" section in the updated manuscript.

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.

Reviewer #3 (Public Review):

Summary:

It has been proposed in the literature, that the ATP release channel Panx1 can be activated in various ways, including by tyrosine phosphorylation of the Panx1 protein. The present study reexamines the commercial antibodies used previously in support of the phosphorylation hypothesis and the presented data indicate that the antibodies may recognize proteins unrelated to Panx1. Consequently, the authors caution about the use and interpretation of results obtained with these antibodies.

Strengths:

The manuscript by Ruan et al. addresses an important issue in Panx1 research, i.e. the activation of the channel formed by Panx1 via protein phosphorylation. If the authors' conclusions are correct, the previous claims for Panx1 phosphorylation on the basis of the commercial anti-phospho-Panx1 antibodies would be in question.

This is a very detailed and comprehensive analysis making use of state-of-the-art techniques, including mass spectrometry and phos-tag gel electrophoresis.

In general, the study is well-controlled as relating to negative controls.

The value of this manuscript is, that it could spawn new, more function-oriented studies on the activation of Panx1 channels.

Weaknesses:

Although the manuscript addresses an important issue, the activation of the ATP-release channel Panx1 by protein phosphorylation, the data provided do not support the firm conclusion that such activation does not exist. The failure to reproduce published data obtained with commercial anti-phospho Panx1 antibodies can only be of limited interest for a subfield.

(1) The title claiming that "Panx1 is NOT phosphorylated..." is not justified by the failure to reproduce previously published data obtained with these antibodies. If, as claimed, the antibodies do not recognize Panx1, their failure cannot be used to exclude tyrosine phosphorylation of the Panx1 protein. There is no positive control for the antibodies.

The full title of our manuscript is “Human Pannexin 1 Channel is NOT Phosphorylated by Src Tyrosine Kinase at Tyr199 and Tyr309”. The major conclusion of our manuscript shall not be extended to the claim that “Panx1 is NOT phosphorylated”. This is by no means our conclusion. In fact, the LC-MS/MS data from both ours and others have shown that PANX1 is phosphorylated at both serine and tyrosine sites1. However, we provided solid evidence that Tyr199 and Tyr309 of human PANX1 are not effective substrate of the Src kinase.

We did provide several positive controls for the antibodies in our study. We showed that the anti-PANX1 and anti-Src antibodies unambiguously recognized PANX1 and Src, respectively (Figure 3A), and that a pan-specific phosphotyrosine antibody (P-Tyr-100) unambiguously recognized phosphorylated Src (Figure 3A)—as expected—but did not recognize PANX1. In addition, we demonstrated that the two antibodies in question (anti-PANX1-pY198 and anti-PANX1-pY308) did produce signals in our western blot analysis, but we provided compelling evidence that the bands produced by these antibodies do not correspond to PANX1 (Figure 2B).

(2) The authors claim that exogenous SRC expression does not phosphorylate Y198. DeLalio et al. 2019 show that Panx1 is constitutively phosphorylated at Y198, so an effect of exogenous SRC expression is not necessarily expected.

We have unambiguously identified peptide fragments containing non-phosphorylated Y198 in our LC-MS/MS experiment, none corresponds to a phosphorylated Y198. Therefore, our LC-MS/MS data doesn’t support the notion that Panx1 is constitutively phosphorylated at Y198.

(3) The authors argue that the GFP tag of Panx1at the COOH terminus does not interfere with folding since the COOH modified (thrombin cleavage site) Panx1 folds properly, forming an amorphous glob in the cryo-EM structure. However, they do not show that the COOH-modified Panx1 folds properly. It may not, because functional data strongly suggest that the terminal cysteine dives deep into the pore. For example, the terminal cysteine, C426, can form a disulfide bond with an engineered cysteine at position F54 (Sandilos et al. 2012).

Our manuscript included results of using a non-GFP tagged PANX1 construct (Figure 2-figure supplement 1). We didn’t notice any difference for PANX1 phosphorylation between GFP-tagged and non-GFP-tagged PANX1. Therefore, the folding of the C-terminal tail of PANX1 doesn’t affect the conclusion of our study.

(4) The authors dismiss the additional arguments for tyrosine phosphorylation of Panx1 given by the various previous studies on Panx1 phosphorylation. These studies did not, as implied, solely rely on the commercial anti-phospho-Panx1 antibodies, but also presented a wealth of independent supporting data. Contrary to the authors' assertion, in the previous papers the pY198 and pY308 antibodies recognized two protein bands in the size range of glycosylated and partial glycosylated Panx1.

We didn’t dismiss additional arguments for the Src-dependent PANX1 regulation. In fact, in the discussion of our manuscript, we acknowledged the fact that Src may still be involved in PANX1 regulation, but probably through indirect mechanisms. In the two previous studies2,3, it’s unclear if the multimeric bands detected by pY198/pY308 antibodies correspond to glycosylated PANX1 or not, as the authors did not overlay the protein markers with their blots. In particular, the migration pattern of PANX1 changes across different western blot images from DeLalio et al2. It’s also worth noting that none of these “independent supporting data” in the two previous studies provided direct evidence that Src can phosphorylate pY198/pY308.

(5) A phosphorylation step triggering channel activity of Panx1 would be expected to occur exclusively on proteins embedded in the plasma membrane. The membrane-bound fraction is small in relation to the total protein, which is particularly true for exogenously expressed proteins. Thus, any phosphorylated protein may escape detection when total protein is analyzed. Furthermore, to be of functional consequence, only a small fraction of the channels present in the plasma membrane need to be in the open state. Consequently, only a fraction of the Panx1 protein in the plasma membrane may need to be phosphorylated. Even the high resolution of mass spectroscopy may not be sufficient to detect phosphorylated Panx1 in the absence of enrichment processes.

We agree with the reviewer that only plasma membrane-residing Panx1 phosphorylation is functionally relevant. Interestingly, however, previous studies actually analyzed total protein from cell lysate and concluded that PANX1 is phosphorylated at Y198 and Y3082,3. This has motivated our analysis, in which we found that the phosphorylation events cannot be detected when using whole cell lysate. Therefore, we have also conducted an electrophysiology experiment by comparing conditions with/without active Src kinase (Figure 7). Our result indicates that PANX1 current is not affected by the presence of Src. This result suggests that even if there might be minor Src kinase phosphorylation beyond detection limit of western blot or mass spectrometry, they may not be functionally significant as well.

(6) In the electrophysiology experiments described in Figure 7, there is no evidence that the GFP-tagged Panx1 is in the plasma membrane. Instead, the image in Figure 7a shows prominent fluorescence in the cytoplasm. In addition, there is no evidence that the CBX-sensitive currents in 7b are mediated by Panx1-GFP and are not endogenous Panx1. Previous literature suggests that the hPanx1 protein needs to be cleaved (Chiu et al. 2014) or mutated at the amino terminus (Michalski et al 2018) to see voltage-activated currents, so it is not clear that the currents represent hPANX1 voltage-activated currents.

Our previous analysis has already shown that endogenous current of non-transfected cells is not sensitive to CBX4. Therefore, the CBX-sensitive current in cells overexpressed PANX1 is from PANX1-GFP. It should be noted that when protein is overexpressed, it tends to accumulate at different intracellular membranes during protein synthesis/maturation. However, this doesn’t affect a portion of the protein to be trafficked to the plasma membrane. In the paper from Michalski et al 2018, it was shown that WT human/mouse PANX1 displayed voltage-dependent activation5. Although the current is relatively small, it is clearly distinguishable from non-transfected HEK and CHO cells. This voltage-dependent activation is also sensitive to CBX, consistent with our measurement (Figure 7)4. When GS is introduced at the N-terminus, the voltage-dependent activation of human/mouse PANX1 is significantly boosted, likely due to the altered NTH conformation resulting from the N-terminal extension.

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

Literature quotes are still problematic. Why are secondary papers quoted instead of the original work? At least quote reviews by authors who published the original findings.

We appreciate the reviewer pointing this out. We have carefully checked our references and made sure that the original literature is cited.

Why does wtPanx1 run close to the 37 kD marker (Figure 2 supplement 1) instead of close to 50 kD as shown in the previous papers using the pY198 and pY308 antibodies?

It is a common observation that membrane proteins migration in SDS-PAGE gel doesn’t correlate with their formula molecular weight, also known as “gel shifting”6–8. The molecular mechanism of this phenomenon remains complex. Therefore, simply relying on protein molecular standard could not unambiguously identify PANX1 protein band. This is an issue for identifying PANX1 band, especially in light of the fact that some antibodies may not be very specific (see Figure 6B). In our experiment, we have correlated the in-gel fluorescence and western blot signal which allowed us to determine the protein band corresponding to PANX1. It is worth noting that in Figure S3 of DeLalio 2019, the PANX1 is detected at 37 kDa2. However, in many other panels of the paper, PANX1 is detected at close to 50 kDa (for example, Figure S2B).

Figure 6, supplement 1: why are there oligomers observed in the absence of crosslinking? Why is there no shift in the size of the "oligomers" in response to glycosidase F?

It is common to observe multimeric membrane proteins, including PANX1, forming oligomeric bands in SDS-PAGE gels, likely because they are not fully denatured or disassembled. PANX1 also contains several free cysteines, which may non-specifically crosslink subunits. There is actually a small shift for the 75 kDa band (dimer) in Figure 6, supplement 1. For higher molecular weight bands, this small shift may not be apparent due to the limited resolution of the gel.

A positive control for the antibodies used is missing. The authors argue that such controls are not available, since these commercial antibodies are "proprietary".

We did provide several positive controls for the antibodies in our study. We showed that the anti-PANX1 and anti-Src antibodies unambiguously recognized PANX1 and Src, respectively (Figure 3A), and that a pan-specific phosphotyrosine antibody (P-Tyr-100) unambiguously recognized phosphorylated Src (Figure 3A)—as expected—but did not recognize PANX1. In addition, we demonstrated that the two antibodies in question (anti-PANX1-pY198 and anti-PANX1-pY308) did produce signals in our western blot analysis, but we provided compelling evidence that the bands produced by these antibodies do not correspond to PANX1 (Figure 2B).

Unfortunately, the epitopes that Millipore Sigma used to generate anti-PANX1-pY198 and anti-PANX1-pY308 are not available. The description of the immunogen from Millipore Sigma website states that “A linear peptide corresponding to 12 amino acids surrounding phospho-Tyr198 of murine Pannexin-1” and “A linear peptide corresponding to 13 amino acids surrounding phosphotyrosine 308 of rat pannexin-1”. However, these immunogen peptides are not available for us to purchase.

References

(1) Nouri-Nejad, D. et al. Pannexin 1 mutation found in melanoma tumor reduces phosphorylation, glycosylation, and trafficking of the channel-forming protein. Mol Biol Cell 32, (2021).

(2) DeLalio, L. J. et al. Constitutive SRC-mediated phosphorylation of pannexin 1 at tyrosine 198 occurs at the plasma membrane. Journal of Biological Chemistry 294, (2019).

(3) Weilinger, N. L. et al. Metabotropic NMDA receptor signaling couples Src family kinases to pannexin-1 during excitotoxicity. Nat Neurosci 19, (2016).

(4) Ruan, Z., Orozco, I. J., Du, J. & Lü, W. Structures of human pannexin 1 reveal ion pathways and mechanism of gating. Nature 584, (2020).

(5) Michalski, K., Henze, E., Nguyen, P., Lynch, P. & Kawate, T. The weak voltage dependence of pannexin 1 channels can be tuned by N-terminal modifications. Journal of General Physiology 150, (2018).

(6) Rath, A., Cunningham, F. & Deber, C. M. Acrylamide concentration determines the direction and magnitude of helical membrane protein gel shifts. Proc Natl Acad Sci U S A 110, (2013).

(7) Rath, A. & Deber, C. M. Correction factors for membrane protein molecular weight readouts on sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Anal Biochem 434, (2013).

(8) Rath, A., Glibowicka, M., Nadeau, V. G., Chen, G. & Deber, C. M. Detergent binding explains anomalous SDS-PAGE migration of membrane proteins. Proc Natl Acad Sci U S A 106, (2009).

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

(1) Aydin, J., et al., Nonshivering thermogenesis protects against defective calcium handling in muscle. Faseb j, 2008. 22(11): p. 3919-24.

(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 previous reviews.

Reviewer #1:

Comments on revised version:

The authors have satisfactorily addressed my concerns.

I suggest some minor edits, however. Line 747 does not mention MARK3 and neither does the figure 8 legend (just MARK2). It would be helpful if the authors could include references to the papers reporting the shown structures in the Figure 8 legend

We have added MARK3 and related references in the revised Figure 8 legend.

Reviewer #2:

I would recommend that the catalog numbers from the different antibodies used in the study, mainly CST and Invitrogen are depicted in material and methods (see Methods/Recombinant proteins and general reagents).

Thank you for the comment. We have now added the antibody catalog numbers in the revised methods section.

I have one remark related to question number 5 (my question was not clear enough). I meant if the authors did look at the functional relevance of the residues implicated in the identified salt-bridge network/tethers. What happens to the proteins functionally when you mutate those residues? (represented on Fig. 8).

Otherwise, the authors have satisfactorily addressed my concerns.

Yes, we have analyzed the stability of the salt bridge interaction in the context of cysteine mutations, and our findings are described in the results section titled “Cysteine mutations alter critical structural interactions required for kinase allosteric regulation Figure 6)”. However, we have not performed mutational analysis of the salt bridge residues as we feel this would be beyond the scope of the current study.

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

(1) Pensalfini, A., Albay, R., 3rd, Rasool, S., Wu, J. W., Hatami, A., Arai, H., Margol, L., Milton, S., Poon, W. W., Corrada, M. M., Kawas, C. H., and Glabe, C. G. (2014) Intracellular amyloid and the neuronal origin of Alzheimer neuritic plaques. Neurobiol Dis 71C, 53-61

(2) Lee, J. H., Yang, D. S., Goulbourne, C. N., Im, E., Stavrides, P., Pensalfini, A., Chan, H., Bouchet-Marquis, C., Bleiwas, C., Berg, M. J., Huo, C., Peddy, J., Pawlik, M., Levy, E., Rao, M., Staufenbiel, M., and Nixon, R. A. (2022) Faulty autolysosome acidification in Alzheimer’s disease mouse models induces autophagic build-up of Abeta in neurons, yielding senile plaques. Nat Neurosci 25, 688-701

(3) Yang, A. J., Chandswangbhuvana, D., Margol, L., and Glabe, C. G. (1998) Loss of endosomal/lysosmal membrane impermeability is an early event in amyloid Aß1-42 pathogenesis. J. Neurosci. Res. 52, 691-698

(4) Yang, A. J., Knauer, M., Burdick, D. A., and Glabe, C. (1995) Intracellular A beta 1-42 aggregates stimulate the accumulation of stable, insoluble amyloidogenic fragments of the amyloid precursor protein in transfected cells. J Biol Chem 270, 14786-14792

(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

References

Boehmer, T., Enninga, J., Dales, S., Blobel, G., and Zhong, H. (2003). Depletion of a single nucleoporin, Nup107, prevents the assembly of a subset of nucleoporins into the nuclear pore complex. Proc Natl Acad Sci U S A 100, 981-985.

D'Angelo, M.A., Raices, M., Panowski, S.H., and Hetzer, M.W. (2009). Age-dependent deterioration of nuclear pore complexes causes a loss of nuclear integrity in postmitotic cells. Cell 136, 284-295.

Eftekharzadeh, B., Daigle, J.G., Kapinos, L.E., Coyne, A., Schiantarelli, J., Carlomagno, Y., Cook, C., Miller, S.J., Dujardin, S., Amaral, A.S., et al. (2018). Tau Protein Disrupts Nucleocytoplasmic Transport in Alzheimer's Disease. Neuron 99, 925-940 e927.

Liu, J., and Hetzer, M.W. (2022). Nuclear pore complex maintenance and implications for agerelated diseases. Trends Cell Biol 32, 216-227.

Lord, A., Kalimo, H., Eckman, C., Zhang, X.Q., Lannfelt, L., and Nilsson, L.N. (2006). The Arctic Alzheimer mutation facilitates early intraneuronal Abeta aggregation and senile plaque formation in transgenic mice. Neurobiol Aging 27, 67-77.

Mertens, J., Paquola, A.C., Ku, M., Hatch, E., Bohnke, L., Ladjevardi, S., McGrath, S., Campbell, B., Lee, H., Herdy, J.R., et al. (2015). Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell stem cell 17, 705-718.

Wu, X., Kasper, L.H., Mantcheva, R.T., Mantchev, G.T., Springett, M.J., and van Deursen, J.M. (2001). Disruption of the FG nucleoporin NUP98 causes selective changes in nuclear pore complex stoichiometry and function. Proc Natl Acad Sci U S A 98, 3191-3196.

Youmans, K.L., Tai, L.M., Kanekiyo, T., Stine, W.B., Jr., Michon, S.C., Nwabuisi-Heath, E., Manelli, A.M., Fu, Y., Riordan, S., Eimer, W.A., et al. (2012). Intraneuronal Abeta detection in 5xFAD mice by a new Abeta-specific antibody. Molecular neurodegeneration 7, 8.

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

Araya, E. I., Baggio, D. F., Koren, L. O., Andreatini, R., Schwarting, R. K. W., Zamponi, G. W., & Chichorro, J. G. (2020). Acute orofacial pain leads to prolonged changes in behavioral and affective pain components. Pain, 161(12), 2830-2840. https://doi.org/10.1097/j.pain.0000000000001970

Barker, D. J., Root, D. H., Ma, S., Jha, S., Megehee, L., Pawlak, A. P., & West, M. O. (2010). Dose-dependent differences in short ultrasonic vocalizations emitted by rats during cocaine self-administration. Psychopharmacology (Berl), 211(4), 435-442. https://doi.org/10.1007/s00213-010-1913-9

Barroso, A. R., Araya, E. I., de Souza, C. P., Andreatini, R., & Chichorro, J. G. (2019). Characterization of rat ultrasonic vocalization in the orofacial formalin test: Influence of the social context. Eur Neuropsychopharmacol, 29(11), 1213-1226. https://doi.org/10.1016/j.euroneuro.2019.08.298

Biały, M., Podobinska, M., Barski, J., Bogacki-Rychlik, W., & Sajdel-Sulkowska, E. M. (2019). Distinct classes of low frequency ultrasonic vocalizations in rats during sexual interactions relate to different emotional states. Acta Neurobiol Exp (Wars), 79(1), 1-12. https://www.ncbi.nlm.nih.gov/pubmed/31038481

Briefer, E. F., Padilla de la Torre, M., & McElligott, A. G. (2012). Mother goats do not forget their kids' calls. Proc Biol Sci, 279(1743), 3749-3755. https://doi.org/10.1098/rspb.2012.0986

Browning, J. R., Browning, D. A., Maxwell, A. O., Dong, Y., Jansen, H. T., Panksepp, J., & Sorg, B. A. (2011). Positive affective vocalizations during cocaine and sucrose self administration: a model for spontaneous drug desire in rats. Neuropharmacology, 61(1-2), 268-275. https://doi.org/10.1016/j.neuropharm.2011.04.012

Brudzynski, S. M. (2015). Pharmacology of Ultrasonic Vocalizations in adult Rats: Significance, Call Classification and Neural Substrate. Curr Neuropharmacol, 13(2), 180-192. https://doi.org/10.2174/1570159x13999150210141444

Brudzynski, S. M., & Bihari, F. (1990). Ultrasonic vocalization in rats produced by cholinergic stimulation of the brain. Neurosci Lett, 109(1-2), 222-226. https://doi.org/10.1016/0304-3940(90)90567-s

Brudzynski, S. M., Bihari, F., Ociepa, D., & Fu, X. W. (1993). Analysis of 22 kHz ultrasonic vocalization in laboratory rats: long and short calls. Physiol Behav, 54(2), 215-221. https://doi.org/10.1016/0031-9384(93)90102-l

Hinchcliffe, J. K., Jackson, M. G., & Robinson, E. S. (2022). The use of ball pits and playpens in laboratory Lister Hooded male rats induces ultrasonic vocalisations indicating a more positive affective state and can reduce the welfare impacts of aversive procedures. Lab Anim, 56(4), 370-379. https://doi.org/10.1177/00236772211065920

Matochik, J. A., White, N. R., & Barfield, R. J. (1992). Variations in scent marking and ultrasonic vocalizations by Long-Evans rats across the estrous cycle. Physiol Behav, 51(4), 783-786. https://doi.org/10.1016/0031-9384(92)90116-j

Olszyński, K. H., Polowy, R., Małż, M., Boguszewski, P. M., & Filipkowski, R. K. (2020). Playback of Alarm and Appetitive Calls Differentially Impacts Vocal, Heart-Rate, and Motor Response in Rats. iScience, 23(10), 101577. https://doi.org/10.1016/j.isci.2020.101577

Olszyński, K. H., Polowy, R., Wardak, A. D., Grymanowska, A. W., & Filipkowski, R. K. (2021). Increased Vocalization of Rats in Response to Ultrasonic Playback as a Sign of Hypervigilance Following Fear Conditioning. Brain Sci, 11(8). https://doi.org/10.3390/brainsci11080970

Olszyński, K. H., Polowy, R., Wardak, A. D., Grymanowska, A. W., Zieliński, J., & Filipkowski, R. K. (2022). Spontaneously hypertensive rats manifest deficits in emotional response to 22-kHz and 50-kHz ultrasonic playback. Prog Neuropsychopharmacol Biol Psychiatry, 120, 110615. https://doi.org/10.1016/j.pnpbp.2022.110615

Saito, Y., Tachibana, R. O., & Okanoya, K. (2019). Acoustical cues for perception of emotional vocalizations in rats. Scientific Reports, 9(1), 10539.

Sales, G. D. (1979). Strain Differences in the Ultrasonic Behavior of Rats (Rattus norvegicus) Am Zool, 19(2), 513-527. https://www.jstor.org/stable/3882331

Shimoju, R., Shibata, H., Hori, M., & Kurosawa, M. (2020). Stroking stimulation of the skin elicits 50-kHz ultrasonic vocalizations in young adult rats. J Physiol Sci, 70(1), 41. https://doi.org/10.1186/s12576-020-00770-1

Silkstone, M., & Brudzynski, S. M. (2019a). The antagonistic relationship between aversive and appetitive emotional states in rats as studied by pharmacologically-induced ultrasonic vocalization from the nucleus accumbens and lateral septum. Pharmacology Biochemistry and Behavior, 181, 77-85. https://doi.org/10.1016/j.pbb.2019.04.009

Silkstone, M., & Brudzynski, S. M. (2019b). Intracerebral injection of R-(-)-Apomorphine into the nucleus accumbens decreased carbachol-induced 22-kHz ultrasonic vocalizations in rats. Behavioural Brain Research, 364, 264-273. https://doi.org/10.1016/j.bbr.2019.01.044

Willey, A. R., & Spear, L. P. (2013). The effects of pre-test social deprivation on a natural reward incentive test and concomitant 50 kHz ultrasonic vocalization production in adolescent and adult male Sprague-Dawley rats. Behav Brain Res, 245, 107-112. https://doi.org/10.1016/j.bbr.2013.02.020

Wöhr, M., Borta, A., & Schwarting, R. K. (2005). Overt behavior and ultrasonic vocalization in a fear conditioning paradigm: a dose-response study in the rat. Neurobiol Learn Mem, 84(3), 228-240. https://doi.org/10.1016/j.nlm.2005.07.004

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

(5) Williams JM, Tsai B. Intracellular trafficking of bacterial toxins. Curr Opin Cell Biol. 2016 Aug;41:51–56. PMCID: PMC4983527

(6) Mesquita FS, van der Goot FG, Sergeeva OA. Mammalian membrane trafficking as seen through the lens of bacterial toxins. Cell Microbiol. 2020 Apr;22(4):e13167. PMCID: PMC7154709

(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

(8) Donovan JJ, Middlebrook JL. Ion-conducting channels produced by botulinum toxin in planar lipid membranes. Biochemistry. 1986 May 20;25(10):2872–2876. PMID: 2424493

(9) Fischer A, Montal M. Single molecule detection of intermediates during botulinum neurotoxin translocation across membranes. Proc Natl Acad Sci U S A. 2007 Jun 19;104(25):10447–10452. PMCID: PMC1965533

(10) Fischer A, Nakai Y, Eubanks LM, Clancy CM, Tepp WH, Pellett S, Dickerson TJ, Johnson EA, Janda KD, Montal M. Bimodal modulation of the botulinum neurotoxin protein-conducting channel. Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1330–1335. PMCID: PMC2635780

(11) Fischer A, Montal M. Crucial role of the disulfide bridge between botulinum neurotoxin light and heavy chains in protease translocation across membranes. J Biol Chem. 2007Oct 5;282(40):29604–29611. PMID: 17666397

(12) Koriazova LK, Montal M. Translocation of botulinum neurotoxin light chain protease through the heavy chain channel. Nature structural biology. 2003. p. 13–18. PMID: 12459720

(13) Moreau D, Kumar P, Wang SC, Chaumet A, Chew SY, Chevalley H, Bard F.Genome-wide RNAi screens identify genes required for Ricin and PE intoxications. Dev Cell. 2011 Aug 16;21(2):231–244. PMID: 21782526

(14) Bassik MC, Kampmann M, Lebbink RJ, Wang S, Hein MY, Poser I, Weibezahn J, Horlbeck MA, Chen S, Mann M, Hyman AA, Leproust EM, McManus MT, Weissman JS. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell. 2013 Feb 14;152(4):909–922. PMCID: PMC3652613

(15) Tian S, Muneeruddin K, Choi MY, Tao L, Bhuiyan RH, Ohmi Y, Furukawa K, Furukawa K, Boland S, Shaffer SA, Adam RM, Dong M. Genome-wide CRISPR screens for Shiga toxins and ricin reveal Golgi proteins critical for glycosylation. PLoS Biol. 2018 Nov;16(11):e2006951. PMCID: PMC6258472

Author response:

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

Reviewer #1:

(1) Importantly, it would be useful to have provided more detailed information on the structure and histological properties of the murine cysts and how such findings relate to human lung cysts. Also, the authors should examine whether there is any information on Bmpr1a in human cyst formation (i.e GWAS data).

We fully agree that it is important to examine Bmpr1a in human cyst pathology. Unfortunately, there is no GWAS data on this. From the published RNA-seq data, which were obtained from postnatal lung specimen of congenital pulmonary airway malformation (CPAM) patients, “integrated suppression of BMP signaling pathway” was reported although altered expression of BMPR1A was not presented. We speculate that (1) BMPR1A is critical in embryonic development and a germline deficiency of BMPR1A may lead to early embryonic lethality prior to lung formation as supported by mouse data; (2) As suggested by our previously published study related to TGF-beta signaling and prenatal pulmonary cysts (Miao et al., Am J Physiol Lung Cell Mol Physiol 2021), dysregulation of BMPR1A-mediated signaling in a particular time window of fetal lung development may be sufficient to cause cyst formation, so that BMPR1A alteration may not be persistent to postnatal lung specimens.

(2) Throughout the paper, there is a lack of quantification for the histological findings. Littermate controls should also be clearly defined genetically,

We thank the reviewer for this suggestion and acknowledge the importance of quantitative measurement for the changes. We now add quantitative data on branching number and size of the airway tips to define the difference between wild-type and Bmpr1a CKO mouse lungs in Fig.1. “The littermate controls were the mice without any gene deletion due to lack of transgenes Tbx4-rtTA and/or TetO-Cre”, which is now added in Materials and Methods.

(3) Figure 1 suppl: "Doxycycline" is misspelled.

This has been corrected.

(4) Figure1c Suppl: Hard to discern clear-cut expression of Bmpr1a protein in mesenchyme in WT. Comparable images with similar sizes of airways should be used.

To provide a clearer comparison of Bmpr1a expression patterns between Bmpr1a CKO and control lungs, we enlarge the fluorescent stained lungs presented in Supplemental Figure 1C as suggested by the editor. Additionally, dotted lines have been added to delineate the airway boundaries from the surrounding mesenchyme to better visualize the Bmpr1a distribution in lung mesenchyme. Bmpr1a expression in fetal lung mesenchyme is easily detected at E15.5 when significant dilation of airways is presented in Bmpr1a CKO lung. It is rare to have comparable sizes of peripheral airways in the Bmpr1a CKO lung at this point.

(5) Figure 2a: Expression of several genes studied and altered should be identified on scatter plot.

As suggested by the reviewer, we now highlight the related genes, including Acta2, Myocd, Eln, Bmp4, Sox2, etc., in the scatter plot. In addition, we also highlight these critical genes in the heatmap (Fig. 2B and Fig. 7B).

(6) Figure 2c: Authors should also consider staining for other smooth muscle markers.

We now include a panel of Myh11 immunostaining in Figure 2E. Myh11 is another common marker for smooth muscle cells. Lack of Myh11 staining in Bmpr1a CKO lung airways further supports our conclusion that loss of mesenchymal Bmpr1a leads to defective airway smooth muscle growth.

(7) Figure 3: ELN expression should be defined in a clear quantitative manner.

We have presented RNA-seq data, Real-time PCR results, immunostaining, and western blot data for in vivo samples. Additionally, we have included in vitro experiment illustrating that Bmp4 induces Eln expression, suggesting that BMP signaling regulates Eln expression. We believe that these datasets collectively support our conclusion.

(8) Figure 4: Additional information on p38 dependent signaling (Including in vivo studies) would potentially help to understand key molecular events and perhaps could help to address key mechanistic events, including their location and identity.

We sincerely appreciate the insightful suggestion from the reviewer. While the study of p38-dependent signaling is definitely important to dissect the entire mechanisms, we are not going to include such experiments in this manuscript due to time constraints associated with in vivo studies.

(9) Figure 6: Would be helpful to know whether Bmpr1a receptor is expressed in Myocd KO.

Bmpr1a expression is not changed in Myocd KO lungs, which is now included as Figure 6C. Together with other data, this suggests that Myocd is a downstream target directly mediating Bmpr1a-regulated airway smooth muscle development.

(10) Figure 7: Not clear how these findings, though interesting, relate to the body of studies and the pathogenesis of cyst formation. Other points: 1) The authors should re-examine/repeat co-staining in the KO mouse lung (right 2 images in the top group of 4) for Foxj1, Sox2, and CDH (right 2 images, Figure 7A). For one thing, the cadherin stain in the 2 KO images seems localized to the lumen. Secondly, the pattern of cadherin staining looks exactly the same in both KO images, suggesting an error and/or duplication 2) authors should place arrows on the heat map showing the location of SPC, Sox2, Sox9, and FoxJ1 bands 3) figure 7D graph needs numbers on y axis.

Fig.7 provides an additional potential mechanism by which deficient Bmp signaling leads to abnormally increased Bmp ligand expression, which disrupts the formation of epithelial proximal-distal axis, and results in cystic defects. Further in vivo experiments are needed to test this, which is beyond the scope of this paper.

The E-cadherin staining signal in the lumen is caused by the tissue section positioned at an interface between lumen and the apical membrane of the lining epithelial cells where the E-cadherin is localized.

Triple immunostaining of E-Cadherin, Sox2, and FoxJ1 was performed for the same tissue section (upper two panels of Figure 7A) as these antibodies were derived from different species, but the images are presented in two different combinations for simplicity and clarity. For the lower two panels of Figure 7A, double immunostaining of Sox9/E-Cadherin and Spc/E-Cadherin were performed separately on different tissue sections due to both anti-Sox9 and anti-Spc antibodies were produced from rabbits.

The genes listed in the heatmap are canonical and putative marker genes for differential lung epithelial cell lineages, such as Scgb1a1 for Clara cells and FoxJ1 for ciliated cells. Therefore, progenitor cell marker Sox2 and Sox9 were not included. In the updated heatmap, four widely acknowledged epithelial cell markers—Scgb1a1, FoxJ1, Sftpb, and Sftpc have been distinguished by utilizing a distinct font color (red) to enhance their visibility.

Label for the y axis of Fig.7D is now added.

Reviewer #2 (Public Review):

(1) The authors may be aware that a recent paper (https://doi.org/10.1038/s41598-022-24858-3) reported on transcriptional changes seen in human CPAM. It would seem that some of the molecular changes seen in human CPAM move in the opposite direction of what is reported in mice lacking mesenchymal Bmrp1a. Perhaps the authors could comment on these differences in the discussion and whether they potentially explain the etiology of CPAM or branching morphogenesis in general.

We thank the reviewer for referring this paper regarding human CPAM study. CPAM has a variety of histopathology. The type 1 CPAM is assumed to develop from more proximal bronchial/bronchiolar airways while type 2 CPAM is developed from relatively distal bronchiolar airways. In that publication, surgical resected lung specimens were collected from type 1 CPAM patients postnatally (0.5-1 year), in which the cysts were lined with ciliated pseudostratified columnar epithelial cells. Gene expression was compared between cystic lung tissues and adjacent non-cystic lung tissues. Interestingly, integrated suppression of BMP signaling pathway was shown by their data analysis. In our mouse model, the histopathology appears as human type 2 CPAM, such as back-to-back cysts lining with a simple layer of epithelial cells. Therefore, several factors could explain the differences between their published data and our study at the molecular level: (1) Different types of CPAM based on the histopathology; (2) Different sampling time points, developing cysts at fetal stage in mouse sample vs. developed cysts in postnatal huma samples; (3) Different comparison of diseased and normal tissues: separate normal lungs vs. cystic lungs in mice while in human cystic tissues vs. non-cystic tissues in the same lungs. We now include this reference in the Discussion.

(2) Figure 4 shows that BMP4 increases SMADs, p38, and several muscle genes in mesenchymal cells. Figure 5 extends this finding with a clever strategy to label airway and vascular smooth muscle with different fluorescent molecules used to isolate different types of mesenchymal cells. It shows that non-vascular smooth muscle cells but not perivascular smooth muscles are responsive to BMP4 signaling as defined by increased expression of Myh11. Are there cell-restricted responses to the other genes shown in Figure 4? Given the lack of SMAD signaling and the increase seen in p38 signaling, would blocking p38 signaling influence the BMP responsiveness of these nonvascular smooth muscle cells?

We thank the reviewer for this constructive comment. As we have addressed above, we will leave p38-mediated signaling and cyst formation to next step study due to time constraints associated with these studies.

(3) Figure 6 shows that mesenchymal loss of Myocd causes a deficiency of airway smooth muscle cells, but this was not sufficient to create cysts. Did the authors ever check to see if it changed Sox2-Sox9 staining in the airway epithelium?

There is no significant change in Sox2 expression in proximal airway epithelia of Myocd CKO lungs as detected by immunostaining. The result was not included in this manuscript.

(4) Figure 7 shows that mesenchymal loss of Bmpr1a proximalizes the distal airway as defined by loss of Sox2 and FoxJ1 (a ciliated marker) and gain in (Sox9 and SP-C) staining. But Club cells expressing Scgb1a1 and Cyp2F2 are the predominant epithelial cells in the distal airway. The transcriptomics data in panel B shows expression of these genes is less in the mutant mice. Does this mean they fail to generate Club cells or there is just less expression per cell? In other words, what are the primary epithelial cells present in the airways of mice with loss of mesenchymal Bmpr1a?

As shown in the heatmap of Fig.7b, the dysregulated gene expression in the Bmpr1a CKO extends beyond the featured epithelial cell markers, encompassing alterations in numerous putative marker genes. For example, several putative Club cell markers in addition to Scgb1a1 and Cyp2F2 were reduced in the Bmpr1a CKO lungs, suggesting a compromised differentiation of Club cells. Additionally, we observed upregulations of some molecular markers for distal progenitors and differentiated cells in the proximal region of airways, again suggesting a significant disruption in epithelial differentiation in the Bmpr1a CKO lungs. These abnormal cells can be further defined by a single cell transcriptomic approach in future.

Recommendations for Authors:

Reviewer #1 (Recommendations For The Authors):

As discussed above, there may be an issue with the histological images and staining in 2 images in Figure 7A. The precise images, problems and suggestions to resolve the issue are in the Review.

Please see our response to Reviewer 1 above.

Reviewer #2 (Recommendations For The Authors):

Minor Weaknesses:

(1) Please enlarge the fluorescent stained lungs presented in Supplemental Figure 1C.

We have revised this panel accordingly.

(2) Figure 1D and E show that loss of Bmpr1a does not change proliferation or apoptosis on E15.5. Was that also seen through E18.5?

We thank the reviewer for the thoughtful question about proliferation and apoptosis at later embryonic stages. Our focus here was to elucidate the mechanisms underlying abnormal branching morphogenesis and lung cyst initiation that occur prior to E15.5 in our model. Measuring the dynamic changes in cell proliferation and apoptosis at later timepoints will help to understand cyst progression, which will be our next focus.

(3) BMP inhibitors used in Figure 4 show that BMP signaling regulates mesenchymal myogenesis independent of SMAD. But the experiments don't show how the inhibitors impact the control cells.

We have examined the effects of the BMPR1 inhibitor LDN on the control cells. At the same dose (200 nM) and serum-free culture condition, LDN did not affect the basal level of BMP signaling (data not included) but blocked exogeneous BMP4-induced signaling elevation (Fig.4E).

(4) Bmpr1a was deleted by administering doxycycline to pregnant dams prior to lung bud formation. It caused cystic disorders by disrupting proximal airspace. Could the authors speculate on why it does not impact tracheal and bronchiolar development? In other words, does the TBX4 promoter not target these cells? Do these cells not express Bmpr1a?

The Tbx4 enhancer does target mesenchymal cells surrounding the trachea and bronchioles. Deletion of Bmpr1a in tracheal mesenchymal cells result in disruption of tracheal cartilage formation and smooth muscle differentiation. These phenotypes are evident in the gross view of lungs from E15.5 and later (Fig.1A). However, our manuscript is focusing on the phenotype of prenatal lung cysts, and we have chosen not to include complex data on tracheal development.

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:

We extend our gratitude to the two reviewers and the editors at eLife for their meticulous examination of our manuscript, as well as for their valuable feedback and positive assessment. We are particularly pleased to observe in both the reviews and the editorial evaluation the recognition of the importance of our findings. Through this provisional response, we wish to convey to the editors, reviewers, and the readership of eLife our intention to enhance the paper by incorporating a detailed description of the sections pertaining to MAD analysis, data interpretation with combined HS-AFM and PCA methods, and specific portions of the discussions. This will involve editing the manuscript accordingly and providing separate explanations in the "author response”. We acknowledge that such additions will strengthen the comprehensiveness of our work and render it more self-contained.

Moreover, in alignment with the recommendations from the review team, we will provide a thorough discussion of published data and offer a clearer explanation of our utilized methods, thereby providing a more robust foundation for our conclusions.

Author response:

eLife assessment

This study investigates associations between retrotransposon element expression and methylation with age and inflammation, using multiple public datasets. The study is valuable because a systematic analysis of retrotransposon element expression during human aging has been lacking. However, the data provided are incomplete due to the sole reliance on microarray expression data for the core analysis of the paper.

Both reviewers found this study to be important. We have selected the microarray datasets of human blood adopted by a comprehensive study of ageing published in Nature Communications (DOI: doi: 10.1038/ncomms9570). We only included the datasets specifically collected for ageing studies. Therefore, the large RNA-seq cohorts for cancer, cardiovascular, and neurological diseases were not relevant to this study and cannot be included.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Tsai and Seymen et al. investigate associations between RTE expression and methylation and age and inflammation, using multiple public datasets. The concept of the study is in principle interesting, as a systematic analysis of RTE expression during human aging is lacking.

We thank the reviewer for the positive comment.

Unfortunately, the reliance on expression microarray data, used to perform the core analysis of the paper places much of the study on shaky ground. The findings of the study would not be sufficiently supported until the authors validate them with more suitable methods.

In our discussion section in the manuscript, we have clarified that “we are aware of the limitations imposed by using microarray in this study, particularly the low number of intergenic probes in the expression microarray data. Our study can be enriched with the advent of large RNA-seq cohorts for aging studies in the future.” However, the application of microarray for RTE expression analysis was introduced previously. In fact, in a manuscript published by Reichmann et al. (DOI: 10.1371/journal.pcbi.1002486) which was cited 76 times, the authors showed and experimentally verified that cryptic repetitive element probes present in Illumina and Affymetrix gene expression microarray platforms can accurately and sensitively monitor repetitive element expression data. Inspired by this methodological manuscript with reasonable acceptance by other researchers, we trusted that the RTE microarray probes could accurately quantify RTE expression at class and family levels.

Strengths:

This is a very important biological problem.

Weaknesses:

RNA microarray probes are obviously biased to genes, and thus quantifying transposon analysis based on them seems dubious. Based on how arrays are designed there should at least be partial (perhaps outdated evidence) that the probe sites overlap a protein-coding or non-coding RNA.

We disagree with the reviewer that quantifying transposon analysis based on microarray data is dubious. As previously shown by Reichmann et al., the quantification is reliable as long as the probes do not overlap with annotated genes and they are in the correct orientation to detect sense repetitive element transcripts. Reichman et al. identified 1,400 repetitive element probes in version 1.0, version 1.1 and version 2.0 of the Illumina Mouse WG-6 Beadchips by comparing the genomic locations of the probes with the Repeatmasked regions of the mouse genome. We applied the same criteria for Illumina Human HT-12 V3 (29431 probes) and V4 (33963) to identify the RTE-specific probes.

The authors state they only used intergenic probes, but based on supplementary files, almost half of RTE probes are not intergenic but intronic (n=106 out of 264).

All our identified RTE probes overlap with intergenic regions. However, due to their repetitive natures, some probes overlap with intronic regions, too. We can replace "intergenic" with "noncoding" in our revision to show that they do not overlap with the exons of protein-coding genes. However, we do not rule out the possibility that some of our detected RTE probes might overlap noncoding RNAs. In fact, the border between coding and non-coding genomes has recently become very fuzzy with new annotations of the genome. RTE RNAs can be easily considered as non-coding RNAs if we challenge our junk DNA view.

This is further complicated by the fact that not all this small subset of probes is available in all analyzed datasets. For example, 232 probes were used for the MESA dataset but only 80 for the GTP dataset. Thus, RTE expression is quantified with a set of probes which is extremely likely to be highly affected by non-RTE transcripts and that is also different across the studied datasets. Differences in the subsets of probes could very well explain the large differences between datasets in multiple of the analyses performed by the authors, such as in Figure 2a, or 3a. It is nonetheless possible that the quantification of RTE expression performed by the authors is truly interpretable as RTE expression, but this must be validated with more data from RNA-seq. Above all, microarray data should not be the main type of data used in the type of analysis performed by the authors.

In this study, we did not compare MESA with GTP etc. We have analysed each dataset separately based on the available data for that dataset. Therefore, sacrificing one analysis because of the lack of information from the other does not make sense. We would do that if we were after comparing different datasets. Moreover, the datasets are not comparable because they were produced from different blood cell types.

Reviewer #2 (Public Review):

Summary:

Yi-Ting Tsai and colleagues conducted a systematic analysis of the correlation between the expression of retrotransposable elements (RTEs) and aging, using publicly available transcriptional and methylome microarray datasets of blood cells from large human cohorts, as well as single-cell transcriptomics. Although DNA hypomethylation was associated with chronological age across all RTE biotypes, the authors did not find a correlation between the levels of RTE expression and chronological age. However, expression levels of LINEs and LTRs positively correlated with DNA demethylation, and inflammatory and senescence gene signatures, indicative of "biological age". Gene set variation analysis showed that the inflammatory response is enriched in the samples expressing high levels of LINEs and LTRs. In summary, the study demonstrates that RTE expression correlates with "biological" rather than "chronological" aging.

Strengths:

The question the authors address is both relevant and important to the fields of aging and transposon biology.

We thank the reviewer for finding this study relevant and important.

Weaknesses:

The choice of methodology does not fully support the primary claims. Although microarrays can detect certain intergenic transposon sequences, the authors themselves acknowledge in the Discussion section that this method's resolution is limited. More critical considerations, however, should be addressed when interpreting the results. The coverage of transposon sequences by microarrays is not only very limited (232 unique probes) but also predetermined. This implies that any potential agerelated overexpression of RTEs located outside of the microarray-associated regions, or of polymorphic intact transposons, may go undetected. Therefore, the authors should be more careful while generalising their conclusions.

This is a bioinformatics study, and we have already admitted and discussed the limitations in the discussion section of this manuscript. All technologies have their own limitations, and this should not stop us from shedding light on scientific facts because of inadequate information. In the manuscript, we have discussed that all large and proper ageing studies were performed using microarray technology. Peters et al. (DOI: doi: 10.1038/ncomms9570) adopted all these microarray data in their transcriptional landscape of ageing manuscript. Our study essentially applies the Reichmann et al. method to the peripheral blood-related data from the Peters et al. manuscript. Since hypomethylation due to ageing is a well-established and broad epigenetic reprogramming, it is unlikely that only a fraction of RTEs is affected by this phenomenon. Therefore, the subsampling of RTEs should not affect the result so much. Indeed, this is supported in our study by the inverse correlation between DNA methylation and RTE expression for LINE and SINE classes despite having limited numbers of probes for LINE and SINE expressions.

Additionally, for some analyses, the authors pool signals from RTEs by class or family, despite the fact that these groups include subfamilies and members with very different properties and harmful potentials. For example, while sequences of older subfamilies might be passively expressed through readthrough transcription, intact members of younger groups could be autonomously reactivated and cause inflammation. The aggregation of signals by the largest group may obscure the potential reactivation of smaller subgroups. I recommend grouping by subfamily or, if not possible due to the low expression scores, by subgroup. For example, all HERV subfamilies are from the ERVL family.

We agree with the reviewer that different subfamilies of RTEs play different roles through their activation. However, we will lose our statistical power if we study RTE subfamilies with a few probes. Global epigenetic alteration and derepression of RTEs by ageing have been observed to be genome-wide. While our systematic analysis across RTE classes and families cannot capture alterations in subfamilies due to statistical power, it is still relevant to the research question we are addressing.

Next, Illumina arrays might not accurately represent the true abundance of TEs due to non-specific hybridization of genomic transposons. Standard RNA preparations always contain traces of abundant genomic SINEs unless DNA elimination is specifically thorough. The problem of such noise should be addressed.

We have checked the RNA isolation step from MESA, GTP, and GARP manuscripts. The total RNA was isolated using the Qiagen mini kit following the manufacturer’s recommendations. The authors of these manuscripts did not mention whether they eliminated genomics DNA, but we assumed they were aware of the DNA contamination and eliminated it based on the manufacturer’s recommendations. We have looked up the literature about non-specific hybridization of RTEs but could not find any evidence to support this observation. We would appreciate the reviewers providing more evidence about such RTE contaminations.

Lastly, scRNAseq was conducted using 10x Genomics technology. However, quantifying transposons in 10x sequencing datasets presents major challenges due to sparse signals.

Applying the scTE pipeline (https://www.nature.com/articles/s41467-021-21808-x), we have found that the statical power of quantifying RTE classes (LINE, SINE, and LTR) or RTE families (L1, L2, All, ERVK, etc.) are as good as each individual gene. However, our proposed method cannot analyse RTE subfamilies, and we did not do that.

Smart-seq single-cell technology is better suited to this particular purpose.

We agree with the reviewer that Smart-seq provides higher yield than 10x, but there is no Smart-seq data available for ageing study.

Anyway, it would be more convincing if the authors demonstrated TE expression across different clusters of immune cells using standard scRNAseq UMAP plots instead of boxplots.

Since the number of RTE reads per cell is low, showing the expression of RTEs per cell in UMAP may not be the best statistical approach to show the difference between the aged and young groups. This is why we chose to analyse with pseudobulk and displayed differential expression using boxplot rather than UMAP for each immune cell type.

I recommend validating the data by RNAseq, even on small cohorts. Given that the connection between RTE overexpression and inflammation has been previously established, the authors should consider better integrating their observations into the existing knowledge.

Until recently, there were no publicly-available, non-cancerous, large cohort of RNA-seq data for ageing studies. We tried to gain access to the two RNA-seq datasets suggested by reviewer 2: Marquez et al. 2020 (phs001934.v1.p1, controlled access) and Morandini et al. 2023 (GSE193141, public access).

Unfortunately, Marquez et al. 2020 data is not accessible because the authors only provide the data for projects related to cardiovascular diseases. However, we did analyse Morandini et al. 2023 data, and we can confirm that no association was observed between any class and family of RTEs with chronological ageing, which is the second strong piece of evidence supporting the statement in the manuscript. However, as expected, we found a positive correlation between RTE expression and IFNI signature score.

Author response:

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

eLife assessment

This study provides an important finding that the local abundance of metabolites impacts the biology of the tumor microenvironment by utilizing kidney tumors from patients and adjacent normal tissues. The evidence supporting the claims of the authors is convincing although certain caveats need to be taken into consideration as the authors acknowledged in the paper. The work will be of interest to the research community working on metabolism and on kidney cancer especially.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The present study addresses how the local abundance of metabolites impacts the biology of the tumor microenvironment. The authors enroll patients harboring kidney tumors and use freshly resected tumor material for metabolic studies. Specifically, the authors separate the adjacent normal kidney tissue from the tumor material and then harvest the interstitial fluid from the normal kidney (KIF) or the tumor (TIF) for quantitative metabolomics. The plasma samples from the patient are used for comparison. Additionally, the authors also compare metabolite levels in the plasma of patients with kidney versus lung cancer (or healthy donors) to address how specific tumor types might contribute to circulating levels of metabolites. Altogether, the authors find that the metabolite levels in the KIF and TIF, although vastly different than plasma, are largely overlapping. These findings indicate that tissue of origin appears to have a stronger role in determining the local metabolic environment of tumors than the genetics or biochemistry of the tumor itself.

Strengths:

The biggest strength of the current study is the use of human patient-derived samples. The cohort size (~50 patients) is relatively large, which adds to the rigor of the work. The work also relies on a small pool of metabolites that can be quantitatively measured using methods developed by the authors. Focusing on a smaller metabolic pool also likely increases the signal-to-noise ratio and enables the more rigorous determination of any underlying differences. The manuscript is well-written and highlights both the significance of the findings and also acknowledges many of the caveats. The recognition of the metabolic contributions of surrounding normal tissue as the primary driver of local nutrient abundance is a novel finding in the work, which can be leveraged in future studies.

We thank the Reviewer for their careful evaluation of the study and for their supportive comments.

Weaknesses:

The work has certain caveats, some of which have been already recognized by the authors. These include the use of steady-state metabolites and the possibility of cross-contamination of some TIF into the adjacent KIF. This study is also unable to distinguish the mechanisms driving the metabolic changes in KIF/TIF relative to circulating levels in plasma.

We agree with the Reviewer that these are important caveats to consider when interpreting the results of this study.

The relative similarity of KIF and TIF is quite surprising. However, this interpretation is presently based on a sampling of only ~100 polar metabolites and ~200 lipid molecules. It is, perhaps, possible that future technological developments that enable more comprehensive quantitative metabolic profiling might distinguish between KIF and TIF composition.

The Reviewer raises another important point that our interpretation of KIF vs TIF is limited to the ~300 metabolites we measured. We agree it would be worthwhile quantifying more metabolites where technically feasible to further characterize similarities and differences in nutrient availability between tumor and normal tissues.

In vitro, tissue culture is recognized to suffer from ‘non-physiological’ nutrient dependencies, which are impacted by the composition of culture media. Thus, in vivo studies remain our current gold-standard in mechanistic studies of tumor metabolism. It is presently unclear whether the findings of this work will be recapitulated in any of the kidney cancer in vivo models and thus be functionally testable.

We thank the Reviewer for calling attention to the limitations of cell culture media in studying tumor metabolism. While both in vitro and in vivo approaches have inherent limitations, formulating culture media based on metabolite concentrations measured here and in other studies provides a tool to study the influence of nutrient availability on kidney cell or kidney cancer cell phenotypes in vitro. We also agree with the Reviewer that determining whether the findings in our study are recapitulated in mouse models of kidney cancer, as this might enable investigation into the factors that modulate nutrient availability in this tissue context.

Reviewer #2 (Public Review):

The study employs quantitative metabolomic and lipidomic analyses to scrutinize tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from renal cell carcinoma (RCC) patients. The authors delve into the intricate world of renal cell carcinoma and its tumor microenvironment, shedding light on the factors that shape nutrient availability in both cancerous and adjacent normal tissues. The authors prove that non-cancer-driven tissue factors play a dominant role in shaping nutrient availability in RCC. This finding opens up new avenues for research, suggesting that the tumor microenvironment is profoundly influenced by factors beyond the presence of cancer cells. This study not only contributes valuable insights into RCC metabolism but also prompts a reevaluation of the factors governing nutrient availability in tumor microenvironments more broadly. Overall, it represents a significant step forward in our understanding of the intricate interplay between cancer and its surrounding milieu.

We thank the Reviewer for their evaluation of our work and for their supportive comments.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. Since the authors exclusively employed samples from RCC patients and did not include kidney interstitial fluid and plasma samples from healthy individuals, we cannot accurately assess the true significance and applicability of the results until the role of cancer cells in reshaping KIF is understood. In essence, some metabolite levels in the tumor interstitial fluid did not show an increase or decrease compared to the adjacent normal kidney interstitial fluid. However, the levels of these metabolites in both TIF and KIF might be higher or lower than those in kidney interstitial fluid from healthy individuals, and the roles of these metabolites should not be overlooked. Similar concerns extend to plasma levels, emphasizing the importance of metabolites that synchronously change in RCC TIF, KIF, and plasma-whether elevated or reduced.

We agree with the Reviewer that an important caveat in considering the study findings is that we do not have KIF values from healthy individuals. Since resection of normal kidney is not a common procedure, obtaining KIF samples from healthy patients was not possible to complement our analysis. We further agree that the metabolite levels we measured in KIF or plasma are plausibly impacted by the presence of RCC. We did compare the composition of polar metabolites in the plasma from RCC, lung cancer, and healthy patients, highlighting how cystine is affected by tumor presence and/or sample collection methodology. We also point out that factors such as diet will impact metabolites in both blood and tissues.

Reviewer #3 (Public Review):

In this study, the authors utilized mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma. This showed that nutrient availability in tumor interstitial fluid was similar to that of interstitial fluid in adjacent normal kidney tissue, but that nutrients found in both interstitial fluid compartments were different from those found in plasma. This suggests that the nutrients in kidney tissue differ from those found in blood and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. Those data could be useful as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

We thank the Reviewer for their time considering our paper and for their supportive comments.

In Figures 1D and 1E, there were about 30% of polar metabolites and 25% of lipids significantly different between TIF and KIF, which could be key factors for RCC tumors. This reviewer considers that the authors should make comments on this.

We agree with the Reviewer that the metabolites that significantly differ between TIF and KIF are of interest, particularly for those studying RCC tumor metabolism. We comment on some of the metabolites driving differences between TIF and KIF in our discussion of Figure 2, and in the revised manuscript we now include a new figure showing a heatmap that enables visualization of these metabolites (Figure 2-Supplement 1A-B).

Recommendations for the authors:

From the Reviewing Editor:

Figure 2 needs to plot heatmaps for both upregulated and downregulated metabolites in TIF.

We agree and now include heatmaps for significantly differing polar metabolites and lipids in TIF vs KIF as requested by Reviewer 3 (Figure 2-Supplement 1A-B). For completeness, we also include heatmaps for metabolites differing between healthy and RCC plasma (Figure 2-Supplement 2C) and for NSCLC and RCC plasma (Figure 2-Supplement 2D).

There is a need to show whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals.

Unfortunately, it has not been possible for us to collect KIF from healthy individuals. Since resection of normal kidney is not a common procedure, we have no way to obtain sufficient KIF samples from healthy patients for this measurement. We discuss this as a limitation of the study.

Reviewer #1 (Recommendations For The Authors):

a. The authors should provide additional details about the methodology to separate the KIF and TIF. Contaminating metabolites from surrounding tissue or the peritoneal fluids could impact interpretation and it would be helpful to understand how these challenges were addressed during tissue collection for this study. Additionally, was the collected tissue minced or otherwise dissociated? If so, could these procedures cause tissue lysis and contaminate the KIF/TIF with intracellular components?

We thank the Reviewer for the suggestions to include more information about the sampling methodology. Care was taken to minimize cell lysis incurred by the processing methodology as tissues were not minced, smashed, nor dissociated, however there is still a possibility of some level of tissue lysis that is pre-existing or occurs during the isolation procedure. We note this caveat in the text (lines 218-220) and have updated the Methods with more details of the sampling and processing of the samples.

b. Although the authors focus on metabolites that are elevated in TIF (relative to KIF and plasma), it would be equally relevant to consider the converse. Metabolites that are reduced in TIF, either due to underproduction or overconsumption, could render the tumors auxotrophic for some critical dependencies and identify some novel metabolic vulnerabilities. In this regard, Figure 2 could have a heatmap of the top metabolites that are elevated and depleted specifically in the TIF.

We agree with the Reviewer it is useful to include heatmaps to better display the metabolites that significantly differ between TIF and KIF and now include these in Figure 2-Supplement 1A-B.

c. The future utilization of this knowledge would depend on our ability to model these differences. Would interstitial tissue from a normal mouse kidney or tumor-bearing mouse kidney recapitulate the same differences relative to mouse plasma?

We agree with the Reviewer that it would be worth determining whether the findings in our study are recapitulated in mouse models of kidney cancer, which would support future investigation into the factors that modulate nutrient availability. This is an interesting question, but we did not have access to endogenously arising models of RCC, which have been a limitation for the field, and comparison of normal mouse kidney metabolite data to human metabolite data is problematic for obvious reasons. Thus, we had no choice but to discuss this as a limitation of the study.

Reviewer #2 (Recommendations For The Authors):

In this study, Abbott et al. investigated the metabolic profile of renal cell carcinoma (RCC) by analyzing the tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from patients. The results indicate that nutrient composition in TIF closely resembles that of KIF, suggesting that tissue-specific factors, rather than tumor-driven alterations, have a more significant impact on nutrient levels. These findings are interesting. The study is overall well-constructed, including appropriate analysis, and the manuscript is written clearly and supported by high-quality figures. However, some issues are raised which if addressed, would strengthen the paper.

We thank the Reviewer for their suggestions to improve the paper.

The authors found a difference in the number of metabolites when comparing TIF or KIF lipid composition with plasma. The discoveries are intriguing; however, I am keen to understand whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals. I am particularly interested in identifying which metabolites could serve as potential diagnostic markers, intervention targets, or potentially reshape the tumor microenvironment. Because, even though some metabolite levels show no difference between TIF and KIF in RCC patients, I wonder if these metabolite levels in KIF increase or decrease compared to the interstitial fluid in healthy individuals. I am intrigued by the metabolites that simultaneously increase or decrease in both TIF and KIF compared to the kidney interstitial fluid in healthy individuals.

We agree with the Reviewer that it would be interesting to measure kidney interstitial fluid from healthy patients to be able to compare metabolites changing due to the presence of RCC tumor. As we discuss in response to the public review, this was not possible as we could not obtain material from healthy individuals for analysis. Nevertheless we agree it warrants future study if material were available.

The analysis conducted using plasma from healthy donors, as applauded by the author, is noteworthy. The author seems to have found that cystine levels do not differ between RCC patient plasma and tissue interstitial fluid. However, considering that in patient plasma, the cystine concentration is approximately two-fold higher than in plasma from healthy individuals, likely, cystine levels in patient tissue fluid have also increased nearly two-fold compared to levels in the interstitial fluid of normal kidney tissues. This finding aligns with the discovery of elevated GSH levels in cancer cells.

We agree with the Reviewer that a higher cystine concentration in RCC patient plasma and interstitial fluid is interesting, and also considered this in relationship to past findings including reports of elevated GSH levels in RCC. However, we think this observation is driven at least in part by the fasting status of the patients pre-surgery. This does not rule out some part being related to the presence of the tumor, as this would be consistent with elevated GSH levels as noted by the Reviewer. Future studies will be needed to further delineate the factors that impact elevated cystine levels in both interstitial fluid and plasma.

Some minor typos, such as "HIF1􀀀-driven" should be corrected.

We thank the Reviewer for pointing out this typo and we have corrected it in the revised manuscript.

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.

Reference list

Bengtsson Gonzales C, Hunt S, Munoz-Manchado AB, McBain CJ, Hjerling-Leffler J (2020) Intrinsic electrophysiological properties predict variability in morphology and connectivity among striatal Parvalbumin-expressing Pthlh-cells. Scientific Reports, 10, 15680. https://doi.org/10.1038/s41598-020-72588-1

Bertero A, Zurita H, Normandin M, Apicella AJ (2020) Auditory long-range parvalbumin cortico-striatal neurons. Frontiers in Neural Circuits, 14, 45. http://doi.org/ 10.3389/fncir.2020.00045

Chamberland S, Nebet ER, Valero M, Hanani M, Egger R, Larsen SB, Eyring KW, Buzsáki G, Tsien RW (2023) Brief synaptic inhibition persistently interrupts firing of fast-spiking interneurons. Neuron, 111, 1264–1281. http://doi.org/10.1016/j.neuron.2023.01.017

Chehrazi P, Lee KKY, Lavertu-Jolin M, Abbasnejad Z, Carreño-Muñoz MI, Chattopadhyaya B, Di Cristo G (2023). The p75 Neurotrophin Receptor in Preadolescent Prefrontal Parvalbumin Interneurons Promotes Cognitive Flexibility in Adult Mice. Biol Psychiatry, 94, 310-321. doi: 10.1016/j.biopsych.2023.04.019.

Elabbady L, Seshamani S, Mu S, Mahalingam G, Schneider-Mizell C, Bodor AL, Bae JA, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Dorkenwald S, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K…Collman F (2024) Perisomatic features enable efficient and dataset wide cell-type classifications across large-scale electron microscopy volumes. bioRxiv, https://doi.org/10.1101/2022.07.20.499976

Goldberg EM, Clark BD, Zagha E, Nahmani M, Erisir A, Rudy B (2008) K+ Channels at the axon initial segment dampen near-threshold excitability of neocortical fast-spiking GABAergic interneurons. Neuron, 58, 387–400. https://doi.org/10.1016/j.neuron.2008.03.003

Golomb D, Donner K, Shacham L, Shlosberg D, Amitai Y, Hansel D. (2007). Mechanisms of firing patterns in fast-spiking cortical interneurons. PLoS Computational Biology, 38, e156. http://doi.org/10.1371/journal.pcbi.0030156

Hu H, Martina M, Jonas P (2010). Dendritic mechanisms underlying rapid synaptic activation of fast-spiking hippocampal interneurons. Science, 327, 52–58. http://doi.org/10.1126/science.1177876

Hwang YS, Maclachlan C, Blanc J, Dubois A, Petersen CH, Knott G, Lee SH (2021). 3D ultrastructure of synaptic inputs to distinct gabaergic neurons in the mouse primary visual cortex. Cerebral Cortex, 31, 2610–2624. http://doi.org/10.1093/cercor/bhaa378

Kavalali E (2015) The mechanisms and functions of spontaneous neurotransmitter release. Nature Reviews Neuroscience, 16, 5–16. https://doi.org/10.1038/nrn3875

Kourrich S, Thomas MJ (2009) Similar neurons, opposite adaptations: psychostimulant experience differentially alters firing properties in accumbens core versus shell. Journal of Neuroscience, 29, 12275-12283. http://doi.org:10.1523/JNEUROSCI.3028-09.2009

Kourrich S, Hayashi T, Chuang JY, Tsai SY, Su TP, Bonci A (2013) Dynamic interaction between sigma-1 receptor and Kv1.2 shapes neuronal and behavioral responses to cocaine. Cell, 152, 236–247. http://doi.org/10.1016/j.cell.2012.12.004

Norenberg A, Hu H, Vida I, Bartos M, Jonas P (2010) Distinct nonuniform cable properties optimize rapid and efficient activation of fast-spiking GABAergic interneurons. Proceedings of the National Academy of Sciences, 107, 894–9. http://doi.org/10.1073/pnas.0910716107

Stevens SR, Longley CM, Ogawa Y, Teliska LH, Arumanayagam AS, Nair S, Oses-Prieto JA, Burlingame AL, Cykowski MD, Xue M, Rasband MN (2021) Ankyrin-R regulates fast-spiking interneuron excitability through perineuronal nets and Kv3.1b K+ channels. Elife, 10, e66491. http://doi.org/10.7554/eLife.66491

Russo G, Nieus TR, Maggi S, Taverna S (2013) Dynamics of action potential firing in electrically connected striatal fast-spiking interneurons. Frontiers in Cellular Neuroscience, 7, 209. https://doi.org/10.3389/fncel.2013.00209

Ünal CT, Ünal B, Bolton MM (2020) Low-threshold spiking interneurons perform feedback inhibition in the lateral amygdala. Brain Structure and Function, 225, 909–923. http://doi.org/10.1007/s00429-020-02051-4

Wang H, Kunkel DD, Schwartzkroin PA, Tempel BL (1994) Localization of Kv1.1 and Kv1.2, two K channel proteins, to synaptic terminals, somata, and dendrites in the mouse brain. The Journal of Neuroscience, 14, 4588-4599. https://doi.org/10.1523/JNEUROSCI.14-08-04588.1994

Zhang YZ, Sapantzi S, Lin A, Doelfel SR, Connors BW, Theyel BB (2023) Activity-dependent ectopic action potentials in regular-spiking neurons of the neocortex. Frontiers in Cellular Neuroscience, 17. https://doi.org/10.3389/fncel.2023.1267687

Zurita H, Feyen PLC, Apicella AJ (2018) Layer 5 callosal parvalbumin-expressing neurons: a distinct functional group of GABAergic neurons. Frontiers in Cellular Neuroscience, 12, 53. https://doi.org/10.3389/fncel.2018.00053

Author response:

Reviewer #1

The first is that data on the general health of mice with single and double knockouts is not shown, nor is there any data on effects in any other tissues. This gives the impression that the only phenotype is in the male reproductive system, which would be misleading if there were phenotypes in other tissues that are not reported.

We thank the reviewer for helpful and constructive suggestions that we plan to implement in the revision. We agree with this point and we will add a statement that the effect on the urogenital system was not the only observed phenotype, although it was the most striking histological feature that we found. We did notice some other physiological differences that we are examining in detail and determining their mechanisms, for future publications.

Furthermore, data for the genitourinary system in single knockouts are very sparse; data are described for fertility in Figure 1H, ploidy, and cell number in Figures 2B and C, plasma testosterone and luteinizing hormone levels in Figures 5C and 5D, and morphology of testis and prostate tissue for single Cdk8 knockout in Supplementary Figure 1C (although in this case the images do not appear very comparable between control and CDK8 KO, thus perhaps wider fields should be shown), but, for example, there is no analysis of different meiotic stages or of gene expression in single knockouts. It is worth mentioning that single knockouts seem to show a corresponding upregulation of the level of the paralogue kinase, indicating that any lack of phenotypes might be due to feedback compensation, which would be an interesting finding if confirmed; this has not been mentioned.

We agree that a description of the single KO could be beneficial, but we expect no big differences with the WT or Cre-Ert. We found neither histological differences nor changes in cell counts or ratios of cell types. Our ethical committee also has concerns about sacrificing mice without major phenotypic changes, without a well formulated hypothesis about the observed effects. We plan to add histological pictures to the next version of the article.

We thank the reviewer for raising an important point about the paralog upregulation. Indeed, our data on primary cells (supplementary 1B) suggests the upregulation of CDK19 in CDK8KO and vice versa. We will point this out in disc We plan to examine the data for the testis as soon as more tissues are available.

The second major weakness is that the correlation between double knockout and reduced expression of genes involved in steroid hormone biosynthesis is portrayed as a causal mechanism for the phenotypes observed. While this is a possibility, there are no experiments performed to provide evidence that this is the case. Furthermore, there is no evidence showing that CDK8 and/or CDK19 are directly responsible for the transcription of the genes concerned.

We agree with the reviewer that the effects on CDK8/CDK19/CCNC could lead to the observed transcriptional changes in multiple indirect steps. There are, however, major technical challenges in examining the binding of transcription factors in the tissue, especially in Leydig cells which are a relatively minor population. We will clarify it in the revision, and strengthen this point in the discussion.

Finally, the authors propose that the phenotypes are independent of the kinase activity of CDK8 or CDK19 because treatment of mice for a month with an inhibitor does not recapitulate the effects of the knockout, and nor does expression of two steroidogenic genes change in cultured Leydig cells upon treatment with an inhibitor. However, there are no controls for effective target inhibition shown.

We thank the reviewer for raising this concern, which we will address in the revision. This study used the same CDK8/19 inhibitor (SNX631-6) as in the recently published study on prostate cancer (doi: 10.1172/JCI176709). That study describes the inhibitor, its target engagement in cell-free and cell-based assays, its anticancer potency, and its transcriptomic effects in vivo, the same dosage strength as in the present study, which phenocopy the effects of CDK8/19 knockdown. Additional data will be included in the revision.

Reviewer #2

The claim of reproductive defects in the induced double knockout of CDK8/19 resulted from the loss of CCNC via a kinase-independent mechanism is interesting but was not supported by the data presented. While the construction and analysis of the systemic induced knockout model of Cdk8 in Cdk19KO mice is not trivial, the analysis and data are weakened by the systemic effect of Cdk8 loss, making it difficult to separate the systemic effect from the local testis effect.

We agree with the reviewer that the effects on the testis could be due to the systemic loss of CDK8 rather than specifically in the testis, and we will clarify it in the revision. We will also clarify that although our results are suggestive that the effects of CDK8/19 knockout are kinase-independent, and that the loss of Cyclin C is a likely explanation for the kinase independence but we do not claim that it is the mechanism.

The analysis of male sterile phenotype is also inadequate with poor image quality, especially testis HE sections. The male reproductive tract picture is also small and difficult to evaluate.

Unfortunately, during the submission process through Biorxiv the quality of the image worsened. We uploaded the high resolution pictures for the journal but probably they were not presented for the reviewer. We will re-send the high resolution images.

The mice crossing scheme is unusual as you have three mice to cross to produce genotypes, while we could understand that it is possible to produce pups of desired genotypes with different mating schemes, such a vague crossing scheme is not desirable and of poor genetics practice.

We thank the reviewer for this suggestion. Indeed, our scheme is not a representation of the actual breeding scheme but just a brief explanation of lineages used for the acquisition of the triple transgenic mice. We will include the full crossing scheme into the revision.

Also using TAM-treated wild type as control is ok, but a better control will be TAM-treated ERT2-cre; CDK8f/f or TAM-treated ERT2 Cre CDK19/19 KO, so as to minimize the impact from the well-recognized effect of TAM.

We used TAM-treated ERT2-cre for most of the experiments, and did not observe any major histological or physiological differences with the WT+TAM. We will make sure to present them in the revision.

While the authors proposed that the inducible loss of CDK8 in the CDK19 knockout background is responsible for spermatogenic defects, it was not clear in which cells CDK8/19 genes are interested and which cell types might have a major role in spermatogenesis. The authors also put forward the evidence that reduction/loss of Testosterone might be the main cause of spermatogenic defects, which is consistent with the expression change in genes involved in steroigenesis pathway in Leydig cells of inducible double knockout. However it is not clear how the loss of Testosterone contributed to the loss of CcnC protein.

We agree with the reviewer that the spermatogenic defects could be caused by the effects on gene expression in tissues other than Leydig cells. Nevertheless, this is our primary hypothesis since these changes resemble the effects of chemical castration in rats (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408499/), and in SCARKO mice (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968405/).

Our hypothesis is actually the reversed scenario proposed by the reviewer. We think that the loss of steroidogenic gene expression is caused by the loss of CDK8/19 and Cyclin C in Leydig cells. This, in turn, leads to a drop of testosterone levels. We will expand this explanation for clarity.

The authors should clarify or present the data on where CDK8 and CDK19 as well as CcnC are expressed so as to help the readers understand which tissues both CDK might be functioning in and cause the loss of CcnC. It should be easier to test the hypothesis of CDK8/19 stabilizing CcnC protein using double knock-out primary cells, instead of the whole testis.

The stabilizing effect of Cdk8/19 on CcnC has been previously discovered and reported in cell culture (doi: 10.1093/nar/gkad538.), and here we have confirmed it at the level of whole tissue. Due to a limited sensitivity of single cell sequencing (only ~5,000 transcripts are sequenced from total of average 500,000 transcripts per cell, so the low expressed transcripts are not sequenced in all cells) it is challenging to firmly establish CDK8/19 positive and -negative tissues from single cell data because both transcripts are minor. This image will be included in the next version. We plan to resolve this matter using two approaches. First, we will try immunohistochemistry. If this method is not sufficiently sensitive we will analyze published single cell sequencing data from mouse databases and re-analyze our data. So far the former approach was challenging for us due to the absence of anti-mouse antibodies which are specific for CDK8 and CDK19 and work on tissue sections. We and others could not produce a tissue-specific staining, with the currently available commercially available antibodies. The only published specific antibody is currently not available.

Since CDK8KO and CDK19KO have significantly reduced fertility compared to the wild type, it might be important to measure the sperm quantity and motility among CDK8 KO, CDK19KO, and induced DKO to evaluate spermatogenesis based on their sperm production.

We agree that this is an interesting question. We did not do spermograms for single KOs but we don’t think that a decreased sperm count would explain CDK8KO infertility as the vasectomized males are able to produce copulative plugs in females whereas CDK8KO males do not, suggesting the absence of mating behavior as a reason for low fertility in the latter genotype.

Some data for the inducible knockout efficiency of Cdk8 were presented in Supplemental Figure 1, but there is no legend for the supplemental figures, it was not clear which band represented the deletion band, and which tissues were examined. Tail or testis?

We apologize for the accidental loss of supplementary figure legends, which will be presented in the next version. The efficiency of CDK8 KO in different tissues was previously examined by us in https://www.ncbi.nlm.nih.gov/gene/264064. The western blot in the MS represents deletion data for the testis.

It seems that two months after the injection of Tam, all the Cdk8 were completely deleted, indicating extremely efficient deletion of Tam induction by two months post administration. Were the complete deletion of Cdk8 happening even earlier?

The complete deletion of CDK8 occurs within a week or even as early as 2-3 days in culture, and at least after at two weeks in vivo. We chose the two mo. period to prevent the effect of tamoxifen on gene expression. We examined other time points (Figure 6) and registered the beginning of effects at 2 weeks and maximum effect by one mo.

The authors found that Sertoli cells re-entered the cell cycle in the inducible double knockout but stopped short of careful characterization other than increased expression of cell cycle genes.

We agree with the reviewer, and we will add Ki67 (or equivalent) staining along with Sertoli cell markers.

Dko should be appropriately named iDKO (induced dKO).

We will make the corresponding change.

We performed necropsy ? not the right wording here. Colchicine-lke apoptotic bodies ? what does this mean? Not clear.

We will amend the next version to address these minor points, and we thank the reviewer for careful reading of the manuscript.

Images throughout the manuscript suffer from poor resolution and are often blurry and hard to evaluate.

As mentioned above, we had a problem with image quality during the submission through Biorxiv and we will provide high resolution images in the next version.

To pinpoint the meiotic stage defect of iDKO, it is better to use the meiotic chromosome spread approach.

Unfortunately, meiotic spreads would not be feasible or informative, due to a low number of surviving cells in iDKO and the fact that there were evidently no cells in stages after SYCP3+.

Author response:

eLife assessment

This is an important study describing a neuromuscular junction co-culture system using human cells that the authors use to study the synaptic consequences of ALS mutations. The data supporting the system are solid and show the value of using myotubes and motor neurons from the same donor. The study will be of interest to researchers who model neuromuscular junction disorders, however, the authors could more comprehensively compare and contrast their system with previous literature describing other similar models. There are also technical weaknesses that limit the interpretation of specific findings.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors propose an improved neuro-muscle co-culture system to study ALS-related functional differences in human pluripotent stem cell lines.

Strengths:

A simple co-culture system with functional readout.

We appreciate the recognition that this is a simplified co-culture system with a straight-forward functional evaluation.

Weaknesses:

There are concerns about the lack of novelty, rigor, and clarity in the approach. The strength of the study is undermined by its reliance on transcription factors used more than a decade ago, low myocyte activity, and inadequate validation methods, such as the lack of single-cell transcriptome analysis and detailed neuromuscular synapse characterization. The evidence presented requires substantial validation through rigorous experimental approaches and resolution of the identified concerns for the study's findings to be considered significant and reliable.

The muscle differentiation protocol used in our work is an adaptation of the Albini S, et al. Cell Rep. 2013. This protocol was selected due to its efficiency to differentiate skeletal muscles from pluripotent stem cells (PSCs). Modifications from the original publications were made in the plasmids (MYOD and BAF60C) used, such as the inclusion of selection genes, puromycin and blasticidin, to improve efficiency. Moreover, a criticism of the previously used overexpression system, especially overexpression of MYOD, is that it introduces artificial expression of this gene throughout muscle differentiation, when it is only supposed to be expressed early in myogenesis. Thus, the constructs used in our work are dox inducible, which enables us to control the expression of MYOD and restrict it to the first 48 hours. This protocol resulted in a highly efficient skeletal muscle differentiation, as noted in our manuscript. “The PSC-derived skeletal muscles were characterized by the presence of Desmin (DES) and Myosin Heavy Chain (MHC), and as early as day 8 of differentiation nearly 100% of the cells co-expressed these markers.” We agree with the reviewer that the myocyte activity identified in our work is lower compared to Albini et al. (2013), mostly explained by the modification we made to the method, from a 3D to a 2D culture. In Albini et al. (2013) the electrophysiological properties were assayed in skeletal myospheres (3D), which are known to improve contractility measurements. Conversely, in 2D cultures when the contractility intensifies the cells detach from the plate. Thus, a tight regulation of cell concentration for optimal maturation and formation of contractile skeletal muscle culture without premature detachment of the cells is required. We believe that single-cell or single-nuclei transcriptome analysis from the co-culture setting of two well-defined cell types might yield little value for method characterization, however, as part of a follow up study we are performing morphological NMJ characterization and applying single-nuclei transcriptome analysis in the fALS disease context to identify specific molecular mechanisms that result in synaptic dysfunction.

Reviewer #2 (Public Review):

The manuscript by Chen et al from the group of Helen Miranda aims to describe an improved neuromuscular junction (NMJ) model to study synaptic dysfunction in several cases of familial ALS. Overall, the system described in the paper appears as a valid platform to study disease phenotypes with exciting results showing specific effects of GDNF on non-SOD1 ALS patient lines. The strength of the paper lies in the use of myotubes, and motor neurons derived from the same donor. However, the current study: (1) lacks a clear comparison of the current system with numerous previously described systems; (2) is limited by the number of repeat experiments in the study and (3) has no description of the synaptic phenotype observed in the study. These major points are discussed in more detail below.

We appreciate the recognition that “the system described in the paper appears as a valid platform to study disease phenotypes with exciting results showing specific effects of GDNF on non-SOD1 ALS patient lines” and the careful evaluation of our work. We plan to address the points raised by this reviewer in the revision.

Major points:

(1) In the introduction the authors state (p. 4): "Finally, recent human NMJ models have been established from PSCs by differentiating these cells into both skeletal muscles and motor neurons in 2D and 3D formats. These previous systems present a remarkable advancement to the studies of human NMJs, however, they require long NMJ formation and maturation time (40 to 60 days), which, restricts their sensitivity and scalability [42]"

In fact, a number of studies have described various in-vitro NMJ systems, with the same timeframes for NMJ formation. For example, in studies by Osaki et al, 2018, Sci Adv; Bellmann et al, 2019, Biomat; Demestre et al, 2015, Stem Cell Res; Badu-Mensah et al, 2022, Biomat (this is just an exemplar selection of the papers); NMJ formation was observed as early as 14 d in culture, in line with or at least slightly longer than reported by Chen et al. With the exception of the study by Osaki et al, all co-culture systems cited above are 2D-based. The authors need to expand on this further or provide a quantitative assessment of why their system is better compared to previously published models.

Indeed, there are previous publications that have described neuromuscular junctions (NMJs) in cocultures of iPSC-derived skeletal muscles and motor neurons. Some of the publications mentioned above did show NMJ formation within ~20ish days, albeit with several caveats such as culture heterogeneity, i.e. 50% motor neuron differentiation efficiency. We agree with the reviewer that this needs to be expanded and clarified, and we will address this concern in the revision.

(2) Further, when comparing their results with other work it is hard to claim how the current system is (p. 5) "more reproducible, and offers a 6-fold increase in scalability compared to previous models [40-43]".

The authors need to expand on this further.

This is an important aspect of this work, and we believe that our protocol offers a higher reproducibility due to, at least partially, the homogeneity of the starting cultures of iPSC-derived skeletal muscles and iPSC-derived motor neurons, and that the direct 2D co-culture approach is more suitable for miniaturization compared to 3D cultures or microfluidic chamber devices. Thus, we will expand on this idea in the revision.

(3) Although mentioned, there were no examples of the modularity of the system, which of course would strengthen the paper and help to uncover ALS mechanisms of synaptic formation, for example by combining WT myotubes and fALS motor neurons (see point 4 below). The authors should show how they would adapt to 96 well plate format to showcase the scalability of the system. Based on their data on the efficacy of synaptic formation (60 per 0.7 cm2 area), is further miniaturization allowed?

We appreciate the points raised by the reviewer. The “mix-and-match” approach to co-culture wild-type and affected iPSC-derived skeletal muscles with iPSC-derived motor neurons is a main focus of our lab and an advantage to protocols like ours, where cells are differentiated independently and later co-cultured together; however, a comprehensive characterization of various mix-match combinations is beyond the scope of this Tools and Resources article. Since the initial submission of this manuscript, we have extensively optimized the scalability of the co-cultures from the initial 0.7 cm2 to 0.32 cm2 (96-well plates). Further miniaturization is also being optimized to 0.136 cm2 (384-well plates). This point will be clarified in the revision.

(4) A lot of a-bungarotoxin staining corresponds to AChR clusters that do not seem to be associated with muscle and do not form normal rings of clustering (pretzel-like) associated with the NMJ in vivo. This is seen clearly in Figure 3B and Figure 5B. Figures 3B and 5B only show low-magnification images which makes it difficult to assess the specificity of localization of the pre-/post-synaptic markers. The authors should clearly show the morphologies of the NMJs formed in WT and fALS lines at high magnification. In addition, the authors should show co-localization images for a-bungarotoxin and myosin-heavy chains to confirm the localization of the bungarotoxin signal on the myotubes.

In addition to that, the authors report that the number of functional synapses formed on a plate varies from 30 (fASL) to 60 (Ctrl) per 10,000 neurons spread over the 0.7 cm2 area (0.6%). How do the authors explain an extensive loss of a-bungarotoxin signal in Figure 5B the majority of which likely corresponds to AChR clusters that are formed outside of neuronal connections? Such clustering can be usually observed in immature co-cultures and in vivo prior to the innervation of myotubes. One explanation could be that myotubes derived from fALS PSC are less capable of synaptic formation. Noteworthy, a study of PSCderived myotubes and motor neurons from PSC lines with various SOD1 mutations has already been published, but not cited by Chen et al (Badu-Mensah et al). Given the importance of those confounding factors, the authors should test cell-intrinsic (motor neuron-related) vs non-cell-intrinsic mechanisms by co-culturing healthy myotubes with fALS-derived motor neurons followed by NMJ quantification.

The iPSC-derived skeletal muscle cultures were plated as a monolayer and even though the abungarotoxin staining does not show the pretzel-like shape NMJs, similar to other in vitro NMJ protocols (Badu-Mensah et al, Biomat 2023; Pereira et al., Nat Commun 2021; Uzel et al., Sci Adv 2016), abungarotoxin does show association with the muscles. For quantification purposes we omitted the MHC staining to decrease background, however we will include it in the revision in response to the reviewer’s concern.

We agree with the reviewer that the suggested approaches would yield insight into disease mechanism but are beyond the scope of this method development study. In fact, we are very excited about our follow up study pursuing a more in-depth analysis of cell-autonomous vs non-cell autonomous pathogenesis to understand the NMJ dysfunction in fALS. We apologize that the “Badu-Mensah et al” work was not included, this was our oversight and will be added in the revision.

(5) The authors present the advantage of optogenetic stimulation, but they only show the proof-ofprinciple and never really apply it to their studies. Specifically, with regard to Figure 6, are motor units derived from fALS PSCs incapable of being ontogenetically activated to the same extent as control motor units? Does the dysfunction stem from fALS motor neurons or fALS myotubes?

We agree that these are important questions to be addressed and are actively pursuing these experiments as part of the natural follow up investigation from the present Tools and Resources article.

(6) Figures 6 B and C appear to be identical except for the addition of the GDNF effect on the fALS lines. This should all be put in one figure. The authors should also show whether GDNF-induced functional recovery is associated with recovery in the number of motor units or with merely synaptic function by quantifying the NMJ number in the presence of GDNF.

We will combine Figures 6B and 6C in the revision. Our follow up study also includes the interrogation of the mechanism through which GDNF rescues fALS NMJ dysfunction.

(7) Figure 5 and Figure 6. The authors only use one line per fALS mutation and their corresponding isogenic controls. They state that the n=6 for these experiments represents the technical replication of the experiment. These experiments should be performed at least n=3 times starting from neuronal differentiation, and not by seeding replicate wells representing a true replication of each experiment. This would significantly strengthen their argument that their method is robust and the results are easily reproducible.

We will clarify that the technical replicates originated from independent differentiations in the revision.

(8) In the discussion the authors may want to mention that the lack of function of GDNF on the SOD1 lines may relate to the fact that SOD1 mutations do not lead to TDP43 pathology. Although speculative this suggests that in cases with TDP43 mutations (their data) or sporadic disease GDNF may be effective.

We appreciate this suggestion and will highlight this as possible inclusion criteria for GDNF treatment in the discussion of our revised version of the manuscript.

(9) Although beyond the scope of this paper, it would of course be interesting to see if sporadic forms of ALS had this same phenotype.

We agree with the reviewer and we hope to include iPSC derived NMJs from sporadic ALS patients in a future study.

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.

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.

Author response:

Overall recommendations.

A brief summary of the main reviewers' recommendations that should be prioritized is listed below. Detailed recommendations as suggested by each individual reviewer are also included.

-Better justification of the choice of the substitutions for the mutations should be added. In addition, authors should strongly consider adding more mutations to enable mechanistic tests of the proposed model for lipid conduction.

We will characterize more mutations to the key residues at the TM4-TM6 interface. In addition to the TM4 lysine mutations shown in the original manuscript, we will include mutations to alanine and glutamate, and justify our choice of the substitutions in the revised manuscript. Furthermore, we will also test if introducing lysine mutations in TM6 will convert the ion channels into lipid scramblases. These additional experiments will greatly strengthen our conclusion.

-Blockers to validate the concern that the recorded currents indeed arise from TMEM16A or OSCA/TMEM63 channels should be tested. Do the pore blockers also block scramblase activity in the gating mutants?

TMEM16A and OSCA1.2 are readily expressed on cell surface. OSCA1.2 also has large conductance. This is the reason why we can record huge current even with inside-out patches. We will include TMEM16A inhibitor Ani9 and a non-specific inhibitor of OSCA channels to further validate. We have reported that Ani9 can inhibit a TMEM16A-derived lipid scramblase (L543K in TM4) in our previo3us publication (PMID: 31015464). We will test if Ani9 can also inhibit other TMEM16A scramblases reported in this study. We will also examine if Gd3+ is capable of blocking lipid scrambling of the OSCA1.2 gating mutations.

-Include details of missing experimental conditions for scramblase activity.

We will conduct a thorough revision to include detailed experimental conditions for scramblase activity measurement.

-Additional mutants above and below the putative lysine gate as suggested by reviewer 3 to better assess the model.

As we explained in Response #1, we will extend our mutations around the putative activation gate.

-Concern about whether osmolarity changes are in fact activating OSC and TMEM63. As suggested by reviewers 1 and 3. This could be addressed by assessing scramblase activity and currents at different osmolarity levels.

We will test the engineered OSCA1.2 scramblases in response to solutions with different osmolarity.

Reviewer #1 (Public Review):

Summary:

TMEM16, OSCA/TMEM63, and TMC belong to a large superfamily of ion channels where TMEM16 members are calcium-activated lipid scramblases and chloride channels, whereas OSCA/TMEM63 and TMCs are mechanically activated ion channels. In the TMEM16 family, TMEM16F is a well-characterized calcium-activated lipid scramblase that plays an important role in processes like blood coagulation, cell death signaling, and phagocytosis. In a previous study, the group demonstrated that lysine mutation in TM4 of TMEM16A can enable the calcium-activated chloride channel to permeate phospholipids too. Based on this they hypothesize that the energy barrier for lipid scramblase in these ion channels is low, and that modification in the hydrophobic gate region by introducing a charged side chain between the TM4/6 interface in TMEM16 and OSCA/TMEM63 family can allow lipid scramblase. In this manuscript, using scramblase activity via Annexin V binding to phosphatidylserine, and electrophysiology, the authors demonstrate that lysine mutation in TM4 of TMEM16F and TMEM16A can cause constitutive lipid scramblase activity. The authors then go on to show that analogous mutations in OSCA1.2 and TMEM63A can lead to scramblase activity.

Strengths:

Overall, the authors introduce an interesting concept that this large superfamily can permeate ions and lipids.

Weaknesses:

The electrophysiology data does not entirely support their claims.

We appreciate your positive comments. We will conduct more experiments including more electrophysiology characterizations as suggested.

Reviewer #2 (Public Review):

This concise and focused study by Lowry and colleagues identifies a motif in the pores of three families of channel/scramblase proteins that regulate exclusive ion permeation and lipid transport. These three ion channel families, which include the TMEM16s, the plant-expressed and stress-gated cation channel OSCA, and the mammalian homolog and mechanosensitive cation channel, TMEM63 share low sequence similarity between them and have seemingly differing functions, as anion (TMEM16s), or stress-activated cation channels (OSCA/TMEM63). The study finds that in all three families, mutating a single hydrophobic residue in the ion permeation pathway of the channels confers lipid transport through the pores of the channels, indicating that TMEM16 and the related OSCA and TMEM63 channels have a conserved potential for both ion and lipid permeation. The authors interpret the findings as revealing that these channel/scramblase proteins have a relatively low "energetic barrier for scramblase" activity. The experiments themselves seem to be done with a high level of rigor and the paper is well written. A weakness is the limited scope of the experiments which, if fixed, could open up a new line of inquiry.

We appreciate the positive comments from the reviewer. We will conduct more experiments listed in our responses to the Overall Recommendations to improve the scope and quality of our study.

Reviewer #3 (Public Review):

This study was focused on the conserved mechanisms across the Transmembrane Channel/Scramblase superfamily, which includes members of the TMEM16, TMEM63/OSCA, and TMC families. The authors show that the introduction of lysine residues at the TM4-TM6 interface can disrupt gating and confer scramblase activity to non-scramblase proteins. Specifically, they show this to be true for conserved TM4 residues across TMEM16F, TMEM16A, OSCA1.2, and TMEM63A proteins. This breadth of data is a major strength of the paper and provides strong evidence for an underlying linked mechanism for ion conduction and phospholipid transport. Overall, the confocal imaging experiments, patch clamping experiments, and data analysis are performed well.

However, there are several concerns regarding the scope of experiments supporting some claims in the paper. Although the authors propose that the TM4/TM6 interface is critical to ion conduction and phospholipid scramblase activity, in each case, there is very narrow evidence of support consisting of 1-3 lysine substitutions at specific residues on TM4. Given that the authors postulate that the introduction of a positive charge via the lysine side chain is essential to the constitutive activity of these proteins, additional mutation controls for side chain size (e.g. glutamine/methionine) or negative charge (e.g. glutamic acid), or a different positive charge (i.e. arginine) would have strengthened their argument. To more comprehensively understand the TM4/TM6 interface, mutations at locations one turn above and one turn below could be studied until there is no phenotype. In addition, the equivalent mutations on the TM6 side should be explored to rule out the effects of conformational changes that arise from mutating TM4 and to increase the strength of evidence for the importance of side-chain interactions at the TM6 interface. The experiments for OSCA1.2 osmolarity effects on gating and scramblase in Figure 4 could be improved by adding different levels of osmolarity in addition to time in the hypotonic solution.

We appreciate the positive and constructive comments from the reviewer. As we outlined in our responses to the Overall Recommendations, we will include more mutations at the TM4 and TM6 interface to further strengthen our conclusion.

Author response:

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

The reviewer comments have been helpful, and we have revised the manuscript to address the concerns of reviewer 2. In addition to text changes, we also added a negative control to Figure 1 to address concerns about photobleaching or DNA unwrapping.

Reviewer #1:

This manuscript presents an extremely exciting and very timely analysis of the role that the nucleosome acidic patch plays in SWR1-catalyzed histone exchange. Intriguingly, SWR1 loses activity almost completely if any of the acidic patches are absent. To my knowledge, this makes SWR1 the first remodeler with such a unique and pronounced requirement for the acidic patch. The authors demonstrate that SWR1 affinity is dramatically reduced if at least one of the acidic patches is absent, pointing to a key role of the acidic patch in SWR1 binding to the nucleosome. The authors also pinpoint a specific subunit - Swc5 - that can bind nucleosomes, engage the acidic patch, and obtain a cryo-EM structure of Swc5 bound to a nucleosome. They also identify a conserved arginine-rich motif in this subunit that is critical for nucleosome binding and histone exchange in vitro and for SWR1 function in vivo. The authors provide evidence that suggests a direct interaction between this motif and the acidic patch.

Strengths:

The manuscript is well-written and the experimental data are of outstanding quality and importance for the field. This manuscript significantly expands our understanding of the fundamentally important and complex process of H2A.Z deposition by SWR1 and would be of great interest to a broad readership.

We thank the reviewer for their enthusiastic and positive comments on our work.

Reviewer #2:

Summary:

In this study, Baier et al. investigated the mechanism by which SWR1C recognizes nucleosomal substrates for the deposition of H2A.Z. Their data convincingly demonstrate that the nucleosome's acidic patch plays a crucial role in the substrate recognition by SWR1C. The authors presented clear evidence showing that Swc5 is a pivotal subunit involved in the interaction between SWR1C and the acidic patch. They pared down the specific region within Swc5 responsible for this interaction. However, two central assertions of the paper are less convincing. First, the data supporting the claim that the insertion of one Z-B dimer into the canonical nucleosome can stimulate SWR1C to insert the second Z-B dimer is somewhat questionable (see below). Given that this claim contradicts previous observations made by other groups, this hypothesis needs further testing to eliminate potential artifacts. Secondly, the claim that SWR1C simultaneously recognizes the acidic patch on both sides of the nucleosome also needs further investigation, as the assay used to establish this claim lacks the sensitivity necessary to distinguish any difference between nucleosomal substrates containing one or two intact acidic patches.

Strengths:

As mentioned in the summary, the authors presented clear evidence demonstrating the role of Swc5 in recognition of the nucleosome acidic patch. The identification of the specific region in Swc5 responsible for this interaction is important.

We thank the reviewer for their careful critique of our work. Below we address each major concern.

Major comments: (1) Figure 1B: It is unclear how much of the decrease in FRET is caused by the bleaching of fluorophores. The authors should include a negative control in which Z-B dimers are omitted from the reaction. In the absence of ZB dimers, SWR1C will not exchange histones. Therefore, any decrease in FRET should represent the bleaching of fluorophores on the nucleosomal substrate, allowing normalization of the FRET signal related to A-B eviction.

In this manuscript, as well as in our two previous publications (Singh et al., 2019; Fan et al.,2022), we have presented the results of no enzyme controls, +/- ZB dimers, no ATP controls, or AMP-PNP controls for our FRET-based, H2A.Z deposition assay (see also Figure S3). We do not observe significant levels of photobleaching in this assay, either during ensemble measurements or in an smFRET experiment. To aid the reader, we have added the AMP-PNP data for the experiment shown in Figure 1B. The results show there is less than a 10% decrease in FRET over 30’, and the signal from the double acidic patch disrupted nucleosome is identical to this negative control.

(2) Figure S3: The authors use the decrease in FRET signal as a metric of histone eviction. However, Figure S3 suggests that the FRET signal decrease could be due to DNA unwrapping. Histone exchange should not occur when SWR1C is incubated with AMP-PNP, as histone exchange requires ATP hydrolysis (10.7554/eLife.77352). And since the insertion of Z-B dimer and the eviction of A-B dimer are coupled, the decrease of FRET in the presence of AMP-PNP is unlikely due to histone eviction or exchange. Instead, the FRET decrease is likely due to DNA unwrapping (10.7554/eLife.77352). The authors should explicitly state what the loss of FRET means.

We agree with the reviewer, that loss of FRET can be due to DNA unwrapping from the nucleosome. We have previously demonstrated this activity by SWR1C in our smFRET study (Fan et al., 2022). However, DNA unwrapping is highly reversible and has a time duration of only 1-3 seconds. We and others have not observed stable unwrapping of nucleosomes by SWR1C, but rather the stable loss of FRET reports on dimer eviction. We assume the reviewer is concerned about the rather large decrease in FRET signal shown in the AMP-PNP controls for Figure S3, panels A and D. For the other 7 panels, the decrease in FRET with AMP-PNP are minimal. In fact, if we average all of the AMP-PNP data points, the rate of FRET loss is not statistically different from no enzyme control reactions (nucleosome plus ZB dimers).

Data for panels A and D used a 77NO nucleosomal substrate, with Cy3 labeling the linker distal dimer. This is our standard DNA fragment, and it was used in Figure 1B. The only difference between data sets is that the data shown in Fig 1B used nucleosome reconstituted with a Cy5-labelled histone octamer, rather than the hexasome assembly method used for Fig S3. Three points are important. First, for all of these substrates, we assembled 3 independent nucleosomes, and the results are highly reproducible. Two, we performed a total of 6 experiments for the 77NO-Cy5 substrates to ensure that the rates were accurate (+/-ATP). Third, and most important, we do not see this decrease in FRET signal in the absence of SWR1C (no enzyme control). This data was included in the data source file. Thus, it appears that there is significant SWR1C-induced nucleosome instability for these two hexasome-assembled substrates. We now note this in the legend to Figure S3. Key for this work, however, is that there is a large increase in the rate of FRET loss in the presence of ATP, and this rate is faster when a ZB dimer was present at the linker proximal location. In response to the last point, we state in the first paragraph of the results: “The dimer exchange activity of SWR1C is monitored by following the decrease in the 670 nm FRET signal due to eviction of the Cy5-labeled AB-Cy5 dimer (Figure 1A).”

(3) Related to point 2. One way to distinguish nucleosomal DNA unwrapping from histone dimer eviction is that unwrapping is reversible, whereas A-B eviction is not. Therefore, if the authors remove AMP-PNP from the reaction chamber and a FRET signal reappears, then the initial loss of FRET was due to reversible DNA unwrapping. However, if the removal of AMP-PNP did not regain FRET, it means that the loss of FRET was likely due to A-B eviction. The authors should perform an AMP-PNP and/or ATP removal experiment to make sure the interpretation of the data is correct.

See response to item 2 above

(4) The nature of the error bars in Figure 1C is undefined; therefore, the statistical significance of the data is not interpretable.

We apologize for not making this more explicit for each figure. The error bars report on 95% confidence intervals from at least 3 sets of experiments. This statement has been added to the legend.

(5) The authors claim that the SWR1C requires intact acidic patches on both sides of the nucleosomes to exchange histone. This claim was based on the experiment in Figure 1C where they showed mutation of one of two acidic patches in the nucleosomal substrate is sufficient to inhibit SWR1C-mediated histone exchange activity. However, one could argue that the sensitivity of this assay is too low to distinguish any difference between nucleosomes with one (i.e., AB/AB-apm) versus two mutated acidic patches (i.e., AB-apm/AB-apm). The lack of sensitivity of the eviction assay can be seen when Figure 1B is taken into consideration. In the gel-shift assay, the AB-apm/AB-apm nucleosome exhibited a 10% SWR1C-mediated histone exchange activity compared to WT. However, in the eviction assay, the single AB/AB-apm mutant has no detectable activity. Therefore, to test their hypothesis, the authors should use the more sensitive in-gel histone exchange assay to see if the single AB/AB-apm mutant is more or equally active compared to the double AB-apm/AB-apm mutant.

Our pincher model is based on three, independent sets of data, not just Figure 1C. First, as noted by the reviewer, we find that disruption of either acidic patch cripples the dimer exchange activity of SWR1C in the FRET-based assay. Whether the defect is identical to that of the double APM mutant nucleosome does not seem pertinent to the model. In a second set of assays, we used fluorescence polarization to quantify the binding affinity of SWR1C for wildtype nucleosomes, a double APM nucleosome, or each single APM nucleosome. Consistent with the pincher model, each single APM disruption decreases binding affinity at least 10-fold (below the sensitivity of the assay). Finally, we monitored the ability of different nucleosomes to stimulate the ATPase activity of SWR1C. Consistent with the pincher model, a single APM disruption was sufficient to eliminate nucleosome stimulation.

(6) The authors claim that the AZ nucleosome is a better substrate than the AA nucleosome. This is a surprising result as previous studies showed that the two insertion steps of the two Z-B dimers are not cooperative (10.7554/eLife.77352 and 10.1016/J.CELREP.2019.12.006). The authors' claim was based on the eviction assay shown in Fig 1C. However, I am not sure how much variation in the eviction assay is contributed by different preparations of nucleosomes. The authors should use the in-gel assay to independently test this hypothesis.

For all data shown in our manuscript, at least three different nucleosome preparations were used. The impact of a ZB dimer on the rates of dimer exchange was highly reproducible among different nucleosome preparations and experiments. We also see reproducible ZB stimulation for three different substrates – with ZB on the linker proximal side, the linker distal side, and on one side of a core particle. We do not believe that our data are inconsistent with previous studies. First, the previous work referenced by the reviewer performed dimer exchange reactions with a large excess of nucleosomes to SWR1C (catalytic conditions), whereas we used single turnover reactions. Secondly, our study is the first to use a homogenous, ZA heterotypic nucleosome as a substrate for SWR1C. All previous studies used a standard AA nucleosome, following the first and second rounds of dimer exchange that occur sequentially. And finally, we observe only a 20-30% increase in rate by a ZB dimer (e.g. 77N0 substrates), and such an increase was unlikely to have been detected by previous gel-based assays.

Minor comments:

(1) Abstract line 4: To say 'Numerous' studies have shown acidic patch impact chromatin remodeling enzymes activity may be too strong.

Removed

(2) Page 15, line 15: The authors claim that swc5∆ was inviable on formamide media. However, the data in Figure 8 shows cell growth in column 1 of swc5∆.

The term ‘inviable’ has been replaced with ‘poor’ or ‘slow growth’

(3) The authors should use standard yeast nomenclature when describing yeast genes and proteins. For example, for Figure 8 and legend, Swc5∆ was used to describe the yeast strain BY4741; MATa; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0; YBR231c::kanMX4. Instead, the authors should describe the swc5∆ mutant strain as BY4741 MAT a his3∆1 leu2∆0 met15∆0 ura3∆0 swc5∆::kanMX4. Exogenous plasmid should also be indicated in italics and inside brackets, such as [SWC5-URA3] or [swc5(R219A)-URA3].

We apologize for missing this mistake in the Figure 8 legend. We had inadvertently copied this from the euroscarf entry and forgot to edit the entry. We decided not to add all the plasmid names to the figure, as it was too cluttered. We state in the figure legend that the panels show growth of swc5 deletion strains harboring the indicated swc5 alleles on CEN/ARS plasmids.

(4) According to Lin et al. 2017 NAR (doi: 10.1093/nar/gkx414), there is only one Swc5 subunit per SWR1C. Therefore, the pincher model proposed by the authors would suggest that there is a missing subunit that recognizes the second acidic patch. The authors should point out this fact in the discussion. However, as mentioned in Major comment 6, I am not sure if the pincer model is substantiated.

In our discussion, we had noted that the published cryoEM structure had suggested that the Swc2 subunit likely interacts with the acidic patch on the dimer that is not targeted for replacement, and we proposed that Swc5 interacts with the acidic patch on the exchanging H2A/H2B dimer. We have now made this more clear in the text.

Author response:

We thank the reviewers for the feedback on our manuscript; we are planning to address the raised concerns in the following manner:

We will be more explicit about the novelty of this method framing it more concretely within the scope of current research. From some comments of the reviewers, we understand that it is not clear that our method is an extension of an already existing method and model that has been extensively validated with pre-trained models brought online. Consequently, the details of the model as well as the training cohort are only covered briefly, referencing relevant published works on this topic. We will improve the clarity in this respect in the full responses. Nevertheless, we agree that the work would benefit from a simulation study that formally evaluates the performance of our method compared with more traditional approaches and will add it in our full responses. We will take care specifically of investigating the effect of assumptions like the centile-stability in healthy controls as suggested by the Reviewer 2.

The novelty of this work lies in introducing a mathematically transparent method to use normative modelling for evaluating studies with a longitudinal design, using normative models trained on cross sectional data. We emphasise strongly that this is otherwise not possible using current methods. Furthermore, by building on a pre-trained model, this method enjoys the benefits of big (cross-sectional) data (by the pre-trained model being fitted on an extensive population sample) without the need to have direct access to them, or a ‘big’ longitudinal dataset from the cohort at hand. This is crucial in neuroimaging, where longitudinal data are much more scarce than cross-sectional data.

We strongly disagree with the notion raised by Reviewer 1 that after the first episode cortical thickness alterations are expected to become more severe. There is now increasing evidence that: (i) trajectories of cortical thickness are highly variable across different individuals after the first psychotic episode and (ii) that individuals treated with second-generation antipsychotics and with careful clinical follow-up can show normalisation of cortical thickness atypicalities after the first episode. Indeed, we can provide evidence for this in an independent cohort, with different analytical methodologies, where precisely this occurs (https://www.medrxiv.org/content/10.1101/2024.04.19.24306008v1, https://pubmed.ncbi.nlm.nih.gov/36805840/). In the full revision, we would be happy to provide further discussion of evidence in support of this.

We  would also like to re-emphasise  that the data were processed with the utmost rigour using state of the art processing pipelines including quality control.

We will take care to improve the flow of the manuscript with special attention to the theoretical part and sections highlighted by the Reviewer 2. 

We agree with the challenge outlined by the Reviewer 2 regarding the limitations in interpretation of overall trends when the position in the visit one is different between the subjects. However, this is a much broader challenge and is not specific to this study. The non-random sampling of large cohort studies is problematic for nearly all studies using such cohorts, and regardless of the  statistical approach used. We will explicitly acknowledge these limitations in the full response.

Author response:

Reviewer #1 (Public Review):

Given that this is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C, the manuscript would benefit from placing these findings in the context of what has been reported in other populations, for example, how decay rates of intact and defective genomes compare with that of other subtypes where known.

Most published studies are from men living with HIV-1 subtype B and the studies are not from the hyperacute infection phase and therefore a direct head-to-head comparison with the FRESH study is difficult. However, we can cite/highlight and contrast our study with a few examples from other acute infection studies as follows.

(1) Peluso et. al., JCI, 2020, showed that in Caucasian men (SCOPE study), with subtype B infection, initiating ART during chronic infection virus intact genomes decayed at a rate of 15.7% per year, while defective genomes decayed at a rate of 4% per year. In our study we showed that in chronic treated participants genomes decreased at a rate of 25% (intact) and 3% (defective) per month for the first 6 months of treatment.

(2) White et. al., PNAS, 2021, demonstrated that in a cohort of African, white and mixed-race American men treated during acute infection, the rate of decay of intact viral genomes in the first phase of decay was <0.3 logs copies in the first 2-3 weeks following ART initiation. In the FRESH cohort our data from acute treated participants shows a comparable decay rate of 0.31 log copies per month for virus intact genomes.

(3) A study in Thailand (Leyre et. al., 2020, Science Translational Medicine), of predominantly HIV-1 CRF01-AE subtype compared HIV-reservoir levels in participants starting ART at the earliest stages of acute HIV infection (in the RV254/SEARCH 010 cohort) and participants initiating ART during chronic infection (in SEARCH 011 and RV304/SEARCH 013 cohorts). In keeping with our study, they showed that the frequency of infected cells with integrated HIV DNA remained stable in participants who initiated ART during chronic infection, while there was a sharp decay in these infected cells in all acutely treated individuals during the first 12 weeks of therapy. Rates of decay were not provided and therefore a direct comparison with our data from the FRESH cohort is not possible.

(4) A study by Bruner et. al., Nat. Med. 2016, described the composition of proviral populations in acute treated (within 100 days) and chronic treated (>180 days), predominantly male subtype B cohort. In comparison to the FRESH chronic treated group, they showed that in chronic treated infection 98% (87% in FRESH) of viral genomes were defective, 80% (60% in FRESH) had large internal deletions and 14% (31% in FRESH) were hypermutated. In acute treated 93% (48% in FRESH) were defective and 35% (7%) in FRESH were hypermutated. The differences frequency of hypermutations could be explained by the differences in timing of infection specifically in the acute treated groups were FRESH participants initiate ART at a median of 1 day after infection. It is also possible that sex- or race-based differences in immunological factors that impact the reservoir may play a role.

This study also showed that large deletions are non-random and occur at hotspots in the HIV-1 genome. The design of the subtype B IPDA assay (Bruner et. al., Nature, 2019) is based on optimal discrimination between intact and deleted sequences - obtained with a 5′ amplicon in the Ψ region and a 3′ amplicon in Envelope. This suggest that Envelope is a hotspot for large while deletions in Ψ is the site of frequent small deletions and is included in larger 5′ deletions. In the FRESH cohort of HIV-1 subtype C, genome deletions were most frequently observed between Integrase and Envelope relative to Gag (p<0.0001–0.001).

(5) In 2017, Heiner et. al., in Cell Rep, also described genetic characteristics of the latent HIV-1 reservoir in 3 acute treated and 3 chronic treated male study participants with subtype B HIV. Their data was similar to Bruner et. al. above showing proportions of intact proviruses in participants who initiated therapy during acute/early infection at 6% (94% defective) and chronic infection at 3% (97% defective). In contrast the frequencies in FRESH in acute treated were 52% intact and 48% defective and in chronic infection were 13% intact and 87% defective. These differences could be attributed to the timing of treatment initiation where in the aforementioned study early treatment ranged from 0.6-3.4 months after infection.

Indeed, in the abstract, the authors indicate that treatment was initiated before the peak. The use of the term 'peak' viremia in the hyperacute-treated group could perhaps be replaced with 'highest recorded viral load'. The statistical comparison of this measure in the two groups is perhaps more relevant with regards to viral burden over time or area under the curve viral load as these are previously reported as correlates of reservoir size.

We will edit the manuscript text to describe the term peak viraemia in hyperacute treated participants more clearly. We will perform an analysis of area under the curve to compare viral burden in the two study groups.

Reviewer #2 (Public Review):

Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections.)

We agree that these factors could play a role however participants in this study were of similar age (18-23), and information on co-morbidities and coinfections are not known.

Reviewer #3 (Public Review):

The word reservoir should not be used to describe proviral DNA soon after ART initiation. It is generally agreed upon that there is still HIV DNA from actively infected cells (phase 1 & 2 decay of RNA) during the first 6-12 months of ART. Only after a full year of uninterrupted ART is it really safe to label intact proviral HIV DNA as an approximation of the reservoir. This should be amended throughout.

We agree and will amend the use of the word reservoir to only refer to the proviral DNA load after full viral suppression, i.e., during undetectable viral load.

All raw, individualized data should be made available for modelers and statisticians. It would be very nice to see the RNA and DNA data presented in a supplementary figure by an individual to get a better grasp of intra-host kinetics.

We will make all relevant data available and accessible to interested parties.

The legend of Supplementary Figure 2 should list when samples were taken.

The data in this figure represents an overall analysis of all sequences available for each participant at all time points. This will be explained more clearly in the manuscript and added to the figure legend.

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.”

Author Response

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

Reviewer #1 (Public Review):

Summary:

Through an unbiased genomewide KO screen, the authors identified loss of DBT to suppress MG132-mediated death of cultured RPE cells. Further analyses suggested that DBT reduces ubiquitinated proteins by promoting autophagy. Mechanistic studies indicated that DBT loss promotes autophagy via AMPK and its downstream ULK and mTOR signaling. Furthermore, loss of DBT suppresses polyglutamine- or TDP-43-mediated cytotoxicity and/or neurodegeneration in fly models. Finally, the authors showed that DBT proteins are increased in ALS patient tissues, compared to non-neurological controls.

Strengths:

The idea is novel, the evidence is mostly convincing, and the data are clean. The findings have implications for human diseases.

Reply: We thank the reviewer for the supportive comments.

Weaknesses:

More experiments are needed to establish the connections between DBT and autophagy. The mechanistic studies are somewhat biased, and it's unclear whether the same mechanism (i.e., AMPK-->mTOR) can be applied to TDP-43-mediated neurodegeneration. Also, some data interpretation has to be more accurate.

Reply: We thank the reviewer for raising these questions, and we have provided additional evidence in the revised manuscript to support the model that DBTKO can enhance autophagy and induce resistance to TDP-43-associated toxicity. This is described in greater detail below.

(1) To provide further evidence for the connection between DBT and autophagy, we have introduced additional controls. For the additional controls, we have included the AMPK shRNA and drug treatment controls (Fig.4D, Fig.S4B), and these results suggest that reducing the AMPK level renders DBTKO cells sensitive to MG132 toxicity. We also added the TSC1 shRNA and mTOR agonist treatment controls (Fig.5E, Fig.S4G), and the results show that increasing mTOR levels also make the DBTKO cells sensitive to MG132.

(2) To further confirm the roles of AMPK and mTOR in DBTKO cells, we introduced the AMPK agonist (EX229) and mTOR inhibitors (RAD001 and AZD8055) in co-treatment experiments with MG132 and then measured cell survival (Fig.S4D, S4G). The results indicate that promoting AMPK activation or inhibiting mTOR can enhance cell resistance to MG132-induced toxicity.

(3) Additionally, we included the overexpression and rescue experiments for DBT and analyzed the AMPK-ULK1 signaling in WT RPE1 and DBTKO cells (Fig.S5D, S5E). The results indicate that the increase of DBT can significantly reduce the phosphorylation of AMPK/ULK1 and the levels of the autophagy marker LC3II. Together, these results suggest that DBT plays an important role in autophagy.

(4) We had shown in the original version of the manuscript that DBTKO renders cells more resistant to TDP-43-associated toxicity, similar to the tolerance of MG132-induced toxicity. Here we further show that expression of TDP-43M337V enhances the phosphorylation of AMPK in the DBTKO cells (Fig. S7A), similar to the effect of the MG132 treatment. These results suggest that the resistance of DBTKO cells to MG132 or TDP-43-assoicated toxicity shares a similar mechanism of activated the AMPK signaling.

Reviewer #2 (Public Review):

Summary:

Hwang, Ran-Der et al utilized a CRISPR-Cas9 knockout in human retinal pigment epithelium (RPE1) cells to evaluate for suppressors of toxicity by the proteasome inhibitor MG132 and identified that knockout of dihydrolipoamide branched chain transacylase E2 (DBT) suppressed cell death. They show that DBT knockout in RPE1 cells does not alter proteasome or autophagy function at baseline. However, with MG132 treatment, they show a reduction in ubiquitinated proteins but with no change in proteasome function. Instead, they show that DBT knockout cells treated with MG132 have improved autophagy flux compared to wildtype cells treated with MG132. They show that MG132 treatment decreases ATP/ADP ratios to a greater extent in DBT knockout cells, and in accordance causes activation of AMPK. They then show downstream altered autophagy signaling in DBT knockout cells treated with MG132 compared to wild-type cells treated with MG132. Then they express the ALS mutant TDP43 M337 or expanded polyglutamine repeats to model Huntington's disease and show that knockdown of DBT improves cell survival in RPE1 cells with improved autophagic flux. They also utilize a Drosophila model and show that utilizing either a RNAi or CRISPR-Cas9 knockout of DBT improves eye pigment in TDP43M337V and polyglutamine repeat-expressing transgenic flies. Finally, they show evidence for increased DBT in postmortem spinal cord tissue from patients with ALS via both immunoblotting and immunofluorescence.

Strengths:

This is a mechanistic and well-designed paper that identifies DBT as a novel regulator of proteotoxicity via activating autophagy in the setting of proteasome inhibition. Major strengths include careful delineation of a mechanistic pathway to define how DBT is protective. These conclusions are largely justified, but additional experiments and information would be useful to clarify and extend these conclusions.

Reply: We thank the reviewer for the supportive comments.

Weaknesses:

The large majority of the experiments are evaluating suppression of drug (MG132) toxicity in an in vitro epithelial cell line, so the generalizability to disease is unclear. Indeed, MG132 itself has been shown to modulate autophagy, and off-target effects of MG132 are not addressed. While this paper is strengthened by the inclusion of mouse-induced motor neurons, Drosophila models, and postmortem tissue, the putative mechanisms are minimally evaluated in these models.

Also, this effect is only seen with MG132 treatment, at a dose that causes markedly impaired cell survival. In this setting, it is certainly plausible that changes in autophagy could be the result of differences in cell survival, as opposed to an underlying mechanism for cell survival. Additional controls would be useful to increase confidence that DBT knockdown is protective via modulation of autophagy.

While the authors report increased DBT in postmortem ALS tissue as suggestive that DBT may modulate proteotoxicity in neurodegeneration, this point would be better supported with the evaluation of overexpression of DBT in their model.

Reply: We appreciate the reviewer for raising these questions, and we have provided further evidence in the revised manuscript to support the proposed mechanism that DBTKO confers resistance to MG132-induced toxicity through activation of autophagy. This is discussed in greater detail below.

(1) To provide further mechanistic analysis, we have included additional controls for the analysis of AMPK signaling in Fig. 4D and Fig. S4B. These results demonstrate that using drugs or shRNAs to reduce AMPK activity can decrease DBTKO survival. We have also shown that that an increasing the AMPK activity with an activator enhances the survival of both WT and DBTKO cells under MG132 treatment (Fig. S4D), suggesting that DBTKO cells resist MG132-induced toxicity through the activation of AMPK signaling.

(2) We have included additional controls for the analysis of mTOR signaling in Fig. 5E and Fig. S4F. The results in Fig. 5E show that reducing TSC1 using shRNAs can decrease DBTKO survival. We also added the experiments with mTOR agonist MHY1485 as a control in Fig. S4F. These results indicate that mTOR activation can promote DBTKO cells' sensitivity to MG132 toxicity. To further confirm the importance of mTOR in DBTKO-mediated resistance to MG132 toxicity, we included the mTOR inhibitors RAD001 and AZD8055 in the co-treatment experiments with MG132, and then measured cell survival (Fig. S4G). The results show that both mTOR inhibitors can enhance cell resistance to MG132-induced toxicity (Fig. S4G). These findings suggest that mTOR inhibition is required for DBTKO-mediated cell survival under MG132 treatment.

(3) To further test the hypothesis that DBT knockdown is protective via modulation of autophagy, we have introduced the overexpression of DBT and the rescue of DBT in DBTKO cells to analyze the AMPK signaling that regulates autophagy (Fig. S5E). The results demonstrate that overexpression of DBT significantly reduced the phosphorylation of AMPK and ULK1 (Fig. S5E). In the rescue experiment, the results mirror those of the overexpression experiment, showing a significant reduction in the phosphorylation of AMPK and ULK1 (Fig. S5E). We also analyzed the autophagy marker LC3II in both the overexpression and rescue experiments, and the results indicate that increasing the DBT level specifically reduces the LC3II level (Fig. S5D). These results support the model that loss of DBT promotes the activation of autophagy.

(4) To test the hypothesis that DBT may modulate proteotoxicity in neurodegeneration, we included the studies with TDP-43M337V and found that the expression of the mutant TDP43 enhanced the phosphorylation of AMPK in the DBTKO cells (Fig. S7A), consistent with the observations made with MG-132 treatment. Together with other findings in the manuscript, these results indicate that DBTKO can sensitize the activation of the AMPK signaling and confer the resistance to TDP-43-associated toxicity.

Author response:

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

Suggestions to the authors:

• Please re-analyze findings by omitting from all Tables and Figures all data of comparators who were not randomized (BAC). I understand the difficulties of running this trial but the results of excess reduction of mortality do not allow the publication of a trial where comparators do not come from the randomized patient population.

We wish to thank the editors and reviewers for their useful comments. Given that the study was designed with both randomised and CC participants we can’t easily exclude the CC analysis from the paper. However, we do provide graphs for both randomised only and randomised and CC participants for the primary and secondary endpoints. The fact that the primary endpoint (CRP) results are mirrored in both instances is also informative form a trial design perspective and indicative of the effect of dornase alfa therapy on inflammation being robust enough to yield the same results with small and larger cohorts.

We agree that there are potential drawbacks of using contemporary controls. To address these potential biases we used CC patients recruited at the same time period at single site using the same selection criteria as the randomised group, which minimised potential bias. However, the enrolment and comparison of CRP in CC-BAC participants to concurrent randomised control R-BAC patients indicated that the two groups responded to BAC treatment in the same manner (Table 2, LS means log(CRP) 3.78 vs 3.53, P=0.386), whereas the R-BAC+DA vs R-BAC group comparison yielded significant differences (Table 2, LS means log(CRP) 3.1 vs 3.59, P=0.041). These comparisons mitigate to a large degree these potential problems.

Still, to make easy to distinguish the groups we now use the following unique nomenclature throughout the manuscript which is clearly defined on ln. 111 and state that comparisons of treated participants were performed with both control groups separately and combined.

R-BAC: Randomised BAC CC-BAC: Contemporary control BAC R-BAC+DA : Randomised BAC+ dornase alfa T-BAC: R-BAC + CC-BAC

In fact, the most important bias in our study, might actually be the placebo effect, given that participants randomised to BAC did not receive a nebulized control substance. We now discuss these points in more detail in the manuscript and modified the title by removing the reference to a randomised trial and clinical outcomes.

• The presentation remains confusing and the manuscript should be critically revised for clarity. There is a repetition of methods (e.g. lines 176-187 repeat 160-175) and redundant results (e.g. Figure S2, Table 3).

We apologise for the repetition. We removed the repeated text in the Exclusion criteria (lines 176-187 in the old manuscript).

Figure S2 is not related to Table 3. Figure S2 depicts baseline characteristics, whereas Table 3 complements the graph in Figure 3A but lists the mean daily value of the primary endpoint as requested by Reviewer 1 in the first round of revision.

At Table 4: the authors should select one method of illustration for lab results, either Table or figure, without repetitions

We agree and have removed Table 4 leaving the graphs instead.

• Regarding inclusion criteria, it is unclear whether high radiological suspicion is sufficient for inclusion or whether PCR based confirmation is required in all instances (differences in wording between lines 153 and 191), and under which oxygen requirements (lines 155 and 192)

We thank the reviewer for pointing this out. Indeed, radiological suspicion was not sufficient and all participants in this study had a positive PCR test as part of their diagnosis prior to inclusion in the study. The entire eligibility section was rewritten to reflect this important point.

• Table 1 should be merged with Table S2 and a better description of cohort baseline severity (P/F, SOFA, APACHE, organ support, number of patients in each point of the WHO severity score) and treatments should be made available.

We thank the reviewer for this suggestion. We have now merged Table 1 and S2 and included WHO ordinal severity information in Table 1, with median, average, SD, min and max values which reflect the participant distribution. Unfortunately, although the additional requested information was recorded, it was not systematically collected for the analysis of the trial and it was not straight forward to compile at this stage.

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:

Reviewer #1 (Public Review):

Summary:

Zhang et al. demonstrate that CD4+ single positive (SP) thymocytes, CD4+ recent thymic emigrants (RTE), and CD4+ T naive (Tn) cells from Cd11c-p28-flox mice, which lack IL-27p28 selectively in Cd11c+ cells, exhibit a hyper-Th1 phenotype instead of the expected hyper Th2 phenotype. Using IL-27R-deficient mice, the authors confirm that this hyper-Th1 phenotype is due to IL-27 signaling via IL-27R, rather than the effects of monomeric IL-27p28. They also crossed Cd11c-p28-flox mice with autoimmune-prone Aire-deficient mice and showed that both T cell responses and tissue pathology are enhanced, suggesting that SP, RTE, and Tn cells from Cd11c-p28-flox mice are poised to become Th1 cells in response to self-antigens. Regarding mechanism, the authors demonstrate that SP, RTE, and Tn cells from Cd11c-p28-flox mice have reduced DNA methylation at the IFN-g and Tbx21 loci, indicating 'de-repression', along with enhanced histone tri-methylation at H3K4, indicating a 'permissive' transcriptional state. They also find evidence for enhanced STAT1 activity, which is relevant given the well-established role of STAT1 in promoting Th1 responses, and surprising given IL-27 is a potent STAT1 activator. This latter finding suggests that the Th1-inhibiting property of thymic IL-27 may not be due to direct effects on the T cells themselves.

Strengths:

Overall the data presented are high quality and the manuscript is well-reasoned and composed. The basic finding - that thymic IL-27 production limits the Th1 potential of SP, RTE, and Tn cells - is both unexpected and well described.

Weaknesses:

A credible mechanistic explanation, cellular or molecular, is lacking. The authors convincingly affirm the hyper-Th1 phenotype at epigenetic level but it remains unclear whether the observed changes reflect the capacity of IL-27 to directly elicit epigenetic remodeling in developing thymocytes or knock-on effects from other cell types which, in turn, elicit the epigenetic changes (presumably via cytokines). The authors propose that increased STAT1 activity is a driving force for the epigenetic changes and resultant hyper-Th1 phenotype. That conclusion is logical given the data at hand but the alternative hypothesis - that the hyper-STAT1 response is just a downstream consequence of the hyper-Th1 phenotype - remains equally likely. Thus, while the discovery of a new anti-inflammatory function for IL-27 within the thymus is compelling, further mechanistic studies are needed to advance the finding beyond phenomenology.

Thanks for the comments. Following the suggestions of the reviewer, further studies will be performed to test whether developing thymocytes are the direct targets of IL-27 using Cd4-IL-27ra knockout mice or mixed bone marrow chimeras of wildtype and IL-27ra knockout cells.

To address the potential autocrine loop in the STAT1 hyperactivation, we added IFN-γ antibody into CD4+ T cell cultures and saw no obvious impact on STAT1 phosphorylation. If deemed necessary, we could further test this possibility in vivo using Cd4-Ifng and CD11c-p28 double knockout mice.

The detailed mechanisms underlying the hyperactivation of STAT1 remain to be determined. IL-27p28 has recently been shown to act as an antagonist of gp130-mediated signaling. In addition, structural studies have demonstrated that IL-27p28 has the interface with EBI3, as well as the two receptor subunits IL-27Rα and gp130. Taken into consideration of these findings and the fact that p28 and IL-27ra deficiency exhibits similar phenotype, we speculate that deficiency in either p28 or IL-27ra makes more gp130 available to transduce signals elicited by other cytokines. We will next focus on gp130 related cytokines to search for the candidate(s) which ultimately leads to enhanced STAT1 activation in the absence of p28. Alternatively, release of EBI3 in the absence of p28 may facilitate its coupling with other cytokine subunits. IL-35, which is composed of EBI3 and p35, is of particular interest as IL-27Rα is also involved in its signaling.

To narrow down the candidate cytokines, we will first examine the expression of IL-35 and gp130 related cytokines, including IL-6, IL-11, LIF, CT1, OSM, IL-31, CLCF1, CNTF in the thymus and thymocyte-depleted thymic stromal cells by mining public databases and by RT-PCR. Similarly, CD4+ thymocytes will be examined for the expression of receptor subunits which can couple with gp130, including IL-6R, IL-11R, LIFR, OSMRβ, IL-31Rα, CNTFRα, IL-23R, and IL-12Rβ2.

We next will select those cytokines expressed in the thymus or thymic stromal cells with cognate receptor expression in CD4+ thymocytes and test their effect on STAT1 phosphorylation of wildtype and p28-deficient CD4+ thymocytes. If deemed necessary, double knockout mice will be engaged to rescue the hyper-Th1 phenotype.

Reviewer #2 (Public Review):

Summary:

Naïve CD4 T cells in CD11c-Cre p28-floxed mice express highly elevated levels of proinflammatory IFNg and the transcription factor T-bet. This phenotype turned out to be imposed by thymic dendritic cells (DCs) during CD4SP T cell development in the thymus [PMID: 23175475]. The current study affirms these observations, first, by developmentally mapping the IFNg dysregulation to newly generated thymic CD4SP cells [PMID: 23175475], second, by demonstrating increased STAT1 activation being associated with increased T-bet expression in CD11c-Cre p28-floxed CD4 T cells [PMID: 36109504], and lastly, by confirming IL-27 as the key cytokine in this process [PMID: 27469302]. The authors further demonstrate that such dysregulated cytokine expression is specific to the Th1 cytokine IFNg, without affecting the expression of the Th2 cytokine IL-4, thus proposing a role for thymic DC-derived p28 in shaping the cytokine response of newly generated CD4 helper T cells. Mechanistically, CD4SP cells of CD11c-Cre p28-floxed mice were found to display epigenetic changes in the Ifng and Tbx21 gene loci that were consistent with increased transcriptional activities of IFNg and T-bet mRNA expression. Moreover, in autoimmune Aire-deficiency settings, CD11c-Cre p28-floxed CD4 T cells still expressed significantly increased amounts of IFNg, exacerbating the autoimmune response and disease severity. Based on these results, the investigators propose a model where thymic DC-derived IL-27 is necessary to suppress IFNg expression by CD4SP cells and thus would impose a Th2-skewed predisposition of newly generated CD4 T cells in the thymus, potentially relevant in autoimmunity.

Strengths:

Experiments are well-designed and executed. The conclusions are convincing and supported by the experimental results.

Weaknesses:

The premise of the current study is confusing as it tries to use the CD11c-p28 floxed mouse model to explain the Th2-prone immune profile of newly generated CD4SP thymocytes. Instead, it would be more helpful to (1) give full credit to the original study which already described the proinflammatory IFNg+ phenotype of CD4 T cells in CD11c-p28 floxed mice to be mediated by thymic dendritic cells [PMID: 23175475], and then, (2) build on that to explain that this study is aimed to understand the molecular basis of the original finding. In its essence, this study mostly rediscovers and reaffirms previously reported findings, but with different tools. While the mapping of epigenetic changes in the IFNg and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, these are expected results, and they only reaffirm what would be assumed from the literature. Thus, there is only incremental gain in new insights and information on the role of DC-derived IL-27 in driving the Th1 phenotype of CD4SP cells in CD11c-p28 floxed mice.

Indeed, the present study is based on the finding of enhanced IFN-γ production by CD4+ T cells from CD11c-p28 floxed mice, which was originally reported by Zhang et al. and repeatedly cited in the our manuscript. We revisited this phenomenon in the context of functional bias of newly generated CD4+ T cells and sought to reveal the mechanisms underlying the hyper-Th1 phenotype in the absence of thymic DC-derived IL-27. We showed that deletion of p28 resulted in an unexpected hyperactivation of STAT1, which was accompanied by epigenetic changes in favor of Th1 bias. However, the gap remains between p28 deficiency and STAT1 activation.

Altogether, the major issues of this study remain unresolved:

(1) It is still unclear why the p28-deficiency in thymic dendritic cells would result in increased STAT1 activation in CD4SP cells. Based on their in vitro experiments with blocking anti-IFNg antibodies, the authors conclude that it is unlikely that the constitutive activation of STAT1 would be a secondary effect due to autocrine IFNg production by CD4SP cells. However, this possibility should be further tested with in vivo models, such as Ifng-deficient CD11c-p28 floxed mice. Alternatively, is this an indirect effect by other IFNg producers in the thymus, such as iNKT cells? It is necessary to explain what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells in the first place.

Thanks for the suggestions. Further studies will be performed to test the potential autocrine loop for IFN-γ production in vivo using Cd4-Ifng and CD11c-p28 double knockout mice. This model should also be helpful to exclude the possibility of indirect role of IFN- production by such cells as iNKT.

As pointed out by the reviewer, a critical unanswered question is what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells. Several lines of evidence point to the possibility that p28 deficiency increases the responsiveness of developing thymocytes to STAT1-activating cytokines. Firstly, IL-27p28 has recently been shown to act as an antagonist of gp130-mediated signaling. Secondly, structural studies have demonstrated that IL-27p28 is centrally positioned in the complex formed with EBI3, as well as the two receptor subunits IL-27Rα and gp130. Thirdly, we observed similar hyper-Th1 phenotype in the absence of either p28 and IL-27ra. Therefore, it is speculated that more gp130 should be available to transduce signals elicited by other cytokines in such a scenario. We will next seek to determine the candidate cytokine(s) responsible for the enhanced STAT1 activation in the absence of p28 as outlined in the response to Reviewer 1.

(2) It is also unclear whether CD4SP cells are the direct targets of IL-27 p28. The cell-intrinsic effects of IL-27 p28 signaling in CD4SP cells should be assessed and demonstrated, ideally by CD4SP-specific deletion of IL-27Ra, or by establishing bone marrow chimeras of IL-27Ra germline KO mice.

Thanks for the suggestions. Further studies will be performed to test whether developing thymocytes are the direct targets of IL-27 using Cd4-IL-27ra knockout mice or mixed bone marrow chimeras of wildtype and IL-27ra knockout cells.

Author Response:

We thank the editors for their assessment of our manuscript. We appreciate the reviewers’ thoughtful comments and plan to incorporate their feedback into a revised manuscript. We agree that incorporating an additional, more common ablation tool would be highly complementary to our Kir2.1 ablation studies. We also agree that images across timepoints should be expanded for contact analyses, connectomics data can be better leveraged, additional quantifications can be performed as suggested by the reviewers to better support claims, and that the introduction and discussion can be revised to better position our work in the context of previous studies. We also strongly agree that providing data on receptor RNA and protein expression in the GF across timepoints would be extremely informative, however we have found acquiring these data, at the necessary resolution, would require new approaches and tools that may be outside the scope of the project.

Author response:

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

Reviewer #1 (Public Review):

Farhat-Younis and colleagues demonstrate tumor-specific IgM's capacity to induce tumor cell death in monocyte-derived dendritic cell cultures. They subsequently designed a chimeric receptor based on high-affinity FcRI. However, the authors found that the transfection process was more efficient when either the variable light or heavy chain was transfected individually rather than the entire scFv. This scFv construct led to an endoplasmic reticulum (ER) stress response and scFv degradation. A considerable portion of the manuscript is dedicated to the negative scFv expression results. The authors pivoted to a modified FcgRI capable of transmitting IgM signals. This represents a tremendous amount of work in the development of this chimeric receptor, the critical experiment showing efficacy in vivo was not presented, and instead various in vitro assays are shown. Thus, this manuscript will markedly benefit from showing improved responses to tumors in vivo when macrophages express FcgRI-IgM.

We deeply thank the reviewer for his thoughtful comments and overall favorable review of our manuscript.

1) In a mouse tumor model, the authors demonstrated that monocyte-derived dendritic cells (MoDCs) treated with IgG immune complexes (ICs) were more effective at preventing tumor growth compared to those treated with IgM ICs (as shown in Figure 1B). In Figure 1C, their in vitro experiments revealed that IgM resulted in tumor cell death, as well as increased production of nitric oxide (NO) and granzyme B. How do the authors reconcile IgG IC-treated MoDCs performing better in preventing tumors in vivo than IgM IC-treated MoDCs, despite the in vitro results with IgM-ICs. The authors speculate that IgG IC-treated MoDCs might trigger T cell immunity but do not show T cell involvement.

We apologize for not making this point clearer. We have extensively studied this phenomenon and published two papers that detailed the underlying mechanism in two consecutive papers (PMID: 27812544, PMID: 25924063). Briefly, we showed that DC activated with IgM-IC DC undergo cell death concomitantly to their release of lytic granules and lysis of tumor cells. As a result, they do not migrate to the lymph nodes where they should induce reactive T cell clones. In contrast, DC activated with IgG-IC do not elicit in vitro cytotoxicity but rather process the IC to present its derived antigens of MHC-II. We addressed that issue in the revised version and cited the relevant paper to further clarify it.

(2) The authors report distinct functional consequences of MoDCs incubated with tumor-IgG complexes and tumor IgM complexes. Tumor growth was inhibited and T cell immunity induced with the former. The latter, however, elicited robust anti-tumor killing. What happens if MoDCs are incubated with both IgG and IgM complexes? If this combined treatment induces effective killing and T cell memory, would this impact the design of the chimeric receptor to include IgG responsiveness as well?

This is a very interesting point. As mentioned above, our previous publications strongly suggest that tumor binding IgG and IgM induce different processes in myeloid cells. Yet, since MoDC naturally express the high affinity receptors for IgG FcRI, we speculate that treating tumor-bearing mice modified monocyte, alone or in combination with tumor-binding IgG, would shed some light into that. Indeed, such treatment elicit a strong T cell immunity in these mice and the data was added to Supplementary Data Figure S4J. With that being said, a complete analysis of this question is very complicated and extent beyond the scope of this work. We would like to emphasize that the purpose of this work is to highlight some of the challenges unique to genetic manipulation in myeloid cells and to suggest one alternative scaffold for integrating signaling in these cells. We do not argue that the specific solution presented here is the most potent one and more work is required before promoting such treatment into the clinic. We have added a sentence to the Discussion section that stress that issue.

(3) In Figure 5H, the authors demonstrate the ability of the chimeric receptor construct to deplete tumor cells in vitro. The ms would improve if the authors could show the chimeric receptor construct results in tumor cell death and/or prevention in an in vivo model. Similarly, if combined stimulation with IgG and IgM complexes enhances tumor response, this should be incorporated into the therapeutic strategy.

This is a wonderful suggestion. To address that, we challenged C57Bl/6 mice with B16F10 melanoma and allowed them to grow until it reached a palpable size of approximately 25 mm2. Concomitantly, we cultured bone marrow dendritic cells from syngeneic mice and transfected them with a linear mRNA of the alpha/mu construct. Tumor bearing mice were then treated with alpha/mu and sham transduced BMDC alone, or in combination with antibody against the melanoma antigen Trp1 (TA99). The results were added as Figure 5K and to Supplementary Figure S4h-S4I.

Reviewer #2 (Public Review):

Summary:

While a significant portion of immunotherapy research has focused on the pivotal role of T cells in tumor immunity, their effectiveness may be limited by the suppressive nature of the tumor environment. On the other hand, myeloid cells are commonly found within tumors and can withstand these adverse conditions. However, these cells often adopt an immunosuppressive phenotype when infiltrating tumors. Therefore, manipulating myeloid cells could potentially enhance the anti-tumor potential of immunotherapy.

In this manuscript, Farhat-Younes and colleagues have demonstrated that activating the IgM receptor signaling in myeloid cells induces an oxygen burst, the secretion of Granzyme B, and the lysis of adjacent tumor cells. Furthermore, they have outlined a strategy to utilize these features to generate CAR macrophages. However, they have identified a limitation: the expression of scFv in myeloid cells induces ER stress and the degradation of misfolded proteins. To address this issue, chimeric receptors were designed based on the high-affinity FcγRI for IgG. When macrophages transfected with these receptors were exposed to tumor-binding IgG, extensive tumor cell killing, and the release of reactive oxygen species and Granzyme B were observed.

Strengths:

In general, I consider this work to be significant, and the results are compelling. It emphasizes the specific considerations and requirements for successful manipulation in myeloid cells, which could further advance the field of cellular engineering for the benefit of immunotherapy

We thank the reviewer for his thoughtful comments and overall appreciation of our findings.

Weaknesses:

Nevertheless, there are several minor issues that should be addressed:

(1) TCR fragments are commonly used to induce ER stress in non-immune cells. Therefore, it would be interesting to investigate whether TCR fragments can be expressed in myeloid cells and if they induce ER stress. Addressing this issue would support the notion that these cells lack the ER chaperones required for folding immunoglobulin variable chains.

This is a wonderful suggestion. To assess that possibility, we cloned the alpha chain of anti-Trp1 TCR and transfected RAW 264.7 macrophages. Importantly, we could not detect expression on this construct in macrophages, further supporting our findings with scFv in these cells. We added this result to Figure 4J and Supplementary Figure S3C.

(2) It would be valuable to determine whether, after the degradation of scFv fragments by myeloid cells, they are presented on MHC-I and MHC-II.

This is a very interesting point. To address that, we generated a genetic construct where we fused the anti-CD19 scFv to a polypeptide composed from the MHCI and the MHCII fragments of Ova Albumin. Next, DC 2.4 were transfected with this construct and measured their capacity to stimulate the proliferation of CD8+ T cells from OT-I and CD4+ from OT-II mice. DC transfected with this construct efficiently stimulated the proliferation of both T cells, suggesting that both Ova fragments are indeed presented on MHCI and MHCII. Nonehteless, DC transfected with polypeptide of MHCI and MHCII fragments of Ova Albumin only (with no scFv), were almost equally effective in stimulating OT-I and OT-II T cell proliferation. We added that result to Supplementary Figure S3D-S3E.

(3) Some methodological details, such as the vaccination protocol and high-resolution microscopy procedures, are missing from the text.

We thank the reviewer for pointing out these issues. We added the missing details to the revised version of the manuscript.

Author response:

We thank both reviewers for their feedback and for underlying the potential of our new tool and experimental approach to identify signalling molecules that can improve the in vitro derivation of specific cell types from human pluripotent stem cells. To address the reviewers' points we plan to carry out further analysis that should solidify our conclusions. We will also edit the text to temper conclusions where appropriate.

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.

Public Review:

Summary:

In this manuscript, the authors set out to understand how different TLR4 agonists trigger Myddosome assembly and seek to examine how the potent LPS agonist induces a heightened TLR4 response. A strength of the study is that the authors employ a novel light sheet imaging modality coupled to nanopipette delivery of TLR4 ligands. The authors use this technological innovation to resolve the dynamics of Myddosome formation within the whole cell volume of macrophage cell lines expressing MyD88-YFP. The main finding is that the kinetics of Myddosome formation is slower for the weaker agonist Abeta than LPS. However, Abeta amyloids resulted in the formation of larger MyD88-YFP puncta that persisted for longer. The authors suggest the slower kinetics of formation and larger puncta size reflect how Abeta amyloids are a less efficient TLR4 agonist. Many Toll-like receptors are now known to recognize endogenous produced danger signals and microbially derived molecules. This work is the first to compare the signaling kinetics of endogenous versus microbially derived TLR agonists.

Strengths:

A key strength of this work is the technological achievement of imaging Myddosomes within the entire cell volume and using a nanopipette to administer ligands directly to single cells. The authors also combine this light sheet microscopy with STORM imaging to gain a super-resolved view of the assembly of Myddosomes. These findings suggest that Myddosomes formed in response to Abeta have a more irregular morphology. We conclude that these technological achievements are significant in improving our understanding of the dynamics of TLR4 signaling in response to diverse agonists. Given the limited literature on the molecular dynamics of innate immune signal transduction, this study is an important addition to the field.

Weaknesses:

One limitation of the paper is that a suitable explanation for how larger Myddosomes would contribute to an attenuated downstream signaling response. Do the larger clusters of nucleated MyD88 polymers reflect inefficiency in assembling fully formed Myddosomes that contain IRAK4/2? Could the MyD88-GFP puncta be stained with antibodies against IRAK4 (or IRAK2) to determine the frequency and probably of the two ligands to stimulate signal transduction beyond MyD88 assembly?

A second weakness is the discussion. The authors should explore other explanations for the observed differences in Myddosome formation between TLR4 agonists. For example, could the observed delay in Myddosome assembly in response to Abeta be due to different binding affinity or kinetics to TLR4? Can this be ruled out?

We thank the reviewer for these comments.

To address the first comment we have added a section on the limitations of the current study and suggested that future work could use IRAK4 or 2 staining to identify Myddosomes that are functional as well as working with cells where the Myddosome expression levels is at physiological levels, which may reduce the formation of larger Myddosomes.

The reviewer is correct that the difference in delay time for Myddosome formation could be due slow formation of a TLR4 dimer or binding to the TLR4 dimer, rather than the time take to assemble the Myddosome after TLR4 dimerisation and binding since we have only measured the delay time for Myddosome formation when triggered by LPS or Aβ aggregates. This delay times involves dimerization of TLR4, binding of LPS or Aβ aggregates to the TLR4 dimer followed by Myddosome formation. These other processes might contribute to the difference in delay time that we observed between LPS or Aβ aggregates. It is worth noting that in our experiments we deliver the LPS or Aβ aggregates directly onto the surface for 5 seconds and that we previously showed the presence of the preformed TLR4 dimers on the cell surface (Latty et al., 2018). The affinity of Aβ aggregates for TLR4 is not known but LPS has a high affinity for TLR4, estimated to ∼3 nM for lipid A–TLR4-MD-2 (Akashi et al., 2003). However, even with this high affinity which implies fast binding, direct delivery directly onto the surface and the presence of preformed TLR4 dimers on the cell surface we observed that it took 80 s to observe Myddosome formation. This indicates that Myddosome formation is the slow step for LPS triggering. This is likely to be the case Aβ aggregates, since pM concentrations of aggregates can trigger TLR4 signalling (Hughes et al., 2020) indicating high affinity. However, it is not possible to rule out a contribution of a difference in affinity to observed difference in delay time without measuring the affinity directly.

We have added both these points to a new paragraph on the limitations of the study in the Discussion.

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.

Reviewer #1 (Public Review):

Granados-Aparici et al., investigate somatic-germline interactions in female mice. Mammalian oocytes are nurtured in multi-cellular ovarian follicles and communication with surrounding somatic cells is critical for oocyte development. This study focused on transzonal projections (TZP) extending from granulosa cells to the surface of oocytes and documented the importance of SMAD4, a TGF- β mediator, in regulating the TZPs. They propose a model in which individual TZPs contact the surface of the oocyte and stably attach if there is sufficient N-cadherin. In SMAD4-depleted cells, there is insufficient N-cadherin to stabilize the attachment. The TZP continues to elongate but eventually retracts. Their model is well supported by their experimental evidence and the manuscript is both well-formulated and written.

Reviewer #2 (Public Review):

Summary:

This study proposed a new mechanism by which the TGF-beta signaling pathway promotes contacts between oocytes and the surrounding somatic cells in mice, by regulating the numbers of transzonal projections (TZPs).

Strengths:

The conditional Smad4 knockout and three-dimensional observation of transzonal projections are solid and sufficiently support the major conclusions.

Weaknesses:

The physiological significance of SMAD4-dependent formation of transzonal projection networks is not assessed in this study.

Previous studies have shown that physical contact and gap junctional communication with the granulosa cells is essential for normal oocyte development. A recent study has also shown that depleting Myo10 in granulosa cells reduces the number of TZPs and leads to abnormalities in oocyte and embryo development. Thus, the importance of TZPs is well-established. These findings, which were insufficiently brought out in the Introduction of the original manuscript, have now been made more clearly (Introduction, 2nd paragraph). We recognize that these reports do not directly test a role for SMAD4-dependent TZPs. Unfortunately, it is beyond our technical capacity to obtain embryos following meiotic maturation and fertilization of oocytes that have grown in vitro, which wold be necessary for us to fully test the physiological role of SMAD4-dependent TZPs.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors switch from Amhr2-cre to ER-cre to increase the number of GFP-positive granulosa cells in 12 d/o ovaries. To avoid disruption of FSH secretion by SMAD4, they use an in vitro model that requires 6 days in GEO culture (1 d tamoxifen + 5 d). Could it be that Amhr2-cre didn't work because most follicles would not have reached the atretic preantral stage in 12 d/o ovaries? Did the authors consider 6 days in vitro GEO culture to determine if Amhr2-cre would be efficient and avoid exposure to tamoxifen?

Please see below.

When is Amhr2 expressed?

Previous studies (Jorgez et al, 2004; Pangas et al, 2006) report that Amhr2 is expressed in growing follicles that have progressed beyond a single layer of granulosa cells (often defined as secondary and primary follicles, respectively). As shown in Fig. 1C, we did not observe evidence of widespread Cre activity in multilayer follicles. At least two factors may contribute why we observed relatively weak Cre activity. One possibility is that, on the genetic background our mice, Amhr2 is expressed relatively late during follicular growth. Thus, we might have observed more GFP-positive granulosa cells in antral or pre-ovulatory follicles. Because the granulosa cells of these late-stage follicles would already have produced many TZPs, the number of new TZPs generated in wild-type but not SMAD4-depleted cells after Amhr2 activation would be a relatively small proportion of the total population. This would make it more difficult to detect a reduction in TZP number in the absence of SMAD4.

A second point is that we used pre-puberal mice whereas Jorgez et al examined Amhr2 expression in ovaries of adult mice. Pangas et al evaluated both prepuberal and adult females. It may be that Amhr2 is expressed earlier or more strongly in granulosa cells of adult mice. Regarding the suggestion to culture complexes obtained from mice on the Amhr2-Cre background, as this might allow widespread expression of Cre without the need for tamoxifen, this is an excellent idea. If there is considerable heterogeneity among cells in the timing of Amhr2-Cre activity, though, this may further cloud efforts to uncover the role of SMAD4 in the production or stability of TZPs, as noted above.

(2) Did most of the GEO cultured in vitro reach the antral follicle stage after 6 days?

Since GOCs were treated with collagenase, the thecal layer was removed. Therefore, development of an antrum does not occur. We observed that, in some cases, the oocyte was extruded from the granulosa cell mass. These abnormal complexes were discarded.

(3). Was the development/diameter of the oocyte in the GEO comparable to the oocyte growing in vivo?

We did not compare the diameter of the oocytes grown in vitro to those grown in vivo. Thus, we cannot say whether the oocytes grown in vitro reached the same size as those grown in vivo. We did, however, compare the diameter of the oocytes in the wt and ko groups and observed no difference (Figure 2). This indicates that depletion of SMAD4 in the granulosa cells does not impair oocyte growth. Importantly for our studies, it excludes the possibility that the reduction in TZP-number is simply due to a smaller surface area of the oocyte.

(4) SMAD4 depletion in granulosa cells disrupts steroidogenesis leading to increased progesterone levels and precocious luteinization of granulosa cells (Pangas et al., 2006). Did the authors determine the expression level of luteal markers of granulosa cells in the in vitro GEO culture Smad4 knockout model? Are their observations direct effects of the absence of SMAD4?

This is an excellent point. We checked our previously performed RNA-seq analysis of the wild-type and knockout granulosa cells, but found no difference in the quantities of Cyp11a1, Sfrp4, Star or Ptgfr. This is now described in the Discussion (4th paragraph). One potentially important difference between our study and that of Pangas et al (2006) is that they observed premature luteinization when prepuberal (3-week old) mice were injected with the FSH analogue, equine serum gonadotropin, whereas we studied granulosa-oocyte complexes cultured in vitro. This could underlie the apparent differences with respect to luteinization.

(5) Could the reduced number of TZPs in ER-cre+; Smad4fl/fl GOCs be explained by luteinization?

This interesting and logical possibility is related to the previous point. In other words, luteinization could be considered as a default pathway of differentiation that is suppressed by SMAD signaling. It is possible that luteinized cells are unable to generate or maintain TZPs. This model offers a potential mechanistic basis for our observation, and we now raise it in the Discussion (3rd paragraph).

Reviewer #3 (Recommendations For The Authors):

The expression and localization of N-cadherin should be observed in Smad4 and control granulosa cell-oocyte complexes.

We agree that this would be an excellent approach to confirm the decreased expression of N-cadherin in the granulosa cells that was observed by immunoblotting. We were confronted by two challenges, however. First, we were unable to consistently obtain strong staining of granulosa cell membranes in the inner layers of multilayer granulosa-oocyte complexes. Other antibodies are able to stain structures at the oocyte surface, indicating that antibodies are not physically blocked from penetrating the complex. More likely, the anti-N-cadherin does not bind its target strongly enough to generate a robust signal that can be detected through multiple overlying layers of cells. Second, whereas for immunoblotting we collect all granulosa cells from culture complexes, for immunofluorescence we are only able to examine those that remain in the complex. This means that, for immunofluorescence, we essentially but unavoidably select against cells that are only loosely attached – as would be expected for N-cadherin-deficient cells – to their neighbours. Given these challenges, we believe that the immunoblotting approach, which produced highly reproducible results over six biological replicates (Fig. 6), is the most reliable.

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:

We extend our sincere gratitude to the editor and three reviewers for their invaluable feedback, which not only included positive comments but also provided constructive suggestions for enhancing the quality of our manuscript.

Of potential interest to you is our forthcoming investigation into vaccine efficacy, where we will compare the effectiveness of our live-attenuated vaccine with an mRNA-based alternative.

Moreover, we acknowledge and fully endorse the recommendation to elucidate why immunization with our live-attenuated vaccine confers protection against viral challenge, even in the absence of sufficient neutralizing antibodies. As pointed out by the reviewers, this phenomenon may be attributed to mucosal immunity. Consequently, we have outlined plans to investigate whether the attenuated live vaccine elicits mucosal immunity as part of our ongoing research.

We are currently working to gather the necessary data to address these inquiries comprehensively, and are aiming to resubmit our manuscript at the earliest opportunity.

Reviewer #1: We sincerely appreciate the insightful comments provided by Reviewer #1. In response to this feedback, we will conduct a comparative analysis of efficacy between our live-attenuated vaccine and an mRNA-based alternative. Furthermore, we will thoroughly examine and delineate the advantages and limitations of this/our live-attenuated vaccine in our discussion.

Reviewer #2: We express our sincere appreciation to Reviewer #2 for invaluable suggestions. In light of the insightful observation concerning the weakness of our study, related to the poor assessment/evaluation of the induction of mucosal immunity by our vaccine candidate, we have resolved to undertake a comprehensive analysis in this regard.

Furthermore, we will take into account this reviewer's recommendation to compare BK2102 results with those of an mRNA vaccine. We are currently in the process of planning additional experiments to thoroughly address this aspect.

Reviewer #3: We are very grateful to Reviewer #3 for the positive feedback and invaluable suggestions. In order to further explore the immune mechanisms underlying the protection against the Omicron variant in the absence of detectable neutralizing antibodies, we are currently devising plans for experiments focused on evaluating mucosal immunity.

Moreover, in accordance with Reviewer #3's suggestion, we are considering the incorporation of an ELISPOT assay experiment. However, we acknowledge uncertainties regarding the feasibility of establishing an experimental system for this purpose.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Day et al. present a high-throughput version of expansion microscopy to increase the throughput of this well-established super-resolution imaging technique. Through technical innovations in liquid handling with custom-fabricated tools and modifications to how the expandable hydrogels are polymerized, the authors show robust ~4-fold expansion of cultured cells in 96-well plates. They go on to show that HiExM can be used for applications such as drug screens by testing the effect of doxorubicin on human cardiomyocytes. Interestingly, the effects of this drug on changing DNA organization were only detectable by ExM, demonstrating the utility of HiExM for such studies.

Overall, this is a very well-written manuscript presenting an important technical advance that overcomes a major limitation of ExM - throughput. As a method, HiExM appears extremely useful, and the data generally support the conclusions.

Strengths:

Hi-ExM overcomes a major limitation of ExM by increasing the throughput and reducing the need for manual handling of gels. The authors do an excellent job of explaining each variation introduced to HiExM to make this work and thoroughly characterize the impressive expansion isotropy. The dox experiments are generally well-controlled and the comparison to an alternative stressor (H2O2) significantly strengthens the conclusions.

Weaknesses:

(1) Based on the exceedingly small volume of solution used to form the hydrogel in the well, there may be many unexpanded cells in the well and possibly underneath the expanded hydrogel at the end of this. How would this affect the image acquisition, analysis, and interpretation of HiExM data?

The hydrogel footprint covers approximately 5% of the surface within an individual well and only cells within this area are embedded in the polymerized hydrogel for subsequent processing steps. Cells that are outside of this footprint are not incorporated into the gel, meaning that these cells are digested by Proteinase K and subsequently washed away by the excess water exchange in the gel swelling step. Note that different cell types may require higher or lower concentrations of Proteinase K to adequately digest cells for expansion while maintaining fluorescence signal. Given the compatibility of HiExM with 96-well plates, this titration can be performed rapidly in a single experiment. Although cells outside of the hydrogel footprint are removed prior to imaging, we do occasionally observe Hoechst signal that appears to be underneath the gels. We believe this signal is likely from excess DNA from digested cells that was not fully washed out in the gel swelling step. This signal is both spatially and morphologically distinct from the nuclear signal of intact cells and it does not affect image acquisition, analysis, or data interpretation.

(2) It is unclear why the expansion factor is so variable between plates (e.g., Figure 2H). This should be discussed in more detail.

The variability in expansion factor across plates can likely be attributed to the small volume (~250 nL) deposited by the device posts. Small variations in gel volume could impact gel polymerization compared to standard ExM gels. For example, gels in HiExM are more sensitive to evaporation because they are ~1000x smaller than standard expansion gel preparations due to an increased air-liquid-interface. Evaporation in HiExM gels increases monomer and cross linker concentrations, leading to variation in expansion factor across plates. We note that expansion factor is robust within well plates and that variance is slightly increased between plates. These differences will be discussed in the revised manuscript.

(3) The authors claim that CF dyes are more resistant to bleaching than other dyes. However, in Figure. S3, it appears that half of the CF dyes tested still show bleaching, and no data is shown supporting the claim that Alexa dyes bleach. It would be helpful to include data supporting the claim that Alexa dyes bleach more than CF dyes and the claim that CF dyes in general are resistant to bleaching should be modified to more accurately reflect the data shown.

We did not show data using Alexa dyes because these fluorophores are highly sensitive to photobleaching using Irgacure and thus we could not obtain images. In contrast, some CF dyes are more robust to bleaching in HiExM including CF488A, CF568, and CF633 dyes. We have recently adapted our protocol to PhotoExM chemistry which is compatible with a wider range of fluorophores as described by Günay et al. (2023) and as shown in current Fig. S11.

(4) Related to the above point, it appears that Figure S11 may be missing the figure legend. This makes it hard to understand how HiExM can use other photo-inducible polymerization methods and dyes other than CF dyes.

The following figure legend will be included in the revised manuscript. Fig. S11: Example of a cell expanded in HiExM using Photo-ExM gel chemistry. Photo-ExM does not require an anoxic environment for gel deposition and polymerization, improving ease of use of HiExM. Mitochondria were stained with an Alexa 647 conjugated secondary antibody, indicating that HiExM is compatible with additional fluorophores when combined with Photo-ExM.

(5) The use of automated high-content imaging is impressive. However, it is unclear to me how the increased search space across the extended planar area and focal depths in expanded samples is overcome. It would be helpful to explain this automated imaging strategy in more detail.

We imaged plates on the Opera Phenix using the PreciScan Acquisition Software in Harmony. In brief, each well is imaged at 5x magnification in the Hoechst channel to capture the full well at low resolution. Hoechst is used for this step given its signal brightness, ubiquity across established staining protocols, and spectral independence from most fluorophores commonly conjugated to secondary antibodies. Using this information, the microscope detects regions of interest (nuclei) based on criteria including size, brightness, circularity, etc. Finally, the positional information for each region is stored, and the microscope automatically images those regions at 63x magnification. The working distance for the objective used in this study is 600 µm which is sufficient to capture the entirety of expanded cells in the Z direction. This strategy allows minimizes off-target imaging and allows robust image acquisition even in cultures with lower seeding density. A detailed description of the automated imaging strategy will be included in the revised manuscript.

(6) The general method of imaging pre- and post-expansion is not entirely clear to me. For example, on page 5 the authors state that pre-expansion imaging was done at the center of each gel. Is pre-expansion imaging done after the initial gel polymerization? If so, this would assume that the gelation itself has no effect on cell size and shape if these gelled but not yet expanded cells are used as the reference for calculating expansion factor and isotropy.

Pre-expansion imaging is performed after staining is complete, but prior to the application of AcX, which is the first step of the HiExM protocol. Following staining and imaging, plates can be sealed with paraffin and stored at 4˚C for up to a week prior to starting the expansion protocol. We typically image 61 fields of view at the center of the well plate (where the gel will be deposited) to obtain sufficient pre-expansion images as shown in Figure 2b (left). After pre-expansion imaging, we perform the HiExM protocol followed by image acquisition. We then tile all the images, as shown in Figure 2b, and compare tiled images from the same well pre- and post-expansion to manually identify the same cells. Comparisons of the pre- and post-expansion images of the same cell are then used to calculate expansion factor and isotropy measurements as described. This detailed description will be included in the revised manuscript.

(7) In the dox experiments, are only 4 expanded nuclei analyzed? It is unclear in the Figure 3 legend what the replicates are because for the unexpanded cells, it says the number of nuclei but for expanded it only says n=4. If only 4 nuclei are analyzed, this does not play to the strengths of HiExM by having high throughput.

We performed the DOX titration assay across four different well plates (i.e. n=4). For each condition, the total number of nuclei measured was 56, 71, 64, 92, and 62 for DMSO, 1nM, 10nM, 100nM, and 1µM, respectively. For SEM calculations, we included the number of technical replicates to avoid underestimating error. We have revised the Figure 3 legend to better reflect the experimental details.

(8) I am not sure if the analysis of dox-treated cells is accurate for the overall phenotype because only a single slice at the midplane is analyzed. It would be helpful to show, at least in one or two example cases, that this trend of changing edge intensity occurs across the whole 3D nucleus.

We will repeat our analysis on a subset of images using multiple optical sections for each nucleus reported. These new data will be included in the revised manuscript.

(9) It would be helpful to provide an actual benchmark of imaging speed or throughput to support the claims on page 8 that HiExM can be combined with autonomous imaging to capture thousands of cells a day. What is the highest throughput you have achieved so far?

The parameters that dictate imaging speed in HiExM include exposure time, z-stack height, and number of channels. Depending on the signal intensity for a given channel, exposure times vary from 200ms to 1000ms. For z-stack height, we found that imaging 65 sections with 1µm spacing allowed for robust identification of each region of interest in the 5x pre-scan. As an example, collecting images for a full well plate (e.g., 20 images per well with 4 channels) requires approximately 24 hours of autonomous image acquisition using the Opera Phenix. Depending on cell size, this yields imaging data for between 1200 cells (1 cell per field of view) to 6000 cells (5 cells per field of view). Different autonomous imagers as well as improving staining techniques that increase signal:noise can be expected to significantly decrease the exposure time as it will reduce the number of z-stacks needed for each region.

Reviewer #2 (Public Review):

Summary:

In the present work, the authors present an engineering solution to sample preparation in 96-well plates for high-throughput super-resolution microscopy via Expansion Microscopy. This is not a trivial problem, as the well cannot be filled with the gel, which would prohibit the expansion of the gel. A device was engineered that can spot a small droplet of hydrogel solution and keep it in place as it polymerizes. It occupies only a small portion of space at the center of each well, the gel can expand into all directions, and imaging and staining can proceed by liquid handling robots and an automated microscope.

Strengths:

In contrast to Reference 8, the authors' system is compatible with standard 96 well imaging plates for high-throughput automated microscopy and automated liquid handling for most parts of the protocol. They thus provide a clear path towards high-throughput ExM and high-throughput super-resolution microscopy, which is a timely and important goal.

Weaknesses:

The assay they chose to demonstrate what high-throughput ExM could be useful for, is not very convincing. But for this reviewer that is not important.

We appreciate this reviewer’s point. We believe the data provide an example of the power of HiExM for collecting thousands of nanoscale images that would benefit experiments that require many samples (e.g., conditions, replicates, timepoints, etc.). The ability to generate large data sets also enables quantitative analysis of images with appropriate statistical power. The intention of this experiment was to provide a proof-of-concept example of the robustness, accessibility, and experimental design flexibility of HiExM.

Reviewer #3 (Public Review):

Summary:

Day et al. introduced high-throughput expansion microscopy (HiExM), a method facilitating the simultaneous adaptation of expansion microscopy for cells cultured in a 96-well plate format. The distinctive features of this method include 1) the use of a specialized device for delivering a minimal amount (~230 nL) of gel solution to each well of a conventional 96-well plate, and 2) the application of the photochemical initiator, Irgacure 2959, to successfully form and expand the toroidal gel within each well.

Strengths:

This configuration eliminates the need for transferring gels to other dishes or wells, thereby enhancing the throughput and reproducibility of parallel expansion microscopy. This methodological uniqueness indicates the applicability of HiExM in detecting subtle cellular changes on a large scale.

Weaknesses:

To demonstrate the potential utility of HiExM in cell phenotyping, drug studies, and toxicology investigations, the authors treated hiPS-derived cardiomyocytes with a low dose of doxycycline (dox) and quantitatively assessed changes in nuclear morphology. However, this reviewer is not fully convinced of the validity of this specific application. Furthermore, some data about the effect of expansion require reconsideration.

The application we chose was intended as a proof of concept. We believe the data provide an example of the power of HiExM for collecting thousands of nanoscale images that would benefit experiments that require many samples (e.g., conditions, replicates, timepoints, etc.). The ability to generate large data sets also enables quantitative analysis of images with appropriate statistical power. The intention of this experiment was to provide a proof-of-concept example of the robustness, accessibility, and experimental design flexibility of HiExM.

The variability in expansion factor across plates can likely be attributed to the small volume (~250 nL) deposited by the device posts. Small variations in gel volume could impact gel polymerization compared to standard ExM gels. For example, gels in HiExM are more sensitive to evaporation because they are ~1000x smaller than standard expansion gel preparations due to an increased air-liquid-interface. Evaporation in HiExM gels increases monomer and cross linker concentrations, leading to variation in expansion factor across plates. We note that expansion factor is robust within well plates and that variance is slightly increased between plates. These differences will be discussed in the revised manuscript.